Reconstructing and modeling regulatory networks is an active area of research in bioinformatics and systems biology. Hence, various computational methods have been published, often successfully modeling one aspect of regulatory control. Gene regulation, however, is a process that depends on many different components such as transcription factors (TFs), cis-regulatory motifs and their temporal and spatial coordination. Accordingly, a promising new direction for computational analysis is the incorporation of multiple data types to discover, for instance, cluster membership, the spatial organization of cis-regulatory motifs and TFs that bind to these motifs. Here, we present such a data-driven framework, comprising four stages, to infer gene regulatory networks (GRNs) by modeling: 1. motif presence in the promoter, 2. spatial motif arrangement in co-regulated genes, 3. TFs that bind the respective motifs, and 4. dynamic properties of the GRN. A novel method is presented in stage 2, where we optimize for the spatial motif properties: orientation, occurrence of multiple motifs, relative distance between two motifs and distance to the Transcription Start Site (TSS). To find optimal distance based properties in efficient time we describe a dynamic programming approach. To combine multiple motif properties that are shared by genes with similar expression profiles a Hill-climber is employed. Subsequently, in stage 3 and 4, we infer GRNs by assigning TFs to the derived motifs and model time-dependent regulatory relationships between them with the Inferelator approach. None of the stages require the user to manually adjust any parameter, and thus derived properties can be analyzed without the bias introduced by parametrization. We applied this approach to S. cerevisiae data and obtained insight into individual and general properties of the spatial assembly of regulatory elements and inferred the corresponding GRN.

An important problem in systems biology is parameter estimation for biochemical system models. Our work concentrates on the metabolic subnetwork of the valine and leucine biosynthesis in Corynebacterium glutamicum, an anaerobic actinobacterium of high biotechnological importance. Using data of an in vivo experiment measuring 13 metabolites during a glucose stimulus-response experiment we investigate the performance of various evolutionary algorithms on the parameter inference problem in biochemical modeling. Due to the inconclusive information on the reversibility of the reactions in the pathway, we develop both a reversible and an irreversible differential equation model based on the recent convenience kinetics approach. As the reversible model allows better approximation on the whole, we use it to analyze the impact of different settings on four especially promising EAs. We show that Particle Swarm Optimization as well as Differential Evolution are useful methods for parameter estimation on convenience kinetics models outperforming Genetic Algorithm and Evolution Strategy approaches and nearly reaching the quality of independent spline approximations on the raw data.

Parameter estimation for biochemical model systems has become an important problem in systems biology. Here we focus on the metabolic subnetwork of the valine and leucine biosynthesis in C. glutamicum. Due to the lack of indisputable information regarding reversibility of the reactions in the pathway we derived two alternative ordinary differential equation models based on the formalisms of the generalized mass-action rate law. We introduced two alternative modeling approaches for feedback inhibition and evaluated the applicability of six optimization procedures (multi start Hill Climber, binary and real valued Genetic Algorithm, standard and covariance matrix adaption Evolution Strategy as well as Simulated Annealing) to the problem of parameter fitting. The model considering irreversible reactions performed worse and was therefore rejected from further analysis. We benchmarked the impact of different mutation and crossover operators as well as the influence of the population size on the remaining system and the two best optimization procedures namely binary Genetic Algorithm and the Evolution Strategy. The GA performed best on average and found the best total result based on the relative squared error.

BACKGROUND: Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets. RESULTS: In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters. CONCLUSION: We successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.

MOTIVATION: JCell is a Java-based application for reconstructing gene regulatory networks from experimental data. The framework provides several algorithms to identify genetic and metabolic dependencies based on experimental data conjoint with mathematical models to describe and simulate regulatory systems. Owing to the modular structure, researchers can easily implement new methods. JCell is a pure Java application with additional scripting capabilities and thus widely usable, e.g. on parallel or cluster computers. AVAILABILITY: The software is freely available for download at http://www-ra.informatik.uni-tuebingen.de/software/JCell.