Abstract

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.

Highlights

  • The wealth of experimental data from high-throughput technologies in different areas of biology allows such data to be incorporated as networks of interactions

  • prior knowledge networks (PKNs) are inclusive by nature because they usually merge altogether interactions previously described in different biological contexts such as different cell types, tissues or experimental conditions in the attempt to collect all relevant biological events

  • The results showed that, the models were only trained to explain the existence of specific stable states, a fraction of the contextualized networks exhibited the expected response under perturbation (20%, 80%, 32% and 36% for epithelial to mesenchymal transition (EMT), Th1-Th2, induction of pluripotent stem cells (iPSC) and human embryonic stem cells (hESC)-cardiomyocyte respectively)

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Summary

Introduction

The wealth of experimental data from high-throughput technologies in different areas of biology allows such data to be incorporated as networks of interactions. PKNs are inclusive by nature because they usually merge altogether interactions previously described in different biological contexts such as different cell types, tissues or experimental conditions in the attempt to collect all relevant biological events. They suffer from undetermined network incompleteness, which is biased due to historical reasons. PKNs are available in specific databases [1,2,3] or can be constructed using dedicated software tools. A number of both commercial and free software resources are available to assist with the reconstruction of PKNs, for example Pathway Studio (www.elsevier.com/online-tools/pathwaystudio), Ingenuity Pathway Analysis (www.ingenuity.com), Metacore A number of both commercial and free software resources are available to assist with the reconstruction of PKNs, for example Pathway Studio (www.elsevier.com/online-tools/pathwaystudio), Ingenuity Pathway Analysis (www.ingenuity.com), Metacore (www.thomsonreuters. com/metacore), Transfac (www.biobase-international.com/product/transcription-factorbinding-sites) and GenMania (www.genemania.org)

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