In mammalian cell biology, an ongoing challenge is to bridge the gaps in our understanding of processes at the molecular, cellular, and tissue levels. Central to this hierarchy of biological complexity is the field of signal transduction, which deals with the biochemical mechanisms and pathways by which cells respond to external stimuli. The over-arching goal of this project is to move the signal transduction field from a linear, pathway-centric framework to a network-centric one;to do this;we are quantifying the complexities of feedback regulation and crosstalk interactions, having demonstrated our approach in elucidating dynamical system features of growth factor receptor-mediated signaling in fibroblasts. Quantitative experiments canvassing an array of cell stimulation and molecular perturbation conditions, together with computational modeling, have comprehensively elucidated the dynamic features of Ras- and phosphoinositide 3-kinase (PI3K)-dependent signaling integrated by ERK, the best-characterized mitogen- activated protein kinase (MAPK) in mammalian cells. Certain challenges remain and will be addressed in the proposed effort using molecular and computational approaches: 1) Mapping the molecular determinants of crosstalk and regulatory feedback onto dynamic features of the signaling network;2) Probing the diversity of PI3K/Erk signaling responses at the single-cell level;3) Comparative analysis of signaling networks among receptor and cell systems;and 4) Elucidating mechanisms of chronically perturbed signaling networks in cells harboring oncogenes.
The goals of this project are to study the complex interactions between specific biochemical pathways that control cell growth and survival during wound healing and which contribute to the progression of cancer. By analyzing these mechanisms quantitatively and using mathematical models, we hope to be able to predict the outcomes of interventions targeting the molecular players in these pathways.
|Rahman, Anisur; Haugh, Jason M (2017) Kinetic Modeling and Analysis of the Akt/Mechanistic Target of Rapamycin Complex 1 (mTORC1) Signaling Axis Reveals Cooperative, Feedforward Regulation. J Biol Chem 292:2866-2872|
|Herring, Laura E; Grant, Kyle G; Blackburn, Kevin et al. (2015) Development of a tandem affinity phosphoproteomic method with motif selectivity and its application in analysis of signal transduction networks. J Chromatogr B Analyt Technol Biomed Life Sci 988:166-74|
|Ahmed, Shoeb; Grant, Kyle G; Edwards, Laura E et al. (2014) Data-driven modeling reconciles kinetics of ERK phosphorylation, localization, and activity states. Mol Syst Biol 10:718|
|Rahman, Anisur; Haugh, Jason M (2014) Deactivation of a negative regulator: a distinct signal transduction mechanism, pronounced in Akt signaling. Biophys J 107:L29-32|
|Asokan, Sreeja B; Johnson, Heath E; Rahman, Anisur et al. (2014) Mesenchymal chemotaxis requires selective inactivation of myosin II at the leading edge via a noncanonical PLC?/PKC? pathway. Dev Cell 31:747-60|
|Haugh, Jason M (2012) Cell regulation: a time to signal, a time to respond. Bioessays 34:528-9|
|Cirit, Murat; Grant, Kyle G; Haugh, Jason M (2012) Systemic perturbation of the ERK signaling pathway by the proteasome inhibitor, MG132. PLoS One 7:e50975|
|Cirit, Murat; Haugh, Jason M (2012) Data-driven modelling of receptor tyrosine kinase signalling networks quantifies receptor-specific potencies of PI3K- and Ras-dependent ERK activation. Biochem J 441:77-85|
|Chylek, Lily A; Hu, Bin; Blinov, Michael L et al. (2011) Guidelines for visualizing and annotating rule-based models. Mol Biosyst 7:2779-95|
|Cirit, Murat; Haugh, Jason M (2011) Quantitative models of signal transduction networks: How detailed should they be? Commun Integr Biol 4:353-6|
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