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Locasale, Jason Wei

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Locasale

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Jason Wei

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Locasale, Jason Wei
Author's Publications: search.filters.isAuthorOfPublication.6e30118c-094b-4ae7-b8fe-f86dcb85c9aa

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    Signal Duration and the Time Scale Dependence of Signal Integration in Biochemical Pathways
    (BioMed Central, 2008) Locasale, Jason Wei
    Background: Signal duration (e.g. the time over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation.Results: Here we present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales. We compute the dynamic, frequency dependent gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales. We identify topological features of these networks that allow for different frequency dependent signal processing properties. Conclusion: We show that multi-staged cascades are effective in integrating signals of long duration whereas multi-staged cascades that operate in the presence of negative feedback are effective in integrating signals of short duration. Our studies suggest principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous motif in biochemical systems.
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    Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data
    (Public Library of Science, 2009) Wolf-Yadlin, Alejandro; Nussinov, Ruth; Locasale, Jason Wei
    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.
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    Altered Metabolism in Cancer
    (BioMed Central, 2010) Locasale, Jason Wei; Cantley, Lewis C.
    Cancer cells have different metabolic requirements from their normal counterparts. Understanding the consequences of this differential metabolism requires a detailed understanding of glucose metabolism and its relation to energy production in cancer cells. A recent study in BMC Systems Biology by Vasquez et al. developed a mathematical model to assess some features of this altered metabolism. Here, we take a broader look at the regulation of energy metabolism in cancer cells, considering their anabolic as well as catabolic needs. See research article: http://www.biomedcentral.com/1752-0509/4/58/