Integrative approaches for finding modular structure in biological networks

Abstract

A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function— i.e. ‘modules’. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

“…Nevertheless, as previously discussed, cellular states are characterized by different combinations of gene expression profiles of individual cells present in the population 2, 3, 4, 5. Indeed, in recent years there has been an increasing interest in dynamical differential network analysis approaches that are starting to replace static descriptions of biological networks 67, 69, 70, 71, 72, 73, 74. These approaches have been mainly used to infer and compare cell‐type/condition specific networks; and they rely on multiple considerations, such as combining literature‐based information with cell‐type/condition specific data – i.e.…”

“…Moreover, in order to direct cell fate commitment, an appropriate strategy could rely on shifting the cell population distribution toward the subpopulation state primed for a specific cell fate. We propose that the generation of integrative GRNs models 72, 97, 98, gathering information at the epigenetics, transcriptional, and signaling levels, will be essential for devising novel strategies for increasing the efficiency and fidelity of differentiation. Computational network‐based approaches aiming at providing a mechanistic description of the regulation of heterogeneity in the pluripotent state, and cell fate commitment, have recently been developed 74, 99, 100, 101.…”

“…More realistic integrative network models will allow the identification of the network stability determinants 4, 72, 75, 102, 103 sensing and transducing niche's signals, and how these signals trigger differentiation in specific PSCs subpopulations (Fig. 1).…”

Applicability of Computational Systems Biology in Toxicology

“…Combining such complementary data types may add value in creating realistic models of potential toxic or adverse effects of chemicals [3,4]. A key strategy to handle multiple data sources is data integration, the use of which has been demonstrated in several applications as reviewed by Mitra et al [5], for example for the identification of new biomarkers for Alzheimer's disease [6].…”

“…Combining such complementary data types may add value in creating realistic models of potential toxic or adverse effects of chemicals [3,4]. A key strategy to handle multiple data sources is data integration, the use of which has been demonstrated in several applications as reviewed by Mitra et al [5], for example for the identification of new biomarkers for Alzheimer's disease [6].Computational toxicology integrates molecular biology and chemistry of toxicological interest with mathematical modelling and computational science [7,8] and can therefore be considered a separate branch within computational systems biology. The use of computational toxicology has, for example, proven useful in the development of predictive signatures for various in vivo end-points by integration of the ToxCast data with in vivo data [9][10][11][12][13] and for the proposal of an adverse outcome pathway for disruption of embryonic vascular development [14].…”