TY  - JOUR
AB  - © The Institution of Engineering and Technology.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.
AU  - Kondofersky, I.
AU  - Theis, F.J.
AU  - Fuchs, C.
C1  - 48517
C2  - 41110
CY  - Hertford
SP  - 210-218
TI  - Inferring catalysis in biological systems.
JO  - IET Syst. Biol.
VL  - 10
IS  - 6
PB  - Inst Engineering Technology-iet
PY  - 2016
SN  - 1751-8849
ER  - 

TY  - JOUR
AB  - In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with acceptable accuracy and ask the question whether this is because of the model lacking important external regulations. Real-world examples for such entities range from microRNAs to metabolic fluxes. To improve the prediction, they propose an algorithm to systematically extend the network by an additional latent dynamic variable which has an exogenous effect on the considered network. This variable's time course and influence on the other species is estimated in a two-step procedure involving spline approximation, maximum-likelihood estimation and model selection. Simulation studies show that such a hidden influence can successfully be inferred. The method is also applied to a signalling pathway model where they analyse real data and obtain promising results. Furthermore, the technique can be employed to detect incomplete network structures.
AU  - Kondofersky, I.
AU  - Fuchs, C.
AU  - Theis, F.J.
C1  - 44578
C2  - 36921
SP  - 193-203
TI  - Identifying latent dynamic components in biological systems.
JO  - IET Syst. Biol.
VL  - 9
IS  - 5
PY  - 2015
SN  - 1751-8849
ER  - 

TY  - JOUR
AB  - The authors propose piecewise deterministic Markov processes as an alternative approach to model gene regulatory networks. A hybrid simulation algorithm is presented and discussed, and several standard regulatory modules are analysed by numerical means. It is shown that despite of the partial simplification of the mesoscopic nature of regulatory networks such processes are suitable to reveal the intrinsic noise effects because of the low copy numbers of genes.
AU  - Zeiser, S.
AU  - Franz, U.*
AU  - Wittich, O.*
AU  - Liebscher, V.*
C1  - 3529
C2  - 25639
SP  - 113-135
TI  - Simulation of genetic networks modelled by piecewise deterministic Markov processes.
JO  - IET Syst. Biol.
VL  - 2
IS  - 3
PB  - IET
PY  - 2008
SN  - 1751-8849
ER  -