Inferring catalysis in biological systems. Issue 6 (1st December 2016)

• Record Type: Journal Article
• Authors: Kondofersky, Ivan; Theis, Fabian J.; Fuchs, Christiane
• Is Part Of: IET systems biology. Volume 10:Issue 6(2016)
• Page Start: 210
• Page End: 218
• Publication Date: 2016-12-01
• Subjects: catalysis -- catalysts -- biochemistry -- genetics -- enzymes -- biology computing -- calibration -- molecular clusters; inferring catalysis -- biological systems -- systems biology -- communication patterns -- genes -- enzymes -- proteins -- time‐resolved experiments -- time‐resolved communication -- reaction networks -- gene regulatory networks -- biochemical networks -- signalling pathways -- mathematical data modelling -- catalytic reactions -- calibration -- catalyst -- multiple‐step procedure -- latent variables -- similarity analysis methods -- noninformative data -- differentiation apoptotic pathway -- cluster; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2015.0087
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple‐step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non‐informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.