A Quantitative Study of the Hog1 MAPK Response to Fluctuating Osmotic Stress in Saccharomyces cerevisiae

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

SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool

<p>Abstract</p> <p>Background</p> <p>It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.</p> <p>Results</p> <p>This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.</p> <p>Conclusion</p> <p>SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.</p

Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics

Mathematical modeling and experimental analyses reveal that TGF-β ligand depletion has an important role in converting short-term graded signaling responses to long-term switch-like responses

Cell-type-specific role of CHK2 in mediating DNA damage-induced G2 cell cycle arrest

Cancer is a life-threatening disease that affects one in three people. Although most cases are sporadic, cancer risk can be increased by genetic factors. It remains unknown why certain genes predispose for specific forms of cancer only, such as checkpoint protein 2 (CHK2), in which gene mutations convey up to twofold higher risk for breast cancer but do not increase lung cancer risk. We have investigated the role of CHK2 and the related kinase checkpoint protein 1 (CHK1) in cell cycle regulation in primary breast and lung primary epithelial cells. At the molecular level, CHK1 activity was higher in lung cells, whereas CHK2 was more active in breast cells. Inhibition of CHK1 profoundly disrupted the cell cycle profile in both lung and breast cells, whereas breast cells were more sensitive toward inhibition of CHK2. Finally, we provide evidence that breast cells require CHK2 to induce a G2–M cell cycle arrest in response of DNA damage, whereas lung cells can partially compensate for the loss of CHK2. Our results provide an explanation as to why CHK2 germline mutations predispose for breast cancer but not for lung cancer

Constraint-Based Modeling and Kinetic Analysis of the Smad Dependent TGF-β Signaling Pathway

Background Investigation of dynamics and regulation of the TGF-β 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-β 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-β 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-β. Conclusions/Significance The simulation results indicate that the signal response to TGF-β 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

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Mathematical modeling and kinetic analysis of cellular signaling pathways

Aufgrund des wachsenden Interesses an der Systembiologie werden zunehmend mathematische Modelle in Kombination mit Experimenten für die Analyse von Stoffwechselnetzwerken, Genregulationsnetzwerken und zellulären Signalweiterleitungswegen verwendet. Diese Dissertationsschrift benutzt die mathematische Modellierung und kinetische Untersuchungsmethoden zum Studium von zelluären Signalwegen, insbesondere des Netzwerkes zur Festlegung der Rezeptorlokalisation und des Tumorwachstumsfaktor-beta-Signalweges. Ergänzend wurde ein Computerwerkzeug (SBML-PET) entwickelt, das die Modellentwicklung unterstützt und der Parameterschätzung dient. Mit diesem Werkzeug kann man Modelle bearbeiten, die in der Systems Biology Markup Language (SBML) formuliert sind. In dieser Arbeit wird ein quantitatives mathematisches Modell benutzt, um die Signalantwort in unterschiedlichen Rezeptorlokalisationsnetzwerken in Abhängigkeit von der Ligandenanzahl und der Zelldichte zu untersuchen. Die rechnergestützte Analyse des Modells hat ergeben, dass der Zustand eines Rezeptorlokalisationsnetzwerkes potenziell eine sigmoide Abhängigkeit von dem Verhältnis zwischen Ligandenanzahl und Oberflächenrezeptoranzahl pro Zelle zeigen. Dieses Verhältnis ist die entscheidende Kontrollgröße der Signalantwort in Rezeptorlokalisationsnetzwerken. Mit Hilfe des SBML-PET Software-Paketes haben wir eine Modellierungsmethode mit Randbedingungen vorgeschlagen, um ein umfangreiches mathematisches Modell für den Smad-abh?ngigen TGF-beta Signalweg zu erstellen und dessen Parameter aus experimentellen Daten unter Berücksichtigung qualitativer Nebenbedingungen zu fitten. Die Ergebnisse der kinetischen Untersuchung dieses Modells legen nahe, dass die Signalantwort auf einen TGF-beta-Reiz durch die Balance zwischen clathrin-abhängier Endozytose und clathrin-unabhängiger Endozytose reguliert wird.With growing interests in systems biology, mathematical models, paired with experiments, have been widely used for the studies on metabolic networks, gene regulatory networks and cellular signaling pathways. This dissertation employs the mathematical modeling and kinetic analysis method to study cellular signaling pathways, in particular, the receptor trafficking network and TGF-beta signaling pathway. On the other hand, a systems biology markup language (SBML) based parameter estimation tool (SBML-PET), was developed for facilitating the modeling process. A quantitative mathematical model is employed to investigate signal responses in different receptor trafficking networks by simultaneous perturbations of the ligand concentration and cell density. The computational analysis of the model revealed that receptor trafficking networks have potentially sigmoid responses to the ratio between ligand number and surface receptor number per cell, which is a key factor to control the signaling responses in receptor trafficking networks. Using the SBML-PET software package, 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. Kinetic analysis results indicate that the signal response to TGF-beta is regulated by the balance between clathrin dependent endocytosis and non-clathrin mediated endocytosis

SBML-PET: a Systems Biology Markup Language-based Parameter Estimation Tool

Summary: The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling pathways, gene regulation networks and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data from different experimental conditions. SBML-PET has a unique feature of supporting event definition in the SMBL model. SBML models can also be simulated in SBML-PET. Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system