Issuu on Google+

Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks Dan Siegal-Gaskins1,2*, Maria Katherine Mejia-Guerra2, Gregory D. Smith3, Erich Grotewold2 1 Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio, United States of America, 2 Department of Molecular Genetics and Plant Biotechnology Center, The Ohio State University, Columbus, Ohio, United States of America, 3 Department of Applied Science, The College of William and Mary, Williamsburg, Virginia, United States of America

Abstract Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes). We find that there exist reaction rate constants leading to bistability in ,90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the twogene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion. Citation: Siegal-Gaskins D, Mejia-Guerra MK, Smith GD, Grotewold E (2011) Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks. PLoS Comput Biol 7(5): e1002039. doi:10.1371/journal.pcbi.1002039 Editor: JO¨rg Stelling, ETH Zurich, Switzerland Received November 23, 2010; Accepted March 21, 2011; Published May 12, 2011 Copyright: ! 2011 Siegal-Gaskins et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by NRI Grant 2007-35318-17805 from the USDA CSREES, DOE Grant DE-FG02-07ER15881 and NSF grant DBI-0701405 to EG, NSF Grant DMS-0443843 to GDS, and an NIH T32 training grant from the Division of Human Cancer Genetics to DSG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: dsg@mbi.osu.edu

Introduction

Chemical reaction network theory (CRNT), which gives conditions for the existence, multiplicity, and stability of steady states in systems of nonlinear ODEs derived from mass-action kinetics [16–18], offers a novel framework for the rapid identification of network topologies with the capacity for bistability (herein referred to as bistable networks). Importantly, CRNT is applicable without specific knowledge of the system parameters. This ability to study network characteristics in a parameter-free context is particularly beneficial in cell and developmental biology, given the high level of uncertainty in parameter values [19]. As a result, CRNT has found a number of biological applications [20– 23]. Still, considering its potential for large-scale analyses, the use of CRNT has been fairly limited. Here, we apply CRNT to reaction network models representing a broad class of small gene regulatory networks (GRNs): those consisting of two transcription factor (TF)-coding genes and their associated proteins. Our comprehensive parameter-free survey resulted in the identification of 36,771 bistable GRN architectures (out of a total of 40,680), including eleven without the TF cooperativity typically associated with switch-like circuits. Approximately 40% of the bistable systems were confirmed as such using existing computational tools, with the remainder identified

Bistability–the coexistence of two stable equilibria in a dynamical system–is responsible for the switch-like behavior seen in a wide variety of cell biological networks, such as those involved in signal transduction [1], cell fate specification [2–4], cell cycle regulation [5], apoptosis [6–8], and in regulating extracellular DNA uptake (competence development) [9]. Evidence for bistable networks has been found in experimental observations of the hysteretic (i.e., history dependent) response to stimuli that is commonly associated with bistability [10,11], for example in the Cdc2 activation circuit in Xenopus egg extracts [12,13] and in the lactose utilization network in E. coli [14]. Complementing experimental analyses, mathematical tools such as bifurcation theory can be used to determine if a particular network–written as a set of ordinary differential equations (ODEs) –is bistable [15]. However, because the dynamical behavior of a network is dependent on the values of the system parameters (e.g., reaction rates), and the number of parameters required for an accurate description of even simple systems is typically large and uncertain, new bistable circuit architectures tend to be identified only slowly and on a network-by-network basis. PLoS Computational Biology | www.ploscompbiol.org

1

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

or they may remain unoccupied. We further assume that, while degradation is considered for monomeric TFs, all TF dimers are stable to proteolytic degradation; the validity of this assumption and its implications are discussed below. A variety of networks may be constructed by combining these reactions, subject to certain logical constraints (e.g., the presence of a dimer-promoter binding reaction requires the inclusion of the dimer formation reaction) and with the requirement that every network includes the necessary basal TF production and degradation reactions. In the two-gene case (N = 2), there are 4 essential reactions and 23 additional reactions (Table 1) that may be combined to form 40,680 different networks. The total number of networks is smaller than might be expected (i.e., less than 223 ) as a result of reaction dependencies (Table 1) and network symmetries; for example, the network consisting of reactions k, q, and w is functionally equivalent to the that with reactions i, l, and r, and as a result we do not include the latter and other symmetric networks like it in the total. It should be noted that within this set of two-gene networks there are a small number for which there is no coupling between the two genes. Given that there are twelve possible one-gene networks for both X1 /P1 and X2 /P2 independently (see [23]), the total number of unique decoupled two-gene networks is 12(12+1)/ 2 = 78, the number of distinct pairs of one-gene circuits. The presence of 78 decoupled two-gene networks was verified by searching through the full list of 40,680 networks for those lacking the basic coupling reactions b, c, j, n, and o (Table 1).

Author Summary Switch-like behavior is found across a wide range of biological systems, and as a result there is significant interest in identifying the various ways in which biochemical reactions can be combined to yield a switch-like response. In this work we use a set of mathematical tools from chemical reaction network theory that provide information about the steady-states of a reaction network irrespective of the values of network rate constants, to conduct a large computational study of a family of model networks consisting of only two protein-coding genes. We find that a large majority of these networks (,90%) have (for some set of parameters) the mathematical property known as bistability and can behave in a switch-like manner. Interestingly, the capacity for switch-like behavior is often maintained as networks increase in size through the introduction of new reactions. We then demonstrate using published yeast data how theoretical parameter-free surveys such as this one can be used to discover possible switch-like circuits in real biological systems. Our results highlight the potential usefulness of parameter-free modeling for the characterization of complex networks and to the study of network evolution, and are suggestive of a role for it in the development of novel synthetic biological switches. through the novel concept of network ancestry, in which the presence of a bistable subnetwork can under certain conditions be used to establish bistability in a larger network if the condition that the two network structures have an identical stoichiometric subspace is met (see the following section on CRNT basics). Despite its large size, the entire two-gene bistable network family can be understood as descended from a set of only eleven minimal bistable networks, that is, bistable networks that do not contain within them a smaller bistable subnetwork and, as a consequence, are rendered monostable by the removal of one or more network reactions. Using experimental protein-protein interaction, proteinDNA interaction, and gene expression data from Saccharomyces cerevisiae, we demonstrate how a general theoretical survey of this kind has unique predictive power to identify bistable modules in organisms that have not been fully explored from a functional genomics perspective. Our results are further suggestive of a role for parameter-free modeling in simplifying the study of complex regulatory networks, understanding network evolution, and designing new synthetic biological circuits.

Chemical reaction network theory basics Given the centrality of CRNT to our analysis, we provide here a primer on the relevant aspects of the theory and illustrate them with the rudimentary two-gene network that consists of only the essential basal protein production and degradation reactions (Figure 1). At the heart of the theory is the concept of network complexes, formally the chemical species or linear combinations of species which occur on either side of a chemical equation. A reaction network can be visualized as a directed graph where each of these complexes appears only once at the heads and/or tails of reaction arrows. A collection of complexes connected by arrows is referred to as a linkage class. The complexes and linkage classes for our rudimentary network are highlighted in Figure 1 in yellow and with dashed lines, respectively. Every complex in the network can also be represented as a vector in an appropriate vector space; in a network of N species, the complex vectors lie in RN . Reactions also have associated vectors (termed reaction vectors), which are constructed by subtracting the reactant complex vectors from the product complex vectors. The size of the largest linearly independent set of reaction vectors is the rank of the network, and the set of all possible linear combinations of reaction vectors (i.e., their span) is referred to as the stoichiometric subspace of the network. This subspace plays an important role in setting boundaries on the system behavior: although the species’ concentrations may evolve with time, they are ultimately constrained within surfaces that are parallel translations of the stoichiometric subspace. Exactly which surface (or stoichiometric compatibility class) the concentrations are constrained to is defined by the initial conditions. For a system with n complexes, l linkage classes, and rank s, the network deficiency d is defined as d = n2l2s. A number of theorems regarding the stability properties of networks are based on the deficiency, including the deficiency zero and deficiency one theorems [16,17].

Results Two-gene network construction As done previously [23], we assume classical chemical kinetics and specify gene regulatory networks (GRNs) as sets of elementary biochemical reactions. For a network consisting of N transcription factor genes Xi and associated proteins Pi (i~1, . . . ,N), the essential reactions are basal protein production (Xi ? Xi zPi ) and degradation (Pi ?1). Networks may also contain protein dimerization reactions (Pi zPj ' Pi Pj ), binding of both TF monomers and dimers to the gene promoters (Xi zPj ' Xi Pj and Xi zPj Pk ' Xi Pj Pk ), and protein production from a bound gene (Xi Pj ? Xi Pj zPi and Xi Pj Pk ? Xi Pj Pk zPi ). For reactions of this last type, under our parameter-free framework, the rate of protein production from a bound gene is unspecified and thus may be either higher or lower than the basal rate but cannot be zero. For simplicity, we assume that the promoter of each gene may only be bound by a single monomer or dimer species at any given time, PLoS Computational Biology | www.ploscompbiol.org

2

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

Table 1. Reactions combined to generate the 40,680 unique networks of two genes and two gene products.

Reaction label ri

Reaction

Dependencies

Biochemical process

*

X1 R X1+P1

gene X1 basal protein production

*

X2 R X2+P2

gene X2 basal protein production

*

P1 R 0/

protein P1 degradation

*

P2 R 0/

protein P2 degradation

a

X1+P1 ( X1 P1

binding of P1 to the X1 promoter

b

X1+P2 ( X1 P2

binding of P2 to the X1 promoter

c

X2+P1 ( X2 P1

binding of P1 to the X2 promoter

d

X2+P2 ( X2 P2

binding of P2 to the X2 promoter

e

X1 P1 R X1 P1+P1

a

production of P1 from a P1-bound gene

f

X1 P2 R X1 P2+P1

b

production of P1 from a P2-bound gene

g

X2 P1 R X2 P1+P2

c

production of P2 from a P1-bound gene

h

X2 P2 R X2 P2+P2

d

production of P2 from a P2-bound gene

i

P1+P1 ( P1 P1

homodimerization of P1

j

P1+P2 ( P1 P2

heterodimerization of P1 and P2

k

P2+P2 ( P2 P2

homodimerization of P2

l

X1+P1 P1 ( X1 P1 P1

i

binding of P1 P1 dimer to the X1 promoter

m

X1+P1 P2 ( X1 P1 P2

j

binding of P1 P2 dimer to the X1 promoter

n

X1+P2 P2 ( X1 P2 P2

k

binding of P2 P2 dimer to the X1 promoter

o

X2+P1 P1 ( X2 P1 P1

i

binding of P1 P1 dimer to the X2 promoter

p

X2+P1 P2 ( X2 P1 P2

j

binding of P1 P2 dimer to the X2 promoter

q

X2+P2 P2 ( X2 P2 P2

k

binding of P2 P2 dimer to the X2 promoter

r

X1 P1 P1 R X1 P1P1+P1

i, l

production of P1 from a P1 P1-bound gene

s

X1 P1 P2 R X1 P1P2+P1

j, m

production of P1 from a P1 P2-bound gene

t

X1 P2 P2 R X1 P2P2+P1

k, n

production of P1 from a P2 P2-bound gene

u

X2 P1 P1 R X2 P1P1+P2

i, o

production of P2 from a P1 P1-bound gene

v

X2 P1 P2 R X2 P1P2+P2

j, p

production of P2 from a P1 P2-bound gene

w

X2 P2 P2 R X2 P2P2+P2

k, q

production of P2 from a P2 P2-bound gene

*These reactions occur in every network. doi:10.1371/journal.pcbi.1002039.t001

in [24] and implemented in the Chemical Reaction Network Toolbox software package (http://www.chbmeng.ohio-state.edu/ ,feinberg/crntwin/), constructs and attempts to solve systems of equalities and inequalities that are based on the network structure. If no solutions (which together with the equality and inequality systems are known as ‘signatures’ of the reaction network) can be found, then the network does not have the qualitative capacity to support multiple steady states. However, if signatures can be found, then the network can support multiple steady states, and the Toolbox will produce example rate constants and associated steady states consistent with the mass-action ODE description of the network, as well as report the stability characteristics of the steady states. It should be emphasized that ADT cannot guarantee bistability even if the network does support multiple steady states, as they may be unstable. Nevertheless, with its substantial analytical power and ease of use, ADT has played a role in a number of recent studies [23,25–28].

Advanced deficiency theory (ADT) [18] is required for networks with a deficiency greater than one. The ADT algorithm, detailed

Preliminary bistable network identification All of the two-gene networks modeled were found by the Chemical Reaction Network Toolbox (herein referred to as simply the Toolbox) to have a deficiency of two or more, necessitating the use of ADT in their analyses. Screening the Toolbox-generated ADT analysis reports, we determined that of the 40,680 networks

Figure 1. Rudimentary two-gene network consisting of only basal protein production and degradation. In the ‘CRNT picture’, complexes are highlighted in yellow and linkage classes are identified with dashed lines. Labeling scheme is adopted from [77]. doi:10.1371/journal.pcbi.1002039.g001

PLoS Computational Biology | www.ploscompbiol.org

3

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

well-established (see, e.g., [29,30]). Bistability via positive autoregulation has also been demonstrated experimentally with synthetic gene circuits in both prokaryotes [31] and eukaryotes [32].

Identifying bistability through network ancestry The bistable networks shown in Figure 3, each containing seven reactions, can be ‘grown’ into new eight-reaction networks through the addition of reactions from Table 1: reactions a, b, d, g, i, j, q, or t to the circuit shown in Figure 3A, and reactions a, b, c, d, i, j, or n to the circuit shown in Figure 3B. In all cases, the new larger networks were also confirmed by the Toolbox to be bistable. We may then ask: is bistability, once established in a ‘parent’ network of N reactions, guaranteed in any ‘descendant’ network of Nz1 reactions? ADT alone is not sufficient to answer this question, since systems were less likely to be characterizable as they increased in size (Figure 2). However, CRNT does provide a basis for establishing bistability in networks which contain subnetworks known to be bistable: if following the addition of a reaction the stoichiometric subspace of the

Figure 2. Fraction of networks which cannot have their stability established by advanced deficiency theory (ADT), as a function of network size. doi:10.1371/journal.pcbi.1002039.g002

surveyed, 18,352 (*45%) have the capacity for multiple steady states, with 14,721 of these being confirmed as bistable with example rate constants (see Materials and Methods for a description of the screening procedure). Only 2,654 networks (*6.5%) cannot be bistable regardless of the parameter values. For the remaining 19,674 networks, ADT could neither establish nor rule out the capacity for multiple steady states, and as a result we refer to these as ‘unknown’ networks. It is noteworthy that the fraction of networks of a given size (that is, a given number of reactions) that are unknown increases as the size increases; for example, w90% of networks with more than 21 reactions, and all networks with more than 24 reactions, are unknown (Figure 2). As expected, the stabilities of the decoupled two-gene networks are the same as the constituent one-gene systems previously studied [23]. The two smallest bistable networks identified exhibit canonical switch topologies (Figure 3). In the double negative feedback circuit shown in Figure 3A, we find that dimerization of only one of the TFs is sufficient for bistability. The autoregulatory positive feedback network shown in Figure 3B is an example of a decoupled two-gene network, with bistability in the concentration of one TF only. We note that while CRNT does not take into account the strength of the regulation in determining a network’s capacity for multiple steady states, the fact that an autoregulatory circuit requires positive feedback in order to achieve bistability is

Figure 4. The fraction of networks of each size that were established as bistable by ADT, bistable by network ancestry, having multiple steady states with unconfirmed stability, monostable, or with an unknown capacity for multiple stable steady states. Network size is determined only by the number of reactions (from Table 1) that are present. The total number of networks of each size is shown in parentheses. doi:10.1371/journal.pcbi.1002039.g004

Figure 3. The smallest two-gene bistable networks found with ADT. (A) A double negative feedback circuit, in which dimerization of only one of the TFs is sufficient for bistability. (B) An autoregulatory positive feedback circuit. The two genes are uncoupled and the bistability is in the concentration of one TF only. In both (A) and (B), degradation of the TF monomers is not shown. doi:10.1371/journal.pcbi.1002039.g003

PLoS Computational Biology | www.ploscompbiol.org

4

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

tion from a TF-bound gene (Xi Pj ? Xi Pj zPi , since the reaction vectors can be written as linear combinations of the vectors associated with Xi zPj ' Xi Pj , Pi ?1, and Pj ?1). Beginning with the 14,721 known bistable networks and using this ‘ancestry’-based method, we identified an additional 22,050 bistable networks. Some of these networks had been previously found by the Toolbox to have the capacity for multiple steady states, but for which no example parameter sets leading to stable equilibria were given. The number of networks of each type– bistable by ADT, bistable by ancestry, multiple steady states with unconfirmed stability, monostable, or unknown–are shown as a function of network size in Figure 4.

descendant network is identical to that of the parent, then the larger network is also bistable for some set of parameter values. As an intuitive example, one can imagine a situation in which a reaction is added to an existing network, that the surface containing the dynamical trajectories of the network species’ concentrations is not changed as a result of the addition, and that the added reaction has only a very small rate constant. In this case we should not expect a change from whatever qualitative phenomena were there before. Thus, if the parent network had two stable equilibria, the descendant network will also have two stable equilibria. Example reactions that do not result in a change in the stoichiometric subspace if added include protein produc-

Figure 5. Minimal bistable networks. Only 11 of the 36,771 bistable networks identified lose bistability by the removal of any network reaction. That is, only 11 of the bistable networks contain no subset of reactions which is also bistable. Dashed-and-colored lines indicate regulation by heterodimer. Horizontal bars represent purely-repressive TF binding, and arrows indicate TF production from a bound gene (at a non-zero rate that may be either higher or lower than the basal rate). Degradation of the TF monomers is not shown. doi:10.1371/journal.pcbi.1002039.g005

PLoS Computational Biology | www.ploscompbiol.org

5

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

Minimal bistable networks

Two-gene networks in S. cerevisiae

Of the 36,771 bistable systems identified, only eleven do not contain within them a smaller subnetwork that is also bistable. For these eleven networks, the removal of any single reaction would result in a loss of bistability. We refer to these networks as minimal bistable networks (MBNs). Named according to the reaction labels in Table 1, the MBNs are: kqw, ckn, bcdh, ikno, jmpsv, bfjpv, abejp, jknptv, jkmnps, dhjknp, and aejknp. The two networks shown in Figure 3 are minimal (kqw and ckn); the full set is shown in Figure 5. Arrows containing the symbol (+) are used in the figure and all that follow to emphasize that, in assessing a networks capacity for multiple steady states, CRNT does not distinguish between upregulation and down-regulation that results in reduced but nonzero expression. With the exception of bcdh (discussed in more detail in the following section), all of these networks contain one or more of the TF dimerization reactions common in bistable GRNs [23]. It can also be seen that each MBN contains feedback loops that for some parameter sets will be made positive, a characteristic shown to be generally necessary for multiple steady states in a system of ODEs [33].

To investigate how an in silico network topology survey such as this can be used to better understand experimental results, we searched for real biological examples of the bistable networks identified in this study in the model organism S. cerevisiae. To our knowledge, there is no single database that contains S. cerevisiae GRN architecture, thus we combined protein-protein and proteinDNA interaction data with gene expression data to establish the large-scale empirical network shown in Supplementary Figure S1. Included in this network are 148 TFs participating in 205 proteinprotein interactions (61 heterodimerization and 144 homodimerization reactions), along with 1,249 interactions between 139 TFs and 208 genes (37 ‘self-binding’ and 1,212 ‘cross-binding’ reactions). To establish which of the two-gene bistable circuits are present in the yeast network, it was first necessary to ‘translate’ the bistable models from their ideal, theoretical description (that distinguishes between and allows for each elementary reaction) into a format that is more amenable to experimental data mining; see Supplementary Text S1. We were then able to identify in the yeast data a total of 1,289 two-gene GRNs, twelve of which have topologies consistent with members of the MBN set (Table 2). Examples of these are highlighted in the next section.

Cooperativity-free switches Although cooperativity in gene regulation–via either the nonindependent binding of TFs to multiple promoter sites or the multimerization of TFs into functional units–is an important component of some bistable networks [34,35], it is not necessary for bistability. Indeed, a number of recent mathematical models of GRNs have shown deterministic bistability without cooperativity of any kind [36–38]. Among the 40,680 two-gene networks are 45 lacking cooperativity, and of these 31 were found to be monostable, eight were identified as bistable directly by ADT, and three more were identified as bistable by network ancestry. All of the bistable networks lacking cooperativity can be derived from the MBN bcdh, which is shown in Figure 6 along with a bifurcation diagram showing the existence of two stable equilibria (and an unstable equilibrium) for a range of P1 degradation rate constants. The complete set of cooperativity-free bistable networks is shown in Figure 7. An essential feature of these circuits is the competitive binding of P1 and P2 to the X2 promoter. Similar competitive or sequestration-type processes have been found to be key components in some switch-like systems [36–40].

Discussion The idea of studying theoretical network models generated via ‘random wiring’ was suggested at least fifty years ago by Monod and Jacob [41]. Only recently, with the development of powerful computational tools, have a variety of simple gene regulatory and metabolic network topologies been studied with surveys over large ranges of parameter space [42,43]. Parameter-free techniques such as CRNT are particularly well-suited for general surveys aimed at bistable network discovery, as they may more definitively answer questions regarding a mass action system’s ability to support multiple steady states. For example, using only the advanced deficiency theory (ADT) algorithm implemented in the Chemical Reaction Network Toolbox we were able to establish that *36% of the 40,680 possible unique two-gene networks are bistable for at least some sets of network parameters, another *9% have the capacity for multiple steady states (which may or may not be stable), and only *6.5% are monostable regardless of the network parameters. As network size and complexity increases, the ability of ADT to draw conclusions becomes limited (Figure 2). One method put forward as a way to extend the usefulness of CRNT to larger networks involves the analysis of simpler subnetworks corresponding with elementary flux modes of the system [25]. We have introduced a complementary subnetwork analysis method for identifying bistability, termed network ancestry, which requires only a topological sorting of the networks based on the presence or absence of individual reactions followed by inspection of the network reaction vectors. If the parent network is determined to be bistable, and if the reaction vectors of the bistable parent and unknown descendant have the same span (i.e., the networks have an identical stoichiometric subspace), then the descendant is also bistable. As a result of network ancestry, we were able to identify an additional 22,050 networks with previously unknown stability as bistable, *54% of the total (Figure 4). We emphasize that a change in the size of the stoichiometric subspace does not in and of itself imply that bistability will be lost; however, from a purely topological perspective, it may not be obvious what the effect of the change may be. Our network ancestry method may thus be considered a relatively conservative one for establishing bistability in larger networks.

Figure 6. Example of a bistable network lacking cooperativity. TF P2 plays a dual role as an activator of X2 and a repressor of X1 . The bifurcation plot shows the stable (solid lines) and unstable (dashed lines) steady state protein concentrations, in units relative to the DNA concentration, for one set of parameter values as a function of the P1 degradation rate. The network ODEs and parameter values are given in the Supplementary Text S1. doi:10.1371/journal.pcbi.1002039.g006

PLoS Computational Biology | www.ploscompbiol.org

6

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

Figure 7. Bistable networks without cooperativity. Of the 45 two-gene networks lacking dimerization, 11 were identified as bistable either directly by advanced deficiency theory analysis or via network ancestry. All the dimer-free bistable networks shown here can be derived from the minimal bistable network bcdh through the addition of reactions from Table 1. Horizontal bars represent purely-repressive TF binding, and arrows indicate TF production from a bound gene (at a non-zero rate that may be either higher or lower than the basal rate). Degradation of the TF monomers is not shown. doi:10.1371/journal.pcbi.1002039.g007

The assumption of mass action kinetics is an important aspect of CRNT. Consequently, Michaelis-Menten and Hill-type expressions are not used in our CRN approach, as they require approximations to mass action that cannot be validated in a parameter-free context. In addition, it was recently demonstrated for a generic two-protein interaction network that bistability present under the ‘inconsistent’ assumption of Michaelis-Menten kinetics is lost when the system is ‘unpacked’ into its fundamental chemical steps [44]. For our two-gene networks, the MichaelisMenten and CRN descriptions could be approximately equivalent only for specific parameters, and only if those parameters PLoS Computational Biology | www.ploscompbiol.org

were such that 1) the DNA-binding reactions reach their equilibria much more quickly than other reactions in the network, and 2) the equilibrium concentrations of any dimer species were proportional to the product of their constituent monomer concentrations [45]. In addition to the inherent consistency of CRN models, the mathematical theory applicable to deterministic CRNs offers significant computational advantages over other methods, in particular stochastic simulation. Furthermore, many deterministically bistable networks have been shown to retain two long-lived states when their models are reformulated to take stochasticity into 7

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

allow for even more comprehensive computational studies. In the meantime, network ancestry offers an attractive solution to the problem of scalability and applicability of CRNT to more complex networks: once all fundamental chemical reactions involved in any network of interest are identified, one could assemble the minimal network topologies covering all possible unique stoichiometric subspaces and probe that smaller set of networks for bistability. In essence, network ancestry allows for the reduction of the problem of determining a large network’s qualitative capacity for bistability to one of identifying the minimal bistable subnetworks within it. There is a strong biological motivation to consider individual networks as parents and descendants with a topological ordering: rather than appearing de novo, modern GRNs grow from ancestor network kernels through mechanisms such as gene duplication and the accretion of protein domains [56–59]. Domain accretion, for example the acquisition of a DNA-binding domain by a monomer (modeled in this work by the addition of one of the promoter binding reactions a, b, c, or d), has been proposed to be particularly important for eukaryotic evolution [60,61]. And there is evidence suggesting an even more direct role for bistability in evolution: it is the primary requirement for epigenetic inheritance mechanisms known to have important evolutionary effects [62,63], and can also lead to increased population fitness in stressful or changing environments [64,65] by driving an increase in phenotypic heterogeneity [66]. Thus, the eleven MBNs identified here (Figure 5), which differ from monostable networks by just a single reaction, may represent an interesting class of networks from the standpoint of evolutionary biology, as it may be that similarly-minimal networks have played an important role in functional development and/or speciation. We used the results of our in silico analysis to motivate a search of–and add functional context to–existing yeast protein-DNA and protein-protein interaction data, and in doing so were able to identify a number of two-gene systems with topologies consistent with bistability. For example, the FKH1 and FKH2 genes (and their associated proteins Fkh1p and Fkh2p, which compete for target promoter occupancy [67]) compose a network with a topology similar to the MBN bcdh (Table 2). FKH1 and FKH2 belong to the pervasive winged-helix/forkhead (FOX) family of TFs and are essential for proper regulation of the yeast cell cycle [68]. Other FOX genes have previously been shown to be involved in important biological functions including cell cycle regulation and cell differentiation [69], two processes for which GRN bistability has been implicated [2–5]. Additional gene pairs of interest include NRG1/RIM101 and OAF1/PIP2, which are components of GRNs with topologies similar to that of MBNs abejp and aejknp, respectively. The Rim101p and Nrg1p proteins, both identified previously as transcriptional repressors, are components in an extracellular pH-responsive differentiation pathway in yeast [70]. Further evidence suggestive of bistability in this system can be found in C. albicans, in which Rim101p and Nrg1p homologs regulate the morphological switch [71] associated with a dramatic change in the pathogen’s virulence [72]. Oaf1p and Pip2p, on the other hand, are involved in the production of peroxisomal proteins in the presence of fatty acids [73], and have been shown to be involved in the coordination of two different transcriptional responses to oleate [74]. We emphasize that while the two-gene networks identified through our analysis are not guaranteed to be bistable, their known topologies and functions make them excellent bistable network candidates, providing powerful hypotheses for further experimentation. The same approach may be used

Table 2. Two-gene networks found in S. cerevisiae that have topologies consistent with members of the minimal bistable network set.

Bistable model*

X1

X2

ckn

PDR1 (YGL013C)

RPN4 (YDL020C)

bcdh

FHL1 (YPR104C)

MSN4 (YKL062W)

bcdh

HMS1 (YOR032C)

YAP6 (YDR259C)

bcdh

IXR1 (YKL032C)

PHD1 (YKL043W)

bcdh

RPN4 (YDL020C)

YAP1 (YML007W)

bcdh

FKH1 (YIL131C)

FKH2 (YNL068C)

jknptv

MTH1 (YDR277C)

RGT1 (YKL038W)

aejknp

OAF1 (YAL051W)

PIP2 (YOR363C)

abejp

NRG1 (YDR043C)

RIM101 (YHL027W)

abejp

IFH1 (YLR223C)

RAP1 (YNL216W)

bfjpv

KSS1 (YGR040W)

CST6 (YIL036W)

bfjpv

OPI1 (YHL020C)

INO2 (YDR123C)

*Model names refer to the constituent reactions as labeled in Table 1. doi:10.1371/journal.pcbi.1002039.t002

account [37,44,46,47]. Still, as biochemical noise has been shown to drive some systems to exhibit switch-like behavior not predicted by deterministic models [37,47–50], it should be considered in any complete study of a specific network of interest. For models already formulated as CRNs, stochastic simulation is relatively straightforward (see, e.g., [44,51]). We attempted to capture the most prevalent and basic biochemical processes involved in transcriptional regulation in our network model construction, but our formalism is by no means exhaustive. One mechanism not included and through which networks can achieve the nonlinearity required for bistability is the direct degradation of TF dimers (Pi Pj ?1) [38]. Given that dimerization regularly protects against proteolysis (see, e.g., [52,53]), its exclusion from our reaction set is reasonable. Furthermore, for most of the networks analyzed here, the addition of a dimer degradation reaction would have no effect on their capacity for bistability: since the reaction vector for Pi Pj ?1 can be written as a linear combination of the vectors associated with reactions Pi zPj ' Pi Pj , Pi ?1 and Pj ?1, any descendant network grown from a bistable parent via the addition of a dimer degradation reaction would have the same stoichiometric subspace and would be bistable as a result of network ancestry. There remains a large amount of additional biological detail which could be incorporated in future surveys, including posttranslational modification, multiple promoter binding sites, and the location of regulatory elements relative to the genes (which has been shown to play a role in network bistability [54]). However, any increase in the level of detail would result in an increase in the combinatorial complexity and the size of the survey. For example, whereas the set of one-gene networks are constructed using combinations of 5 different reactions [23], and our two-gene networks using 23 reactions (Table 1), the addition of a third gene alone would lead to 60 different reactions that could be ‘wired’ together. With the current version of the Toolbox taking (at best) many seconds to import, analyze, and export the results for every network model, it is perhaps not an ideal software package for surveys significantly larger than this one. New software implementations of CRNT continue to be developed (e.g., [55]), and we anticipate that future programs will PLoS Computational Biology | www.ploscompbiol.org

8

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

Figure 8. Screening networks for different steady state behaviors. Networks are initially screened by the content of analysis reports produced by the Chemical Reaction Network Toolbox. The networks designated ‘multiple steady states’ are those determined by ADT to have the capacity for multiple steady states but for which no example pair of asymptotically stable steady states could be found by the program. Bistable networks are those for which an example pair of asymptotically stable steady states was reported. The complete sorting procedure is described in Materials and Methods. doi:10.1371/journal.pcbi.1002039.g008

It is worth emphasizing that the region of parameter space supporting bistability in any individual network cannot be determined via parameter-free techniques alone. For example, it may be that the necessary parameter values lie outside the range of biological reality or are difficult to engineer, or that the size of the bistable region of parameter space is exceedingly small. However, in many large-scale studies, such as those that resulted in the yeast data sets used in this work, a high degree of biochemical detail is simply nonexistent. While this lack of quantitative detail can make some analyses of biological networks challenging, it also opens up opportunities for parameter-free studies to provide experimental guidance and new functional insights [78]. Once identified, potentially interesting network architectures may be analyzed in more detail, with rate constants chosen, for example, by Monte Carlo sampling of parameter space.

to provide guidance or functional context to any system for which the necessary interaction data is available. High-throughput parameter-free analysis holds potential, not just as a tool for the study of natural systems, but also as a design aid in the growing field of synthetic biology [75,76]. For example, a survey such as this can provide inspiration for the development of new bistable switches and a library of models to draw from; already we have proposed a set of novel bistable networks that lack cooperativity and which may be particularly good designs as a result (e.g., because they do not require any ‘extra’ engineering of dimerization domains). At the very least, such a broad application of CRNT may be used to rule out (possibly large numbers of) designs incapable of bistability. CRNT can be similarly used to rule out circuits without the capacity for sustained oscillations [16] or those which cannot exhibit ‘absolute concentration robustness’ [77]. PLoS Computational Biology | www.ploscompbiol.org

9

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

Materials and Methods

Identification of bistable networks in S. cerevisiae A set of 228 yeast genes previously established as coding for transcriptional regulators [79,80] was used as the primary source for candidate network TF genes (Supplementary Table S1). Protein-protein interactions were retrieved from the BioGRID database [81] (Supplementary Table S2) and protein-DNA interactions were retrieved from the Yeastract database [82] (Supplementary Table S3). The effect of the protein-DNA interactions on target gene expression (activation or repression) is usually unknown, and any information suggestive of a particular effect was used supplementarily in the network discovery process (Supplementary Table S4).

Two-gene network construction Two-gene networks were generated in MATLAB (2009a, The MathWorks, Inc.) by first enumerating all possibilities and then removing one network from each symmetric pair (defined by two functionally-equivalent networks which can be made identical through a simple change of component subscripts). The heterodimers P1 P2 and P2 P1 were assumed to be equivalent.

Chemical reaction network theory analysis and network screening Advanced deficiency theory analysis was done using a preliminary version of the Chemical Reaction Network Toolbox v2.0 (http://www.chbmeng.ohio-state.edu/,feinberg/crntwin/) made available to us by M. Feinberg and automated with AutoIt v3 (http://www.autoitscript.com/autoit3/index.shtml). Networks were screened based on the content of the analysis reports generated by the Toolbox. These reports, though unique to each network, all contain one of three statements: either the network ‘‘DOES have the capacity for multiple steady states’’, ‘‘CANNOT admit multiple positive steady states’’, or ‘‘MAY have the capacity for multiple steady states’’. Networks with reports containing one of the latter two statements were labeled monostable and unknown, respectively. If a network was determined by ADT to have the capacity for multiple steady states, the analysis report also contained one (or more) example set(s) of rate constants and the associated pair(s) of distinct steady states. However, each steady state may be either asymptotically stable, unstable, or with a stability that is ‘‘left undetermined’’. Only those networks that could support multiple steady states and for which an example pair of asymptotically stable steady states was given were deemed to be bistable networks. This is not to imply that multiple steady state networks without such an example are not bistable, only that we were unable to confirm their bistability with ADT. The screening procedure is shown schematically in Figure 8. Network ancestry and minimal bistable network analysis was done using MATLAB. Parent and descendant network pairs were found by simple comparison of the networks stoichiometric subspaces and their constituent reactions (descendant networks contain all the same reactions as their parents plus one additional reaction). Cooperativity-free networks were identified by their lack of dimerization reactions, since by construction, the model genes do not have two TF binding sites that could be occupied simultaneously and there are no multi-protein complexes larger than dimers. Additional data analysis was done with MATLAB and Mathematica (Wolfram Research, Inc.). The bifurcation plot shown in Figure 6 was generated using XPPAUT (http://www. math.pitt.edu/,bard/xpp/xpp.html).

Supporting Information Figure S1 Large-scale GRN in S. cerevisiae. GRN was generated

through the combination of protein-protein interaction, proteinDNA interaction, and gene expression data. (EPS) Table S1 List of genes/proteins considered as transcriptional regulators in yeast. Data taken from [79,80]. (XLS) Table S2 List of protein-protein interactions. Physical proteinprotein interactions between yeast transcriptional regulators extracted from BioGRID database [81]. (XLS) Table S3 List of protein-DNA interactions. Physical protein-

DNA interactions were extracted from Yeastract database [82]. (XLS) Table S4 Transcriptional effect of protein-DNA interactions.

(XLS) Text S1 ODEs and parameter values for Fig. 6, and the method

used in translating bistable network models into the experimental data mining format. (PDF)

Acknowledgments We are grateful to M. Feinberg for the preliminary version of the Chemical Reaction Network Toolbox v2.0 and for many useful discussions, and to G. Craciun, J. Stelling, A. P. Arkin, J. Paulsson, and J. J. Collins for their comments as well. DSG was jointly mentored by GDS and EG.

Author Contributions Conceived and designed the experiments: DSG GDS EG. Performed the experiments: DSG MKMG GDS. Analyzed the data: DSG MKMG. Contributed reagents/materials/analysis tools: DSG GDS. Wrote the paper: DSG MKMG GDS EG.

References 6. Bagci EZ, Vodovotz Y, Billiar TR, Ermentrout GB, Bahar I (2006) Bistability in apoptosis: roles of Bax, Bcl-2, and mitochondrial permeability transition pores. Biophys J 90: 1546–59. 7. Eissing T, Conzelmann H, Gilles ED, Allgo¨wer F, Bullinger E, et al. (2004) Bistability analyses of a caspase activation model for receptor-induced apoptosis. J Biol Chem 279: 36892–7. 8. Legewie S, Blu¨thgen N, Herzel H (2006) Mathematical modeling identifies inhibitors of apoptosis as mediators of positive feedback and bistability. PLoS Comput Biol 2: e120. 9. Avery SV (2005) Cell individuality: the bistability of competence development. Trends Microbiol 13: 459–62.

1. Pomerening JR (2008) Uncovering mechanisms of bistability in biological systems. Curr Opin Biotechnol 19: 381–8. 2. Lai K, Robertson MJ, Schaffer DV (2004) The sonic hedgehog signaling system as a bistable genetic switch. Biophys J 86: 2748–57. 3. Laslo P, Spooner CJ, Warmflash A, Lancki DW, Lee HJ, et al. (2006) Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 126: 755–66. 4. Huang S, Guo YP, May G, Enver T (2007) Bifurcation dynamics in lineagecommitment in bipotent progenitor cells. Dev Biol 305: 695–713. 5. Cross FR, Archambault V, Miller M, Klovstad M (2002) Testing a mathematical model of the yeast cell cycle. Mol Biol Cell 13: 52–70.

PLoS Computational Biology | www.ploscompbiol.org

10

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

10. Ninfa AJ, Mayo AE (2004) Hysteresis vs. graded responses: the connections make all the difference. Sci STKE 2004: pe20. 11. Guidi G, Goldbeter A (1997) Bistability without hysteresis in chemical reaction systems: a theoretical analysis of irreversible transitions between multiple steady states. J Phys Chem A 101: 9367–9376. 12. Pomerening JR, Sontag ED, Ferrell JE (2003) Building a cell cycle oscillator: hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 5: 346–51. 13. ShaW, Moore J, Chen K, Lassaletta AD, Yi CS, et al. (2003) Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts. Proc Natl Acad Sci USA 100: 975–80. 14. Ozbudak EM, Thattai M, Lim HN, Shraiman BI, van Oudenaarden A (2004) Multistability in the lactose utilization network of Escherichia coli. Nature 427: 737–40. 15. Tyson JJ, Chen KC, Nova´k B (2001) Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2: 908–16. 16. Feinberg M (1987) Chemical reaction network structure and the stability of complex isothermal reactors–I. The Deficiency Zero and Deficiency One Theorems. Chem Eng Sci 42: 2229–2268. 17. Feinberg M (1988) Chemical reaction network structure and the stability of complex isothermal reactors. II: Multiple steady states for networks of deficiency one. Chem Eng Sci 43: 1–25. 18. Ellison P, Feinberg M (2000) How catalytic mechanisms reveal themselves in multiple steady-state data: I. Basic principles. J Mol Catal A-Chem 154: 155–167. 19. Kaltenbach HM, Dimopoulos S, Stelling J (2009) Systems analysis of cellular networks under uncertainty. FEBS Lett 583: 3923–30. 20. Conradi C, Saez-Rodriguez J, Gilles ED, Raisch J (2005) Using chemical reaction network theory to discard a kinetic mechanism hypothesis. Syst Biol 152: 243–8. 21. Otero-Muras I, Banga JR, Alonso AA (2009) Exploring multiplicity conditions in enzymatic reaction networks. Biotechnol Prog 25: 619–31. 22. Craciun G, Tang Y, Feinberg M (2006) Understanding bistability in complex enzyme-driven reaction networks. Proc Natl Acad Sci USA 103: 8697–702. 23. Siegal-Gaskins D, Grotewold E, Smith GD (2009) The capacity for multistability in small gene regulatory networks. BMC Syst Biol 3: 96. 24. Ellison P (1998) The advanced deficiency algorithm and its applications to mechanism discrimination [PhD thesis]. Rochester (New York): Department of Chemical Engineering, University of Rochester. 25. Conradi C, Flockerzi D, Raisch J, Stelling J (2007) Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. Proc Natl Acad Sci USA 104: 19175–80. 26. Flockerzi D, Conradi C (2008) Subnetwork analysis for multistationarity in mass action kinetics. J Phys: Conf Ser 138: 012006. 27. Miller CA, Beard DA (2008) The effects of reversibility and noise on stochastic phosphorylation cycles and cascades. Biophys J 95: 2183–92. 28. Saez-Rodriguez J, Hammerle-Fickinger A, Dalal O, Klamt S, Gilles ED, et al. (2008) Multistability of signal transduction motifs. IET Syst Biol 2: 80–93. 29. Keller AD (1995) Model genetic circuits encoding autoregulatory transcription factors. J Theor Biol 172: 169–85. 30. Hasty J, Isaacs F, Dolnik M, McMillen D, Collins JJ (2001) Designer gene networks: Towards fundamental cellular control. Chaos 11: 207–220. 31. Isaacs FJ, Hasty J, Cantor CR, Collins JJ (2003) Prediction and measurement of an autoregulatory genetic module. Proc Natl Acad Sci USA 100: 7714–9. 32. Becskei A, Se´raphin B, Serrano L (2001) Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J 20: 2528–35. 33. Cinquin O, Demongeot J (2002) Positive and negative feedback: striking a balance between necessary antagonists. J Theor Biol 216: 229–41. 34. Cherry JL, Adler FR (2000) How to make a biological switch. J Theor Biol 203: 117–33. 35. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403: 339–42. 36. Franc¸ois P, Hakim V (2004) Design of genetic networks with specified functions by evolution in silico. Proc Natl Acad Sci USA 101: 580–5. 37. Lipshtat A, Loinger A, Balaban NQ, Biham O (2006) Genetic toggle switch without cooperative binding. Phys Rev Lett 96: 188101. 38. Buchler NE, Louis M (2008) Molecular titration and ultrasensitivity in regulatory networks. J Mol Biol 384: 1106–19. 39. Sedlak TW, Oltvai ZN, Yang E, Wang K, Boise LH, et al. (1995) Multiple Bcl-2 family members demonstrate selective dimerizations with Bax. Proc Natl Acad Sci USA 92: 7834–8. 40. Basak S, Shih VFS, Hoffmann A (2008) Generation and activation of multiple dimeric transcription factors within the NF-kB signaling system. Mol Cell Biol 28: 3139–50. 41. Monod J, Jacob F (1961) Teleonomic mechanisms in cellular metabolism, growth, and differentiation. Cold Spring Harb Symp Quant Biol 26: 389–401. 42. Ramakrishnan N, Bhalla US (2008) Memory switches in chemical reaction space. PLoS Comput Biol 4: e1000122. 43. Ma W, Trusina A, El-Samad H, Lim WA, Tang C (2009) Defining network topologies that can achieve biochemical adaptation. Cell 138: 760–73. 44. Sabouri-Ghomi M, Ciliberto A, Kar S, Nova´k B, Tyson JJ (2008) Antagonism and bistability in protein interaction networks. J Theor Biol 250: 209–18. 45. Bundschuh R, Hayot F, Jayaprakash C (2003) Fluctuations and slow variables in genetic networks. Biophys J 84: 1606–15.

PLoS Computational Biology | www.ploscompbiol.org

46. Stamatakis M, Mantzaris NV (2009) Comparison of deterministic and stochastic models of the lac operon genetic network. Biophys J 96: 887–906. 47. Kepler TB, Elston TC (2001) Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys J 81: 3116–36. 48. Blake WJ, Kaern M, Cantor CR, Collins JJ (2003) Noise in eukaryotic gene expression. Nature 422: 633–7. 49. Samoilov M, Plyasunov S, Arkin AP (2005) Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proc Natl Acad Sci USA 102: 2310–5. 50. Arkin AP, Ross J, McAdams HH (1998) Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149: 1633–48. 51. Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58: 35–55. 52. Jenal U, Hengge-Aronis R (2003) Regulation by proteolysis in bacterial cells. Curr Opin Microbiol 6: 163–72. 53. Johnson PR, Swanson R, Rakhilina L, Hochstrasser M (1998) Degradation signal masking by heterodimerization of MATa2 and MATa1 blocks their mutual destruction by the ubiquitinproteasome pathway. Cell 94: 217–27. 54. Kelemen JZ, Ratna P, Scherrer S, Becskei A (2010) Spatial epigenetic control of mono- and bistable gene expression. PLoS Biol 8: e1000332. 55. Soranzo N, Altafini C (2009) ERNEST: a toolbox for chemical reaction network theory. Bioinformatics 25: 2853–4. 56. Chothia C, Gough J, Vogel C, Teichmann SA (2003) Evolution of the protein repertoire. Science 300: 1701–3. 57. Force A, Cresko WA, Pickett FB, Proulx SR, Amemiya C, et al. (2005) The origin of subfunctions and modular gene regulation. Genetics 170: 433–46. 58. Lynch M, Conery JS (2003) The origins of genome complexity. Science 302: 1401–4. 59. Buljan M, Frankish A, Bateman A (2010) Quantifying the mechanisms of domain gain in animal proteins. Genome Biol 11: R74. 60. Babushok DV, Ostertag EM, Kazazian HH (2007) Current topics in genome evolution: molecular mechanisms of new gene formation. Cell Mol Life Sci 64: 542–54. 61. Koonin EV, Aravind L, Kondrashov AS (2000) The impact of comparative genomics on our understanding of evolution. Cell 101: 573–6. 62. Veening JW, Smits WK, Kuipers OP (2008) Bistability, epigenetics, and bethedging in bacteria. Annu Rev Microbiol 62: 193–210. 63. Jablonka E, Lamb M (1998) Epigenetic inheritance in evolution. J Evol Biol 11: 159–183. 64. Bishop AL, Rab FA, Sumner ER, Avery SV (2007) Phenotypic heterogeneity can enhance rare-cell survival in ‘stress-sensitive’ yeast populations. Mol Microbiol 63: 507–20. 65. Kussell E, Leibler S (2005) Phenotypic diversity, population growth, and information in fluctuating environments. Science 309: 2075–8. 66. Dubnau D, Losick R (2006) Bistability in bacteria. Mol Microbiol 61: 564–72. 67. Hollenhorst PC, Pietz G, Fox CA (2001) Mechanisms controlling differential promoter-occupancy by the yeast forkhead proteins Fkh1p and Fkh2p: implications for regulating the cell cycle and differentiation. Genes Dev 15: 2445–56. 68. Zhu G, Spellman PT, Volpe T, Brown PO, Botstein D, et al. (2000) Two yeast forkhead genes regulate the cell cycle and pseudohyphal growth. Nature 406: 90–4. 69. Hannenhalli S, Kaestner KH (2009) The evolution of Fox genes and their role in development and disease. Nat Rev Genet 10: 233–40. 70. Lamb TM, Mitchell AP (2003) The transcription factor Rim101p governs ion tolerance and cell differentiation by direct repression of the regulatory genes NRG1 and SMP1 in Saccharomyces cerevisiae. Mol Cell Biol 23: 677–86. 71. Bensen ES, Martin SJ, Li M, Berman J, Davis DA (2004) Transcriptional profiling in Candida albicans reveals new adaptive responses to extracellular pH and functions for Rim101p. Mol Microbiol 54: 1335–51. 72. Kumamoto CA, Vinces MD (2005) Contributions of hyphae and hypha-coregulated genes to Candida albicans virulence. Cell Microbiol 7: 1546–54. 73. Baumgartner U, Hamilton B, Piskacek M, Ruis H, Rottensteiner H (1999) Functional analysis of the zn(2)cys(6) transcription factors oaf1p and pip2p. different roles in fatty acid induction of beta-oxidation in saccharomyces cerevisiae. J Biol Chem 274: 22208–16. 74. Smith JJ, Ramsey SA, Marelli M, Marzolf B, Hwang D, et al. (2007) Transcriptional responses to fatty acid are coordinated by combinatorial control. Mol Syst Biol 3: 115. 75. Andrianantoandro E, Basu S, Karig DK, Weiss R (2006) Synthetic biology: new engineering rules for an emerging discipline. Mol Syst Biol 2: 2006.0028. 76. Ellis T, Wang X, Collins JJ (2009) Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol 27: 465–71. 77. Shinar G, Feinberg M (2010) Structural sources of robustness in biochemical reaction networks. Science 327: 1389–91. 78. Bailey JE (2001) Complex biology with no parameters. Nat Biotechnol 19: 503–4. 79. Harbison C, Gordon D, Lee T, Rinaldi N, Macisaac K, et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99–104. 80. Drobna E, Bialkova A, Subik J (2008) Transcriptional regulators of seven yeast species: Comparative genome analysis - review. Folia Microbiol 53: 275–287.

11

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of Switch-Like Behavior in Networks

82. Teixeira MC, Monteiro P, Jain P, Tenreiro S, Fernandes AR, et al. (2006) The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res 34: D446–D451.

81. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, et al. (2008) The BioGRID interaction database: 2008 update. Nucleic Acids Res 36: D637–D640.

PLoS Computational Biology | www.ploscompbiol.org

12

May 2011 | Volume 7 | Issue 5 | e1002039


Emergence of switch-like behavior in a large family of simple biochemical networks Dan Siegal-Gaskins1,2, Maria Katherine Mejia-Guerra2, Gregory D. Smith3, and Erich Grotewold2 1. Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA 2. Department of Plant Cellular and Molecular Biology and Plant Biotechnology Center, The Ohio State University, Columbus, OH 43210, USA

3. Department of Applied Science, The College of William and Mary, Williamsburg, VA 23187, USA

Supplementary Information ODEs and parameter values for Fig. 7 The dimer-free network shown in Fig. 7 can be described with the following set of ODEs: tot

P1' = k1*(X1

tot

- X1P2) - k7*(X2

tot

- X2P1 - X2P2)*P1 + k8*X2P1 - k11*P1 tot

tot

P2' = k2*(X2 - X2P1 - X2P2) - k5*(X1 - X1P2)*P2 + k6*X1P2 - k9*(X2 k10*X2P2 - k12*P2 + k16*X2P2 tot

- X1P2)*P2 - k6*X1P2

tot

- X2P1 - X2P2)*P1 - k8*X2P1

tot

- X2P1 - X2P2)*P2 - k10*X2P2

X1P2' = k5*(X1 X2P1' = k7*(X2 X2P2' = k9*(X2

- X2P1 - X2P2)*P2 +

where the Pi are the concentrations of the free protein monomers, the XiPj are the concentrations of the tot protein-DNA complexes, the Xi are the total DNA (free + bound) concentrations, and the ki are model tot tot parameters. The bifurcation plot was generated with the following parameter values: X1 = X2 = 10 C -1 -1 -1 -1 (C being an arbitrary unit of concentration), k1 = 7.22 time , k2 = 0.63 time , k5 = 0.36 C time , k6 = 0.40 -1 -1 -1 -1 -1 -1 -1 -1 time , k7 = 0.63 C time , k8 = 0.25 time , k9 = 0.057 C time , k10 = 0.17 time , k12 = 0.5 time , and k16 -1 = 1.71 time . The degradation rate k11 is used as the bifurcation parameter. Identification of bistable networks in S. cerevisiae (supplementary methods) Because limitations in the data-gathering techniques do not allow for the identification of interactions between heterodimers and DNA (reactions m, p, s, and v) with any certainty, we did not consider these reactions when searching for networks. Similarly, it cannot be determined from the data whether the TFs bind to DNA as monomers or dimers; for example, although we distinguish in our modeling framework between P1 binding to X1 and P1P1 binding to X1, that resolution does not exist in the experimental data. We therefore generated new ‘experimental evidence’ labels for pairs of reactions that cannot be distinguished: reactions b and n are referred to with label b, c and o with label c, a and l with label a, and d and q with label d. Lastly, for most of the pairs of TFs and promoters which are known to associate, the effect of that association on target gene expression (activation or repression) is usually unknown. Information suggestive of a particular effect, gathered from a large number of TF deletion strains and listed in Supplementary Table S4, was viewed as only supplementary in the process of network discovery. With these experimental limitations in mind, networks may be ‘translated’ from their theoretical description into one that takes the limitations into account. For example, the minimal bistable networks may be written as (theoretical name ! translated name) are:


1) kqw ! dk(w) 2) ckn ! bck 3) bcdh ! bcd(h) 4) abejp ! ab(e)j 5) bfjpv ! b(f)j 6) jmpsv ! j 7) ikno ! bcik 8) jknptv ! bjk(t) 9) aejknp ! ab(e)jk 10) jkmnps ! bjk 11) abdfjkmnpq ! 12) dhjknp ! bd(h)jk Labels in parenthesis indicate a reaction that is supplementary to the network discovery. Note that network abdfjkmnpq cannot be translated because it contains both monomeric and dimeric TF-promoter binding explicitly.

Supplementary Table S1. List of genes/proteins considered as transcriptional regulators in yeast, taken from [1] and [2]. Supplementary Table S2. List of protein-protein interactions. Physica protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database [3]. Supplementary Table S3. List of protein-DNA interactions. Physical protein-DNA interactions were extracted from YEASTRACT database [4]. Supplementary Table S4. Protein-DNA binding reactions which renders activation or repression extracted from literature. Supplementary Figure S1. Large-scale GRN in S. cerevisiae, generated through the combination of protein-protein interaction, protein-DNA interaction, and gene expression data.


References 1. Harbison C, Gordon D, Lee T, Rinaldi N, Macisaac K, Danford T, Hannett N, Tagne J, Reynolds D, Yoo J, Jennings E, Zeitlinger J, Pokholok D, Kellis M, Rolfe P, Takusagawa K, Lander E, Gifford D, Fraenkel E, and Young R (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431:99-104. 2. Drobna E, Bialkova A, and Subik J (2008) Transcriptional regulators of seven yeast species: Comparative genome analysis - Review. Folia Microbiol., 53:275–287. 3. Breitkreutz B, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Bahler J, Wood V, Dolinski K, and Tyers M (2008) The BioGRID interaction database: 2008 update. Nucleic Acids Res, 36:D637–D640. 4. Teixeira MC, Monteiro P, Jain P, Tenreiro S, Fernandes AR, Mira NP, Alenquer M, Freitas AT, Oliveira AL, and Sá-Correia I (2006) The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res, 34:446451.


Page 1 of 6

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Systematic Gene Name

Common Gene Name

Protein Name

YCR040W YKL112W YER045C YLR131C YDR216W YGL071W YPL202C YMR042W YML099C YDR421W YPR199C YKL185W YGR097W YOR113W YKR099W YKL005C YDR423C YJR060W YLR098C YOR028C YNL027W YIL036W YPL177C YKR034W YIR023W YNL314W YML113W YPL049C YER088C YLR228C YBR033W YNR054C YDL166C YPR104C YIL131C YNL068C YGL254W YDR009W YPL248C YML051W YFL021W YLR013W YEL009C

A1 ABF1 ACA1 ACE2 ADR1 AFT1 AFT2 ARG80 ARG81 ARO80 ARR1 ASH1 ASK10 AZF1 BAS1 BYE1 CAD1 CBF1 CHA4 CIN5 CRZ1 CST6 CUP9 DAL80 DAL81 DAL82 DAT1 DIG1 DOT6 ECM22 EDS1 ESF2 FAP7 FHL1 FKH1 FKH2 FZF1 GAL3 GAL4 GAL80 GAT1 GAT3 GCN4

A1p Abf1p Aca1p Ace2p Adr1p Aft1p Aft2p Arg80p Arg81p Aro80p Arr1p Ash1p Ask10p Azf1p Bas1p Bye1p Cad1p Cbf1p Cha4p Cin5p Crz1p Cst6p Cup9p Dal80p Dal81p Dal82p Dat1p Dig1p Dot6p Ecm22p Eds1p Esf2p Fap7p Fhl1p Fkh1p Fkh2p Fzf1p Gal3p Gal4p Gal80p Gat1p Gat3p Gcn4p


Page 2 of 6

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Systematic Gene Name

Common Gene Name

Protein Name

YPL075W YNL199C YER040W YGL181W YJL110C YPR008W YFL031W YOL089C YLR256W YGL237C YBL021C YKL109W YOR358W YBL008W YOR038C YJR140C YOR032C YJR147W YLR113W YGL073W YLR223C YJR094C YGL192W YDR123C YOL108C YKL032C YNL132W YGR040W YLR451W YMR021C YGR288W YBR297W YOR298C-A YDL056W YMR043W YGL197W YIL128W YIR017C YPL038W YDR253C YNL103W YGR249W YGL035C

GCR1 GCR2 GLN3 GTS1 GZF3 HAA1 HAC1 HAL9 HAP1 HAP2 HAP3 HAP4 HAP5 HIR1 HIR2 HIR3 HMS1 HMS2 HOG1 HSF1 IFH1 IME1 IME4 INO2 INO4 IXR1 KRE33 KSS1 LEU3 MAC1 MAL13 MAL33 MBF1 MBP1 MCM1 MDS3 MET18 MET28 MET31 MET32 MET4 MGA1 MIG1

Gcr1p Gcr2p Gln3p Gts1p Gzf3p Haa1p Hac1p Hal9p Hap1p Hap2p Hap3p Hap4p Hap5p Hir1p Hir2p Hir3p Hms1p Hms2p Hog1p Hsf1p Ifh1p Ime1p Ime4p Ino2p Ino4p Ixr1p Kre33p Kss1p Leu3p Mac1p Mal13p Mal33p Mbf1p Mbp1p Mcm1p Mds3p Met18p Met28p Met31p Met32p Met4p Mga1p Mig1p


Page 3 of 6

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Systematic Gene Name

Common Gene Name

Protein Name

YGL209W YER028C YER068W YMR070W YOL116W YMR037C YKL062W YMR164C YDR277C YOR372C YHR124W YGR089W YDR043C YAL051W YHL020C YDR081C YGL013C YBL005W YKL043W YDL106C YFR034C YOR363C YLR014C YKL015W YNL216W YMR075W YOR380W YCR106W YBR049C YLR176C YMR182C YKL038W YHL027W YPL089C YGR044C YPR065W YER169W YIL119C YDL020C YJR127C YOL067C YBL103C YOR077W

MIG2 MIG3 MOT2 MOT3 MSN1 MSN2 MSN4 MSS11 MTH1 NDD1 NDT80 NNF2 NRG1 OAF1 OPI1 PDC2 PDR1 PDR3 PHD1 PHO2 PHO4 PIP2 PPR1 PUT3 RAP1 RCO1 RDR1 RDS1 REB1 RFX1 RGM1 RGT1 RIM101 RLM1 RME1 ROX1 RPH1 RPI1 RPN4 RSF2 RTG1 RTG3 RTS2

Mig2p Mig3p Mot2p Mot3p Msn1p Msn2p Msn4p Mss11p Mth1p Ndd1p Ndt80p Nnf2p Nrg1p Oaf1p Opi1p Pdc2p Pdr1p Pdr3p Phd1p Pho2p Pho4p Pip2p Ppr1p Put3p Rap1p Rco1p Rdr1p Rds1p Reb1p Rfx1p Rgm1p Rgt1p Rim101p Rlm1p Rme1p Rox1p Rph1p Rpi1p Rpn4p Rsf2p Rtg1p Rtg3p Rts2p


Page 4 of 6

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Systematic Gene Name

Common Gene Name

Protein Name

YOR140W YLR403W YNL257C YJL089W YHR206W YNL167C YPR054W YBR182C YDR477W YGL131C YMR016C YJL127C YER161C YKL020C YCR018C YNL309W YMR053C YMR019W YHR178W YKL072W YHR084W YDR463W YHR006W YDL048C YDR310C YGL162W YPR009W YER111C YDR146C YLR182W YBR150C YBR083W YBR240C YNL139C YGL096W YOR344C YDL170W YDR207C YDR213W YPL230W YML076C YOR230W YOR229W

SFL1 SFP1 SIP3 SIP4 SKN7 SKO1 SMK1 SMP1 SNF1 SNT2 SOK2 SPT10 SPT2 SPT23 SRD1 STB1 STB2 STB4 STB5 STB6 STE12 STP1 STP2 STP4 SUM1 SUT1 SUT2 SWI4 SWI5 SWI6 TBS1 TEC1 THI2 THO2 TOS8 TYE7 UGA3 UME6 UPC2 USV1 WAR1 WTM1 WTM2

Sfl1p Sfp1p Sip3p Sip4p Skn7p Sko1p Smk1p Smp1p Snf1p Snt2p Sok2p Spt10p Spt2p Spt23p Srd1p Stb1p Stb2p Stb4p Stb5p Stb6p Ste12p Stp1p Stp2p Stp4p Sum1p Sut1p Sut2p Swi4p Swi5p Swi6p Sbs1p Tec1p Thi2p Tho2p Tos8p Tye7p Uga3p Ume6p Upc2p Usv1p War1p Wtm1p Wtm2p


Page 5 of 6

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Systematic Gene Name

Common Gene Name

Protein Name

YIL101C YML007W YHL009C YIR018W YDR259C YOL028C YBL054W YBR239C YBR267W YDR026C YDR049W YDR266C YDR520C YER051W YER130C YER184C YFL044C YFL052W YGR067C YDR451C YJL206C YKL222C YKR064W YLR278C YML081W YNR063W YML027W YPR022C YPR196W YOR162C YJL056C YBL066C YBR066C YCR065W YDR006C YDR017C YDR034C YDR096W YDR303C YER164W YGL166W YHR056C YIL130W

XBP1 YAP1 YAP3 YAP5 YAP6 YAP7 YBL054W YBR239C YBR267W YDR026C YDR049W YDR266C YDR520C YER051W YER130C YER184C YFL044C YFL052W YGR067C YHP1 YJL206C YKL222C YKR064W YLR278C YML081W YNR063W YOX1 YPR022C YPR196W YRR1 ZAP1 SEF1 NRG2 HCM1 SOK1 KCS1 LYS14 GIS1 RSC3 CHD1 CUP2 RSC30 ASG1

Xbp1p Yap1p Yap3p Yap5p Yap6p Yap7p Ybl054wp Ybr239cp Ybr267wp Ydr026cp Ydr049wp Ydr266cp Ydr520cp Yer051wp Yer130cp Yer184cp Yfl044cp Yfl052wp Ygr067cp Yhp1p Yjl206cp Ykl222cp Ykr064wp Ylr278cp Yml081wp Ynr063wp Yox1p Ypr022cp Ypr196wp Yrr1p Zap1p Sef1p Nrg2p Hcm1p Sok1p Kcs1p Lys14p Gis1p Rsc3p Chd1p Cup2p Rsc30p Asg1p


Page 6 of 6

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Systematic Gene Name

Common Gene Name

Protein Name

YJL103C YLR266C YMR168C YMR213W YMR280C YOR172W YOR337W YPL133C YPR186C YER109C YMR172W YIR033W YCL055W

GSM1 PDR8 CEP3 CEF1 CAT8 YRM1 TEA1 RDS2 PZF1 FLO8 HOT1 MGA2 KAR4

Gsm1p Pdr8p Cep3p Cef1p Cat8p Yrm1p Tea1p Rds2p Pzf1p Flo8p Hot1p Mga2p Kar4p


Page 1 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

A1p Adr1p Adr1p Aft1p Aft1p Aft1p Aft1p Arg80p Arg80p Arg80p Arg80p Arg80p Arg80p Arg80p Arg80p Arg81p Arg81p Arg81p Arg81p Arg81p Ash1p Ash1p Bas1p Bas1p Bas1p Bas1p Bas1p Cbf1p Cbf1p Cbf1p Cbf1p Cbf1p Cbf1p Cbf1p Cbf1p Cbf1p Crz1p Cst6p Cst6p Dal80p Dal80p Dal80p Dal80p

Mcm1p Snf1p Cat8p Aft1p Aft1p Cbf1p Yap5p Arg81p Arg81p Arg81p Arg81p Mcm1p Mcm1p Mcm1p Ume6p Arg81p Mcm1p Mcm1p Mcm1p Ume6p Ash1p Ume6p Pho2p Pho2p Pho2p Pho2p Pho2p Cbf1p Cbf1p Met28p Met28p Met32p Met4p Met4p Met4p Cep3p Skn7p Kss1p Oaf1p Dal80p Dal80p Gzf3p Ydr520cp

Reconstituted Complex Co-localization Co-localization Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Affinity Capture-MS Affinity Capture-MS Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Two-hybrid PCA Reconstituted Complex Two-hybrid Reconstituted Complex Co-purification Affinity Capture-Western Two-hybrid Reconstituted Complex Reconstituted Complex Reconstituted Complex Biochemical Activity Affinity Capture-MS Two-hybrid Two-hybrid Two-hybrid Two-hybrid

15118075 12167649 15743812 17538022 17538022 16172405 10688190 10632874 12138185 10688655 10688655 10632874 12138185 15289616 10809695 10632874 10632874 10688655 10688655 10809695 16314178 16314178 19528318 12110691 12110691 11689683 11095676 18467557 9894911 8665859 9171357 18308733 18308733 8665859 9171357 11070082 11432834 16319894 16554755 18719252 9791119 9791119 11283351


Page 2 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Dal80p Dal81p Dal82p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Dig1p Ecm22p Esf2p Esf2p Fhl1p Fhl1p Fhl1p Fhl1p Fkh1p Fkh2p Fkh2p Fkh2p Fkh2p Fkh2p Fkh2p Fkh2p

Ydr520cp Dal82p Dal82p Dig1p Gts1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Pho4p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Tec1p Tec1p Tec1p Mot3p Esf2p Kre33p Ifh1p Ifh1p Ifh1p Rap1p Mbp1p Mcm1p Mcm1p Mcm1p Mcm1p Ndd1p Ndd1p Ndd1p

Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Reconstituted Complex Two-hybrid Two-hybrid Biochemical Activity Biochemical Activity Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Two-hybrid FRET Affinity Capture-Western Reconstituted Complex Two-hybrid Affinity Capture-MS Affinity Capture-Western Two-hybrid Affinity Capture-MS Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Affinity Capture-MS Affinity Capture-MS Two-hybrid Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-MS Reconstituted Complex Reconstituted Complex Reconstituted Complex Reconstituted Complex Affinity Capture-Western Reconstituted Complex Affinity Capture-Western

18719252 10906145 10688190 18719252 18719252 18719252 8918885 14734536 8918885 9094309 8918885 11525741 11805837 14660704 17200106 18719252 19079053 8918885 10825185 9343403 12590263 9094309 9094309 16554755 16782869 19218425 16782869 16782869 16783004 15964808 16554755 15620355 15620355 15692568 17452446 11805837 12711672 10959837 10899128 10894549 12865300 15509804 15509804


Page 3 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal3p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal4p Gal80p Gal80p

Gal4p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal4p Gal4p Gal4p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Hap5p Ino2p Rap1p Snf1p Gal80p Gal80p

Reconstituted Complex Affinity Capture-Western Reconstituted Complex Two-hybrid FRET Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Co-purification Co-crystal Structure Reconstituted Complex Co-crystal Structure Co-crystal Structure Affinity Capture-Western Affinity Capture-Western Protein-peptide Reconstituted Complex Reconstituted Complex Two-hybrid Affinity Capture-Western Two-hybrid Affinity Capture-Western Reconstituted Complex Reconstituted Complex Reconstituted Complex Two-hybrid Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Reconstituted Complex Two-hybrid Affinity Capture-Western Co-purification Two-hybrid Reconstituted Complex Two-hybrid Reconstituted Complex PCA Reconstituted Complex

9670023 18245852 18245852 18245852 18952899 9050845 9111326 8628318 9670023 15998719 16219783 11964151 18611375 18611375 1557122 18292341 11418596 7739564 12706896 10966808 15998719 11418596 10523671 10523671 12417740 10809742 9670023 8670900 9159467 3316976 3316976 1985957 11095729 11478912 15695361 15695361 1406674 11418596 15719021 17919657 15719021 18467557 11179228


Page 4 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gal80p Gcn4p Gcn4p Gcn4p Gcn4p Gcn4p Gcr1p Gcr1p Gcr1p Gcr1p Gcr1p Gcr1p Gln3p Gln3p Gln3p Gln3p Gln3p Gts1p Gts1p Gts1p Gzf3p Gzf3p Gzf3p Gzf3p Hac1p Hal9p Hap1p Hap1p Hap2p Hap2p Hap2p Hap2p Hap2p Hap2p Hap2p Hap2p

Gal80p Ino2p Pho2p Put3p Rtg1p Swi5p Yer130cp Hcm1p Gcn4p Gcn4p Mbf1p Mbf1p Met31p Gcr2p Gcr2p Rap1p Rap1p Rap1p Rap1p Gln3p Met4p Snf1p Snf1p Snf1p Gts1p Rgm1p Yap6p Gzf3p Snf1p Ydr520cp Cep3p Hac1p Tbs1p Hap1p Hap1p Hap3p Hap3p Hap3p Hap3p Hap3p Hap4p Hap4p Hap5p

Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Protein-peptide Reconstituted Complex Affinity Capture-Western Reconstituted Complex Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Reconstituted Complex Affinity Capture-MS Affinity Capture-Western Two-hybrid Biochemical Activity Reconstituted Complex Two-hybrid Two-hybrid Two-hybrid Biochemical Activity Two-hybrid Two-hybrid Two-hybrid Two-hybrid Reconstituted Complex Reconstituted Complex Affinity Capture-MS Two-hybrid Reconstituted Complex Affinity Capture-MS Reconstituted Complex Reconstituted Complex Reconstituted Complex Affinity Capture-MS

15695361 18719252 18719252 18719252 18719252 18719252 18719252 18719252 19331323 3678204 9710580 9710580 10688190 7713414 1508187 8508768 8649429 9826662 15300680 19345193 16554755 11809814 11809814 11809814 19345193 18719252 11283351 9791119 16319894 18719252 17634282 8932376 18719252 10428861 8464899 17200106 11283351 10972830 16554755 16278450 9372932 16278450 17200106


Page 5 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Hap2p Hap2p Hap2p Hap2p Hap2p Hap2p Hap2p Hap3p Hap3p Hap3p Hap3p Hap3p Hap3p Hap3p Hap4p Hap4p Hir1p Hir1p Hir1p Hir1p Hir1p Hir1p Hir1p Hir2p Hir2p Hog1p Hog1p Hog1p Hog1p Hog1p Hog1p Hog1p Hog1p Hog1p Hog1p Hog1p Hsf1p Hsf1p Hsf1p Hsf1p Hsf1p Hsf1p Ifh1p

Hap5p Hap5p Hap5p Hap5p Hap5p Hap5p Hap5p Hap4p Hap4p Hap5p Hap5p Hap5p Hap5p Hap5p Hap5p Hap5p Hir1p Hir1p Hir2p Hir2p Hir2p Hir2p Hir3p Hir3p Hir3p Sko1p Sko1p Sko1p Sko1p Smp1p Smp1p Smp1p Rsc3p Hot1p Hot1p Hot1p Msn2p Msn4p Skn7p Snf1p Snf1p Snf1p Rap1p

Two-hybrid Reconstituted Complex Affinity Capture-MS Reconstituted Complex Affinity Capture-MS Affinity Capture-MS Reconstituted Complex Reconstituted Complex Reconstituted Complex Affinity Capture-MS Two-hybrid Reconstituted Complex Reconstituted Complex Affinity Capture-MS Reconstituted Complex Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Affinity Capture-MS Affinity Capture-Western Affinity Capture-Western Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Biochemical Activity Affinity Capture-Western Two-hybrid Biochemical Activity Affinity Capture-Western Two-hybrid Two-hybrid Biochemical Activity Co-localization Co-localization Affinity Capture-Western Biochemical Activity Two-hybrid Biochemical Activity Affinity Capture-Western

11283351 7828851 11805826 10972830 11805837 16554755 16278450 9372932 16278450 17200106 11283351 7828851 10972830 16554755 9372932 16278450 9504914 9001207 17200106 9504914 9001207 16264190 16264190 17200106 16264190 11230135 12086627 12086627 11230135 12482976 12482976 12482976 19153600 10409737 12743037 12743037 18070923 18070923 10888672 18793336 14612437 14612437 17452446


Page 6 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Ime1p Ime1p Ime1p Ime1p Ime1p Ime1p Ime4p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino2p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Kss1p Mac1p Mac1p

Ime1p Ume6p Ume6p Ume6p Ume6p Ume6p Kar4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Ino4p Opi1p Opi1p Opi1p Opi1p Opi1p Opi1p Pho4p Pho4p Rtg1p Rtg3p Rtg3p Tye7p Tye7p Sip4p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Tec1p Tec1p Asg1p Mac1p Mac1p

Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Far Western Reconstituted Complex Reconstituted Complex Two-hybrid Two-hybrid Affinity Capture-Western Affinity Capture-MS Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Affinity Capture-Western Two-hybrid Biochemical Activity Two-hybrid Reconstituted Complex Reconstituted Complex Two-hybrid Reconstituted Complex Biochemical Activity Affinity Capture-MS Affinity Capture-Western Affinity Capture-MS Biochemical Activity Two-hybrid Two-hybrid

8628320 10545448 9372955 9111339 9889189 8628320 11283351 10688190 18542964 11071933 7862526 10747047 10361278 11071933 7862526 8195172 16554755 12753200 11454208 11454208 15819625 15819625 15819625 11071933 11071933 11071933 11071933 11071933 11071933 11071933 16319894 18719252 14734536 9744865 7851759 9393860 11525741 11805837 15558284 11805837 16319894 18719252 10506178


Page 7 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Mac1p Mac1p Mbp1p Mbp1p Mbp1p Mbp1p Mbp1p Mbp1p Mbp1p Mbp1p Mbp1p Mbp1p Mcm1p Mcm1p Mcm1p Mcm1p Mcm1p Met28p Met28p Met28p Met28p Met31p Met31p Met31p Met32p Met32p Met4p Mig1p Mig1p Mig1p Mig1p Mig1p Mig1p Mig1p Mig1p Mig1p Mig3p Mot2p Mot3p Mot3p Msn1p Msn1p Msn2p

Mac1p Mac1p Skn7p Skn7p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Mcm1p Ste12p Ste12p Yhp1p Yox1p Met4p Met4p Met4p Met4p Met4p Met4p Met4p Met4p Met4p Met4p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Mot2p Mot3p Mot3p Ydr520cp Ygr067cp Snf1p

Two-hybrid Two-hybrid Reconstituted Complex Two-hybrid Affinity Capture-MS Two-hybrid Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Two-hybrid Affinity Capture-MS Affinity Capture-MS Two-hybrid Reconstituted Complex Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Two-hybrid Reconstituted Complex Two-hybrid Affinity Capture-Western Two-hybrid Reconstituted Complex Two-hybrid Reconstituted Complex Two-hybrid Affinity Capture-Western Biochemical Activity Affinity Capture-Western Two-hybrid Biochemical Activity Biochemical Activity Biochemical Activity Biochemical Activity Biochemical Activity Biochemical Activity Affinity Capture-Western PCA Reconstituted Complex Two-hybrid Two-hybrid Biochemical Activity

9867833 11297731 10512874 10512874 17200106 19820714 19820714 19820714 8649372 10512874 11805826 16554755 10632874 1756728 8139556 12464633 12464633 17157252 9799240 9171357 8665859 17157252 9799240 9799240 9799240 9799240 11087867 17178716 12748292 9774644 9774644 12684376 12393914 10403407 16319894 16847059 14993292 19707589 18467557 19345193 18719252 18719252 16281053


Page 8 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Msn2p Mss11p Mss11p Mss11p Mth1p Mth1p Mth1p Mth1p Nnf2p Nnf2p Nrg1p Nrg1p Oaf1p Oaf1p Oaf1p Oaf1p Oaf1p Oaf1p Oaf1p Pdr1p Pdr1p Pdr1p Pdr1p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho2p Pho4p Pho4p Pho4p Pho4p

Sok2p Ste12p Tec1p Flo8p Rgt1p Rgt1p Rgt1p Rgt1p Nnf2p Swi5p Rim101p Snf1p Oaf1p Oaf1p Pip2p Pip2p Pip2p Pip2p Pip2p Pdr1p Pdr3p Stb5p Stb5p Pho4p Pho4p Pho4p Pho4p Pho4p Pho4p Pho4p Pho4p Pho4p Pho4p Swi5p Swi5p Swi5p Swi5p Swi5p Swi5p Pho4p Pho4p Pho4p Pho4p

Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Two-hybrid Two-hybrid Reconstituted Complex Two-hybrid Two-hybrid Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Co-localization Reconstituted Complex Co-localization Affinity Capture-Western Reconstituted Complex Co-localization Reconstituted Complex Co-fractionation Affinity Capture-Western Reconstituted Complex Two-hybrid Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Two-hybrid Reconstituted Complex Two-hybrid Reconstituted Complex Reconstituted Complex Reconstituted Complex Reconstituted Complex Reconstituted Complex Reconstituted Complex Two-hybrid Two-hybrid Reconstituted Complex Protein-peptide Co-crystal Structure

11238897 15485921 15485921 15485921 14508605 14508605 15489524 15489524 11087867 11087867 16024810 11404322 18671944 9288897 18671944 18285336 8972187 12709061 9288897 19345193 12453227 15123673 15123673 19528318 19528318 9354395 12136204 7957107 12219216 8676879 10884387 10884387 10320381 9111337 7493941 8355698 7902583 9774660 9774660 18719252 9443961 11967834 9303313


Page 9 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Pho4p Ppr1p Put3p Put3p Put3p Rco1p Rdr1p Reb1p Reb1p Reb1p Reb1p Rfx1p Rgt1p Rim101p Rlm1p Rlm1p Rtg1p Rtg1p Rtg1p Rtg1p Rtg1p Sfl1p Sip3p Sip3p Sip4p Sip4p Sip4p Sip4p Sip4p Sip4p Skn7p Skn7p Skn7p Sko1p Smk1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p Snf1p

Snf1p Ppr1p Put3p Put3p Put3p Rco1p Rdr1p Reb1p Rfx1p Rsc3p Rsc3p Rsc3p Rgt1p Zap1p Rlm1p Smp1p Rtg3p Rtg3p Rtg3p Rtg3p Rtg3p Sfl1p Snf1p Snf1p Sip4p Snf1p Snf1p Snf1p Snf1p Cat8p Skn7p Skn7p Skn7p Sko1p Yap7p Snf1p Snf1p Snf1p Snf1p Nrg2p Nrg2p Gsm1p Rds2p

Affinity Capture-MS Co-crystal Structure Co-crystal Structure Co-crystal Structure Reconstituted Complex Affinity Capture-MS Two-hybrid Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Two-hybrid Two-hybrid Reconstituted Complex Affinity Capture-Western Affinity Capture-MS Two-hybrid Affinity Capture-Western Reconstituted Complex Affinity Capture-MS Affinity Capture-Western Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Protein-peptide Affinity Capture-Western Affinity Capture-MS Co-crystal Structure Reconstituted Complex Affinity Capture-Western Biochemical Activity Affinity Capture-Western Two-hybrid Biochemical Activity Biochemical Activity

14660704 7958913 9303003 9303004 8846888 16286008 11283351 14759368 11805826 11805826 16429126 11805826 15489524 10688190 19345193 9121433 17200106 9242640 10848632 9032238 16554755 12024012 8127709 1496382 11486018 10581241 11486018 10581241 8628258 11486018 10888672 8598053 11967834 11500510 16554755 16531232 16531232 16531232 12748292 11404322 11404322 16319894 17875938


Page 10 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Snf1p Snt2p Sok2p Spt2p Spt23p Spt23p Spt23p Stb1p Stb1p Stb1p Stb4p Stb5p Ste12p Ste12p Ste12p Ste12p Ste12p Ste12p Sum1p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Swi4p Tec1p Tho2p Ume6p Ume6p Wtm1p Wtm1p Wtm1p Wtm1p Wtm1p Wtm1p Wtm2p

Rds2p Xbp1p Sok2p Spt2p Spt23p Spt23p Yfl044cp Swi6p Swi6p Swi6p Swi5p Stb5p Tec1p Tec1p Tec1p Tec1p Tec1p Flo8p Sum1p Swi4p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Swi6p Flo8p Tho2p Ume6p Tea1p Wtm1p Wtm1p Wtm2p Wtm2p Wtm2p Wtm2p Wtm2p

Biochemical Activity Affinity Capture-MS Reconstituted Complex Affinity Capture-Western Two-hybrid Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-MS Affinity Capture-Western Reconstituted Complex Reconstituted Complex Affinity Capture-MS Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-MS Two-hybrid Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Reconstituted Complex Reconstituted Complex Affinity Capture-MS Affinity Capture-MS Affinity Capture-Western Affinity Capture-MS Affinity Capture-MS Two-hybrid Affinity Capture-Western Two-hybrid Two-hybrid Affinity Capture-Western Two-hybrid Affinity Capture-MS PCA

16319894 16554755 19345193 9632800 11733065 11733065 16427015 18794370 10409718 12832490 11805837 15123673 9036858 9234690 11805837 16782869 16782869 15485921 18268008 10490612 17200106 19820714 19820714 19820714 12024050 8649372 10490612 9521763 2649246 1465410 16554755 16429126 15485921 14759368 16314178 11238941 9234739 9234739 18719252 9234739 9234739 11805837 18467557


Page 11 of 11

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein

Experimental Evidence

PubMed ID

Wtm2p Wtm2p Wtm2p Yap5p Ybr239cp Ybr267wp Ybr267wp Ydr266cp Ydr520cp Ydr520cp Yer184cp Ypr022cp Yrr1p Yrr1p Yrr1p Gis1p Rsc3p Rsc3p Rsc3p Rsc3p Chd1p Cep3p Cep3p Cef1p Cat8p Cat8p

Wtm2p Wtm2p Wtm2p Yap5p Rds2p Ybr267wp Ybr267wp Ydr266cp Ydr520cp Ydr520cp Yer184cp Ypr022cp Yrr1p Yrr1p Yrr1p Mga2p Rsc3p Rsc30p Rsc30p Rsc30p Chd1p Cep3p Cep3p Cef1p Cat8p Cat8p

Two-hybrid Affinity Capture-Western Two-hybrid Protein-peptide Two-hybrid Affinity Capture-MS Affinity Capture-Western Protein-peptide Two-hybrid Protein-peptide Two-hybrid Reconstituted Complex Two-hybrid Affinity Capture-Western Reconstituted Complex Two-hybrid Affinity Capture-MS Affinity Capture-MS Affinity Capture-MS Affinity Capture-Western Affinity Capture-MS Co-crystal Structure Co-purification Two-hybrid Two-hybrid Protein-peptide

18719252 9234739 9234739 11967834 11283351 14759368 16651379 11967834 18719252 11967834 18719252 19345193 18719252 15123673 15123673 17043893 14759368 17200106 16429126 16204215 14759368 18064045 10352012 10092627 11486018 11967834


Page 1 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Arr1p Cha4p Dal80p Gat3p Gzf3p Hap4p Rap1p Stp2p Uga3p Yap5p Yap6p Zap1p Abf1p Mal33p Mbp1p Ash1p Cad1p Cin5p Fhl1p Hap5p Mbp1p Mga1p Nrg1p Phd1p Pho2p Rap1p Rim101p Rox1p Sok2p Ste12p Xbp1p Yap6p Flo8p Fkh1p Fkh2p Ino4p Mcm1p Rap1p Zap1p Fhl1p Msn4p Pho4p Rgt1p

YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YCR040W YKL112W YKL112W YKL112W YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YER045C YLR131C YLR131C YLR131C YLR131C YLR131C YLR131C YDR216W YDR216W YDR216W YDR216W

15343339 15343339 15343339 12399584 15343339 15343339 3315231 15343339 15343339 15343339 12399584 15343339 15192094 15343339 16709784 12399584 16709784 16709784 17646381 15343339 12399584 16449570 15343339 16449570 15343339 17646381 15343339 12399584 16449570 19159457 15343339 15343339 16449570 16709784 16709784 16709784 16709784 16709784 12399584 17646381 12399584 19108609 12399584


Page 2 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Ste12p Stp2p Yhp1p Gsm1p Aft1p Aft2p Fhl1p Phd1p Put3p Rap1p Ino4p Ndt80p Skn7p Yap1p Fhl1p Ste12p Arg80p Arg81p Dal82p Gat1p Aro80p Cin5p Yap1p Ace2p Mcm1p Smp1p Sok2p Swi5p Cin5p Phd1p Ste12p Yap6p Rap1p Gat1p Rtg1p Sip4p Yap7p Abf1p Dot6p Gts1p Abf1p Yap5p Cbf1p

YDR216W YDR216W YDR216W YDR216W YGL071W YGL071W YGL071W YGL071W YGL071W YGL071W YPL202C YPL202C YPL202C YPL202C YMR042W YMR042W YML099C YML099C YML099C YML099C YDR421W YDR421W YDR421W YKL185W YKL185W YKL185W YKL185W YKL185W YGR097W YGR097W YGR097W YGR097W YOR113W YKR099W YKR099W YKR099W YKR099W YKL005C YKL005C YKL005C YDR423C YDR423C YJR060W

17638031 16709784 12464632 16785442 15343339 15343339 17646381 16449570 15343339 17646381 16709784 15343339 15343339 18627600 17646381 19159457 12399584 15343339 16709784 15343339 16709784 12399584 18627600 17898805 18303948 15343339 17638031 17898805 12399584 16449570 19159457 12399584 17646381 15343339 12399584 15343339 15343339 18305101 16709784 15343339 16709784 12464632 15343339


Page 3 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Cin5p Pho4p Tos8p Mss11p Stb5p Cin5p Mga1p Phd1p Sko1p Sok2p Ste12p Tec1p Yap1p Yap6p Flo8p Pho2p Pho4p Rap1p Abf1p Ino4p Pho4p Put3p Ste12p Arg80p Arg81p Fhl1p Fkh2p Mac1p Mga1p Phd1p Pho4p Sok2p Stb5p Ste12p Tec1p Flo8p Gln3p Ste12p Sum1p Tec1p Abf1p Yap6p Mga1p

YJR060W YJR060W YJR060W YLR098C YLR098C YOR028C YOR028C YOR028C YOR028C YOR028C YOR028C YOR028C YOR028C YOR028C YOR028C YNL027W YNL027W YNL027W YIL036W YIL036W YIL036W YIL036W YIL036W YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YPL177C YKR034W YKR034W YKR034W YKR034W YIR023W YIR023W YPL049C

12399584 19108609 12464632 12399584 16914749 15343339 16449570 16449570 18931682 16449570 16449570 16449570 18627600 15343339 16449570 15343339 15343339 17646381 16709784 16709784 15343339 12399584 15343339 15343339 15343339 17646381 12399584 16709784 16449570 16449570 19108609 16449570 16914749 17638031 16449570 16449570 9171383 16449570 12399584 16449570 16709784 15343339 16449570


Page 4 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Phd1p Sok2p Ste12p Tos8p Fhl1p Rap1p Skn7p Sok2p Stb5p Cin5p Sok2p Ste12p Hcm1p Hal9p Rap1p Rgt1p Rph1p Dig1p Hap3p Hap5p Hms2p Ppr1p Sfp1p Ste12p Swi6p Upc2p Hcm1p Abf1p Fkh1p Msn4p Fkh2p Fhl1p Fkh1p Fkh2p Rap1p Abf1p Cin5p Gcr2p Gln3p Met31p Met32p Put3p Rtg1p

YPL049C YPL049C YPL049C YPL049C YER088C YER088C YER088C YER088C YER088C YLR228C YLR228C YLR228C YLR228C YBR033W YBR033W YBR033W YBR033W YNR054C YNR054C YNR054C YNR054C YNR054C YNR054C YNR054C YNR054C YNR054C YNR054C YPR104C YPR104C YPR104C YIL131C YNL068C YNL068C YNL068C YNL068C YGL254W YGL254W YGL254W YGL254W YGL254W YGL254W YGL254W YGL254W

16449570 17638031 17638031 12464632 17646381 17646381 12399584 16449570 16914749 16709784 17638031 17638031 12464632 12399584 17646381 12399584 15343339 12399584 12399584 12399584 15343339 15343339 12399584 17638031 12399584 15343339 12464632 18305101 15343339 12399584 15343339 17646381 11562353 18057023 17646381 18305101 12399584 15343339 15343339 15343339 15343339 15343339 15343339


Page 5 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Swi4p Gal4p Mig1p Pdr3p Ste12p Mig1p Stp1p Stp2p Tos8p Fkh2p Gal4p Ace2p Dal80p Dal81p Dal82p Fhl1p Fkh2p Gln3p Hap2p Pho4p Rap1p Skn7p Smp1p Ste12p Swi5p Ume6p Yap5p Nrg1p Rap1p Swi5p Ume6p Fhl1p Gln3p Hap2p Hap4p Mga1p Rap1p Sok2p Ste12p Stp1p Tec1p Yap1p Flo8p

YGL254W YDR009W YDR009W YDR009W YDR009W YPL248C YPL248C YPL248C YPL248C YML051W YML051W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YFL021W YLR013W YLR013W YLR013W YLR013W YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C YEL009C

12399584 16709784 8114729 16914749 19159457 8114729 12399584 12399584 12464632 12399584 16709784 15343339 8622686 15343339 15343339 17646381 15343339 8622686 15343339 19108609 17646381 15343339 12399584 19159457 15343339 15343339 12464632 16709784 16709784 15343339 15343339 17646381 15343339 15343339 12399584 16449570 17646381 17638031 17638031 12399584 17638031 18627600 16449570


Page 6 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Rap1p Ste12p Sum1p Yox1p Ino4p Aft1p Gcn4p Gln3p Mal33p Mig1p Rap1p Reb1p Aft1p Aft2p Dal82p Put3p Rap1p Reb1p Abf1p Gat1p Sip4p Tos8p Rap1p Skn7p Ste12p Swi4p Cbf1p Hac1p Spt23p Ste12p Mga2p Dot6p Fhl1p Hap1p Mcm1p Rap1p Sok2p Ste12p Swi4p Yap1p Aft1p Met32p Oaf1p

YPL075W YPL075W YPL075W YPL075W YNL199C YER040W YER040W YER040W YER040W YER040W YER040W YER040W YGL181W YGL181W YGL181W YGL181W YGL181W YGL181W YJL110C YJL110C YJL110C YJL110C YPR008W YPR008W YPR008W YPR008W YFL031W YFL031W YFL031W YFL031W YFL031W YOL089C YLR256W YLR256W YLR256W YLR256W YLR256W YLR256W YLR256W YLR256W YGL237C YGL237C YGL237C

17646381 19159457 16709784 12464632 16709784 15343339 15343339 9171383 16709784 16709784 16709784 16709784 15343339 15343339 16709784 15343339 17646381 16709784 18305101 15343339 12399584 12464632 17646381 12399584 19159457 12399584 15343339 15009095 16543154 19159457 16543154 16709784 17646381 17706600 18303948 17646381 17638031 19159457 16709784 18627600 15343339 15343339 15343339


Page 7 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Reb1p Rap1p Cin5p Hir2p Mga1p Mig1p Nrg1p Pdr1p Phd1p Rox1p Skn7p Sok2p Stb5p Ste12p Swi4p Tec1p Yap1p Gsm1p Cat8p Rds2p Flo8p Dot6p Fkh1p Rap1p Yox1p Cad1p Spt23p Cbf1p Sok2p Tye7p Yox1p Adr1p Rap1p Cup9p Fhl1p Hal9p Mga1p Nrg1p Phd1p Rap1p Rox1p Sfp1p Sok2p

YGL237C YBL021C YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YKL109W YOR358W YOR358W YOR358W YOR358W YBL008W YBL008W YOR038C YOR038C YOR038C YOR038C YJR140C YJR140C YOR032C YOR032C YOR032C YOR032C YOR032C YOR032C YOR032C YOR032C YOR032C YOR032C

16709784 11455386 16709784 16709784 16449570 8114729 15343339 16914749 16449570 15343339 15343339 16449570 16914749 16449570 16709784 16449570 18627600 16785442 12125049 17875938 16449570 16709784 15343339 17646381 12464632 15343339 15343339 16709784 12464632 15343339 12464632 15343339 17646381 12399584 17646381 12399584 16449570 12399584 16449570 17646381 15343339 12399584 16449570


Page 8 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Ste12p Tec1p Tos8p Yap6p Flo8p Ace2p Cbf1p Cin5p Fhl1p Gcn4p Rap1p Skn7p Swi4p Xbp1p Yap1p Yap6p Aft1p Fhl1p Fkh1p Gat1p Ino4p Mcm1p Pdr1p Phd1p Rap1p Reb1p Rtg3p Sok2p Stb5p Ste12p Swi4p Swi6p Tec1p Yap1p Flo8p Rlm1p Fhl1p Rap1p Ifh1p Rap1p Reb1p Yap5p Adr1p

YOR032C YOR032C YOR032C YOR032C YOR032C YJR147W YJR147W YJR147W YJR147W YJR147W YJR147W YJR147W YJR147W YJR147W YJR147W YJR147W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YLR113W YGL073W YGL073W YLR223C YLR223C YLR223C YLR223C YJR094C

16449570 16449570 12464632 12399584 16449570 15343339 15343339 16709784 17646381 17224918 17646381 12399584 12399584 15343339 18627600 15343339 15343339 17646381 12399584 16709784 16709784 18303948 17158869 16449570 17646381 16709784 12399584 16449570 16914749 16449570 16709784 16709784 17638031 18627600 16449570 16709784 17646381 17646381 15616569 17646381 15343339 18287073 15743812


Page 9 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Cin5p Fkh1p Mcm1p Mga1p Msn2p Msn4p Nrg1p Phd1p Rap1p Rme1p Rox1p Sok2p Ste12p Tec1p Xbp1p Yap6p Yhp1p Flo8p Abf1p Dot6p Hap4p Reb1p Tec1p Dal82p Hsf1p Ino4p Dot6p Ino4p Tos8p Fhl1p Mcm1p Phd1p Rap1p Sok2p Ste12p Tec1p Flo8p Ino4p Phd1p Ste12p Tec1p Yap6p Ace2p

YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YJR094C YGL192W YGL192W YGL192W YGL192W YGL192W YDR123C YDR123C YDR123C YOL108C YOL108C YOL108C YKL032C YKL032C YKL032C YKL032C YKL032C YKL032C YKL032C YKL032C YNL132W YNL132W YNL132W YNL132W YNL132W YGR040W

16709784 12399584 18303948 16449570 9528770 9528770 16709784 16449570 16709784 7851756 12399584 16449570 17638031 17638031 15343339 16709784 10705372 16449570 16709784 16709784 12399584 12399584 15343339 12399584 15343339 16709784 16709784 16709784 12464632 17646381 18303948 16449570 17646381 16449570 17638031 16449570 16449570 16709784 16449570 16449570 16449570 18287073 12399584


Page 10 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Cst6p Fkh1p Ino4p Msn4p Ste12p Swi4p Swi5p Swi6p Tec1p Yap1p Ash1p Gcn4p Pho4p Rsf2p Dal82p Leu3p Sok2p Ste12p Hcm1p Mac1p Mbp1p Hsf1p Mth1p Sok2p Tec1p Cup9p Hal9p Hms1p Pho2p Rap1p Rfx1p Fhl1p Oaf1p Pdr1p Swi5p Yap5p Cbf1p Gcn4p Hsf1p Met28p Met31p Met32p Met4p

YGR040W YGR040W YGR040W YGR040W YGR040W YGR040W YGR040W YGR040W YGR040W YGR040W YLR451W YLR451W YLR451W YLR451W YMR021C YMR021C YGR288W YGR288W YGR288W YBR297W YBR297W YOR298C-A YOR298C-A YOR298C-A YOR298C-A YDL056W YDL056W YDL056W YDL056W YDL056W YDL056W YMR043W YMR043W YIL128W YIL128W YIL128W YIR017C YIR017C YIR017C YIR017C YIR017C YIR017C YIR017C

15343339 12399584 16709784 15343339 17638031 12399584 12399584 12399584 17638031 18627600 16709784 15343339 12399584 12399584 12399584 16923194 17638031 19159457 12464632 15343339 11206552 12399584 12399584 16449570 16449570 12399584 12399584 12399584 16709784 16709784 12399584 17646381 15343339 15343339 15343339 16709784 15343339 17224918 12399584 12399584 9799240 9799240 12399584


Page 11 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Pdr1p Ste12p Tec1p Yap6p Gat1p Leu3p Mal33p Mcm1p Met4p Cbf1p Fhl1p Gcn4p Ino4p Sfp1p Yap1p Cad1p Cin5p Fhl1p Hms2p Hsf1p Mga1p Msn2p Nrg1p Pdr1p Pdr3p Phd1p Rap1p Rim101p Sfl1p Skn7p Sko1p Sok2p Ste12p Swi4p Tec1p Xbp1p Yap5p Yap6p Flo8p Rlm1p Mig1p Pdr3p Rgt1p

YIR017C YIR017C YIR017C YIR017C YDR253C YDR253C YDR253C YDR253C YDR253C YNL103W YNL103W YNL103W YNL103W YNL103W YNL103W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGR249W YGL035C YGL209W YGL209W

16914749 19159457 16449570 12399584 16709784 16923194 16709784 16709784 16709784 15343339 17646381 17224918 16709784 12399584 18627600 16709784 16709784 17646381 15343339 15343339 16449570 15343339 16709784 16914749 16914749 16449570 16709784 15343339 12399584 15343339 18931682 16449570 16449570 16709784 16449570 15343339 12399584 15343339 16449570 12399584 8114729 16914749 14871952


Page 12 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Sok2p Ste12p Hcm1p Cin5p Nrg1p Phd1p Rgt1p Sok2p Ste12p Flo8p Aft1p Arg80p Arg81p Ecm22p Gcn4p Ino4p Msn2p Msn4p Reb1p Fhl1p Hap1p Ino2p Mbp1p Nrg1p Pdr1p Phd1p Skn7p Sko1p Sok2p Stb5p Swi4p Flo8p Abf1p Arg80p Cin5p Dot6p Yap1p Yap5p Cbf1p Gcr2p Hap1p Skn7p Sko1p

YGL209W YGL209W YGL209W YER028C YER028C YER028C YER028C YER028C YER028C YER028C YER068W YER068W YER068W YER068W YER068W YER068W YER068W YER068W YER068W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YMR070W YOL116W YOL116W YOL116W YOL116W YOL116W YOL116W YMR037C YMR037C YMR037C YMR037C YMR037C

17638031 16449570 12464632 16709784 15343339 16449570 15343339 16449570 19159457 16449570 15343339 12399584 12399584 16709784 12399584 16709784 15343339 15343339 15343339 17646381 15343339 15343339 15343339 15343339 16914749 16449570 15343339 16709784 16449570 16914749 16709784 16449570 16709784 12399584 15343339 16709784 12399584 12399584 15343339 15343339 15343339 15343339 16709784


Page 13 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Sok2p Ste12p Tec1p Tye7p Fhl1p Hal9p Hap5p Hir2p Ino2p Msn2p Msn4p Pdr1p Phd1p Rap1p Skn7p Sok2p Stb5p Cin5p Sko1p Ste12p Tec1p Dal81p Gal4p Hir2p Ino4p Rap1p Rgt1p Ste12p Hcm1p Fkh2p Stb1p Swi4p Swi6p Ume6p Gcr2p Ino4p Msn2p Msn4p Rim101p Sfl1p Ste12p Rlm1p Cin5p

YMR037C YMR037C YMR037C YMR037C YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YKL062W YMR164C YMR164C YMR164C YMR164C YDR277C YDR277C YDR277C YDR277C YDR277C YDR277C YDR277C YDR277C YOR372C YOR372C YOR372C YOR372C YHR124W YGR089W YGR089W YGR089W YGR089W YGR089W YGR089W YGR089W YGR089W YDR043C

17638031 17638031 17638031 15343339 17646381 16709784 15343339 12399584 12399584 15343339 12399584 16914749 15343339 17646381 15343339 17638031 16914749 15343339 16709784 16449570 17638031 12399584 15343339 12399584 16709784 17646381 14871952 19159457 12464632 12399584 12399584 16709784 16709784 15343339 12399584 16709784 15343339 15343339 12399584 12399584 19159457 12399584 18287073


Page 14 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Nrg1p Rap1p Rim101p Skn7p Sok2p Ste12p Tec1p Rap1p Ino2p Ino4p Rds2p Dot6p Ecm22p Mbp1p Met31p Nrg1p Rox1p Rpn4p Ste12p Swi4p Hsf1p Pdr1p Pdr3p Rph1p Cin5p Fhl1p Ixr1p Mga1p Phd1p Sko1p Sok2p Ste12p Swi4p Swi6p Tec1p Yap1p Yox1p Flo8p Abf1p Ino4p Pho4p Usv1p Fhl1p

YDR043C YDR043C YDR043C YDR043C YDR043C YDR043C YDR043C YAL051W YHL020C YHL020C YHL020C YGL013C YGL013C YGL013C YGL013C YGL013C YGL013C YGL013C YGL013C YGL013C YBL005W YBL005W YBL005W YBL005W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YKL043W YDL106C YDL106C YDL106C YDL106C YFR034C

16709784 16709784 12509465 15343339 17638031 17638031 17638031 17646381 15343339 16709784 17875938 16709784 16709784 15343339 15343339 15343339 15343339 15343339 19159457 16709784 16556235 16914749 16914749 12399584 16709784 17646381 16709784 16449570 16449570 16709784 16449570 16449570 16709784 16709784 16449570 18627600 12464632 16449570 18305101 16709784 19108609 12399584 17646381


Page 15 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Rap1p Yox1p Flo8p Adr1p Oaf1p Pip2p Ste12p Abf1p Adr1p Cbf1p Gcn4p Hap2p Hap3p Hap4p Hap5p Rap1p Rpn4p Tye7p Rap1p Reb1p Ste12p Ste12p Cin5p Pdr1p Yap6p Cbf1p Crz1p Gal4p Gat3p Rds1p Rgm1p Yap1p Yap5p Yap6p Cbf1p Hsf1p Reb1p Rpn4p Ste12p Fhl1p Oaf1p Rfx1p Aft1p

YFR034C YFR034C YFR034C YOR363C YOR363C YOR363C YLR014C YKL015W YKL015W YKL015W YKL015W YKL015W YKL015W YKL015W YKL015W YKL015W YKL015W YKL015W YNL216W YNL216W YNL216W YMR075W YOR380W YOR380W YOR380W YCR106W YCR106W YCR106W YCR106W YCR106W YCR106W YCR106W YCR106W YCR106W YBR049C YBR049C YBR049C YBR049C YBR049C YLR176C YLR176C YLR176C YMR182C

17646381 12464632 16449570 18285336 18285336 18285336 16449570 15343339 15343339 15343339 16709784 12399584 12399584 15343339 12399584 17646381 15343339 15343339 17646381 2361590 15343339 17638031 12399584 12399584 12399584 15343339 12399584 12399584 12399584 15343339 12399584 18627600 12399584 15343339 15343339 16709784 2204808 15343339 19159457 17646381 15343339 9741624 15343339


Page 16 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Fhl1p Mac1p Reb1p Ste12p Mbp1p Abf1p Gcn4p Smp1p Sok2p Ste12p Swi4p Tos8p Cad1p Cin5p Fhl1p Rap1p Ste12p Yap5p Yap6p Rds2p Cin5p Fhl1p Hap1p Msn4p Pdr1p Rim101p Rox1p Skn7p Sko1p Sok2p Ste12p Tec1p Yap1p Yap6p Flo8p Rap1p Sok2p Ste12p Cad1p Cin5p Cup9p Fhl1p Gln3p

YMR182C YMR182C YMR182C YMR182C YKL038W YHL027W YHL027W YHL027W YHL027W YHL027W YHL027W YHL027W YPL089C YPL089C YPL089C YPL089C YPL089C YPL089C YPL089C YGR044C YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YPR065W YER169W YER169W YER169W YIL119C YIL119C YIL119C YIL119C YIL119C

17646381 12399584 15343339 19159457 11206552 15343339 15343339 15343339 17638031 19159457 16709784 12464632 18287073 18287073 17646381 17646381 19159457 18287073 18287073 17875938 16709784 17646381 15343339 15343339 17158869 15343339 15343339 15343339 16087739 16449570 17638031 17638031 16709784 15343339 16449570 17646381 17638031 19159457 16709784 16709784 15343339 17646381 15343339


Page 17 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Hms2p Ino4p Ixr1p Mga1p Mot3p Nrg1p Pdr1p Pdr3p Phd1p Rap1p Rim101p Rox1p Skn7p Sko1p Sok2p Stb5p Ste12p Tec1p Xbp1p Yap1p Yap6p Hcm1p Flo8p Rlm1p Abf1p Cbf1p Hsf1p Msn4p Pdr1p Pdr3p Yap1p Yap7p Abf1p Aft1p Fkh2p Ino4p Mga1p Phd1p Sok2p Ste12p Tec1p Yap5p Flo8p

YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YIL119C YDL020C YDL020C YDL020C YDL020C YDL020C YDL020C YDL020C YDL020C YJR127C YJR127C YJR127C YJR127C YJR127C YJR127C YJR127C YJR127C YJR127C YJR127C YJR127C

15343339 16709784 15343339 16449570 15343339 16709784 16914749 16914749 16449570 16709784 15343339 15343339 15343339 16087739 16449570 16914749 16449570 16449570 15343339 18627600 16709784 12464632 16449570 12399584 18305101 15343339 16709784 15343339 16914749 16914749 16709784 15343339 15343339 15343339 16709784 16709784 16449570 16449570 16449570 17638031 17638031 12399584 16449570


Page 18 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Azf1p Gat1p Gcn4p Pdr3p Tos8p Leu3p Hcm1p Ace2p Fzf1p Gal4p Gcr2p Leu3p Nrg1p Pdr1p Rap1p Rme1p Skn7p Smp1p Ste12p Swi4p Swi5p Yap5p Fkh2p Rap1p Tec1p Dal80p Ino4p Mss11p Arg80p Arg81p Dal81p Gcn4p Rap1p Sko1p Ume6p Hot1p Dal82p Sok2p Gts1p Ste12p Abf1p Ndt80p Reb1p

YOL067C YOL067C YBL103C YBL103C YBL103C YOR077W YOR077W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YOR140W YLR403W YLR403W YLR403W YNL257C YNL257C YNL257C YJL089W YJL089W YJL089W YJL089W YJL089W YJL089W YJL089W YJL089W YHR206W YHR206W YNL167C YNL167C YPR054W YPR054W YPR054W

12399584 16709784 17224918 16914749 12464632 16923194 12464632 15343339 15343339 15343339 15343339 16923194 16709784 15343339 16709784 15343339 12399584 15343339 19159457 15343339 15343339 12399584 16709784 17646381 17638031 15343339 16709784 15343339 12399584 12399584 12399584 12399584 17646381 18931682 15343339 18931682 16709784 17638031 15343339 19159457 9742114 12832469 12399584


Page 19 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Sum1p Cin5p Fhl1p Phd1p Rap1p Rim101p Smp1p Ste12p Xbp1p Pho4p Cin5p Cup9p Fhl1p Mga1p Nrg1p Pdr1p Phd1p Rap1p Rox1p Skn7p Sko1p Sok2p Ste12p Swi4p Swi6p Tec1p Yap6p Flo8p Ste12p Tos8p Tos8p Abf1p Hap1p Yap1p Yap5p Ste12p Gcn4p Pho4p Rap1p Pho2p Rme1p Aft1p Gat1p

YPR054W YBR182C YBR182C YBR182C YBR182C YBR182C YBR182C YBR182C YBR182C YGL131C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YMR016C YJL127C YJL127C YER161C YKL020C YKL020C YKL020C YKL020C YCR018C YNL309W YNL309W YNL309W YMR053C YMR053C YMR019W YMR019W

12832469 12399584 17646381 16449570 17646381 12509465 16709784 19159457 15343339 12399584 12399584 12399584 17646381 16449570 12399584 16914749 16449570 17646381 12399584 15343339 16709784 16709784 17638031 16709784 12399584 17638031 12399584 16449570 19159457 12464632 12464632 18305101 15343339 15343339 12464632 19159457 17224918 15343339 17646381 15343339 12399584 15343339 15343339


Page 20 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Gcn4p Hal9p Hms2p Msn2p Msn4p Phd1p Put3p Reb1p Rtg1p Rtg3p Skn7p Stp1p Cin5p Hsf1p Rap1p Stb5p Yap1p Yap3p Arg81p Aro80p Cin5p Hap3p Mcm1p Mga1p Rds1p Reb1p Dot6p Fkh1p Phd1p Ste12p Tec1p Abf1p Pdc2p Tec1p Ino4p Mcm1p Tye7p Cin5p Fhl1p Mga1p Phd1p Sok2p Ste12p

YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YMR019W YHR178W YHR178W YHR178W YHR178W YHR178W YHR178W YKL072W YKL072W YKL072W YKL072W YKL072W YKL072W YKL072W YKL072W YHR084W YHR084W YHR084W YHR084W YHR084W YDR463W YDR463W YDR463W YHR006W YHR006W YHR006W YDL048C YDL048C YDL048C YDL048C YDL048C YDL048C

15343339 15343339 15343339 15343339 15343339 15343339 15343339 16709784 15343339 15343339 15343339 15343339 12399584 12399584 17646381 16914749 12399584 15343339 12399584 15343339 16709784 15343339 15343339 15343339 15343339 15343339 16709784 15343339 15343339 16449570 16449570 15343339 15343339 15343339 16709784 18303948 12464632 18287073 17646381 16449570 16449570 16449570 17638031


Page 21 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Swi4p Tec1p Yap6p Flo8p Cha4p Ste12p Sum1p Uga3p Fhl1p Fkh2p Phd1p Sok2p Ste12p Tec1p Yox1p Flo8p Hms2p Skn7p Sok2p Ste12p Swi4p Swi6p Tec1p Abf1p Cbf1p Mal33p Mbp1p Mcm1p Phd1p Sok2p Ste12p Swi4p Swi6p Ume6p Fkh1p Fkh2p Hap3p Ino2p Ino4p Mcm1p Rap1p Reb1p Rtg3p

YDL048C YDL048C YDL048C YDL048C YDR310C YDR310C YDR310C YDR310C YGL162W YGL162W YGL162W YGL162W YGL162W YGL162W YGL162W YGL162W YPR009W YPR009W YPR009W YPR009W YPR009W YPR009W YPR009W YER111C YER111C YER111C YER111C YER111C YER111C YER111C YER111C YER111C YER111C YER111C YDR146C YDR146C YDR146C YDR146C YDR146C YDR146C YDR146C YDR146C YDR146C

16709784 16449570 18287073 16449570 15343339 15343339 16709784 12399584 17646381 15343339 16449570 17638031 16449570 16449570 12464632 16449570 15343339 15343339 17638031 17638031 16709784 16709784 17638031 18305101 16709784 15343339 15343339 16709784 16449570 17638031 19159457 16709784 12399584 15343339 10894549 16709784 12399584 15343339 16709784 16709784 16709784 2181283 16709784


Page 22 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Sfp1p Ste12p Uga3p Yap5p Nrg1p Oaf1p Pip2p Ste12p Tec1p Fhl1p Hap1p Hsf1p Ino4p Pdr1p Phd1p Sok2p Ste12p Sum1p Swi5p Tec1p Flo8p Fhl1p Leu3p Abf1p Tos8p Ash1p Cin5p Fhl1p Mga1p Phd1p Sfp1p Sok2p Ste12p Swi4p Tec1p Flo8p Cbf1p Cin5p Fhl1p Gcr2p Hap5p Hsf1p Ino4p

YDR146C YDR146C YDR146C YDR146C YBR150C YBR150C YBR150C YBR150C YBR150C YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR083W YBR240C YBR240C YNL139C YNL139C YGL096W YGL096W YGL096W YGL096W YGL096W YGL096W YGL096W YGL096W YGL096W YGL096W YGL096W YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C

12399584 15343339 16709784 16709784 15343339 15343339 15343339 19159457 17638031 17646381 15343339 16709784 16709784 16914749 16449570 16449570 16449570 12399584 11572776 16449570 16449570 17646381 16923194 18305101 12464632 16709784 16709784 17646381 16449570 16449570 12399584 16449570 16449570 16709784 16449570 16449570 15343339 16709784 17646381 15343339 15343339 15343339 16709784


Page 23 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Mcm1p Mga1p Msn2p Pdr1p Pdr3p Phd1p Pho2p Pho4p Rap1p Sok2p Stb5p Ste12p Swi4p Swi6p Tec1p Yap1p Flo8p Adr1p Dal81p Dal82p Gcn4p Gln3p Hap2p Hap3p Ste12p Yap1p Rds2p Abf1p Fhl1p Leu3p Mac1p Met32p Mth1p Oaf1p Phd1p Pip2p Rme1p Rox1p Sfp1p Dal82p Hsf1p Ino4p Fhl1p

YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YOR344C YDL170W YDL170W YDL170W YDL170W YDL170W YDL170W YDL170W YDL170W YDL170W YDL170W YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR207C YDR213W YDR213W YDR213W YPL230W

18303948 16449570 15343339 16914749 16914749 16449570 12399584 19108609 17646381 16449570 16914749 16449570 15343339 16709784 16449570 18627600 16449570 16709784 15343339 15343339 16709784 16709784 15343339 15343339 19159457 18627600 17875938 18305101 17646381 16923194 12399584 15343339 12399584 15343339 16449570 15343339 12399584 12399584 12399584 16709784 12399584 16709784 17646381


Page 24 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Nrg1p Phd1p Rap1p Rox1p Skn7p Sok2p Gcn4p Hap1p Ino4p Ace2p Cin5p Fkh2p Gcr2p Gln3p Ino2p Ino4p Mbp1p Phd1p Sok2p Flo8p Cin5p Ino4p Msn4p Aft2p Cin5p Fhl1p Gat1p Ino4p Mal33p Mga1p Nrg1p Phd1p Pip2p Rap1p Rgt1p Rox1p Sko1p Sok2p Ste12p Yap5p Ace2p Aro80p Azf1p

YPL230W YPL230W YPL230W YPL230W YPL230W YPL230W YML076C YML076C YML076C YOR230W YOR230W YOR230W YOR230W YOR230W YOR230W YOR230W YOR230W YOR230W YOR230W YOR230W YOR229W YOR229W YOR229W YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YIL101C YML007W YML007W YML007W

15343339 15343339 17646381 15343339 15343339 17638031 15343339 15343339 16709784 16709784 16709784 15343339 15343339 15343339 15343339 16709784 11206552 16449570 16449570 16449570 12399584 12399584 15343339 15343339 16709784 17646381 12399584 16709784 15343339 16449570 16709784 16449570 15343339 16709784 16709784 16709784 18931682 16449570 19159457 16709784 15343339 12399584 15343339


Page 25 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Hap1p Hap3p Msn2p Msn4p Rpn4p Skn7p Stb5p Ste12p Yap1p Mcm1p Ste12p Usv1p Cbf1p Gcn4p Leu3p Met28p Met4p Pdr1p Skn7p Ste12p Swi4p Tec1p Yap6p Azf1p Cin5p Cup9p Fhl1p Fkh2p Hms1p Hsf1p Mga1p Nrg1p Pdr1p Phd1p Rap1p Rox1p Sok2p Stb5p Ste12p Tec1p Yap1p Yap6p Flo8p

YML007W YML007W YML007W YML007W YML007W YML007W YML007W YML007W YML007W YHL009C YHL009C YHL009C YIR018W YIR018W YIR018W YIR018W YIR018W YIR018W YIR018W YIR018W YIR018W YIR018W YIR018W YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C YDR259C

15343339 12399584 15343339 15343339 15343339 15343339 16914749 19159457 16709784 18303948 17638031 12399584 15343339 17224918 16923194 12399584 12399584 16914749 12399584 19159457 12464632 16449570 12399584 15343339 15343339 15343339 17646381 15343339 12399584 12399584 16449570 16709784 16914749 16449570 16709784 15343339 16449570 16914749 17638031 17638031 18627600 15343339 16449570


Page 26 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Hap1p Nrg1p Yap1p Yap7p Usv1p Mot2p Hcm1p Arg80p Aro80p Gcr2p Hms1p Met31p Smp1p Ste12p Stp1p Abf1p Dot6p Fhl1p Phd1p Rap1p Sok2p Ste12p Ume6p Rox1p Ste12p Tec1p Yap7p Ste12p Fhl1p Gcn4p Nrg1p Ume6p Arg80p Ash1p Fhl1p Fkh1p Fkh2p Ino4p Mbp1p Mcm1p Phd1p Rap1p Sok2p

YOL028C YOL028C YOL028C YOL028C YBR239C YDR026C YDR026C YDR049W YDR049W YDR049W YDR049W YDR049W YDR049W YDR049W YDR049W YDR266C YDR266C YER130C YER130C YER130C YER130C YER130C YER130C YER184C YER184C YER184C YER184C YFL052W YGR067C YGR067C YGR067C YGR067C YDR451C YDR451C YDR451C YDR451C YDR451C YDR451C YDR451C YDR451C YDR451C YDR451C YDR451C

15343339 15343339 18627600 15343339 12399584 12399584 12464632 12399584 12399584 12399584 12399584 12399584 12399584 19159457 12399584 18305101 16709784 17646381 16449570 17646381 17638031 19159457 15343339 15343339 17638031 17638031 15343339 19159457 17646381 15343339 15343339 15343339 15343339 15343339 17646381 16709784 16709784 16709784 16709784 16709784 15343339 16709784 17638031


Page 27 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Ste12p Swi4p Swi6p Tec1p Rap1p Sko1p Ino4p Msn1p Phd1p Sok2p Flo8p Abf1p Aft1p Gat1p Dot6p Mcm1p Fhl1p Mbp1p Mcm1p Swi4p Swi6p Cin5p Sok2p Ecm22p Fkh1p Fkh2p Hsf1p Ino4p Mcm1p Ste12p Cbf1p Pho4p Yap1p Yap7p Leu3p Pho4p Zap1p Hir2p Ino4p Mcm1p Nrg1p Pho4p Skn7p

YDR451C YDR451C YDR451C YDR451C YJL206C YJL206C YLR278C YLR278C YLR278C YLR278C YLR278C YML081W YML081W YML081W YNR063W YNR063W YML027W YML027W YML027W YML027W YML027W YPR022C YPR022C YPR196W YPR196W YPR196W YPR196W YPR196W YPR196W YPR196W YOR162C YOR162C YOR162C YOR162C YJL056C YJL056C YJL056C YBR066C YBR066C YBR066C YBR066C YBR066C YBR066C

17638031 16709784 16709784 17638031 17646381 16709784 16709784 12399584 16449570 17638031 16449570 15343339 15343339 16709784 16709784 16709784 17646381 15965243 18303948 16709784 16709784 15343339 17638031 16709784 16709784 16709784 15343339 16709784 18303948 19159457 15343339 19108609 18627600 15343339 16923194 19108609 16709784 12399584 12399584 18303948 15343339 19108609 15343339


Page 28 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Sok2p Ste12p Tec1p Yap1p Flo8p Fhl1p Gln3p Mal33p Mbp1p Mcm1p Pdr1p Phd1p Pho4p Rox1p Ste12p Swi4p Swi6p Yfl044cp Flo8p Azf1p Fhl1p Gal4p Pdr3p Skn7p Pho4p Gcn4p Gcr2p Hap5p Phd1p Ste12p Tec1p Sok2p Abf1p Fhl1p Rap1p Sok2p Fkh2p Fkh1p Ste12p Ume6p Gsm1p Cbf1p Dal81p

YBR066C YBR066C YBR066C YBR066C YBR066C YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YCR065W YDR006C YDR006C YDR006C YDR006C YDR006C YDR017C YDR034C YDR034C YDR034C YDR034C YDR034C YDR034C YDR096W YER164W YGL166W YGL166W YGL166W YIL130W YJL103C YJL103C YJL103C YJL103C YLR266C YLR266C

16449570 16449570 17638031 18627600 16449570 17646381 15343339 15343339 16709784 18303948 16914749 16449570 12399584 15343339 19159457 16709784 16709784 12399584 16449570 12399584 17646381 15343339 16914749 12399584 19108609 17224918 15343339 15343339 16449570 16449570 16449570 17638031 18305101 17646381 17646381 12464632 15343339 12399584 19159457 15343339 16785442 15343339 16709784


Page 29 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Gcn4p Nrg1p Pho4p Rtg3p Reb1p Abf1p Aft1p Crz1p Reb1p Sok2p Ume6p Cad1p Gcn4p Pho2p Pho4p Rap1p Pho4p Abf1p Hap4p Yjl206cp Sok1p Ste12p Tec1p Cin5p Hcm1p Leu3p Rim101p Skn7p Dal81p Dal82p Gln3p Hap2p Smp1p Ste12p Fkh1p Fkh2p Ino4p Mbp1p Mcm1p Phd1p Rim101p Rme1p Rpn4p

YLR266C YLR266C YLR266C YLR266C YMR213W YMR280C YMR280C YMR280C YMR280C YMR280C YMR280C YOR172W YOR337W YOR337W YOR337W YOR337W YPL133C YPR186C YPR186C YPR186C YER109C YER109C YER109C YMR172W YMR172W YMR172W YMR172W YMR172W YIR033W YIR033W YIR033W YIR033W YIR033W YIR033W YCL055W YCL055W YCL055W YCL055W YCL055W YCL055W YCL055W YCL055W YCL055W

15343339 15343339 15343339 15343339 12399584 18305101 15343339 18362157 15343339 17638031 15343339 12399584 16709784 15343339 15343339 17646381 15343339 16709784 15343339 12399584 17638031 17638031 17638031 15343339 12464632 16923194 15343339 15343339 15343339 15343339 15343339 15343339 12399584 19159457 12399584 12399584 16709784 12399584 16709784 12399584 12399584 12399584 16709784


Page 30 of 30

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Transcription Factor Protein name

Target - Systematic Gene Name

PubMed ID

Ste12p Swi4p

YCL055W YCL055W

19159457 12399584


Page 1 of 1

Supplementary Table S4: Transcriptional effect of protein-DNA interactions extracted from literature.

Transcription Factor Protein name

Target - Systematic Gene Name

Transcriptional Effect

PubMed ID

Abf1p Aft1p Gal3p Gal80p Gal4p Ino4p Nrg1p Rox1p Mga1p Msn2p Mot3p Ash1p Hms2p Tye7p Tye7p Eds1p Hms2p Msn4p Smp1p Stp4p Phd1p Dal80p

YKL112W YGL071W YPL248C YPL248C YML051W YOL108C YHL027W YPR065W YNL167C YNL167C YNL167C YMR016C YLR131C YNL199C YMR043W YNL216W YNL216W YNL216W YHL027W YMR016C YMR016C YMR016C

REPRESSION ACTIVATION ACTIVATION ACTIVATION REPRESSION ACTIVATION ACTIVATION REPRESSION ACTIVATION ACTIVATION ACTIVATION REPRESSION ACTIVATION REPRESSION REPRESSION REPRESSION ACTIVATION REPRESSION REPRESSION REPRESSION REPRESSION REPRESSION

15192094 14534306 9670023 9670023 9670023 1461729 12509465 7768429 16087739 16087739 16087739 11046133 17417638 17417638 17417638 17417638 17417638 17417638 17417638 17417638 17417638 17417638


6XSSOHPHQWDU\)LJXUH6


2011Siegal-GaskinsEtal11(EmergenceOfSwitchLikeBehaviorInALargeFamilyOfSimpleBiochemicalNetworks)[Pre