Inference of Gene Regulatory Networks Using Boolean-Network Inference Methods

Hickman, Graham J.; Hodgman, Charlie

doi:10.1142/s0219720009004448

Cited by 45 publications

(22 citation statements)

References 51 publications

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“…Boolean networks assign binary states to each gene (gene expression on/off), and the state of a gene at a given time depends on the state of all genes at a previous time through a set of logical functions assigned to each gene. See [64] for a review of clustering and Boolean network inference and [96] for a review of Boolean network inference.…”

Section: B Reconstruction Of Gene Regulatory Networkmentioning

confidence: 99%

Inverse statistical problems: from the inverse Ising problem to data science

Nguyen

Zecchina

Berg

2017

Advances in Physics

250

378

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Inverse problems in statistical physics are motivated by the challenges of 'big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetizations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics

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Section: B Reconstruction Of Gene Regulatory Networkmentioning

confidence: 99%

Inverse statistical problems: from the inverse Ising problem to data science

Nguyen

Zecchina

Berg

2017

Advances in Physics

250

378

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“…Besides the conceptual framework afforded by such models, practical uses, such as the identification of suitable drug targets in cancer therapy, result by inferring the structure of the genetic models from experimental data, e.g., from the gene expression profiles . A number of approaches have been proposed for the inference of BNs from both steady‐state and time‐series data . To generate the regulatory interactions uses mutual information, bases its inference approach on minimum description length principle, applies constraints on the levels and lengths of attractor cycles and incorporates biological connectivity knowledge available from databases in the inference procedure.…”

Section: Genetic Regulatory Network Modelsmentioning

confidence: 99%

Modeling and inference of genetic interactions

Pal

2013

WIREs Data Min & Knowl

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The regulatory interactions in a cell form a complicated system, and an important goal of systems biology is to model and infer these interactions. The modeling and inference of genetic regulatory models requires understanding of the true biological interactions while incorporating the technological limitations on observation of the biological entities in estimation of a robust model. This review is structured as follows: (a) a brief description of the biological interactions is provided, (b) currently available technologies for measuring genomic characterizations are outlined, (c) followed by the description of the commonly used approaches for genetic regulatory network modeling, and (d) finally, some of the pertinent issues in modeling and inference of genetic interactions are discussed.

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“…Thus, the “reverse engineering” approaches where the connection architectures are inferred from the perturbation response data are becoming increasingly appreciated. Although reverse engineering methods such as Boolean networks [1], Bayesian networks [2,3], dynamic Bayesian networks [4,5], multivariate regression methods [6-8], linear programming [9], genetic algorithm [10] and information theoretic [11] approaches have been applied to deduce the circuitry of signaling and gene networks, all currently developed methods have significant limitations. For instance, the Boolean network based methods are found to be formidably slow, and their performance degrades with increasing network size [12].…”

Section: Introductionmentioning

confidence: 99%

Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

2013

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BackgroundRecent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations.ResultsWe propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells.ConclusionsThe proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.

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Inference of Gene Regulatory Networks Using Boolean-Network Inference Methods

Cited by 45 publications

References 51 publications

Inverse statistical problems: from the inverse Ising problem to data science

Inverse statistical problems: from the inverse Ising problem to data science

Modeling and inference of genetic interactions

Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

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