Ben Cosgrove

Lauffenburger Lab

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Ben Cosgrove CV email Ph.D. Student Biological Engineering Division webpage Massachusetts Institute of Technology webpage Research Advisors: Linda Griffith webpage Doug Lauffenburger webpage internal wiki Research Summary: "Multivariate Cue-Signal-Response Analysis of TNFα-Induced Hepatocyte Proliferation and Apoptosis" In the liver, the pleoitropic cytokine tumor necrosis factor-α (TNFα) induces hepatocytes to undergo multiple cellular decision processes including proliferation, survival, and apoptosis (programmed cell death) that are highly sensitive to a variety of physiologically relevant co-stimuli such as mitogenic growth factors and viral infection. During liver regeneration, TNFα and other mitogenic stimuli (HGF, EGF, TGFα) are secreted by liver non-parenchymal cells and induce proliferation of differentiated hepatocytes. Upon viral infection by pathogenic species or common gene therapy vectors such as adenovirus, liver non-parenchymal cells secrete TNFα and other pro-inflammatory cytokine that stimulate virus-infected hepatocytes to undergo apoptosis. Our understanding of the mechanisms that mediate TNFα-induced hepatocyte outcomes is limited by (i) the extent of extracellular cross-talk between hepatocytes and liver non-parenchymal cells through paracrine and autocrine mediators, (ii) the extent of intracellular cross-talk between signaling pathways downstream of TNFα and other physiologically relevant co-stimuli in hepatocytes, and (iii) the effects of hepatocyte de-differentiation in standard in vitro culture systems. We propose to characterize TNFα-mediated hepatocyte responses through a multivariate cue-signal-response paradigm by which multiple intracellular signaling activities, extracellular autocrine cascades, and phenotypic cellular outcomes are rigorously quantified upon co-stimulation with either growth factors or a replication-deficient adenovirus using both standard two-dimensional and novel three-dimensional rat hepatocyte culture systems. Multivariate signaling and cellular outcome data will be subsequently analyzed using data-driven modeling and analysis methods such as principal component analysis and partial least squares regression to develop and validate predictive models of hepatocyte decision processes related to the phenomena of liver regeneration and viral infection responses. Member of: MIT Biotechnology Process Engineering center webpage MIT Center for Cell Decision Processes webpage internal wiki MIT Center for Cancer Research webpage Funding: Whitaker Foundation Graduate Research Fellowship webpage MIT-Pfizer Hepatoxicity Signaling Collaboration webpage MIT Center for Cell Decision Processes webpage