Hybrid modeling in biochemical systems theory by means of functional petri nets.
Many biological systems are genuinely hybrids consisting of interacting discrete and continuous components and processes that often operate at different time scales. It is therefore desirable to create modeling frameworks capable of combining differently structured processes and permitting their analysis over multiple time horizons. During the past 40 years, Biochemical Systems Theory (BST) has been a very successful approach to elucidating metabolic, gene regulatory, and signaling systems. However, its foundation in ordinary differential equations has precluded BST from directly addressing problems containing switches, delays, and stochastic effects. In this study, we extend BST to hybrid modeling within the framework of Hybrid Functional Petri Nets (HFPN). First, we show how the canonical GMA and S-system models in BST can be directly implemented in a standard Petri Net framework. In a second step we demonstrate how to account for different types of time delays as well as for discrete, stochastic, and switching effects. Using representative test cases, we validate the hybrid modeling approach through comparative analyses and simulations with other approaches and highlight the feasibility, quality, and efficiency of the hybrid method.
Wu
Jialiang
J
Department of Mathematics, Bioinformatics Program, Georgia Institute of Technology, Atlanta, GA 30332, USA. gtg337v@mail.gatech.edu
Voit
Eberhard
E
D000465:Algorithms; D001671:Biochemistry / Q000379:methods; D001704:Biopolymers / Q000737:chemistry* / Q000378:metabolism*; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D016571:Neural Networks, Computer; D015398:Signal Transduction / Q000502:physiology*
D001704:Biopolymers