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BMC Systems Biology

, 2:70

First Online: 01 August 2008Received: 27 February 2008Accepted: 01 August 2008

Abstract

BackgroundThe current challenge of Systems Biology is to integrate high throughput data sets for simulating the complexity of biological networks, exploit the evolution of nature-designed networks that maintain the robustness of a biological system, and thereby generate novel, experimentally testable hypotheses. In order to simulate non-linear biological complexities, we have previously developed an Enzyme-Centric mechanistic modeling approach and validated it using metabolic network in E. coli. The idea is to use prior knowledge of catalytic and regulatory mechanisms of each enzyme within the metabolic network to build a dynamic model for investigating the network level regulation and thus understand the nature design principle behind the network.

ResultsIn this paper, we further demonstrate the application of complex enzyme catalytic and regulatory modules to simulate nonlinear network regulatory patterns vs. simple linear conversion model. We learned and validated that it is essential to incorporate prior knowledge from the literature to simulate non-linear biological complexities. The network expandability is demonstrated and validated with the complex amino acid biosynthetic network with multi-regulations. Also, we demonstrated the compatibility of mechanistic models within close species. Furthermore, the eukaryotic protein factory model for insuring steady mRNA production is simulated and the coupling of RNA transcription and splicing is validated by both mathematical simulation and experimental analysis.

ConclusionWe demonstrated the importance of modeling complex enzyme catalytic and regulatory mechanisms to further understand nonlinear network regulatory patterns. The simulations presented in this paper reveal how a living system maintains homeostasis and its robustness to continue functioning while facing environmental stresses or genetic mutations.

AbbreviationsBCAAbranched chain amino acids

E. coliEscherichia coli

S. typhSalmonella typhimurium

SMSulfometuron Methyl

Pol IIRNA polymerase II

MCAmetabolic control analysis

GMWCGeneralized Monod, Wyman, Changeux model

ODEordinary differential equation.

Electronic supplementary materialThe online version of this article doi:10.1186-1752-0509-2-70 contains supplementary material, which is available to authorized users.

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Author: Chin-Rang Yang

Source: https://link.springer.com/



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