Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data

By Jason W. Locasale, Alejandro Wolf-Yadlin. 2009

45 citations recorded by the source corpus.

Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data.

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “, for the engineering of stable and fast-folding proteins [39], for the analysis of correlated network states in neural populations [40], regulatory gene network modeling from transcript profiles [41], to extract residue-residue interactions from nucleotide sequences [42–43]), as well as derivation ”
3D Protein Structure Predicted from Sequence (2011, unknown): “, for the engineering of stable and fast-‐folding proteins [37], for the analysis of correlated network states in neural populations [38], regulatory gene network modeling from transcript profiles [39], t”
Inverse statistical problems: from the inverse Ising problem to data science (2017, methodology): “The same approach was used in [128] to analyse the cellular signalling networks mediated by the phosphorylation of specific sites on different proteins.”
Principles of maximum entropy and maximum caliber in statistical physics (2013, background): “…inferring the firing patterns of arrays of neuronal cells (see below) (Schneidman et al., 2006); inferring the structures of networks of proteins (Locasale and Wolf-Yadlin, 2009) and genes (Lezon et al., 2006) from proteomics and microarray data; modeling low-noise regulatory networks assuming…”