Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

We present FGES-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the recall of the true structure while also improving upon it in terms of speed, scaling up to the tens of thousands of variables and being able to use empirical knowledge about the topological structure of gene regulatory networks. We apply this method to learning the gene regulatory network for the full human genome using data from samples of different brain structures (from the Allen Human Brain Atlas). Our goal is to develop a Bayesian network model that predicts interactions between genes in a way that is clear to experts, following the current trends in interpretable artificial intelligence. To achieve this, we also present a new open-access visualization tool that facilitates the exploration of massive networks and can aid in finding nodes of interest for experimental tests.

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“…For a more in-depth overview of the topological properties of GRNs see [7], [20] and [21]. We will summarize these properties here:…”

Section: Topological Properties Of Grnsmentioning

confidence: 99%

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Bernaola-Alvarez

Michiels

Larrañaga

et al. 2020

Preprint

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“…Mapping gene interactions in vivo is challenging task ( Mackay 2014 ; Ehrenreich 2017 ). This makes in silico generated expression data widely accepted ( Sargolzaei and Schenkel 2009 ; Faux et al 2016 ; Angelin-Bonnet et al 2019 ; Liu et al 2019a ). Though, mRNA and protein concentrations form a molecular trait ( Claringbould et al 2017 ; Angelin-Bonnet et al 2019 ), it is not sufficient to consider this as a complex trait in the sense of quantitative genetics.…”

Section: Introductionmentioning

confidence: 99%

“…Primarily, the software performs a detailed mechanistic simulation of gene regulatory interactions for variable genomic architectures and generates transcriptional/translational genes’ products data. Basically, such MeSCoT functionality overlaps with some other software solutions, which have been proposed in the last decades, see the detailed overview of principal algorithms and key features in Angelin-Bonnet et al (2019) . However, besides the detailed mechanistic model of gene regulatory interactions, the major contribution of our approach is due to the novel SNP and omnigenic genetic models implemented within the MeSCoT software.…”

Section: Introductionmentioning

confidence: 99%

MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes’ regulatory interactions

Milkevych

Karaman

Sahana

et al. 2021

G3 Genes|Genomes|Genetics

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This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait’s realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes’ regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions.

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“…Computational biology has also taken up the complexity of diseases to understand their mechanisms, systemic behaviors, and linkages within an organism as well as epidemiology across populations. Computational and mathematical modeling of complex biological systems has flourished [53,54], and impressive progress has been made in synthesis and nanobiology. As a result, now computational biology is spearheading microbiome research.…”

Section: Introductionmentioning

confidence: 99%

Computational Structural Biology: Successes, Future Directions, and Challenges

et al. 2019

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Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous ‘big data’ integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells’ actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

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Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Cited by 12 publications

References 129 publications

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes’ regulatory interactions

Computational Structural Biology: Successes, Future Directions, and Challenges

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