Network inference using steady-state data and Goldbeter-Koshland kinetics
published: Oct. 23, 2012, recorded: September 2012, views: 3016
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Description
Motivation: Network inference approaches are widely used to shed light on regulatory interplay
between molecular players such as genes and proteins. Biochemical processes underlying networks
of interest (e.g. gene regulatory or protein signalling networks) are gene- rally nonlinear. In many
settings, knowledge is available concerning relevant chemical kinetics. However, existing network
inference methods for continuous data are typically rooted in convenient statistical formulations
which do not exploit chemical kinetics to guide inference.
Results: Here we present an approach to network inference for steady-state data that is rooted in
nonlinear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics
to obtain functional forms that are in turn used to infer networks using steady-state data. The
approach we propose is directly applicable to conventional steady-state gene expression or
proteomic data and does not require knowledge of either network topology or any kinetic
parameters; both are simultaneously learned from data. We illustrate the approach in the context
of protein phosphorylation networks, using data simulated from a recent mechanistic model and
proteomic data from cancer cell lines. In the former, the true network is known and used for
assessment, whilst in the latter results are compared against known biochemistry. We find that the
proposed methodology is more effective at estimating network topology than methods based on
linear models.
Availability: MATLAB R2009b code used to produce these results is provided in the Supplemental
Information.
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