scholarly journals Assigning function to natural allelic variation via dynamic modeling of gene network induction

2017 ◽  
Author(s):  
Magali Richard ◽  
Florent Chuffart ◽  
Hélène Duplus-Bottin ◽  
Fanny Pouyet ◽  
Martin Spichty ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Computational Models ◽  
Allelic Variation ◽  
Model Parameters ◽  
Or Efficiency ◽  
Response Network ◽  
Dna Variants ◽  
Gene Regulatory ◽  
Regulatory Models

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.

Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

2016 ◽  
Vol 14 (03) ◽  
pp. 1650010 ◽  
Author(s):  
Sudip Mandal ◽  
Abhinandan Khan ◽  
Goutam Saha ◽  
Rajat Kumar Pal
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Real Life ◽  
Bat Algorithm ◽  
Genetic Network ◽  
Model Parameters ◽  
Mathematical Tool ◽  
Gene Regulatory ◽  
Artificial Network

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

Computational models reconstruct gene regulatory networks

2008 ◽  
Vol 4 (10) ◽  
pp. 993 ◽  
Author(s):  
Anastasios Bezerianos ◽  
Ioannis A. Maraziotis
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Computational Models ◽  
Gene Regulatory

scholarly journals Switching On Static Gene Regulatory Networks to Compute Cellular Decisions

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]

scholarly journals Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Abhinandan Khan ◽  
Sudip Mandal ◽  
Rajat Kumar Pal ◽  
Goutam Saha
Keyword(s):  
Swarm Intelligence ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Model Parameters ◽  
Expression Data ◽  
Hybrid Swarm ◽  
Worst Case ◽  
Time Points ◽  
Gene Regulatory

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

scholarly journals Classification-based Inference of Dynamical Models of Gene Regulatory Networks

2019 ◽  
Author(s):  
David A. Fehr ◽  
Manu ◽  
Yen Lee Loh
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Linear Optimization ◽  
Model Parameters ◽  
Gene Circuit ◽  
Gap Gene ◽  
Non Linear ◽  
Gene Regulatory

AbstractCell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR’s effectiveness on synthetic data as well as experimental data from the gap gene system of Drosophila. FIGR is faster than global non-linear optimization by nearly three orders of magnitude and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before. FIGR Source code is freely available at http://github.com/mlekkha/FIGR.

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