CSB modelling examples

Systems modelling examples

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Examples of defining work in the field of computational systems modelling.

 

Contents

 

E-Cell

Yachie-Kinoshita et al. (2010) A metabolic model of human erythrocytes: practical application of the E-Cell Simulation Environment. J Biomed Biotechnol 2010:642420. (pmid: 20625505)

[ PubMed ] [ DOI ] Abstract The human red blood cell (RBC) has long been used for modeling of complex biological networks, for elucidation of a wide variety of dynamic phenomena, and for understanding the fundamental topology of metabolic pathways. Here, we introduce our recent work on an RBC metabolic model using the E-Cell Simulation Environment. The model is sufficiently detailed to predict the temporal hypoxic response of each metabolite and, at the same time, successfully integrates modulation of metabolism and of the oxygen transporting capacity of hemoglobin. The model includes the mechanisms of RBC maintenance as a single cell system and the functioning of RBCs as components of a higher order system. Modeling of RBC metabolism is now approaching a fully mature stage of realistic predictions at the molecular level and will be useful for predicting conditions in biotechnological applications such as long-term cold storage of RBCs.

 

Virtual Cell

Neves (2011) Developing models in virtual cell. Sci Signal 4:tr12. (pmid: 21954293)

[ PubMed ] [ DOI ] Abstract This Teaching Resource provides lecture notes, slides, and a student assignment for a two-part lecture on mathematical modeling using the Virtual Cell environment. The lectures discuss the steps involved in developing and running simulations using Virtual Cell, with particular focus on spatial partial differential equation models. We discuss how to construct both ordinary differential equation models, in which the cytoplasm is considered a well-mixed cellular compartment, and partial differential equation models, which calculate how chemical species change as a function of both time and location. The Virtual Cell environment is especially well suited for models that explore spatial specificity of cellular reactions. Partial differential equation models in Virtual Cell can give rise to simulations using predefined cellular geometries, which enable direct comparison with imaging data. These models address questions regarding the regulatory capability arising from spatial organization of the cell. Examples are provided of studies that have successfully exploited the Virtual Cell software to address the spatial contribution to signaling.

 

Exercises

   

Further reading and resources

Anesiadis et al. (2008) Dynamic metabolic engineering for increasing bioprocess productivity. Metab Eng 10:255-66. (pmid: 18606241)

[ PubMed ] [ DOI ] Abstract Yield and productivity are critical for the economics and viability of a bioprocess. In metabolic engineering, the main objective is the increase of a target metabolite production through genetic engineering. However, genetic manipulations usually result in lower productivity due to growth impairment. Previously, it has been shown that the dynamic control of metabolic fluxes can increase the amount of product formed in an anaerobic batch fermentation of Escherichia coli. In order to apply this control strategy, the genetic toggle switch is used to manipulate key fluxes of the metabolic network. We have designed and analyzed an integrated computational model for the dynamic control of gene expression. This controller, when coupled to the metabolism of E. coli, resulted in increased bioprocess productivity.

Kim (2009) Ingeneue: a software tool to simulate and explore genetic regulatory networks. Methods Mol Biol 500:169-200. (pmid: 19399429)

[ PubMed ] [ DOI ] Abstract Here I describe how to use Ingeneue, a software tool for constructing, simulating, and exploring models of gene regulatory networks. Ingeneue is an open source, extensible Java application that allows users to rapidly build ordinary differential equation models of a gene regulatory network without requiring extensive programming or mathematical skills. Models can be in a single cell or 2D sheet of cells, and Ingeneue is well suited for simulating both oscillatory and pattern forming networks. Ingeneue provides features to allow rapid model construction and debugging, sophisticated visualization and statistical tools for model exploration, and a powerful framework for searching parameter space for desired behavior. This chapter provides an overview of the mathematical theory and operation of Ingeneue, and detailed walkthroughs demonstrating how to use the main features and how to construct networks in Ingeneue.

Faeder et al. (2009) Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 500:113-67. (pmid: 19399430)

[ PubMed ] [ DOI ] Abstract Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about protein-protein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rule-based modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems.

Shaffer et al. (2009) Modeling molecular regulatory networks with JigCell and PET. Methods Mol Biol 500:81-111. (pmid: 19399431)

[ PubMed ] [ DOI ] Abstract We demonstrate how to model macromolecular regulatory networks with JigCell and the Parameter Estimation Toolkit (PET). These software tools are designed specifically to support the process typically used by systems biologists to model complex regulatory circuits. A detailed example illustrates how a model of the cell cycle in frog eggs is created and then refined through comparison of simulation output with experimental data. We show how parameter estimation tools automatically generate rate constants that fit a model to experimental data.

Mendes et al. (2009) Computational modeling of biochemical networks using COPASI. Methods Mol Biol 500:17-59. (pmid: 19399433)

[ PubMed ] [ DOI ] Abstract Computational modeling and simulation of biochemical networks is at the core of systems biology and this includes many types of analyses that can aid understanding of how these systems work. COPASI is a generic software package for modeling and simulation of biochemical networks which provides many of these analyses in convenient ways that do not require the user to program or to have deep knowledge of the numerical algorithms. Here we provide a description of how these modeling techniques can be applied to biochemical models using COPASI. The focus is both on practical aspects of software usage as well as on the utility of these analyses in aiding biological understanding. Practical examples are described for steady-state and time-course simulations, stoichiometric analyses, parameter scanning, sensitivity analysis (including metabolic control analysis), global optimization, parameter estimation, and stochastic simulation. The examples used are all published models that are available in the BioModels database in SBML format.

Swat et al. (2009) Multicell simulations of development and disease using the CompuCell3D simulation environment. Methods Mol Biol 500:361-428. (pmid: 19399437)

[ PubMed ] [ DOI ] Abstract Mathematical modeling and computer simulation have become crucial to biological fields from genomics to ecology. However, multicell, tissue-level simulations of development and disease have lagged behind other areas because they are mathematically more complex and lack easy-to-use software tools that allow building and running in silico experiments without requiring in-depth knowledge of programming. This tutorial introduces Glazier-Graner-Hogeweg (GGH) multicell simulations and CompuCell3D, a simulation framework that allows users to build, test, and run GGH simulations.

Klipp (2011) Computational yeast systems biology: a case study for the MAP kinase cascade. Methods Mol Biol 759:323-43. (pmid: 21863496)

[ PubMed ] [ DOI ] Abstract Cellular networks and processes can be mathematically described and analyzed in various ways. Here, the case example of a MAP kinase (MAPK) cascade is used to detail steps in the formulation of a system of ordinary differential equations governing the temporal behavior of a signal transduction pathway after stimulation. Different analysis methods for the model are explained and demonstrated, such as stoichiometric analysis, sensitivity analysis, or studying the effect of deletions and protein overexpression. Finally, a perspective on standards concerning modeling in systems biology is given.