Topological augmentation to infer hidden processes in biological systems

• DOI: 10.1093/bioinformatics/btt638
• Publication Date: 2013-12-03T01:49:19Z
• Authors: Mikael Sunnåker; Elias Zamora-Sillero; Adrián López Garc...; Florian Rudroff; Uwe Sauer; Joerg Stelling; Andreas Wagner
• Keywords: 0301 basic medicine; 1303 Biochemistry; Glutamine; Saccharomyces cerevisiae; Models, Biological; 10127 Institute of Evolutionary Biology and Environmental Studies; 03 medical and health sciences; 1312 Molecular Biology; 1706 Computer Science Applications; Humans; Pharmacokinetics; 2613 Statistics and Probability; 0303 health sciences; Systems Biology; Bayes Theorem; Original Papers; 3. Good health; Gastrointestinal Tract; Pharmaceutical Preparations; 570 Life sciences; biology; 590 Animals (Zoology); Amino Acid Transport Systems, Basic; 2605 Computational Mathematics; Algorithms; 1703 Computational Theory and Mathematics

Abstract Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present method called topological augmentation this statistically rigorous systematic way from prior knowledge experimental data. Results: Topological starts simple model that unable explain the data augments by adding new terms capture behavior. process guided representing uncertainty through stochastic differential equations whose trajectories contain information about missing parts. We first apply semiautomatic procedure pharmacokinetic model. example illustrates global sampling parameter space critical for inferring structure. also use our improve glutamine transport yeast. analysis shows dynamics determined permeases with two different kinds kinetics. can not only be applied systems, but any described ordinary equations. Availability implementation: Matlab code examples are available at: http://www.csb.ethz.ch/tools/index. Contact: [e-mail]; [e-mail] Supplementary information: at Bioinformatics online.