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Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method. We apply the method to two BNs which differ in size, modeling formalism, and biological scope. Despite these differences, the method presented here provides a powerful tool for the analysis of both networks. First, our analysis of a BN studying the effect of the microenvironment during angiogenesis shows that the previously defined microenvironments inducing the specialized phalanx behavior in endothelial cells (ECs) additionally induce stalk behavior. We obtain this result from an extended network version which was previously not analyzed. Second, we were able to heuristically detect attractors in a cell cycle control network formalized as a bipartite Boolean model (bBM) with 3158 nodes. These attractors are directly interpretable in terms of genotype-to-phenotype relationships, allowing network validation equivalent to an in silico mutagenesis screen. Our approach contributes to the development of scalable analysis methods required for whole-cell modeling efforts. Author summary: Systems biology requires not only scalable formalization methods, but also means to analyze complex networks. Although Boolean networks (BNs) are a convenient way to formalize biological processes, their analysis suffers from the combinatorial complexity with increasing number of nodes n. Hence, the long standing O(2n) barrier for detection of periodic attractors in BNs has obstructed the development of large, biological BNs. We break this barrier by introducing a novel algorithm using a priori information. We show that the proposed algorithm enables systematic analysis of BNs formulated as bipartite models in the form of in silico mutagenesis screens. [ABSTRACT FROM AUTHOR]

Sperm migration in the female genital tract controls sperm selection and, therefore, reproductive success as male gametes are conditioned for fertilization while their number is dramatically reduced. Mechanisms underlying sperm migration are mostly unknown, since in vivo investigations are mostly unfeasible for ethical or practical reasons. By presenting a spatio-temporal model of the mammalian female genital tract combined with agent-based description of sperm motion and interaction as well as parameterizing it with bovine data, we offer an alternative possibility for studying sperm migration in silico. The model incorporates genital tract geometry as well as biophysical principles of sperm motion observed in vitro such as positive rheotaxis and thigmotaxis. This model for sperm migration from vagina to oviducts was successfully tested against in vivo data from literature. We found that physical sperm characteristics such as velocity and directional stability as well as sperm-fluid interactions and wall alignment are critical for success, i.e. sperms reaching the oviducts. Therefore, we propose that these identified sperm parameters should be considered in detail for conditioning sperm in artificial selection procedures since the natural processes are normally bypassed in reproductive in vitro technologies. The tremendous impact of mucus flow to support sperm accumulation in the oviduct highlights the importance of a species-specific optimum time window for artificial insemination regarding ovulation. Predictions from our extendable in silico experimental system will improve assisted reproduction in humans, endangered species, and livestock. Author summary: Successful fertilisation is indispensable for all mammals' existence. Positioning of living sperms within the female genital tract and migration towards the oviducts is a prerequisite for fusion of sperm and egg in the oviduct and hence successful fertilization. During this journey the number of sperms is drastically reduced. Natural sperm selection consists of an array of physical and chemical processes and interactions. Sperm movement properties were examined in various experiments by closely observing sperms in vitro. For example sperms swim along surfaces (thigmotaxis) and against fluid flows (rheotaxis). However, it is unclear how those interactions affect fertilization success, as it is not possible to observe sperms after natural mating. Therefore, we present a computational model of sperm migration, which approximates the shape of the female reproductive tract and integrates different interactions between sperms and their surroundings. The model gives insights into the impact of different processes on the number of sperms which reaches the oviducts as well as pointing out which sperms are more likely to succeed. Thus, it gives hints which interactions could be most beneficial for fertilization and hence should be considered in selecting sperms for artificial insemination techniques. [ABSTRACT FROM AUTHOR]

BK virus (BKV) associated nephropathy affects 1–10% of kidney transplant recipients, leading to graft failure in about 50% of cases. Immune responses against different BKV antigens have been shown to have a prognostic value for disease development. Data currently suggest that the structural antigens and regulatory antigens of BKV might each trigger a different mode of action of the immune response. To study the influence of different modes of action of the cellular immune response on BKV clearance dynamics, we have analysed the kinetics of BKV plasma load and anti-BKV T cell response (Elispot) in six patients with BKV associated nephropathy using ODE modelling. The results show that only a small number of hypotheses on the mode of action are compatible with the empirical data. The hypothesis with the highest empirical support is that structural antigens trigger blocking of virus production from infected cells, whereas regulatory antigens trigger an acceleration of death of infected cells. These differential modes of action could be important for our understanding of BKV resolution, as according to the hypothesis, only regulatory antigens would trigger a fast and continuous clearance of the viral load. Other hypotheses showed a lower degree of empirical support, but could potentially explain the clearing mechanisms of individual patients. Our results highlight the heterogeneity of the dynamics, including the delay between immune response against structural versus regulatory antigens, and its relevance for BKV clearance. Our modelling approach is the first that studies the process of BKV clearance by bringing together viral and immune kinetics and can provide a framework for personalised hypotheses generation on the interrelations between cellular immunity and viral dynamics. [ABSTRACT FROM AUTHOR]

The spatial architecture of signaling pathways and the interaction with cell size and morphology are complex, but little understood. With the advances of single cell imaging and single cell biology, it becomes crucial to understand intracellular processes in time and space. Activation of cell surface receptors often triggers a signaling cascade including the activation of membrane-attached and cytosolic signaling components, which eventually transmit the signal to the cell nucleus. Signaling proteins can form steep gradients in the cytosol, which cause strong cell size dependence. We show that the kinetics at the membrane-cytosolic interface and the ratio of cell membrane area to the enclosed cytosolic volume change the behavior of signaling cascades significantly. We suggest an estimate of average concentration for arbitrary cell shapes depending on the cell volume and cell surface area. The normalized variance, known from image analysis, is suggested as an alternative measure to quantify the deviation from the average concentration. A mathematical analysis of signal transduction in time and space is presented, providing analytical solutions for different spatial arrangements of linear signaling cascades. Quantification of signaling time scales reveals that signal propagation is faster at the membrane than at the nucleus, while this time difference decreases with the number of signaling components in the cytosol. Our investigations are complemented by numerical simulations of non-linear cascades with feedback and asymmetric cell shapes. We conclude that intracellular signal propagation is highly dependent on cell geometry and, thereby, conveys information on cell size and shape to the nucleus. [ABSTRACT FROM AUTHOR]

After endocytic uptake, influenza viruses transit early endosomal compartments and eventually reach late endosomes. There, the viral glycoprotein hemagglutinin (HA) triggers fusion between endosomal and viral membrane, a critical step that leads to release of the viral segmented genome destined to reach the cell nucleus. Endosomal maturation is a complex process involving acidification of the endosomal lumen as well as endosome motility along microtubules. While the pH drop is clearly critical for the conformational change and membrane fusion activity of HA, the effect of intracellular transport dynamics on the progress of infection remains largely unclear. In this study, we developed a comprehensive mathematical model accounting for the first steps of influenza virus infection. We calibrated our model with experimental data and challenged its predictions using recombinant viruses with altered pH sensitivity of HA. We identified the time point of virus-endosome fusion and thereby the diffusion distance of the released viral genome to the nucleus as a critical bottleneck for efficient virus infection. Further, we concluded and supported experimentally that the viral RNA is subjected to cytosolic degradation strongly limiting the probability of a successful genome import into the nucleus. [ABSTRACT FROM AUTHOR]

Cationic and heavy metal toxicity is involved in a substantial number of diseases in mammals and crop plants. Therefore, the understanding of tightly regulated transporter activities, as well as conceiving the interplay of regulatory mechanisms, is of substantial interest. A generalized thermodynamic description is developed for the complex interplay of the plasma membrane ion transporters, membrane potential and the consumption of energy for maintaining and restoring specific intracellular cation concentrations. This concept is applied to the homeostasis of cation concentrations in the yeast cells of S. cerevisiae. The thermodynamic approach allows to model passive ion fluxes driven by the electrochemical potential differences, but also primary or secondary active transport processes driven by the inter- play of different ions (symport, antiport) or by ATP consumption (ATPases). The model—confronted with experimental data—reproduces the experimentally observed potassium and proton fluxes induced by the external stimuli KCl and glucose. The estimated phenomenological constants combine kinetic parameters and transport coefficients. These are in good agreement with the biological understanding of the transporters thus providing a better understanding of the control exerted by the coupled fluxes. The model predicts the flux of additional ion species, like e.g. chloride, as a potential candidate for counterbalancing positive charges. Furthermore, the effect of a second KCl stimulus is simulated, predicting a reduced cellular response for cells that were first exposed to a high KCl stimulus compared to cells pretreated with a mild KCl stimulus. By describing the generalized forces that are responsible for a given flow, the model provides information and suggestions for new experiments. Furthermore, it can be extended to other systems such as e.g. Candida albicans, or selected plant cells. [ABSTRACT FROM AUTHOR]

Maintenance of cellular size is a fundamental systems level process that requires balancing of cell growth with proliferation. This is achieved via the cell division cycle, which is driven by the sequential accumulation and destruction of cyclins. The regulatory network around these cyclins, particularly in G1, has been interpreted as a size control network in budding yeast, and cell size as being decisive for the START transition. However, it is not clear why disruptions in the G1 network may lead to altered size rather than loss of size control, or why the S-G2-M duration also depends on nutrients. With a mathematical population model comprised of individually growing cells, we show that cyclin translation would suffice to explain the observed growth rate dependence of cell volume at START. Moreover, we assess the impact of the observed bud-localisation of the G2 cyclin CLB2 mRNA, and find that localised cyclin translation could provide an efficient mechanism for measuring the biosynthetic capacity in specific compartments: The mother in G1, and the growing bud in G2. Hence, iteration of the same principle can ensure that the mother cell is strong enough to grow a bud, and that the bud is strong enough for independent life. Cell sizes emerge in the model, which predicts that a single CDK-cyclin pair per growth phase suffices for size control in budding yeast, despite the necessity of the cell cycle network around the cyclins to integrate other cues. Size control seems to be exerted twice, where the G2/M control affects bud size through bud-localized translation of CLB2 mRNA, explaining the dependence of the S-G2-M duration on nutrients. Taken together, our findings suggest that cell size is an emergent rather than a regulatory property of the network linking growth and proliferation. [ABSTRACT FROM AUTHOR]

Communication between cells is a ubiquitous feature of cell populations and is frequently realized by secretion and detection of signaling molecules. Direct visualization of the resulting complex gradients between secreting and receiving cells is often impossible due to the small size of diffusing molecules and because such visualization requires experimental perturbations such as attachment of fluorescent markers, which can change diffusion properties. We designed a method to estimate such extracellular concentration profiles in vivo by using spatiotemporal mathematical models derived from microscopic analysis. This method is applied to populations of thousands of haploid yeast cells during mating in order to quantify the extracellular distributions of the pheromone α-factor and the activity of the aspartyl protease Bar1. We demonstrate that Bar1 limits the range of the extracellular pheromone signal and is critical in establishing α-factor concentration gradients, which is crucial for effective mating. Moreover, haploid populations of wild type yeast cells, but not BAR1 deletion strains, create a pheromone pattern in which cells differentially grow and mate, with low pheromone regions where cells continue to bud and regions with higher pheromone levels and gradients where cells conjugate to form diploids. However, this effect seems to be exclusive to high-density cultures. Our results show a new role of Bar1 protease regulating the pheromone distribution within larger populations and not only locally inside an ascus or among few cells. As a consequence, wild type populations have not only higher mating efficiency, but also higher growth rates than mixed MATabar1Δ/MATα cultures. We provide an explanation of how a rapidly diffusing molecule can be exploited by cells to provide spatial information that divides the population into different transcriptional programs and phenotypes. [ABSTRACT FROM AUTHOR]

We studied the dynamics of the proteome of influenza virus A/PR/8/34 (H1N1) infected Madin-Darby canine kidney cells up to 12 hours post infection by mass spectrometry based quantitative proteomics using the approach of stable isotope labeling by amino acids in cell culture (SILAC). We identified 1311 cell proteins and, apart from the proton channel M2, all major virus proteins. Based on their abundance two groups of virus proteins could be distinguished being in line with the function of the proteins in genesis and formation of new virions. Further, the data indicate a correlation between the amount of proteins synthesized and their previously determined copy number inside the viral particle. We employed bioinformatic approaches such as functional clustering, gene ontology, and pathway (KEGG) enrichment tests to uncover co-regulated cellular protein sets, assigned the individual subsets to their biological function, and determined their interrelation within the progression of viral infection. For the first time we are able to describe dynamic changes of the cellular and, of note, the viral proteome in a time dependent manner simultaneously. Through cluster analysis, time dependent patterns of protein abundances revealed highly dynamic up- and/or down-regulation processes. Taken together our study provides strong evidence that virus infection has a major impact on the cell status at the protein level. [ABSTRACT FROM AUTHOR]

Although considered as essential cofactors for a variety of enzymatic reactions and for important structural and functional roles in cell metabolism, metals at high concentrations are potent toxic pollutants and pose complex biochemical problems for cells. We report results of single dose acute toxicity testing in the model organism S. cerevisiae. The effects of moderate toxic concentrations of 10 different human health relevant metals, Ag+, Al3+, As3+, Cd2+, Co2+, Hg2+, Mn2+, Ni2+, V3+, and Zn2+, following short-term exposure were analyzed by transcription profiling to provide the identification of early-on target genes or pathways. In contrast to common acute toxicity tests where defined endpoints are monitored we focused on the entire genomic response. We provide evidence that the induction of central elements of the oxidative stress response by the majority of investigated metals is the basic detoxification process against short-term metal exposure. General detoxification mechanisms also comprised the induction of genes coding for chaperones and those for chelation of metal ions via siderophores and amino acids. Hierarchical clustering, transcription factor analyses, and gene ontology data further revealed activation of genes involved in metal-specific protein catabolism along with repression of growth-related processes such as protein synthesis. Metal ion group specific differences in the expression responses with shared transcriptional regulators for both, up-regulation and repression were also observed. Additionally, some processes unique for individual metals were evident as well. In view of current concerns regarding environmental pollution our results may support ongoing attempts to develop methods to monitor potentially hazardous areas or liquids and to establish standardized tests using suitable eukaryotic a model organism. [ABSTRACT FROM AUTHOR]

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. [ABSTRACT FROM AUTHOR]

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies. [ABSTRACT FROM AUTHOR]

The immune system protects us from foreign substances or pathogens by generating specific antibodies. The variety of immunoglobulin (Ig) paratopes for antigen recognition is a result of the V(D)J rearrangement mechanism, while a fast and efficient immune response is mediated by specific immunoglobulin isotypes obtained through class switch recombination (CSR). To get a better understanding on how antibody-based immune protection works and how it changes with age, the interdependency between these two parameters need to be addressed. Here, we have performed an in depth analysis of antibody repertoires of 14 healthy donors representing different gender and age groups. For this task, we developed a unique pyrosequencing approach, which is able to monitor the expression levels of all immunoglobulin V(D)J recombinations of all isotypes including subtypes in an unbiased and quantitative manner. Our results show that donors have individual immunoglobulin repertoires and cannot be clustered according to V(D)J recombination patterns, neither by age nor gender. However, after incorporating isotype-specific analysis and considering CSR information into hierarchical clustering the situation changes. For the first time the donors cluster according to age and separate into young adults and elderly donors (>50). As a direct consequence, this clustering defines the onset of immune senescence at the age of fifty and beyond. The observed age-dependent reduction of CSR ability proposes a feasible explanation why reduced efficacy of vaccination is seen in the elderly and implies that novel vaccine strategies for the elderly should include the "Golden Agers". [ABSTRACT FROM AUTHOR]

Eukaryotic cells have evolved mechanisms to sense and adapt to dynamic environmental changes. Adaptation to thermal insults, in particular, is essential for their survival. The major fungal pathogen of humans, Candida albicans, is obligately associated with warm-blooded animals and hence occupies thermally buffered niches. Yet during its evolution in the host it has retained a bona fide heat shock response whilst other stress responses have diverged significantly. Furthermore the heat shock response is essential for the virulence of C. albicans. With a view to understanding the relevance of this response to infection we have explored the dynamic regulation of thermal adaptation using an integrative systems biology approach. Our mathematical model of thermal regulation, which has been validated experimentally in C. albicans, describes the dynamic autoregulation of the heat shock transcription factor Hsf1 and the essential chaperone protein Hsp90. We have used this model to show that the thermal adaptation system displays perfect adaptation, that it retains a transient molecular memory, and that Hsf1 is activated during thermal transitions that mimic fever. In addition to providing explanations for the evolutionary conservation of the heat shock response in this pathogen and the relevant of this response to infection, our model provides a platform for the analysis of thermal adaptation in other eukaryotic cells. [ABSTRACT FROM AUTHOR]

Somatic cells can be reprogrammed to induced pluripotent stem cells by over-expression of OCT4, SOX2, KLF4 and c-MYC (OSKM). With the aim of unveiling the early mechanisms underlying the induction of pluripotency, we have analyzed transcriptional profiles at 24, 48 and 72 hours post-transduction of OSKM into human foreskin fibroblasts. Experiments confirmed that upon viral transduction, the immediate response is innate immunity, which induces free radical generation, oxidative DNA damage, p53 activation, senescence, and apoptosis, ultimately leading to a reduction in the reprogramming efficiency. Conversely, nucleofection of OSKM plasmids does not elicit the same cellular stress, suggesting viral response as an early reprogramming roadblock. Additional initiation events include the activation of surface markers associated with pluripotency and the suppression of epithelial-to-mesenchymal transition. Furthermore, reconstruction of an OSKM interaction network highlights intermediate path nodes as candidates for improvement intervention. Overall, the results suggest three strategies to improve reprogramming efficiency employing: 1) anti-inflammatory modulation of innate immune response, 2) pre-selection of cells expressing pluripotency-associated surface antigens, 3) activation of specific interaction paths that amplify the pluripotency signal. [ABSTRACT FROM AUTHOR]

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback. modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from . [ABSTRACT FROM AUTHOR]

Background: DNA replication begins at specific locations called replication origins, where helicase and polymerase act in concert to unwind and process the single DNA filaments. The sites of active DNA synthesis are called replication forks. The density of initiation events is low when replication forks travel fast, and is high when forks travel slowly. Despite the potential involvement of epigenetic factors, transcriptional regulation and nucleotide availability, the causes of differences in replication times during DNA synthesis have not been established satisfactorily, yet. Methodology/Principal Findings: Here, we aimed at quantifying to which extent sequence properties contribute to the DNA replication time in budding yeast. We interpreted the movement of the replication machinery along the DNA template as a directed random walk, decomposing influences on DNA replication time into sequence-specific and sequence-independent components. We found that for a large part of the genome the elongation time can be well described by a global average replication rate, thus by a single parameter. However, we also showed that there are regions within the genomic landscape of budding yeast with highly specific replication rates, which cannot be explained by global properties of the replication machinery. Conclusion/Significance: Computational models of DNA replication in budding yeast that can predict replication dynamics have rarely been developed yet. We show here that even beyond the level of initiation there are effects governing the replication time that can not be explained by the movement of the polymerase along the DNA template alone. This allows us to characterize genomic regions with significantly altered elongation characteristics, independent of initiation times or sequence composition. [ABSTRACT FROM AUTHOR]

Background: Yeast cells live in a highly fluctuating environment with respect to temperature, nutrients, and especially osmolarity. The Hog1 mitogen-activated protein kinase (MAPK) pathway is crucial for the adaption of yeast cells to external osmotic changes. Methodology/Principal Findings: To better understand the osmo-adaption mechanism in the budding yeast Saccharomyces cerevisiae, we have developed a mathematical model and quantitatively investigated the Hog1 response to osmotic stress. The model agrees well with various experimental data for the Hog1 response to different types of osmotic changes. Kinetic analyses of the model indicate that budding yeast cells have evolved to protect themselves economically: while they show almost no response to fast pulse-like changes of osmolarity, they respond periodically and are well-adapted to osmotic changes with a certain frequency. To quantify the signal transduction efficiency of the osmo-adaption network, we introduced a measure of the signal response gain, which is defined as the ratio of output change integral to input (signal) change integral. Model simulations indicate that the Hog1 response gain shows bell-shaped response curves with respect to the duration of a single osmotic pulse and to the frequency of periodic square osmotic pulses, while for up-staircase (ramp) osmotic changes, the gain depends on the slope. Conclusions/Significance: The model analyses suggest that budding yeast cells have selectively evolved to be optimized to some specific types of osmotic changes. In addition, our work implies that the signaling output can be dynamically controlled by fine-tuning the signal input profiles. [ABSTRACT FROM AUTHOR]

The eukaryotic cell cycle is the repeated sequence of events that enable the division of a cell into two daughter cells. It is divided into four phases: G1, S, G2, and M. Passage through the cell cycle is strictly regulated by a molecular interaction network, which involves the periodic synthesis and destruction of cyclins that bind and activate cyclindependent kinases that are present in nonlimiting amounts. Cyclin-dependent kinase inhibitors contribute to cell cycle control. Budding yeast is an established model organism for cell cycle studies, and several mathematical models have been proposed for its cell cycle. An area of major relevance in cell cycle control is the G1 to S transition. In any given growth condition, it is characterized by the requirement of a specific, critical cell size, PS, to enter S phase. The molecular basis of this control is still under discussion. The authors report a mathematical model of the G1 to S network that newly takes into account nucleo/cytoplasmic localization, the role of the cyclin-dependent kinase Sic1 in facilitating nuclear import of its cognate Cdk1-Clb5, Whi5 control, and carbon source regulation of Sic1 and Sic1-containing complexes. The model was implemented by a set of ordinary differential equations that describe the temporal change of the concentration of the involved proteins and protein complexes. The model was tested by simulation in several genetic and nutritional setups and was found to be neatly consistent with experimental data. To estimate PS, the authors developed a hybrid model including a probabilistic component for firing of DNA replication origins. Sensitivity analysis of PS provides a novel relevant conclusion: PS is an emergent property of the G1 to S network that strongly depends on growth rate. [ABSTRACT FROM AUTHOR]

Although considered as essential cofactors for a variety of enzymatic reactions and for important structural and functional roles in cell metabolism, metals at high concentrations are potent toxic pollutants and pose complex biochemical problems for cells. We report results of single dose acute toxicity testing in the model organism S. cerevisiae. The effects of moderate toxic concentrations of 10 different human health relevant metals, Ag+, Al3+, As3+, Cd2+, Co2+, Hg2+, Mn2+, Ni2+, V3+, and Zn2+, following short-term exposure were analyzed by transcription profiling to provide the identification of early-on target genes or pathways. In contrast to common acute toxicity tests where defined endpoints are monitored we focused on the entire genomic response. We provide evidence that the induction of central elements of the oxidative stress response by the majority of investigated metals is the basic detoxification process against short-term metal exposure. General detoxification mechanisms also comprised the induction of genes coding for chaperones and those for chelation of metal ions via siderophores and amino acids. Hierarchical clustering, transcription factor analyses, and gene ontology data further revealed activation of genes involved in metal-specific protein catabolism along with repression of growth-related processes such as protein synthesis. Metal ion group specific differences in the expression responses with shared transcriptional regulators for both, up-regulation and repression were also observed. Additionally, some processes unique for individual metals were evident as well. In view of current concerns regarding environmental pollution our results may support ongoing attempts to develop methods to monitor potentially hazardous areas or liquids and to establish standardized tests using suitable eukaryotic a model organism. [ABSTRACT FROM AUTHOR]

Background: Yeast cells live in a highly fluctuating environment with respect to temperature, nutrients, and especially osmolarity. The Hog1 mitogen-activated protein kinase (MAPK) pathway is crucial for the adaption of yeast cells to external osmotic changes., Methodology/principal Findings: To better understand the osmo-adaption mechanism in the budding yeast Saccharomyces cerevisiae, we have developed a mathematical model and quantitatively investigated the Hog1 response to osmotic stress. The model agrees well with various experimental data for the Hog1 response to different types of osmotic changes. Kinetic analyses of the model indicate that budding yeast cells have evolved to protect themselves economically: while they show almost no response to fast pulse-like changes of osmolarity, they respond periodically and are well-adapted to osmotic changes with a certain frequency. To quantify the signal transduction efficiency of the osmo-adaption network, we introduced a measure of the signal response gain, which is defined as the ratio of output change integral to input (signal) change integral. Model simulations indicate that the Hog1 response gain shows bell-shaped response curves with respect to the duration of a single osmotic pulse and to the frequency of periodic square osmotic pulses, while for up-staircase (ramp) osmotic changes, the gain depends on the slope., Conclusions/significance: The model analyses suggest that budding yeast cells have selectively evolved to be optimized to some specific types of osmotic changes. In addition, our work implies that the signaling output can be dynamically controlled by fine-tuning the signal input profiles.

Background: Investigation of dynamics and regulation of the TGF-beta signaling pathway is central to the understanding of complex cellular processes such as growth, apoptosis, and differentiation. In this study, we aim at using systems biology approach to provide dynamic analysis on this pathway., Methodology/principal Findings: We proposed a constraint-based modeling method to build a comprehensive mathematical model for the Smad dependent TGF-beta signaling pathway by fitting the experimental data and incorporating the qualitative constraints from the experimental analysis. The performance of the model generated by constraint-based modeling method is significantly improved compared to the model obtained by only fitting the quantitative data. The model agrees well with the experimental analysis of TGF-beta pathway, such as the time course of nuclear phosphorylated Smad, the subcellular location of Smad and signal response of Smad phosphorylation to different doses of TGF-beta., Conclusions/significance: The simulation results indicate that the signal response to TGF-beta is regulated by the balance between clathrin dependent endocytosis and non-clathrin mediated endocytosis. This model is useful to be built upon as new precise experimental data are emerging. The constraint-based modeling method can also be applied to quantitative modeling of other signaling pathways.