Abstract
Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems.
Highlights
Systems biology refers to the quantitative analysis of the dynamic interactions among multiple components of a biological system and aims to understand the characteristics of a system as a whole [1,2]
ordinary differential equations (ODEs) gene-regulation networks (GRNs) models are similar to metabolic models that are formulated based on enzyme kinetics, where each rate law approximates a series of elementary chemical steps
In the tables x and s denote the mean error and error standard deviation, respectively, obtained from four reverse-engineering replications per model. Rows in these tables refer to the GRN systems from which the data was obtained, and columns to omega intervals used to reverse-engineer the GRN models
Summary
Systems biology refers to the quantitative analysis of the dynamic interactions among multiple components of a biological system and aims to understand the characteristics of a system as a whole [1,2]. As the amount of gene expression data is growing, researchers are becoming increasingly interested in the automated inference or reverse-engineering of quantitative dynamic, mechanistic gene-regulatory network models from gene expression time-course data [5,6,4,1,7,8,9]. The quality of such reverse-engineered GRN models is determined mainly by two factors: The quality of a GRN model depends on two factors: the model’s explanatory power (or model completeness) and the model’s predictive power (or model correctness)
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