CANTATA-prediction of missing links in Boolean networks using genetic programming.

Motivation: Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions.

A model of the onset of the senescence associated secretory phenotype after DNA damage induced senescence.

Cells and tissues are exposed to stress from numerous sources. Senescence is a protective mechanism that prevents malignant tissue changes and constitutes a fundamental mechanism of aging. It can be accompanied by a senescence associated secretory phenotype (SASP) that causes chronic inflammation.

ViSiBooL-visualization and simulation of Boolean networks with temporal constraints.

Summary: Mathematical models and their simulation are increasingly used to gain insights into cellular pathways and regulatory networks. Dynamics of regulatory factors can be modeled using Boolean networks (BNs), among others. Text-based representations of models are precise descriptions, but hard to understand and interpret.

GiANT: gene set uncertainty in enrichment analysis.

Unlabelled: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA).

Boolean modeling identifies Greatwall/MASTL as an important regulator in the AURKA network of neuroblastoma.

Aurora Kinase A (AURKA) is often overexpressed in neuroblastoma (NB) with poor outcome. The causes of AURKA overexpression in NB are unknown. Here, we describe a gene regulatory network consisting of core regulators of AURKA protein expression and activation during mitosis to identify potential causes.

BiTrinA--multiscale binarization and trinarization with quality analysis.

Motivation: When processing gene expression profiles or other biological data, it is often required to assign measurements to distinct categories (e.g. 'high' and 'low' and possibly 'intermediate').

Cooperative development of logical modelling standards and tools with CoLoMoTo.

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools.

Multiscale binarization of gene expression data for reconstructing Boolean networks.

Network inference algorithms can assist life scientists in unraveling gene-regulatory systems on a molecular level. In recent years, great attention has been drawn to the reconstruction of Boolean networks from time series. These need to be binarized, as such networks model genes as binary variables (either “expressed” or “not expressed”).

BoolNet--an R package for generation, reconstruction and analysis of Boolean networks.

Motivation: As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks.