Dynamic modeling of biochemical networks using simulated annealing and S-system - Echavez, Michael T. (2008.)
A biochemical network is an abstract model of actual biological metabolic or genetic systems found in nature. Major advancements in technology made it possible for scientists to measure the constituents of these systems even in the cellular level. Micro-array experiments, nuclear magnetic resonance, protein kinase phosphorylation, and mass spectroscopic methods have been utilized to generate the time profiles of such systems. Time profiles contain both quantitative (structural) and qualitative (dynamical) information about the system they were extracted from. Previous studies on biochemical network modeling mainly focused on the estimation of a network's parameters. Such research assumes that the network topology is already known which, in reality, is not usually the case. Only a handful of studies have tackled the problem of inferring a network?s topology (structure). Some of these methods proved to be quite ineffective in inferring a network structure with feedback loops the presence of activator and inhibitor variables. In this work, a new algorithm which solves the network inference problem using Simulated Annealing for S-systems is presented. S-system is a modeling framework based on the power-law formalism which been widely used in modeling biochemical systems because of its simplicity and efficiency in modeling both the quantitative and qualitative aspects. Optimization is done through Simulated Annealing, an algorithm based on the physical phenomenon of creating glass and metal wherein the material is heat treated and slowly cooled. By incorporating the Randomized Pruning Technique and a novel Pruning Term in the fitness function, Simulated Annealing is shown to be able to correctly infer a network's topology.
Thesis (M.S. Computer Science)--University of the Philippines, Diliman.
Keywords: Simulated annealing (Mathematics).; Biochemistry -- Mathematical models; Biological sytems -- Mathematical models