In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different mathematical models on the inference problem. They are used to model the underlying dynamic system of artificial regulatory networks. The dynamics of the artificial systems represent different basic types of behavior,dimensionality and mathematical properties. They are all created with three commonly used approaches, namely linear weight matrices, H-systems, and S-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. However, in many publications only one algorithm is used without any comparison to other optimization methods. Thus, we introduce a framework to systematically apply evolutionary algorithms for further comparative analysis.

In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different evolutionary algorithms on the inference problem. These algorithms are used to evolve an underlying quantitative mathematical model. The dynamics of the regulatory system are modeled with two commonly used approaches, namely linear weight matrices and S-systems and a novel formulation, namely H-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. However, in many publications only one algorithm is used without any comparison to other optimization methods. Thus, we introduce a framework to systematically apply evolutionary algorithms and different types of mutation and crossover operators to the inference problem for further comparative analysis.

During the last years, high throughput experiments have become very popular. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose grouping genes according to their biological function by means of kernel functions, which are similarity measures having special mathematical properties and play a crucial role e.g. in SVM classification. Thereby our kernel functions rely on functional information on the genes provided by Gene Ontology annotation. We investigate and compare several provably symmetric, positive semidefinite kernel functions in combination with spectral clustering, dual k-means and average linkage and demonstrate that our approach leads to good clustering results.

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.

In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multimodal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step

In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. The problem often is multi-modal and therefore appropriate optimization strategies become necessary. We propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks than standard algorithms. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step.

With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose a new method based on spectral clustering for the partitioning of genes according to their biological function. The functional information is based on Gene Ontology annotation, a mechanism to capture functional knowledge in a shareable and computer processable form. Our functional cluster method promises to automates, speed up and therefore improve biological data analysis.

With the invention of microarrays, researchers are capable of measuring thousands of gene expression levels in parallel at various time points of the biological process. To investigate general regulatory mechanisms, biologists cluster genes based on their expression patterns. In this paper, we propose a new memetic co-clustering algorithm for expression profiles, which incorporates a priori knowledge in the form of gene ontology information. Ontologies offer a mechanism to capture knowledge in a shareable form that is also processable by computers. The use of this additional annotation information promises to improve biological data analysis and simplifies the identification of processes that are relevant under the measured conditions.

In this paper we address the problem of finding gene regulatory networks from artificial data sets of DNA microarray experiments. Some researchers suggested evolutionary algorithms for this purpose. We suggest to use an enhancement for evolutionary algorithms to infer the parameters of the nonlinear system given by the observed data more reliably and precisely. At present, we use S-Systems because they are a general mathematical model for simulating the complex interactions of gene regulatory networks. Due to the limited number of available data, the inferring problem is highly under-determined and ambiguous. Further on, the problem often is highly multi-modal and therefore appropriate optimization strategies become necessary. We propose to use an island model to maintain diversity in the EA population to prevent premature convergence and to raise the probability of finding the global optimum.

In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. As underlying mathematical model we used S-Systems, a quantitative model, which recently has found increased attention in the literature. Due to the complexity of the inference problem some researchers suggested evolutionary algorithms for this purpose. We introduce enhancements to this optimization process to infer the parameters of sparsely connected non-linear systems given by the observed data more reliably and precisely. Due to the limited number of available data the inferring problem is under-determined and ambiguous. Further on, the problem often is multi-modal and therefore appropriate optimization strategies become necessary. In this paper, we propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network. This method is compared to standard algorithms found in the literature.

With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need of a functional grouping of genes arises. We propose a new clustering algorithm for the partition of genes or gene products according to their known biological function based on Gene Ontology terms. Ontologies offer a mechanism to capture knowledge in a shareable form that is also processable by computers. Our functional cluster algorithm promises to automatize, speed up and therefore improve biological data analysis.

With the invention of microarray technology, researchers are capable of measuring the expression levels of ten thousands of genes in parallel at various time points of the biological process. During the investigation of gene regulatory networks and general cellular mechanisms, biologists are attempting to group genes based on the time-depending pattern of the obtained expression levels. In this paper, we propose a new memetic algorithm - a genetic algorithm combined with local search-based on a tree representation of the data - a minimum spanning tree minus; for clustering gene expression data. The combination of both concepts is shown to find near-optimal solutions quickly. Due to the minimum spanning tree representation of the data, our algorithm is capable of finding clusters of different shapes. We show that our approach is superior in solution quality compared to classical clustering methods.