A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast

Data integration strategies for whole-cell modeling

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.

Systems Level Modeling of the Cell Cycle Using Budding Yeast

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.

Systems Level Analysis of the Yeast Osmo-Stat

Adaptation is an important property of living organisms enabling them to cope with environmental stress and maintaining homeostasis. Adaptation is mediated by signaling pathways responding to different stimuli. Those signaling pathways might communicate in order to orchestrate the cellular response to multiple simultaneous stimuli, a phenomenon called crosstalk. Here, we investigate possible mechanisms of crosstalk between the High Osmolarity Glycerol (HOG) and the Cell Wall Integrity (CWI) pathways in yeast, which mediate adaptation to hyper- and hypo-osmotic challenges, respectively. We combine ensemble modeling with experimental investigations to test in quantitative terms different hypotheses about the crosstalk of the HOG and the CWI pathways. Our analyses indicate that for the conditions studied i) the CWI pathway activation employs an adaptive mechanism with a variable volume-dependent threshold, in contrast to the HOG pathway, whose activation relies on a fixed volume-dependent threshold, ii) there is no or little direct crosstalk between the HOG and CWI pathways, and iii) its mainly the HOG alone mediating adaptation of cellular osmotic pressure for both hyper- as well as hypo-osmotic stress. Thus, by iteratively combining mathematical modeling with experimentation we achieved a better understanding of regulatory mechanisms of yeast osmo-homeostasis and formulated new hypotheses about osmo-sensing.

Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast

Cells respond to environmental stimuli via specialized signaling pathways. Concurrent stimuli trigger multiple pathways that integrate information, predominantly via protein phosphorylation. Budding yeast responds to NaCl and pheromone via two mitogen-activated protein kinase cascades, the high osmolarity, and the mating pathways, respectively. To investigate signal integration between these pathways, we quantified the time-resolved phosphorylation site dynamics after pathway co-stimulation. Using shotgun mass spectrometry, we quantified 2,536 phosphopeptides across 36 conditions. Our data indicate that NaCl and pheromone affect phosphorylation events within both pathways, which thus affect each other at more levels than anticipated, allowing for information exchange and signal integration. We observed a pheromone-induced down-regulation of Hog1 phosphorylation due to Gpd1, Ste20, Ptp2, Pbs2, and Ptc1. Distinct Ste20 and Pbs2 phosphosites responded differently to the two stimuli, suggesting these proteins as key mediators of the information exchange. A set of logic models was then used to assess the role of measured phosphopeptides in the crosstalk. Our results show that the integration of the response to different stimuli requires complex interconnections between signaling pathways.

Mathematical modelling of arsenic transport, distribution and detoxification processes in yeast

Arsenic has a dual role as causative and curative agent of human disease. Therefore, there is considerable interest in elucidating arsenic toxicity and detoxification mechanisms. By an ensemble modelling approach, we identified a best parsimonious mathematical model which recapitulates and predicts intracellular arsenic dynamics for different conditions and mutants, thereby providing novel insights into arsenic toxicity and detoxification mechanisms in yeast, which could partly be confirmed experimentally by dedicated experiments. Specifically, our analyses suggest that: (i) arsenic is mainly protein-bound during short-term (acute) exposure, whereas glutathione-conjugated arsenic dominates during long-term (chronic) exposure, (ii) arsenic is not stably retained, but can leave the vacuole via an export mechanism, and (iii) Fps1 is controlled by Hog1-dependent and Hog1-independent mechanisms during arsenite stress. Our results challenge glutathione depletion as a key mechanism for arsenic toxicity and instead suggest that (iv) increased glutathione biosynthesis protects the proteome against the damaging effects of arsenic and that (v) widespread protein inactivation contributes to the toxicity of this metalloid. Our work in yeast may prove useful to elucidate similar mechanisms in higher eukaryotes and have implications for the use of arsenic in medical therapy.

Assessing cytokines' talking patterns following experimental myocardial damage by applying Shannon's information theory

BACKGROUND

The simultaneous measurement of multiple cytokines in parallel by using multiplex proteome arrays (MPA) is of great interest to understanding the inflammatory response following myocardial infarction; however, since cytokines are pleiotropic and redundant, increase of information throughput (IT) attained by measuring multiple cytokines remain to be determined. We aimed this study to assess the IT of an MPA system designed to assess 8 cytokines - commercially available at the time of the study - serum levels, before (control state) and after experimental myocardial cryoinjury (activated state) in rats.

METHODS

By assuming that redundant information do not generally increase the IT, we derived Entropy (H) and Redundancy (R) of information by using formulas of Shannon modified accordingly, where a high IT (high H and low R) corresponds to a low level of correlation between cytokines and vice versa for a low IT. The maximum theoretical level of IT and the contribution of each cytokine were also estimated.

RESULTS

In control state, no significant correlations were found between cytokines showing high IT; on the contrary, in activated state, several significant correlations were found supporting a complex cross-talk pattern between cytokines with low IT. Using as reference the maximum theoretical level of IT, in activated state, H was reduced of 67.0% and R was increased of 77.4% supporting a reduction of IT. Furthermore, the contribution of individual cytokines to H value of MPA was variable: in control state, IL-2 gave the most contribution to H value, conversely during activated state IL-10 gave most contribution. Finally during activated state, IL-1β was the only cytokine strongly correlated with values of all other cytokines, suggesting a crucial role in the inflammatory cascade.

CONCLUSIONS

Paradoxically, by analyzing an MPA system designed for redundant analytes such as cytokines, translating the Shannon's information theory from the field of communication to biology, the IT system in our model deteriorates during the activation state by increasing its redundancy, showing maximum value of entropy in the control conditions. Finally, the study of the mutual interdependence between cytokines by the contribution to the IT may allow formulating alternative models to describe the inflammatory cascade after myocardial infarction.

Quantitative measurement of protein relocalization in live cells

Microscope cytometry provides a powerful means to study signaling in live cells. Here we present a quantitative method to measure protein relocalization over time, which reports the absolute fraction of a tagged protein in each compartment. Using this method, we studied an essential step in the early propagation of the pheromone signal in Saccharomyces cerevisiae: recruitment to the membrane of the scaffold Ste5 by activated Gβγ dimers. We found that the dose response of Ste5 recruitment is graded (EC50 = 0.44 ± 0.08 nM, Hill coefficient = 0.8 ± 0.1). Then, we determined the effective dissociation constant (K(de)) between Ste5 and membrane sites during the first few minutes when the negative feedback from the MAPK Fus3 is first activated. K(de) changed during the first minutes from a high affinity of < 0.65 nM to a steady-state value of 17 ± 9 nM. During the same period, the total number of binding sites decreased slightly, from 1940 ± 150 to 1400 ± 200. This work shows how careful quantification of a protein relocalization dynamic can give insight into the regulation mechanisms of a biological system.

When ubiquitination meets phosphorylation: a systems biology perspective of EGFR/MAPK signalling

Ubiquitination, the covalent attachment of ubiquitin to target proteins, has emerged as a ubiquitous post-translational modification (PTM) whose function extends far beyond its original role as a tag for protein degradation identified three decades ago. Although sharing parallel properties with phosphorylation, ubiquitination distinguishes itself in important ways. Nevertheless, the interplay and crosstalk between ubiquitination and phosphorylation events have become a recurrent theme in cell signalling regulation. Understanding how these two major PTMs intersect to regulate signal transduction is an important research question. In this review, we first discuss the involvement of ubiquitination in the regulation of the EGF-mediated ERK signalling pathway via the EGF receptor, highlighting the interplay between ubiquitination and phosphorylation in this cancer-implicated system and addressing open questions. The roles of ubiquitination in pathways crosstalking to EGFR/MAPK signalling will then be discussed. In the final part of the review, we demonstrate the rich and versatile dynamics of crosstalk between ubiquitination and phosphorylation by using quantitative modelling and analysis of network motifs commonly observed in cellular processes. We argue that given the overwhelming complexity arising from inter-connected PTMs, a quantitative framework based on systems biology and mathematical modelling is needed to efficiently understand their roles in cell signalling.

Modelling the activation of alkaline pH response transcription factor PacC in Aspergillus nidulans: involvement of a negative feedback loop

Alkaline pH adaptation represents an important environmental stress response in Aspergillus nidulans. It is mediated by the pal signalling pathway and the PacC transcription factor. Although studied extensively experimentally, the activation mechanism of PacC has not been quantified, and it is not clear how this activation is regulated. Here, by constructing mathematical models, we first show that the pattern of PacC activation observed in previously published experiments cannot be explained based on existing knowledge about PacC activation. Extending the model with a negative feedback loop is necessary to produce simulation results that are consistent with the data, suggesting the existence of a negative feedback loop in the PacC activation process. This extended model is then validated against published measurements for cells with drug treatment and mutant cells. Furthermore, we investigate the role of an intermediate form of PacC in the PacC activation process, and propose experiments that can be used to test our predictions. Our work illustrates how mathematical models can be used to uncover regulatory mechanisms in the transcription regulation, and generate hypotheses that guide further laboratory investigations.

Cell-to-Cell Communication Circuits: Quantitative Analysis of Synthetic Logic Gates

One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae (Regot et al., 2011). It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values.

NOISE-INDUCED ALTERNATIVE RESPONSE IN MAP KINASE PATHWAYS WITH MUTUAL INHIBITION

All organisms have the ability to detect and respond to changes in the environment for survival, and as a result, specific cellular signaling pathways have evolved by which organisms sense their environment and respond to signals that they detect. However, an important unsolved problem in cell biology is to understand how specificity from signal to cellular response is maintained between different signal transduction pathways that share similar or identical components. Here, we show, using mathematical and computational modeling, that two typical signaling pathways in a single cell, hyperosmolar and pheromone motigen-avtivated protein kinase in the yeast Saccharomyces cerevisiae with mutual inhibition, can respond alternatively to two costimulated signals in a stochastically fluctuated environment. Within a bistable region over two input signals, noise plays an essential role in achieving specificity of response, while outside it, these pathways achieve specificity by filtering out spurious crosstalk through mutual inhibition.

CROSSTALK FACILITATES SPATIAL SIGNAL PROPAGATION THROUGH MAPK CASCADES

In intracellular mitogen-activated protein kinase (MAPK) cascades, it has been shown that signals can be propagated across the cell cytosol in the form of phosphoprotein waves arising from the bistable response of MAPK to active MAPK kinase. Without such a bistable response, however, they can not propagate into distant cell compartments, although a long positive feedback endows a mechanistically-distinct bistable response of MAPK to extracellular signal. Here we provide a compensate means that uses crosstalk between parallel identical pathways of MAPK cascades. For a spherical cell, we find that both unidirectional and bidirectional crosstalk with enhancement of phosphorylation can facilitate phosphoprotein signal propagation from the plasma membrane to the periphery of cell nucleus. Moreover, different shallow spatial gradients of biphosphorylated MAPK occur in the cytosol under different strengths of pathway interactions. These results suggest that crosstalk would be utilized by living organisms for spatial information transfer and cellular decision-making processing.

Information theory based approaches to cellular signaling

BACKGROUND

Cells interact with their environment and they have to react adequately to internal and external changes such changes in nutrient composition, physical properties like temperature or osmolarity and other stresses. More specifically, they must be able to evaluate whether the external change is significant or just in the range of noise. Based on multiple external parameters they have to compute an optimal response. Cellular signaling pathways are considered as the major means of information perception and transmission in cells.

SCOPE OF REVIEW

Here, we review different attempts to quantify information processing on the level of individual cells. We refer to Shannon entropy, mutual information, and informal measures of signaling pathway cross-talk and specificity.

MAJOR CONCLUSIONS

Information theory in systems biology has been successfully applied to identification of optimal pathway structures, mutual information and entropy as system response in sensitivity analysis, and quantification of input and output information.

GENERAL SIGNIFICANCE

While the study of information transmission within the framework of information theory in technical systems is an advanced field with high impact in engineering and telecommunication, its application to biological objects and processes is still restricted to specific fields such as neuroscience, structural and molecular biology. However, in systems biology dealing with a holistic understanding of biochemical systems and cellular signaling only recently a number of examples for the application of information theory have emerged. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

How can yeast cells decide between three activated MAP kinase pathways? A model approach

In yeast (Saccharomyces cerevisiae), the regulation of three MAP kinase pathways responding to pheromones (Fus3 pathway), carbon/nitrogen starvation (Kss1 pathway), and high osmolarity/osmotic stress (Hog1 pathway) is the subject of intensive research. We were interested in the question how yeast cells would respond when more than one of the MAP kinase pathways are activated simultaneously. Here, we give a brief overview over the regulatory mechanisms of the yeast MAP kinase pathways and investigate a kinetic model based on presently known molecular interactions and feedbacks within and between the three mitogen-activated protein kinases (MAPK) pathways. When two pathways are activated simultaneously with the osmotic stress response as one of them, the model predicts that the osmotic stress response (Hog1 pathway) is turned on first. The same is true when all three pathways are activated at the same time. When testing simultaneous stimulations by low nitrogen and pheromones through the Kss1 and Fus3 pathways, respectively, the low nitrogen response dominates over the pheromone response. Due to its autocatalytic activation mechanism, the pheromone response (Fus3 pathway) shows typical sigmoid response kinetics and excitability. In the presence of a small but sufficient amount of activated Fus3, a stimulation by pheromones will lead to a rapid self-amplification of the pheromone response. This 'excitability' appears to be a feature of the pheromone pathway that has specific biological significance.

Model-based inference of biochemical parameters and dynamic properties of microbial signal transduction networks

Because of the inherent uncertainty about quantitative aspects of signalling networks it is of substantial interest to use computational methods that allow inferring non-measurable quantities such as rate constants, from measurable quantities such as changes in protein abundances. We argue that true biochemical parameters like rate constants can generally not be inferred using models due to their non-identifiability. Recent advances, however, facilitate the analysis of parameter identifiability of a given model and automated discrimination of candidate models, both being important techniques to still extract quantitative biological information from experimental data.

Signal integration in budding yeast

A complex signalling network governs the response of Saccharomyces cerevisiae to an array of environmental stimuli and stresses. In the present article, we provide an overview of the main signalling system and discuss the mechanisms by which yeast integrates and separates signals from these sources. We apply our classification scheme to a simple semi-quantitative model of the HOG (high-osmolarity glycerol)/FG (filamentous growth)/PH (pheromone) MAPK (mitogen-activated protein kinase) signalling network by perturbing its signal integration mechanisms under combinatorial stimuli of osmotic stress, starvation and pheromone exposure in silico. Our findings include that the Hog1 MAPK might act as a timer for filamentous differentiation, not allowing morphological differentiation before osmo-adaptation is sufficiently completed. We also see that a mutually exclusive decision-making between pheromone and osmo-response might not be taken on the MAPK level and transcriptional regulation of MAPK targets. We conclude that signal integration mechanisms in a wider network including the cell cycle have to be taken into account for which our framework might provide focal points of study.

Modelling and experimental analysis of hormonal crosstalk in Arabidopsis

An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. We have developed the first hormonal crosstalk network in Arabidopsis by iteratively combining modelling with experimental analysis.

We have revealed that the POLARIS gene interacts with the ethylene receptor and regulates both auxin transport and biosynthesis.

Our modelling analysis has reproduced all known mutants. With new experimental data, it has provided new insights into how the POLARIS gene regulates auxin concentration for root development in Arabidopsis, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling.

Modelling and experimental analysis have revealed that a bell-shaped dose–response relationship between endogenous auxin and root length is established via POLARIS.

Hormone signalling systems coordinate plant growth and development through a range of complex interactions. The activities of plant hormones, such as auxin, ethylene and cytokinin, depend on cellular context and exhibit interactions that can be either synergistic or antagonistic. An important question regarding the understanding of those interactions is how genes act on the crosstalk between hormones to regulate plant growth.

Previously, we identified the POLARIS (PLS) gene of Arabidopsis, which transcribes a short mRNA encoding a 36-amino acid peptide that is required for correct root growth and vascular development (Casson et al, 2002). Experimental evidence shows that there is a link between PLS, ethylene signalling, auxin homeostasis and microtubule cytoskeleton dynamics (Chilley et al, 2006). Specifically, mutation of PLS results in an enhanced ethylene-response phenotype, defective auxin transport and homeostasis, and altered sensitivity to microtubule inhibitors. These defects, along with the short-root phenotype, are suppressed by genetic and pharmacological inhibition of ethylene action. The expression of PLS is itself repressed by ethylene and induced by auxin. It was also shown that pls mutant roots are hyper-responsive to exogenous cytokinins and show increased expression of the cytokinin inducible gene ARR5/IBC6 compared with the wild type (Casson et al, 2002). Therefore, PLS may also be required for correct auxin–cytokinin homeostasis to modulate root growth.

In this study, we model PLS gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis.

Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS–ethylene pathway interactions is developed, and used to make quantitative predictions about PLS–hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport (Figures 1 and 4).

The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose–response relationship between endogenous auxin and root length is established via PLS.

In summary, we developed the first hormonal crosstalk model in Arabidopsis and revealed a hormonal crosstalk circuit through PLS and the downstream of ethylene signalling. Our study provides a platform to further integrate hormonal crosstalk in space and time in Arabidopsis.

An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS–ethylene pathway interactions is developed, and used to make quantitative predictions about PLS–hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose–response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants.

Ultrasensitive responses and specificity in cell signaling

Background

Interconnected cell signaling pathways are able to efficiently and accurately transmit a multitude of different signals, despite an inherent potential for undesirable levels of cross-talk. To ensure that an appropriate response is produced, biological systems have evolved network-level mechanisms that insulate pathways from crosstalk and prevent 'leaking' or 'spillover' between pathways. Many signaling pathways have been shown to respond in an ultrasensitive (switch-like) fashion to graded input, and this behavior may influence specificity. The relationship of ultrasensitivity to signaling specificity has not been extensively explored.

Results

We studied the behavior of simple mathematical models of signaling networks composed of two interconnected pathways that share an intermediate component, asking if the two pathways in the network could exhibit both output specificity (preferentially activate their own output) and input fidelity (preferentially respond to their own input). Previous results with weakly-activated pathways indicated that neither mutual specificity nor mutual fidelity were obtainable in the absence of an insulating mechanism, such as cross-pathway inhibition, combinatorial signaling or scaffolding/compartmentalization. Here we found that mutual specificity is obtainable for hyperbolic or ultrasensitive pathways, even in the absence of an insulating mechanism. However, mutual fidelity is impossible at steady-state, even if pathways are hyperbolic or ultrasensitive. Nevertheless, ultrasensitivity does provide advantages in attaining specificity and fidelity to networks that contain an insulating mechanism. For networks featuring cross-pathway inhibition or combinatorial signaling, ultrasensitive activation can increase specificity in a limited way, and can only be utilized by one of the two pathways. In contrast, for networks featuring scaffolding/compartmentalization, ultrasensitive activation of both pathways can dramatically improve network specificity.

Conclusions

There are constraints to obtaining performance objectives associated with signaling specificity; such constraints may have influenced the evolution of signal transduction networks. Notably, input fidelity (preferential response to an authentic input) is a more difficult objective to achieve than output specificity (preferential targeting to an authentic output). Indeed, mutual fidelity is impossible in the absence of an insulating mechanism, even if pathways are ultrasensitive. Ultrasensitivity does, however, significantly enhance the performance of several insulating mechanisms. In particular, the ultrasensitive activation of both pathways can provide substantial improvement to networks containing scaffolding/compartmentalization.

Lose and gain: impacts of ERK5 and JNK cascades on each other

Kinase cascades in ERK5 (Extracellular signal-regulated kinases) and JNK (c-Jun N-terminal kinases) signaling pathways mediate the sensing and processing of stimuli. Cross-talks between signaling cascades is a likely phenomenon that can cause apparently different biological responses from a single pathway, on its activation. Feedback loops have the potential to greatly alter the properties of a pathway and its response to stimuli. Based on enzyme kinetic reactions, mathematical models have been developed to predict and analyze the impacts of cross-talks and feedback loops in ERK5 and JNK cascades. It has been observed that, there is no significant impact on neither ERK5 activation nor JNKs' activation due to cross-talks between them. But it is due to cross-talks and feedback loops in ERK5 and JNK cascade, ERK5 gets activated in a transient manner in the absence of input signals. Planning to obtain the parameter values from the experimentalist and the result should be validated by experimental verification.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-010-9061-4) contains supplementary material, which is available to authorized users.

Computational systems biology of the cell cycle

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

Fus3-triggered Tec1 degradation modulates mating transcriptional output during the pheromone response

The yeast transcription factor Ste12 controls both mating and filamentation pathways. Upon pheromone induction, the mitogen-activated protein kinases, Fus3 and Kss1, activate Ste12 by relieving the repression of two functionally redundant Ste12 inhibitors, Dig1 and Dig2. Mating genes are controlled by the Ste12/Dig1/Dig2 complex through Ste12-binding sites, whereas filamentation genes are regulated by the Tec1/Ste12/Dig1 complex through Tec1-binding sites. The two Ste12 complexes are mutually exclusive. During pheromone response, Tec1 is degraded upon phosphorylation by Fus3, preventing cross-activation of the filamentation pathway. Here, we show that a stable Tec1 also impairs the induction of mating genes. A mathematical model is developed to capture the dynamic formation of the two Ste12 complexes and their interactions with pathway-specific promoters. By model simulations and experimentation, we show that excess Tec1 can impair the mating transcriptional output because of its ability to sequester Ste12, and because of a novel function of Dig2 for the transcription of mating genes. We suggest that Fus3-triggered Tec1 degradation is an important part of the transcriptional induction of mating genes during the pheromone response.

Modelling dynamic processes in yeast

Yeast molecular and cell biology has accumulated large amounts of qualitative and quantitative data of diverse cellular processes. The results are often summarized as verbal or graphical descriptions. Moreover, a series of mathematical models has been developed that should help to interpret such data, to integrate them into a coherent picture and to allow for an understanding of the underlying processes. Dynamic modelling of regulatory processes in yeast focuses on central carbon metabolism, on a number of selected signalling pathways and on cell cycle regulation. These models can explain questions of general relevance, such as whether the dynamics of a network can be understood from the combination of in vitro kinetics of its individual reactions. They help to elucidate complicated dynamic features, such as glycolytic oscillations, effects of feedback regulation or the optimal regulation of gene expression. The availability of comprehensive qualitative information, such as protein interactions or pathway composition, and sets of quantitative data make yeast a perfect model organism. Therefore, yeast-related data are often used to develop and examine computational approaches and modelling methods.

A novel Bayesian DNA motif comparison method for clustering and retrieval

Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.

Regulation of gene expression plays a central role in the activity of living cells and in their response to internal (e.g., cell division) or external (e.g., stress) stimuli. Key players in determining gene-specific regulation are transcription factors that bind sequence-specific sites on the DNA, modulating the expression of nearby genes. To understand the regulatory program of the cell, we need to identify these transcription factors, when they act, and on which genes. Transcription regulatory maps can be assembled by computational analysis of experimental data, by discovering the DNA recognition sequences (motifs) of transcription factors and their occurrences along the genome. Such an analysis usually results in a large number of overlapping motifs. To reconstruct regulatory maps, it is crucial to combine similar motifs and to relate them to transcription factors. To this end we developed an accurate fully-automated method, termed BLiC, based upon an improved similarity measure for comparing DNA motifs. By applying it to genome-wide data in yeast, we identified the DNA motifs of transcription factors and their putative target genes. Finally, we analyze motifs of transcription factor that alter their target genes under different conditions, and show how cells adjust their regulatory program in response to environmental changes.

Kinetic insulation as an effective mechanism for achieving pathway specificity in intracellular signaling networks

Intracellular signaling pathways that share common components often elicit distinct physiological responses. In most cases, the biochemical mechanisms responsible for this signal specificity remain poorly understood. Protein scaffolds and cross-inhibition have been proposed as strategies to prevent unwanted cross-talk. Here, we report a mechanism for signal specificity termed "kinetic insulation." In this approach signals are selectively transmitted through the appropriate pathway based on their temporal profile. In particular, we demonstrate how pathway architectures downstream of a common component can be designed to efficiently separate transient signals from signals that increase slowly over time. Furthermore, we demonstrate that upstream signaling proteins can generate the appropriate input to the common pathway component regardless of the temporal profile of the external stimulus. Our results suggest that multilevel signaling cascades may have evolved to modulate the temporal profile of pathway activity so that stimulus information can be efficiently encoded and transmitted while ensuring signal specificity.

Yeast systems biology: modelling the winemaker's art

Yeast research represents an important nexus between fundamental and applied research. Just as fundamental yeast research transitioned from classical, reductionist strategies to whole-genome techniques, whole-genome studies are advancing to the next level of biological research, referred to as systems biology. Industries that rely on high-performing yeast, such as the wine industry, are therefore poised to reap the many benefits that systems biology can provide. This includes the promise of strain development at speeds and costs which are unobtainable using current techniques. This article reviews the current state of whole-genome techniques available to yeast researchers and outlines how these processes can be used to obtain 'systems-level' information to provide insights into winemaking.

Systems biology analysis of G protein and MAP kinase signaling in yeast

Approximately a third of all drugs act by binding directly to cell surface receptors coupled to G proteins. Other drugs act indirectly on these same pathways, for example, by inhibiting neurotransmitter reuptake or by blocking the inactivation of intracellular second messengers. These drugs have revolutionized the treatment of human disease. However, the complexity of G protein signaling mechanisms has significantly hampered our ability to identify additional new drug targets. Moreover, today's molecular pharmacologists are accustomed to working on narrowly focused problems centered on a single protein or enzymatic process. Here we describe emerging efforts in yeast aimed at identifying proteins and processes that modulate the function of receptors, G proteins and MAP kinase effectors. The scope of these efforts is far more systematic, comprehensive and quantitative than anything attempted previously, and includes integrated approaches in genetics, proteomics and computational biology.

Mathematical models of specificity in cell signaling

Cellular signaling pathways transduce extracellular signals into appropriate responses. These pathways are typically interconnected to form networks, often with different pathways sharing similar or identical components. A consequence of this connectedness is the potential for cross talk, some of which may be undesirable. Indeed, experimental evidence indicates that cells have evolved insulating mechanisms to partially suppress "leaking" between pathways. Here we characterize mathematical models of simple signaling networks and obtain exact analytical expressions for two measures of cross talk called specificity and fidelity. The performance of several insulating mechanisms--combinatorial signaling, compartmentalization, the inhibition of one pathway by another, and the selective activation of scaffold proteins--is evaluated with respect to the trade-off between the specificity they provide and the constraints they place on the network. The effects of noise are also examined. The insights gained from this analysis are applied to understanding specificity in the yeast mating and invasive growth MAP kinase signaling network.

Gene expression during oosporogenesis in heterothallic and homothallic Phytophthora

A large-scale screen for genes induced during sexual development was performed in the heterothallic oomycete Phytophthora infestans, the potato blight agent. Of 15,644 unigenes on an Affymetrix chip, 87 were induced >10-fold during mating, with 28 induced >100-fold. This was validated in independent matings using RNA blots and RT-PCR. Only 44 genes resembled sequences in GenBank. These encoded regulators such as protein kinases, protein phosphatases, and transcription factors, plus enzymes with metabolic, transport, or cell-cycle activities. Several genes were induced during both mating and asexual sporogenesis, suggesting crosstalk between those pathways. In the homothallic species P. phaseoli, 20% of the 87 genes were expressed at higher levels during conditions conducive to oosporogenesis than non-conducive conditions, while the rest were at similar levels. Many of the latter exhibited higher mRNA concentrations in P. phaseoli than in any non-mating culture of P. infestans, suggesting that part of the sexual pathway is active constitutively in homothallics.