Hill Kinetics Meets P Systems: A Case Study on Gene Regulatory Networks as Computing Agents in silico and in vivo
Modeling and simulation of biological reaction networks is an essential task in systems biology aiming at formalization, understanding, and prediction of processes in living organisms. Currently, a variety of modeling approaches for specific purposes coex
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Friedrich-Schiller-Universit¨ at Jena, Bio Systems Analysis Group Ernst-Abbe-Platz 1–4, D-07743 Jena, Germany {hinze,thlenser,naoki,dittrich}@minet.uni-jena.de 2 Universit¨ at des Saarlandes, Computational Biology Group Center for Bioinformatics, P.O. Box 15 11 50, D-66041 Saarbr¨ ucken, Germany [email protected] Abstract. Modeling and simulation of biological reaction networks is an essential task in systems biology aiming at formalization, understanding, and prediction of processes in living organisms. Currently, a variety of modeling approaches for specific purposes coexists. P systems form such an approach which owing to its algebraic nature opens growing fields of application. Here, emulating the dynamical system behavior based on reaction kinetics is of particular interest to explore network functions. We demonstrate a transformation of Hill kinetics for gene regulatory networks (GRNs) into the P systems framework. Examples address the switching dynamics of GRNs acting as NAND gate and RS flip-flop. An adapted study in vivo experimentally verifies both practicability for computational units and validity of the system model.
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Introduction
Along with the development of systems biology, a variety of modeling techniques for biological reaction networks have been established during the last years [1]. Inspired by different methodologies, three fundamental concepts emerged mostly independent of each other: analytic, stochastic, and algebraic approaches. Each paradigm specifically emphasizes certain modeling aspects. Analytic approaches, primarily adopted from chemical reaction kinetics, enable a macroscopic view on species concentrations in many-body systems. Based on differential equations considering generation and consumption rates of species, deterministic monitoring and prediction of temporal or spatial system behavior is efficiently expressed by continuous average concentration gradients. In contrast, stochastic approaches reflect aspects of uncertainty in biological reaction networks by incorporating randomness and probabilities. So, ranges of possible scenarios and their statistical distribution can be studied facilitating a direct comparison with wetlab experimental data. Statistical tools help in discovering correlations between network components. Furthermore, algebraic approaches appear as flexible instruments regarding the level of abstraction for system description. Due to G. Eleftherakis et al. (Eds.): WMC8 2007, LNCS 4860, pp. 320–335, 2007. c Springer-Verlag Berlin Heidelberg 2007
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