scholarly journals EpiLog: A software for the logical modelling of epithelial dynamics

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1145 ◽  
Author(s):  
Pedro L. Varela ◽  
Camila V. Ramos ◽  
Pedro T. Monteiro ◽  
Claudine Chaouiya
Keyword(s):  
Regulatory Networks ◽  
Cellular Responses ◽  
Tissue Level ◽  
Cellular Level ◽  
Environmental Cues ◽  
Link Type ◽  
Qualitative Models ◽  
Logical Modelling ◽  
Gene Regulatory ◽  
External Signals

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued)  framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks.  Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals.  EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

scholarly journals EpiLog: A software for the logical modelling of epithelial dynamics

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1145 ◽  
Author(s):  
Pedro L. Varela ◽  
Camila V. Ramos ◽  
Pedro T. Monteiro ◽  
Claudine Chaouiya
Keyword(s):  
Regulatory Networks ◽  
Cellular Responses ◽  
Tissue Level ◽  
Cellular Level ◽  
Environmental Cues ◽  
Link Type ◽  
Qualitative Models ◽  
Logical Modelling ◽  
Gene Regulatory ◽  
External Signals

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued)  framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks.  Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals.  EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tuqyah Abdullah Al Qazlan ◽  
Aboubekeur Hamdi-Cherif ◽  
Chafia Kara-Mohamed
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Ad Hoc ◽  
Cellular Level ◽  
Biological Information ◽  
Cellular Functions ◽  
Fuzzy Methods ◽  
Gene Regulatory ◽  
Living Organisms ◽  
Different Levels

To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/orad hoccorrecting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

Improved Model Checking Techniques for State Space Analysis of Gene Regulatory Networks

2010 ◽  
pp. 386-404
Author(s):  
Hélio C. Pais ◽  
Kenneth L. McMillan ◽  
Ellen M. Sentovich ◽  
Ana T. Freitas ◽  
Arlindo L. Oliveira
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Basins Of Attraction ◽  
Model Checker ◽  
Biologically Relevant ◽  
Qualitative Models ◽  
A Cell ◽  
Gene Regulatory ◽  
Improved Model ◽  
High Level

A better understanding of the behavior of a cell, as a system, depends on our ability to model and understand the complex regulatory mechanisms that control gene expression. High level, qualitative models of gene regulatory networks can be used to analyze and characterize the behavior of complex systems, and to provide important insights on the behavior of these systems. In this chapter, we describe a number of additional functionalities that, when supported by a symbolic model checker, make it possible to answer important questions about the nature of the state spaces of gene regulatory networks, such as the nature and size of attractors, and the characteristics of the basins of attraction. We illustrate the type of analysis that can be performed by applying an improved model checker to two well studied gene regulatory models, the network that controls the cell cycle in the yeast S. cerevisiae, and the network that regulates formation of the dorsal-ventral boundary in D. melanogaster. The results show that the insights provided by the analysis can be used to understand and improve the models, and to formulate hypotheses that are biologically relevant and that can be confirmed experimentally.

scholarly journals PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

2020 ◽  
Author(s):  
Ming Wu ◽  
Tim Kacprowski ◽  
Dietmar Zehn
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Cellular Level ◽  
Rna Seq ◽  
Analysis Workflow ◽  
Gene Regulatory ◽  
Cell Trajectory ◽  
User Friendly ◽  
Molecular Expression ◽  
Marker Detection

AbstractSummaryThe Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way.AvailabilitySource code and documentation are available: https://github.com/MingBit/[email protected]

scholarly journals Systems Biology Approach to Model the Life Cycle ofTrypanosoma cruzi

2015 ◽  
Author(s):  
Alejandra Carrea ◽  
Luis Diambra
Keyword(s):  
Life Cycle ◽  
Systems Biology ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Environmental Cues ◽  
Cell Fates ◽  
Gene Regulatory ◽  
Cell Phenotypes ◽  
Optimisation Procedure

Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of theTrypanosoma cruzi's life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasite's life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.

scholarly journals Gene regulatory networks controlled by FLOWERING LOCUS C that confer variation in seasonal flowering and life history

2020 ◽  
Author(s):  
Eva Madrid ◽  
John W Chandler ◽  
George Coupland
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Higher Plants ◽  
Floral Transition ◽  
Environmental Cues ◽  
Flowering Locus C ◽  
Gene Regulatory ◽  
Flowering Locus

Abstract Responses to environmental cues synchronize reproduction of higher plants to the changing seasons. The genetic basis of these responses has been intensively studied in the Brassicaceae. The MADS-domain transcription factor FLOWERING LOCUS C (FLC) plays a central role in the regulatory network that controls flowering of Arabidopsis thaliana in response to seasonal cues. FLC blocks flowering until its transcription is stably repressed by extended exposure to low temperatures in autumn or winter and, therefore, FLC activity is assumed to limit flowering to spring. Recent reviews describe the complex epigenetic mechanisms responsible for FLC repression in cold. We focus on the gene regulatory networks controlled by FLC and how they influence floral transition. Genome-wide approaches determined the in vivo target genes of FLC and identified those whose transcription changes during vernalization or in flc mutants. We describe how studying FLC targets such as FLOWERING LOCUS T, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 15, and TARGET OF FLC AND SVP 1 can explain different flowering behaviours in response to vernalization and other environmental cues, and help define mechanisms by which FLC represses gene transcription. Elucidating the gene regulatory networks controlled by FLC provides access to the developmental and physiological mechanisms that regulate floral transition.

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