Computational models reconstruct gene regulatory networks

ABSTRACTMore and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be ‘personalized’ according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

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Toward Intracellular Delivery and Drug Discovery: Stochastic Logic Networks as Efficient Computational Models for Gene Regulatory Networks

Intracellular Delivery II - Fundamental Biomedical Technologies ◽

10.1007/978-94-017-8896-0_17 ◽

2014 ◽

pp. 327-359

Author(s):

Peican Zhu ◽

Jinghang Liang ◽

Jie Han

Keyword(s):

Drug Discovery ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Computational Models ◽

Intracellular Delivery ◽

Gene Regulatory

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Switching On Static Gene Regulatory Networks to Compute Cellular Decisions

10.1101/2020.05.29.122200 ◽

2020 ◽

Author(s):

Clara E. Pavillet ◽

Dimitrios Voukantsis ◽

Francesca M. Buffa

Keyword(s):

Gene Regulatory Networks ◽

Gene Networks ◽

Gene Network ◽

Regulatory Networks ◽

Computational Models ◽

Cell Behaviour ◽

Cellular Processes ◽

Health And Disease ◽

Gene Regulatory ◽

Cellular Behaviour

AbstractMotivationGene networks are complex sets of regulators and interactions that govern cellular processes. Their perturbations can disrupt regular biological functions, translating into a change in cell behaviour and ability to respond to internal and external cues. Computational models of these networks can boost translation of our scientific knowledge into medical applications by predicting how cells will behave in health and disease, or respond to stimuli such as a drug treatment. The development of such models requires effective ways to read, manipulate and analyse the increasing amount of existing, and newly deposited gene network data.ResultsWe developed BioSWITCH, a command-line program using the BioPAX standardised language to “switch on” static regulatory networks so that they can be executed in GINML to predict cellular behaviour. Using a previously published haematopoiesis gene network, we show that BioSWITCH successfully and faithfully automates the network de-coding and re-coding into an executable logical network. BioSWITCH also supports the integration of a BioPAX model into an existing GINML graph.AvailabilitySource code available at https://github.com/CBigOxf/[email protected]; [email protected]

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