Parameter estimation for Boolean models of biological networks

Dimitrova, Elena; García-Puente, Luis David; Hinkelmann, Franziska; Jarrah, Abdul Salam; Laubenbacher, Reinhard; Stigler, Brandilyn; Stillman, Michael; Vera-Licona, Paola

doi:10.1016/j.tcs.2010.04.034

Cited by 28 publications

(22 citation statements)

References 24 publications

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“…This qualitative assumption allows the use of Boolean networks [83] in Type 4 inference problems. Expression values in Boolean network inference approaches are discretized mostly in two states, representing an activity level at each time point [84][85][86]. Regulatory connection inference algorithms try to find a binary function that computes the next state of a gene based on a combination of other genes' states using simple Boolean operations, e.g.…”

Section: Classification Of Inference Algorithmsmentioning

confidence: 99%

Computational approaches to identify regulators of plant stress response using high-throughput gene expression data

Koryachko

Matthiadis

Ducoste

et al. 2015

Current Plant Biology

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a b s t r a c tInsight into biological stress regulatory pathways can be derived from high-throughput transcriptomic data using computational algorithms. These algorithms can be integrated into a computational approach to provide specific testable predictions that answer biological questions of interest. This review conceptually organizes a wide variety of developed algorithms into a classification system based on desired type of output predictions. This classification is then used as a structure to describe completed approaches in the literature, with a focus on project goals, overall path of implemented algorithms, and biological insight gained. These algorithms and approaches are introduced mainly in the context of research on the model plant species Arabidopsis thaliana under stress conditions, though the nature of computational techniques makes these approaches easily applicable to a wide range of species, data types, and conditions.

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Section: Classification Of Inference Algorithmsmentioning

confidence: 99%

Computational approaches to identify regulators of plant stress response using high-throughput gene expression data

Koryachko

Matthiadis

Ducoste

et al. 2015

Current Plant Biology

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“…Several inference methods have one or several of the aforementioned features. Some of these methods fall in the category of coarse-grained models based on discrete variables, such as Boolean networks, Bayesian networks, Petri nets, and polynomial dynamical systems [ 19 - 25 ]; others correspond to fine-grained models based on continuous variables, such as systems of ordinary differential equations, artificial neural networks, hybrid Petri nets, and regression methods [ 26 - 32 ] (For a broad overview of the different methods in the field, we refer the reader to [ 33 - 35 ]). However, there is still a need for inference methods that gather all the previously mentioned properties, and for which their mathematical frameworks can be exploited to improve the methods’ performance.…”

Section: Introductionmentioning

confidence: 99%

An algebra-based method for inferring gene regulatory networks

Vera-Licona

Jarrah

García-Puente

et al. 2014

BMC Systems Biology

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BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

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“…Normally these techniques limit the indegree, , of a node to a small number like 3 or 4. Some examples of reconstruction algorithms include REVEAL, which is based on the information theoretic principle and has a time complexity factor a multiple of [11]; predictor chooser method of Ideker et al that uses minimum set covering [13]; minimal sets algorithm and genetic algorithms by Dimitrova et al [16] Monte-Carlo type randomized algorithm of Akutsu et al [12], which has an exponential time complexity. Best fit extension principle of Lähdesmäki et al again involves a factor of [14], where is the total number of nodes in the network.…”

Section: Introductionmentioning

confidence: 99%

Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems

et al. 2012

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A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.

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Parameter estimation for Boolean models of biological networks

Cited by 28 publications

References 24 publications

Computational approaches to identify regulators of plant stress response using high-throughput gene expression data

Computational approaches to identify regulators of plant stress response using high-throughput gene expression data

An algebra-based method for inferring gene regulatory networks

Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems

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