scholarly journals Input Dataset Survey of <i>In-Silico</i> Tools for Inference and Visualization of Gene Regulatory Networks (GRN)

2015 ◽  
Vol 3 (6) ◽  
pp. 81
Author(s):  
Taiwo Adigun
Keyword(s):  
Gene Regulatory Networks ◽  
In Silico ◽  
Regulatory Networks ◽  
Input Dataset ◽  
Gene Regulatory

scholarly journals Expression pattern determines regulatory logic

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244864
Author(s):  
Carlos Mora-Martinez
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Dna Sequences ◽  
In Silico ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
Evolutionary Strategy ◽  
Specific Gene ◽  
Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

scholarly journals Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

PLoS ONE ◽  
2010 ◽  
Vol 5 (1) ◽  
pp. e8121 ◽  
Author(s):  
Kevin Y. Yip ◽  
Roger P. Alexander ◽  
Koon-Kiu Yan ◽  
Mark Gerstein
Keyword(s):  
Gene Regulatory Networks ◽  
In Silico ◽  
Regulatory Networks ◽  
Perturbation Data ◽  
Gene Regulatory

scholarly journals In Silico Design of Self-Optimizing Integrated Metabolic and Gene Regulatory Networks

2014 ◽  
Author(s):  
Timo Maarleveld ◽  
Bennet K NG ◽  
Herbert Sauro ◽  
Kyung Kim
Keyword(s):  
Gene Regulatory Networks ◽  
Environmental Conditions ◽  
In Silico ◽  
Regulatory Networks ◽  
Metabolic Networks ◽  
Self Regulation ◽  
Protein Signaling ◽  
Target Network ◽  
Gene Regulatory ◽  
Protein Signaling Networks

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed \emph{in silico} evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

scholarly journals Real-time in silico experiments on gene regulatory networks and surgery simulation on handheld devices

2014 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Icíar Alfaro ◽  
David González ◽  
Felipe Bordeu ◽  
Adrien Leygue ◽  
Amine Ammar ◽  
...  
Keyword(s):  
Real Time ◽  
Gene Regulatory Networks ◽  
In Silico ◽  
Regulatory Networks ◽  
Handheld Devices ◽  
Surgery Simulation ◽  
Gene Regulatory
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