Abstract
Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales.
Highlights
Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks
We investigated the robustness to additional rules by systematically adding rules from all specified interactions plus an always active input to all non-input interactions for two target values which resulted in 70*67*2 = 9380 ProbRules models
ProbRules is a novel probabilistic modeling approach for integrating multi-scale knowledge about the dynamics of interactions. It mitigates the costs of an investment into specifying an in-silico model of a biological system in several ways
Summary
Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. Molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. The growth in available knowledge about interactions of genes and proteins[1] inspired efforts to integrate this into mathematical models[2] This was done in order to simulate functions of organisms in silico[3] and in particular, to use the resulting insights for prediction of outcomes in vitro and in vivo[4]. A range of approaches is based on a logical description of a system that allows a formal verification of its properties[19,20,21,22] These aforementioned dynamic modeling approaches require an explicit consideration of the crosstalk of all simultaneous interactions. Such rules can be implemented into mathematical models that can be simulated insilico and analyzed using logical frameworks[30]
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