Christian Spieth

A memetic clustering algorithm for the functional partition of genes based on the gene ontology

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.

Gene expression patterns of epithelial cells modulated by pathogenicity factors of Yersinia enterocolitica

Epithelial cells express genes whose products signal the presence of pathogenic microorganisms to the immune system. Pathogenicity factors of enteric bacteria modulate host cell gene expression. Using microarray  technology  we  have  profiled  epithelial cell gene expression upon interaction with Yersinia enterocolitica. Yersinia enterocolitica wild‐type and isogenic mutant strains were used to identify host genes modulated by invasin protein (Inv), which is involved in enteroinvasion, and Yersinia outer protein P (YopP) which inhibits innate immune responses. Among 22 283 probesets (14 239 unique genes), we found 193 probesets (165 genes) to be regulated by Yersinia infection. The majority of these genes were induced by Inv, whose recognition leads to expression of NF‐κB‐regulated factors such as cytokines and adhesion molecules. Yersinia virulence plasmid (pYV)‐encoded factors counter regulated Inv‐induced gene expression. Thus, YopP repressed Inv‐induced NF‐κB regulated genes at 2 h post infection whereas other pYV‐encoded factors repressed host cell genes at 4 and 8 h post infection. Chromosomally encoded factors of Yersinia, other than Inv, induced expression of genes known to be induced by TGF‐β receptor signalling. These genes were also repressed by pYV‐encoded factors. Only a few host genes were exclusively induced by pYV‐encoded factors. We hypothesize that some of these genes may contribute to pYV‐mediated silencing of host cells. In conclusion, the data demonstrates that epithelial cells express a limited number of genes upon interaction with enteric Yersinia. Both Inv and YopP appear to modulate gene expression in order to subvert epithelial cell functions involved in innate immunity.

Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments

In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. Different approaches to infer the dependencies of gene regulatory networks by identifying parameters of mathematical models like complex S-systems or simple Random Boolean Networks can be found in literature. Due to the complexity of the inference problem some researchers suggested Evolutionary Algorithms for this purpose. We introduce enhancements to the Evolutionary Algorithm optimization process to infer the parameters of the non-linear system 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. We propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network.

A memetic co-clustering algorithm for gene expression profiles and biological annotation

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.

Functional grouping of genes using spectral clustering and Gene Ontology

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.

A memetic inference method for gene regulatory networks based on S-Systems

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.

Comparing Genetic Programming and Evolution Strategies on Inferring Gene Regulatory Networks

In recent years several strategies for inferring gene regulatory networks from observed time series data of gene expression have been suggested based on Evolutionary Algorithms. But often only few problem instances are investigated and the proposed strategies are rarely compared to alternative strategies. In this paper we compare Evolution Strategies and Genetic Programming with respect to their performance on multiple problem instances with varying parameters. We show that single problem instances are not sufficient to prove the effectiveness of a given strategy and that the Genetic Programming approach is less prone to varying instances than the Evolution Strategy.

Comparing evolutionary algorithms on the problem of network inference

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.

Comparing mathematical models on the problem of network inference

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.

Multi-objective model optimization for inferring gene regulatory networks

With the invention of microarray technology, researchers are able to measure the expression levels of ten thousands of genes in parallel at various time points of a biological process. The investigation of gene regulatory networks has become one of the major topics in Systems Biology. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We suggest to use a multi-objective evolutionary algorithm to identify the parameters of a non-linear system given by the observed data. Currently, only limited information on gene regulatory pathways is available in Systems Biology. Not only the actual parameters of the examined system are unknown, also the connectivity of the components is a priori not known. However, this number is crucial for the inference process. Therefore, we propose a method, which uses the connectivity as an optimization objective in addition to the data dissimilarity (relative standard error - RSE) between experimental and simulated data.