Abstract

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

Highlights

  • A major challenge in systems biology is the integration of different types of knowledge about biological processes in order to gain insight into the functioning of the organism as a whole system

  • Gene L appears to be up-regulated in the InR mutant, where we infer a shortest path starting with L inhibition on target of rapamycin (TOR)-C1, followed by TOR-C2 and AKT1 activation, leading to lifespan reduction by FOXO inactivation

  • Such insights cannot be produced by standard functional analysis methods for gene expression data sets

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Summary

Introduction

A major challenge in systems biology is the integration of different types of knowledge about biological processes in order to gain insight into the functioning of the organism as a whole system. An additional challenge is to bridge the gap between knowledge of biological pathways, genetic perturbations with their effects on gene expression and the resulting phenotypes, in order to gain a more thorough understanding of biological mechanisms. We developed a method for integrating pathway with gene expression and phenotypic data sets, making inferences from them and checking their consistency. These include information on changes in gene expression resulting from different gene mutants, biological signalling pathway information at the protein level in the form of activation and inhibition between proteins, and results of phenotypic analyses of the mutant animals

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