Reverse engineering of gene regulation models from multi-condition experiments

Kennedy, Noel; Mizeranschi, Alexandru; Thompson, Paul D.; Zheng, Huiru; Dubitzky, Werner

doi:10.1109/cibcb.2013.6595396

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…However, as experimental techniques improve and become more affordable, more and more relevant data is likely to be produced in the future. We anticipate that multi-stimulus data on the same system is likely to reveal more of the underlying mechanistic details of GRN systems, and modeling approaches as the one presented in this study will become a part of the standard toolbox [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

“…As the amount of gene expression data is growing, researchers are becoming increasingly interested in the automated inference or reverse-engineering of quantitative dynamic, mechanistic gene-regulatory network models from gene expression time-course data [ 5 , 4 , 1 - 9 ]. The quality of such reverse-engineered GRN models is determined mainly by two factors:…”

Section: Introductionmentioning

confidence: 99%

“…- A lack of sufficient amounts of gene expression time-course data. While the number of sampling points is important, far more important is to have multiple stimulus-response data sets from the same system [ 5 ]. This is a challenging requirement for current experimental practice.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks

Mizeranschi

Zheng

Thompson

et al. 2015

BMC Systems Biology

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Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks

Mizeranschi

Zheng

Thompson

et al. 2015

BMC Systems Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Automated reverse-engineering of dynamic mechanistic GRN models from gene-expression time-series data is becoming an area of growing interest in systems biology research [7,8,9,10,11].…”

Section: Reverse-engineering Grn Modelsmentioning

confidence: 99%

“…(1) While the number of sampling points is important (typically, 10 to 50 time points are measured), far more important is to have multiple stimulus-response datasets from the same system under different stimuli [7]. This is a challenging requirement for current experimental practice.…”

Section: Reverse-engineering Grn Modelsmentioning

confidence: 99%

MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks

Mizeranschi

Swain

Scona

et al. 2016

Future Generation Computer Systems

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Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data. Besides a lack of suitable data (in particular multi-condition data from the same system), one of the key challenges of this task is the computational complexity involved. The more genes in the GRN system and the more parameters a GRN model has, the higher the computational load. The computational challenge is likely to increase substantially in the near future when we tackle larger GRN systems. The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models. We present the resulting software called MultiGrain/MAPPER. This software is based on a new architecture and tools supporting multiscale computing in a distributed computing environment. A key feature of MultiGrain/MAPPER is the realization of GRN reverse-engineering based on the underlying distributed computing framework and multi-swarm particle swarm optimization. We demonstrate some of the features of MultiGrain/MAPPER and evaluate its performance using

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Neural model of gene regulatory network: a survey on supportive meta-heuristics

Biswas

Acharyya

2016

Theory Biosci.

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Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

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Reverse engineering of gene regulation models from multi-condition experiments

Cited by 5 publications

References 31 publications

Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks

Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks

MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks

Neural model of gene regulatory network: a survey on supportive meta-heuristics

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