The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.

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SUMMARY The embryonic stem (ES) cell transcriptional and epigenetic networks are critical for the maintenance of ES cell self-renewal. However, it remains unclear whether components of these networks functionally interact and if so, what factors mediate such interactions. Here we show that WD-repeat protein-5 (Wdr5), a core member of the mammalian Trithorax (trxG) complex, positively correlates with the undifferentiated state and is a novel regulator of ES cell self-renewal. We demonstrate that Wdr5, an ‘effector’ of H3K4 methylation, interacts with the pluripotency transcription factor Oct4. Genome-wide protein localization and transcriptome analyses demonstrate overlapping gene regulatory functions between Oct4 and Wdr5. We show that the Oct4-Sox2-Nanog circuitry and trxG cooperate in activating transcription of key self-renewal regulators. Furthermore, Wdr5 expression is required for the efficient formation of induced pluripotent stem (iPS) cells. We propose an integrated model of transcriptional and epigenetic control, mediated by select trxG members, for maintenance of ES cell self-renewal and somatic cell reprogramming.

Constraint-based reconstruction and analysis (COBRA) methods at the genome-scale have been under development since the first whole genome sequences appeared in the mid-1990s. A few years ago this approach began to demonstrate the ability to predict a range of cellular functions including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome-scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This primer will get you started.