Fitting Boolean Networks from Steady State Perturbation Data
- 5 January 2011
- journal article
- research article
- Published by Walter de Gruyter GmbH in Statistical Applications in Genetics and Molecular Biology
- Vol. 10 (1)
- https://doi.org/10.2202/1544-6115.1727
Abstract
Gene perturbation experiments are commonly used for the reconstruction of gene regulatory networks. Typical experimental methodology imposes persistent changes on the network. The resulting data must therefore be interpreted as a steady state from an altered gene regulatory network, rather than a direct observation of the original network. In this article an implicit modeling methodology is proposed in which the unperturbed network of interest is scored by first modeling the persistent perturbation, then predicting the steady state, which may then be compared to the observed data. This results in a many-to-one inverse problem, so a computational Bayesian approach is used to assess model uncertainty.The methodology is first demonstrated on a number of synthetic networks. It is shown that the Bayesian approach correctly assigns high posterior probability to the network structure and steady state behavior. Further, it is demonstrated that where uncertainty of model features is indicated, the uncertainty may be accurately resolved with further perturbation experiments. The methodology is then applied to the modeling of a gene regulatory network using perturbation data from nine genes which have been shown to respond synergistically to known oncogenic mutations. A hypothetical model emerges which conforms to reported regulatory properties of these genes. Furthermore, the Bayesian methodology is shown to be consistent in the sense that multiple randomized applications of the fitting algorithm converge to an approximately common posterior density on the space of models. Such consistency is generally not feasible for algorithms which report only single models. We conclude that fully Bayesian methods, coupled with models which accurately account for experimental constraints, are a suitable tool for the inference of gene regulatory networks, in terms of accuracy, estimation of model uncertainty, and experimental design.Keywords
This publication has 25 references indexed in Scilit:
- Selection of Statistical Thresholds in Graphical ModelsEURASIP Journal on Bioinformatics and Systems Biology, 2009
- Synergistic response to oncogenic mutations defines gene class critical to cancer phenotypeNature, 2008
- A graphical approach to relatedness inferenceTheoretical Population Biology, 2007
- Tumor suppressor p53 restricts Ras stimulation of RhoA and cancer cell motilityNature Structural & Molecular Biology, 2007
- Generating Boolean networks with a prescribed attractor structureBioinformatics, 2005
- A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networksBioinformatics, 2004
- Reconstructing Pathways in Large Genetic Networks from Genetic PerturbationsJournal of Computational Biology, 2004
- Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation DataGenome Research, 2002
- The Hallmarks of CancerCell, 2000
- Metabolic stability and epigenesis in randomly constructed genetic netsJournal of Theoretical Biology, 1969