On the Calculation of System Entropy in Nonlinear Stochastic Biological NetworksReport as inadecuate




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Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan





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Academic Editor: Kevin H. Knuth

Abstract Biological networks are open systems that can utilize nutrients and energy from their environment for use in their metabolic processes, and produce metabolic products. System entropy is defined as the difference between input and output signal entropy, i.e., the net signal entropy of the biological system. System entropy is an important indicator for living or non-living biological systems, as biological systems can maintain or decrease their system entropy. In this study, system entropy is determined for the first time for stochastic biological networks, and a computation method is proposed to measure the system entropy of nonlinear stochastic biological networks that are subject to intrinsic random fluctuations and environmental disturbances. We find that intrinsic random fluctuations could increase the system entropy, and that the system entropy is inversely proportional to the robustness and stability of the biological networks. It is also determined that adding feedback loops to shift all eigenvalues to the farther left-hand plane of the complex s-domain could decrease the system entropy of a biological network. View Full-Text

Keywords: system entropy; thermodynamics; open system; biological network; nonlinear stochastic system; Hamilton-Jacobi inequality HJI; linear matrix inequality LMI system entropy; thermodynamics; open system; biological network; nonlinear stochastic system; Hamilton-Jacobi inequality HJI; linear matrix inequality LMI





Author: Bor-Sen Chen * , Shang-Wen Wong and Cheng-Wei Li

Source: http://mdpi.com/



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