A network biology approach to aging in yeast

The Howard Hughes Medical Institute, Bioinformatics Program, Center for Advanced Biotechnology and Department of Biomedical Engineering. Boston University, 44 Cummington Street, Boston, MA 02215, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 02/2009; 106(4):1145-50. DOI: 10.1073/pnas.0812551106
Source: PubMed


In this study, a reverse-engineering strategy was used to infer and analyze the structure and function of an aging and glucose repressed gene regulatory network in the budding yeast Saccharomyces cerevisiae. The method uses transcriptional perturbations to model the functional interactions between genes as a system of first-order ordinary differential equations. The resulting network model correctly identified the known interactions of key regulators in a 10-gene network from the Snf1 signaling pathway, which is required for expression of glucose-repressed genes upon calorie restriction. The majority of interactions predicted by the network model were confirmed using promoter-reporter gene fusions in gene-deletion mutants and chromatin immunoprecipitation experiments, revealing a more complex network architecture than previously appreciated. The reverse-engineered network model also predicted an unexpected role for transcriptional regulation of the SNF1 gene by hexose kinase enzyme/transcriptional repressor Hxk2, Mediator subunit Med8, and transcriptional repressor Mig1. These interactions were validated experimentally and used to design new experiments demonstrating Snf1 and its transcriptional regulators Hxk2 and Mig1 as modulators of chronological lifespan. This work demonstrates the value of using network inference methods to identify and characterize the regulators of complex phenotypes, such as aging.

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    • "The methods presented here are just a selection of the available methods for graph inference. Several other methods such as Boolean networks (Shmulevich et al., 2002) or differential equation systems (Chen et al., 1999; Lorenz et al., 2009) are commonly used for modelling biological networks. "
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    • "To be able to control and systematically vary the properties of the data, as well as evaluate the performance by comparing to the " true " network, we constructed a network with 10 nodes and 25 links (Figure 5 andTable 1) and used it to generate in silico data. We want to simulate steady-state perturbation experiments of the type previously performed in vivo for inference of a ten gene network of the Snf1 signalling pathway in S. cerevisiae [20] and have therefore tuned the properties such that the network is biologically plausible. It is sparse, each gene has a negative self-loop representing mRNA degradation , the digraph forms one strong component, the degree of interampatteness is 145, it is stable, i.e. all eigenvalues are negative, and the time constants of the system are in the range 0.089 to 12 [24]. "
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