Modeling yeast Osmoadaptation at Different Levels of Resolution

We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e.g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.

[1]  Alexander Bockmayr,et al.  Temporal constraints in the logical analysis of regulatory networks , 2008, Theor. Comput. Sci..

[2]  R. Lew,et al.  Turgor Regulation in Osmotically Stressed Arabidopsis Epidermal Root Cells. Direct Support for the Role of Inorganic Ion Uptake as Revealed by Concurrent Flux and Cell Turgor Measurements1 , 2002, Plant Physiology.

[3]  A. Oudenaarden,et al.  A Systems-Level Analysis of Perfect Adaptation in Yeast Osmoregulation , 2009, Cell.

[4]  James R Faeder,et al.  Efficient modeling, simulation and coarse-graining of biological complexity with NFsim , 2011, Nature Methods.

[5]  S. Hohmann.  Osmotic Stress Signaling and Osmoadaptation in Yeasts , 2002, Microbiology and Molecular Biology Reviews.

[6]  Stefan Hohmann,et al.  Fps1p controls the accumulation and release of the compatible solute glycerol in yeast osmoregulation , 1999, Molecular microbiology.

[7]  Hiroaki Kitano,et al.  Robustness and fragility in the yeast high osmolarity glycerol (HOG) signal-transduction pathway , 2009, Molecular systems biology.

[8]  Johan Hattne,et al.  Stochastic reaction-diffusion simulation with MesoRD , 2005, Bioinform..

[9]  William S. Hlavacek,et al.  BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains , 2004, Bioinform..

[10]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[11]  Stefan Hohmann,et al.  A Simple Mathematical Model of Adaptation to High Osmolarity in Yeast , 2006, Silico Biol..

[12]  Megan N. McClean,et al.  Cross-talk and decision making in MAP kinase pathways , 2007, Nature Genetics.

[13]  Eytan Ruppin,et al.  Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. , 2010, Current opinion in biotechnology.

[14]  Luca Cardelli,et al.  A Compositional Approach to the Stochastic Dynamics of Gene Networks , 2006, Trans. Comp. Sys. Biology.

[15]  E. Klipp,et al.  Signal integration in budding yeast. , 2010, Biochemical Society transactions.

[16]  S. Bhartiya,et al.  Characterization of the adaptive response and growth upon hyperosmotic shock in Saccharomyces cerevisiae. , 2011, Molecular bioSystems.

[17]  J. Ariño,et al.  Alkali Metal Cation Transport and Homeostasis in Yeasts , 2010, Microbiology and Molecular Biology Reviews.

[18]  F. Klauschen,et al.  Computational Modeling of Cellular Signaling Processes Embedded into Dynamic Spatial Contexts , 2012, Nature Methods.

[19]  E. Klipp,et al.  Integrative model of the response of yeast to osmotic shock , 2005, Nature Biotechnology.

[20]  D. Raitt,et al.  Yeast osmosensor Sln1 and plant cytokinin receptor Cre1 respond to changes in turgor pressure , 2003, The Journal of cell biology.

[21]  Reinhart Heinrich,et al.  Mathematical models of protein kinase signal transduction. , 2002, Molecular cell.

[22]  D. Gillespie.  A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[23]  P. Masson,et al.  Yeast Respond to Hypotonic Shock with a Calcium Pulse* , 1996, The Journal of Biological Chemistry.

[24]  J M Thevelein,et al.  Fps1, a yeast member of the MIP family of channel proteins, is a facilitator for glycerol uptake and efflux and is inactive under osmotic stress. , 1995, The EMBO journal.

[25]  Barbara Di Ventura,et al.  From in vivo to in silico biology and back , 2006, Nature.

[26]  Kevin P. Murphy,et al.  Learning the Structure of Dynamic Probabilistic Networks , 1998, UAI.

[27]  Simeone Marino,et al.  Multiscale Computational Modeling Reveals a Critical Role for TNF-α Receptor 1 Dynamics in Tuberculosis Granuloma Formation , 2011, The Journal of Immunology.

[28]  Jens Timmer,et al.  Dynamical modeling and multi-experiment fitting with PottersWheel , 2008, Bioinform..

[29]  Jerome T. Mettetal,et al.  The Frequency Dependence of Osmo-Adaptation in Saccharomyces cerevisiae , 2008, Science.

[30]  Ricard Solé,et al.  Dynamic Signaling in the Hog1 MAPK Pathway Relies on High Basal Signal Transduction , 2009, Science Signaling.

[31]  R. Shamir,et al.  Refinement and expansion of signaling pathways: the osmotic response network in yeast. , 2007, Genome research.

[32]  Tatsuya Maeda,et al.  A two-component system that regulates an osmosensing MAP kinase cascade in yeast , 1994, Nature.

[33]  Vincent Danos,et al.  Rule-Based Modelling of Cellular Signalling , 2007, CONCUR.

[34]  Christoph H. Borchers,et al.  A Systems-Biology Analysis of Feedback Inhibition in the Sho1 Osmotic-Stress-Response Pathway , 2007, Current Biology.

[35]  J. Schaber,et al.  Time-Dependent Quantitative Multicomponent Control of the G1-S Network by the Stress-Activated Protein Kinase Hog1 upon Osmostress , 2011, Science Signaling.

[36]  Markus Kirkilionis,et al.  Exploration of cellular reaction systems , 2010, Briefings Bioinform..

[37]  I. H. Segel.  Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems , 1975 .

[38]  Edda Klipp,et al.  Formal representation of the high osmolarity glycerol pathway in yeast. , 2010, Genome informatics. International Conference on Genome Informatics.

[39]  Edda Klipp,et al.  A Quantitative Study of the Hog1 MAPK Response to Fluctuating Osmotic Stress in Saccharomyces cerevisiae , 2010, PloS one.

[40]  S. Hohmann,et al.  Quantification of cell volume changes upon hyperosmotic stress in Saccharomyces cerevisiae. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[41]  S. Bhartiya,et al.  A model-based study delineating the roles of the two signaling branches of Saccharomyces cerevisiae, Sho1 and Sln1, during adaptation to osmotic stress , 2009, Physical biology.

[42]  Edda Klipp,et al.  Biophysical properties of Saccharomyces cerevisiae and their relationship with HOG pathway activation , 2010, European Biophysics Journal.

[43]  P. Gervais,et al.  Osmotic mass transfer in the yeast Saccharomyces cerevisiae. , 2001, Cellular and molecular biology.

[44]  D. E. Levin,et al.  A Second Osmosensing Signal Transduction Pathway in Yeast , 1995, The Journal of Biological Chemistry.