Reverse engineering of GRNs: an evolutionary approach based on the tsallis entropy
07 Jul 2012-pp 185-192
TL;DR: Results show that the proposed method is a promising approach and that the combination of criterion function based on Tsallis entropy with an heuristic search such as genetic algorithms yields networks up to 50% more accurate when compared to other Boolean-based approaches.
Abstract: The discovery of gene regulatory networks is a major goal in the field of bioinformatics due to their relevance, for instance, in the development of new drugs and medical treatments. The idea underneath this task is to recover gene interactions in a global and simple way, identifying the most significant connections and thereby generating a model to depict the mechanisms and dynamics of gene expression and regulation. In the present paper we tackle this challenge by applying a genetic algorithm to Boolean-based networks whose structures are inferred through the optimization of a Tsallis entropy function, which has been already successfully used in the inference of gene networks with other search schemes. Additionally, wisdom of crowds is applied to create a consensus network from the information contained within the last generation of the genetic algorithm. Results show that the proposed method is a promising approach and that the combination of criterion function based on Tsallis entropy with an heuristic search such as genetic algorithms yields networks up to 50% more accurate when compared to other Boolean-based approaches.
Citations
More filters
[...]
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
7,116 citations
TL;DR: A logical model of the mammalian cell cycle network and its threshold Boolean network equivalent is analyzed, and an omnipresent network with interactions that match with the original model as well as the discovery of new interactions is identified.
Abstract: A common gene regulatory network model is the threshold Boolean network, used for example to model the Arabidopsis thaliana floral morphogenesis network or the fission yeast cell cycle network. In this paper, we analyze a logical model of the mammalian cell cycle network and its threshold Boolean network equivalent. Firstly, the robustness of the network was explored with respect to update perturbations, in particular, what happened to the attractors for all the deterministic updating schemes. Results on the number of different limit cycles, limit cycle lengths, basin of attraction size, for all the deterministic updating schemes were obtained through mathematical and computational tools. Secondly, we analyzed the topology robustness of the network, by reconstructing synthetic networks that contained exactly the same attractors as the original model by means of a swarm intelligence approach. Our results indicate that networks may not be very robust given the great variety of limit cycles that a network can obtain depending on the updating scheme. In addition, we identified an omnipresent network with interactions that match with the original model as well as the discovery of new interactions. The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks.
29 citations
Cites methods from "Reverse engineering of GRNs: an evo..."
...For example in Mendoza et al. (2012), Boolean network models of GRN were inferred using genetic algorithms (GAs) to optimize a Tsallis entropy function....
[...]
TL;DR: A fast and accurate predictor set inference framework which linearly combines some inference methods to increase the accuracy of inferred GRN and the simulation results indicate that the proposed framework is fast and more reliable compared to other recent methods.
5 citations
01 Aug 2017
TL;DR: The proposed formulation was effective to infer bistable lac operon models under the threshold Boolean network paradigm and obtained a good trade-off between effectiveness versus efficiency.
Abstract: The lac operen in E. coli is one of the earliest examples of an inducible system of genes being under both positive and negative control that is capable of showing bistability. In this paper, we present a methodology to infer synthetic threshold Boolean regulatory networks of a reduced model of the lac operon using evolutionary computation. The formulation consists in a vector representation of the solutions (networks) and a fitness function specially designed to correctly simulate the bistability through the models' fixed points. We compared the effectiveness and efficiency (runtime) of the proposed approach using three evolutionary computation techniques: differential evolution, genetic algorithms, and particle swarm optimization. The results showed that the three algorithms are capable of finding solutions, being differential evolution the most effective, whereas genetic algorithms was the least effective and efficient in terms of runtime. Particle swarm optimization obtained a good trade-off between effectiveness versus efficiency. One of the inferred solutions was analyzed showing some interesting biological insights, as well as correctly being able to model bistability without any spurious attractors. Overall, the proposed formulation was effective to infer bistable lac operon models under the threshold Boolean network paradigm.
5 citations
Cites background from "Reverse engineering of GRNs: an evo..."
...Also, for general Boolean networks, in [15], Boolean network models of gene...
[...]
TL;DR: The objective of this work is the proposal of a method based on genetic algorithms to infer gene networks, whose main idea consists in applying one genetic algorithm for each gene independently, instead of applying a unique genetic algorithm to determine the whole network as usually done in the literature.
Abstract: Gene regulatory networks inference from gene expression data is an important problem in systems biology field, in which the main goal is to comprehend the global molecular mechanisms underlying diseases for the development of medical treatments and drugs. This problem involves the estimation of the gene dependencies and the regulatory functions governing these interactions to provide a model that explains the dataset (usually obtained from gene expression data) on which the estimation relies. However, such problem is considered an open problem, since it is difficult to obtain a satisfactory estimation of the dependencies given a very limited number of samples subject to experimental noises. Several gene networks inference methods exist in the literature, including those based on genetic algorithms, which codify whole networks as possible solutions (chromosomes). Given the huge search space of possible networks, genetic algorithms are suitable for the task, even though it is still hard to achieve good networks that explain the data by codifying whole networks as solutions. The objective of this work is the proposal of a method based on genetic algorithms to infer gene networks, whose main idea consists in applying one genetic algorithm for each gene independently, instead of applying a unique genetic algorithm to determine the whole network as usually done in the literature. Besides, the method involves the application of a network inference method to generate the initial populations to serve as more promising starting points for the genetic algorithms than random populations. To guide the genetic algorithms, we propose the use of Akaike information criterion (AIC) as fitness function. Results obtained from inference of artificial Boolean networks show that AIC correlates very well with popular topological similarity metrics even in cases with small number of samples. Besides, the benefit of applying one genetic algorithm per gene starting from initial populations defined by a network inference technique is evident according to the results. Comparative analysis involving a recently proposed genetic algorithm method for the same purpose is presented, showing that our method achieves superior performance.
4 citations
Cites background or methods or result from "Reverse engineering of GRNs: an evo..."
...method executes the genetic algorithm 30 times to obtain a unique consensus network composed by the edges that are most frequent along these networks, following the ‘‘wisdom of crowds’’ principle (Mendoza et al. 2012)....
[...]
...Recently, Mendoza et al. (2012) proposed a genetic algorithm to infer GRNs, modeled as Boolean networks, from experimental data....
[...]
...4.2; (c) comparison of the proposed method (GAs starting from random initial populations and from initial populations generated by ESMI method) with the ESMI method itself; (d) comparison involving the Mendoza et al. method (Mendoza et al. 2012)....
[...]
..., which is also based on genetic algorithm to infer gene networks (Mendoza et al. 2012)....
[...]
...Moreover, the proposed method was compared to a recent technique proposed by Mendoza et al., which is also based on genetic algorithm to infer gene networks (Mendoza et al. 2012)....
[...]
References
More filters
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Abstract: In this final installment of the paper we consider the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now. To a considerable extent the continuous case can be obtained through a limiting process from the discrete case by dividing the continuum of messages and signals into a large but finite number of small regions and calculating the various parameters involved on a discrete basis. As the size of the regions is decreased these parameters in general approach as limits the proper values for the continuous case. There are, however, a few new effects that appear and also a general change of emphasis in the direction of specialization of the general results to particular cases.
65,425 citations
Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
33,771 citations
[...]
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
7,116 citations
"Reverse engineering of GRNs: an evo..." refers methods in this paper
...In order to verify the sensibility of the method on the topology class, we generate AGNs with the uniformly-random Erdös-Rényi (ER, [8]) and the BarabásiAlbert (BA,[5]) models....
[...]
TL;DR: The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”.
4,250 citations