Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments

Spieth, Christian; Streichert, Felix; Speer, N.; Zell, Andreas

doi:10.1007/978-3-540-24653-4_11

Cited by 13 publications

(21 citation statements)

References 11 publications

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“…For example, iterative methods [25] can be used to iteratively identify the correct regulatory network model by incorporating additional microarray data sets. Further on, enhancements to the CBN-EA will be implemented, which introduce an upper limit for the cluster size.…”

Section: Discussionmentioning

confidence: 99%

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Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Spieth¹,

Streichert²,

Speer³

et al.

2005 IEEE Congress on Evolutionary Computation

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Abstract-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.

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Section: Discussionmentioning

confidence: 99%

“…Hence, there exist several network structures that satisfy the given time course dynamics of the experiment. This issue has already been addressed by several publications, one example can be found in [25], where the problem is resolved by exploiting additional data in the inference process. A similar approach has been proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Spieth¹,

Streichert²,

Speer³

et al.

2005 IEEE Congress on Evolutionary Computation

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show abstract

“…Recently, the authors published a method to incorporate data sets obtained by additional experiments [16]. As one future enhancement of the proposed methods, we plan to incorporate such additional methods to identify the correct network.…”

Section: Discussionmentioning

confidence: 99%

Multi-objective Model Optimization for Inferring Gene Regulatory Networks

Spieth

Streichert

Speer

et al. 2005

Lecture Notes in Computer Science

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Abstract. 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.

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“…For example, iterative methods [25] can be used to iteratively identify the correct regulatory network model by incorporating additional microarray data sets.…”

Section: Future Workmentioning

confidence: 99%

Utilizing an island model for EA to preserve solution diversity for inferring gene regulatory networks

Spieth

Streichert

Speer

et al.

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

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Abstract-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 multimodal 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.

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Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments

Cited by 13 publications

References 11 publications

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Clustering-based Approach to Identify Solutions for the Inference of Regulatory Networks

Multi-objective Model Optimization for Inferring Gene Regulatory Networks

Utilizing an island model for EA to preserve solution diversity for inferring gene regulatory networks

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