NetExplore: a web server for modeling small network motifs

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Single-Cell Analyses of ESCs Reveal Alternative Pluripotent Cell States and Molecular Mechanisms that Control Self-Renewal

Analyses of gene expression in single mouse embryonic stem cells (mESCs) cultured in serum and LIF revealed the presence of two distinct cell subpopulations with individual gene expression signatures. Comparisons with published data revealed that cells in the first subpopulation are phenotypically similar to cells isolated from the inner cell mass (ICM). In contrast, cells in the second subpopulation appear to be more mature. Pluripotency Gene Regulatory Network (PGRN) reconstruction based on single-cell data and published data suggested antagonistic roles for Oct4 and Nanog in the maintenance of pluripotency states. Integrated analyses of published genomic binding (ChIP) data strongly supported this observation. Certain target genes alternatively regulated by OCT4 and NANOG, such as Sall4 and Zscan10, feed back into the top hierarchical regulator Oct4. Analyses of such incoherent feedforward loops with feedback (iFFL-FB) suggest a dynamic model for the maintenance of mESC pluripotency and self-renewal.

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Mouse embryonic stem cells grown on serum and LIF contain two subpopulations of cells

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Oct4 and Nanog alternatively regulate a class of pluripotency genes

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We demonstrate stabilization of Oct4 concentration and pluripotency via feedback control

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The “state exchange” model explains self-renewal

Emerging Modeling Concepts and Solutions in Stem Cell Research

Modern stem cell research, as well as other fields of contemporary biology involves quantitative sciences in many ways. Identifying candidates for key differentiation or reprogramming factors, tracing global transcriptome changes, or finding drugs is now broadly involves bioinformatics and biostatistics. However, the next key step, understanding the underlying reasons and establishing causal links leading to differentiation or reprogramming requires qualitative and quantitative biological models describing complex biological systems. Currently, quantitative modeling is a challenging science, capable to deliver rather modest results or predictions. What model types are the most popular and what features of stem cell behavior they are capturing? What new insights do we expect from the computational modeling of stem cells in the foreseeable future? Current review attempts to approach these essential questions by considering published quantitative models and solutions emerging in the area of stem cell research.