YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis.

YEASTRACT+ (http://yeastract-plus.org/) is a tool for the analysis, prediction and modelling of transcription regulatory data at the gene and genomic levels in yeasts. It incorporates three integrated databases: YEASTRACT (http://yeastract-plus.

Prediction of Gene and Genomic Regulation in Candida Species, Using the PathoYeastract Database: A Comparative Genomics Approach.

The ability of living organisms to survive changing environmental conditions is dependent on the implementation of gene expression programs underlying adaptation and fitness. Transcriptional networks can be exceptionally complex: a single transcription factor (TF) may regulate hundreds of genes, and multiple TFs may regulate a single gene-depending on the environmental conditions. Moreover, the same TF may act as an activator or repressor in distinct conditions.

Boolean function metrics can assist modelers to check and choose logical rules.

Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understanding and interpretation of those systems. Defining rules that determine how the output activity of biological entities is regulated by their respective inputs has proven to be challenging.

The N.C.Yeastract and CommunityYeastract databases to study gene and genomic transcription regulation in non-conventional yeasts.

Responding to the recent interest of the yeast research community in non-Saccharomyces cerevisiae species of biotechnological relevance, the N.C.Yeastract (http://yeastract-plus.

From a genome assembly to full regulatory network prediction: the case study of Rhodotorula toruloides putative Haa1-regulon.

Numerous genomes are sequenced and made available to the community through the NCBI portal. However, and, unlike what happens for gene function annotation, annotation of promoter sequences and the underlying prediction of regulatory associations is mostly unavailable, severely limiting the ability to interpret genome sequences in a functional genomics perspective. Here we present an approach where one can download a genome of interest from NCBI in the GenBank Flat File (.

Prediction and Boolean logical modelling of synergistic microRNA regulatory networks during reprogramming of male germline pluripotent stem cells.

Unipotent male germline stem (GS) cells can undergo spontaneous reprogramming to germline pluripotent stem (GPS) cells during in vitro culture. In our previous study, we proposed a Boolean logical model of gene regulatory network (GRN) during reprogramming of GS cells to GPS cells. This study was designed to predict and model synergistic microRNA (miRNA) regulatory network during reprogramming of GS cells into GPS cells.

Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae.

The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks.

A Boolean Logical model for Reprogramming of Testes-derived male Germline Stem Cells into Germline pluripotent stem cells.

Background And Objective: Male germline stem (GS) cells are responsible for the maintenance of spermatogenesis throughout the adult life of males. Upon appropriate in vitro culture conditions, these GS cells can undergo reprogramming to become germline pluripotent stem (GPS) cells with the loss of spermatogenic potential. In recent years, voluminous data of gene transcripts in GS and GPS cells have become available.

Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors.

Epithelial-to-mesenchymal transition (EMT) has been associated with cancer cell heterogeneity, plasticity, and metastasis. However, the extrinsic signals supervising these phenotypic transitions remain elusive. To assess how selected microenvironmental signals control cancer-associated phenotypes along the EMT continuum, we defined a logical model of the EMT cellular network that yields qualitative degrees of cell adhesions by adherens junctions and focal adhesions, two features affected during EMT.

Revision of Boolean Models of Regulatory Networks Using Stable State Observations.

Models of biological regulatory networks are essential to understand cellular processes. However, the definition of such models is still mostly manually performed, and consequently prone to error. Moreover, as new experimental data are acquired, models need to be revised and updated.

YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts.

The YEASTRACT+ information system (http://YEASTRACT-PLUS.org/) is a wide-scope tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in yeasts of biotechnological or human health relevance. YEASTRACT+ is a new portal that integrates the previously existing YEASTRACT (http://www.

Repairing Boolean logical models from time-series data using Answer Set Programming.

Background: Boolean models of biological signalling-regulatory networks are increasingly used to formally describe and understand complex biological processes. These models may become inconsistent as new data become available and need to be repaired. In the past, the focus has been shed on the inference of (classes of) models given an interaction network and time-series data sets.

EpiLog: A software for the logical modelling of epithelial dynamics.

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.

On logical bifurcation diagrams.

Bifurcation theory provides a powerful framework to analyze the dynamics of differential systems as a function of specific parameters. Abou-Jaoudé et al. (2009) introduced the concept of logical bifurcation diagrams, an analog of bifurcation diagrams for the logical modeling framework.

Estimating Attractor Reachability in Asynchronous Logical Models.

Logical models are well-suited to capture salient dynamical properties of regulatory networks. For networks controlling cell fate decisions, cell fates are associated with model attractors (stable states or cyclic attractors) whose identification and reachability properties are particularly relevant. While synchronous updates assume unlikely instantaneous or identical rates associated with component changes, the consideration of asynchronous updates is more realistic but, for large models, may hinder the analysis of the resulting non-deterministic concurrent dynamics.

Large-Scale Simulations of Bacterial Populations Over Complex Networks.

The understanding of bacterial population genetics and evolution is crucial in epidemic outbreak studies and pathogen surveillance. However, all epidemiological studies are limited to their sampling capacities which, by being usually biased or limited due to economic constraints, can hamper the real knowledge of the bacterial population structure of a given species. To this end, mathematical models and large-scale simulations can provide a quantitative analytical framework that can be used to assess how or if limited sampling can infer the true population structure.

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks.

Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks.

Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0.

The logical formalism is well adapted to model large cellular networks, in particular when detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.

YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae.

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT-www.yeastract.com) information system has been, for 11 years, a key tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in Saccharomyces cerevisiae.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species.

Logical Modeling and Dynamical Analysis of Cellular Networks.

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks.

Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis.

Transcriptional regulation is one of the key steps in the control of gene expression, with huge impact on the survival, adaptation, and fitness of all organisms. However, it is becoming increasingly clear that transcriptional regulation is far more complex than initially foreseen. In model organisms such as the yeast Saccharomyces cerevisiae evidence has been piling up showing that the expression of each gene can be controlled by several transcription factors, in the close dependency of the environmental conditions.

Not seeing the forest for the trees: size of the minimum spanning trees (MSTs) forest and branch significance in MST-based phylogenetic analysis.

Trees, including minimum spanning trees (MSTs), are commonly used in phylogenetic studies. But, for the research community, it may be unclear that the presented tree is just a hypothesis, chosen from among many possible alternatives. In this scenario, it is important to quantify our confidence in both the trees and the branches/edges included in such trees.

Model checking to assess T-helper cell plasticity.

Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues.