KINSOLVER: A simulator for computing large ensembles of biochemical and gene regulatory networks
| Autor: | Thiab R. Taha, Yihai Yu, Boanerges Aleman-Meza, Jonathan Arnold, H.-B. Schüttler |
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| Rok vydání: | 2009 |
| Předmět: |
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Computing life Computer science business.industry Interface (Java) Gene regulatory network Synthetic biological circuit Topology (electrical circuits) Model ensemble Visualization Biochemical and gene regulatory network Repressilator Genetic networks Computational Mathematics Software Computational Theory and Mathematics Modeling and Simulation Modelling and Simulation Simulator business Chemical reaction network Simulation |
| Zdroj: | Computers & Mathematics with Applications. 57(3):420-435 |
| ISSN: | 0898-1221 |
| DOI: | 10.1016/j.camwa.2008.10.086 |
| Popis: | With the sequence of many genomes now available the major challenge of functional genomics is ‘reassembling the pieces’. One functional view of a living system is as a chemical reaction network, and new genomics technologies like RNA and protein profiling are providing ways to measure the state of these networks. The goal here is being able to simulate an arbitrary ensemble of hypothesized biochemical and genetic regulatory networks to predict what a cell is doing, i.e. to Compute Life, so that these predictions may be compared with the observed state of the system. The simulator KINSOLVER solves chemical reaction networks, satisfying standard multiplicative mass-action kinetics, of arbitrary size, topology, rate constants, and initial conditions by 5 standard methods (Euler, Modified Euler, Runge–Kutta (RK), Adaptive RK–Fehlberg, and LSODES). The simulator includes a simple Web-based interface for specifying and refining a target reaction network as well as visualization tools to represent the network’s behavior. The simulator is verified as rapidly solving in seconds (with benchmarks relative to GEPASI) some classic biological circuits like the lac operon and qa gene cluster as well as a new circuit, the repressilator, with oscillatory behavior. The LSODES method uniformly outperformed the other methods with a relatively large error tolerance of 0.01 and with a small error tolerance of 1E−6 for a variety of examples. The simulator is written in a nearly platform independent manner to simulate large ensembles of models and has a Web-based interface to interact with the simulator by using Java and C++ at the back-end. Software can be downloaded from http://webster.cs.uga.edu/~boanerg/mams or http://gene.genetics.uga.edu/stc. |
| Databáze: | OpenAIRE |
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