Inferring catalysis in biological systems

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.

Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model

“…While the model is based on extensive literature and was manually curated, interactions might still be missing or incorrect. Some methods have been demonstrated to detect such problems (Kondofersky et al, 2016;Penas et al, 2017); however, it is unlikely that these methods scale sufficiently well. An alternative might be to consider ensembles of models featuring different model structures and employ the concepts of bagging and boosting (Opitz and Maclin, 1999).…”