Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses

Repsilber, Dirk; Liljenström, Hans; Andersson, Siv G. E.

doi:10.1016/s0303-2647(02)00019-9

Cited by 44 publications

(35 citation statements)

References 12 publications

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“…In particular, the ability of asynchronous update adds orders of magnitude to the combinatorial explosion of possibilities. Multiple discrete expression levels were also used in the reverse-engineering method in Repsilber et al (2002), which uses genetic algorithms to explore the parameter space of multistage discrete genetic network models.…”

Section: Approaches To Reverse Engineeringmentioning

confidence: 99%

“…The complexity of this step is Oðm 2 ðgðm; nÞ þ mÞðlog pÞ 2 þ m 2 n 2 Þ as reported in Abbott et al (2000), where gðm; nÞ is Oðm ðnÀ1Þ=n Þ (Berman, 1981). Since g is sublinear in m; the worst case complexity of this step is quadratic in the number of nodes and cubic in the number of time points (Robbiano, 1998). (3) Dube´et al (1986) show that the complexity of reducing a polynomial by an ideal requires Oð2 cd LÞ time, where c is a constant, d is the degree of the polynomial being reduced, and L is the number of terms in the polynomial.…”

Section: Complexity Of the Algorithmmentioning

confidence: 99%

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A computational algebra approach to the reverse engineering of gene regulatory networks

Laubenbacher

Stigler

2004

Journal of Theoretical Biology

233

253

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This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. An analysis of the complexity of the algorithm is performed. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster. r

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Section: Approaches To Reverse Engineeringmentioning

confidence: 99%

Section: Complexity Of the Algorithmmentioning

confidence: 99%

A computational algebra approach to the reverse engineering of gene regulatory networks

Laubenbacher

Stigler

2004

Journal of Theoretical Biology

233

253

View full text Add to dashboard Cite

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“…Mathematical frameworks being applied in this context include Boolean networks [179][180][181], graphical Gaussian networks [182,183], Bayesian models [184,185], correlation analysis [186], graph theory [187,188], and genetic algorithms [189]. Software has been developed for these procedures as GeneTS, utilizing a graphical Gaussian network framework to compute a graph representing correlations between objects based on time series data.…”

Section: Regulatory Context Analysismentioning

confidence: 99%

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

et al. 2006

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Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables -and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

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“…Since initial, now classical models, such as the NK model [1], there has been a steady interest in understanding regulatory networks [2,3,4,5,6,7]. High throughput "post-genomic" techniques, specifically microarrays for measuring gene expression [8,9], currently lead to renewed interest in biological networks [10,11], and various suggestions for reconstructing regulatory networks from gene expression data [12,13,14,15]. However, understanding the relation between regulatory network structure and the resulting gene expression dynamics remains a major challenge [16].…”

Section: Introductionmentioning

confidence: 99%

Effects of Spatial Growth on Gene Expression Dynamics and on Regulatory Network Reconstruction

Kim

2005

Advances in Artificial Life

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Abstract. Morphogenesis and the spatial structure of an organism have repercussions on gene expression. These effects can influence the results of regulatory network reconstruction. An integrated, flexible and extensible computational framework for modelling gene expression dynamics within spatially growing structures is developed and used as a test system for evaluating a reconstruction algorithm. With complex morphological structures, significant effects of spatial organisation on the reconstruction process are observed. The results also reveal that stronger regulatory interactions result in more frequent cases of indirect regulation, posing a challenge for accurate network reconstruction.

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Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses

Cited by 44 publications

References 12 publications

A computational algebra approach to the reverse engineering of gene regulatory networks

A computational algebra approach to the reverse engineering of gene regulatory networks

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

Effects of Spatial Growth on Gene Expression Dynamics and on Regulatory Network Reconstruction

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