Gender	Male
E-mail	ravi.iyengar@mssm.edu
Education and Training	Ph.D., University of Houston
	M.S., University of Houston
	M.Sc., Bombay University
	B.S., Bombay University
	Fellowship, Baylor College of Medicine

Dr. Iyengar is the Dorothy H. and Lewis Rosenstiel Professor and Chairman of the Department of Pharmacology and Systems Therapeutics, Director of the Experimental Therapeutics Institute and Director and Principal Investigator of the NIGMS funded Systems Biology Center New York.

Trained as a biochemist, Dr. Iyengar's research focuses on cell signaling networks with emphasis on heterotrimeric G protein pathways. His laboratory uses a combination of experimental and computational approaches to understand the regulatory and information processing capabilities of cellular signaling networks.

Research Interests:

Systems biology and systems pharmacology
Computational cell biology
Cellular signaling networks
Spatial modeling of cell signaling
G-protein mediated intracellular signaling in neurons
Spatiotemporal organization of cellular networks

Websites:

Iyengar Laboratory
Experimental Therapeutics Institute (ETI)
Systems Biology Center New York (SBCNY)

Education and Training	Ph.D., University of Houston
	M.S., University of Houston
	M.Sc., Bombay University
	B.S., Bombay University
	Fellowship, Baylor College of Medicine

Summary of Research

The Iyengar laboratory is interested in understanding how signals are routed and processed through cellular signaling networks including mechanisms of information sorting and integration.

We are interested in understanding dynamics of network topology. For this we focus on identifying regulatory motifs such as feedback and feedforward loops and determining their information processing capability. We have constructed and analyzed dynamic maps of these motifs to understand how cellular signaling networks engage the various cellular machinery to produce physiological responses to extra-cellular signals. To study complex cell signaling networks we utilize a combination of experimental and theoretical approaches. Multidimensional experimental approaches currently being used in the laboratory include reverse-phase phosphoproteomic arrays, transcription factor arrays, ChIP-Seq and mRNA profiling by sequencing. These experimental approaches are being integrated with theoretical analysis using both graph theory approaches and differential equation based modeling to understand network regulation of cell proliferation and activity induced synaptic plasticity.

We are interested in understanding how spatial organization within cells and tissues contributes to dynamic stability. We are studying the role of cell shape in regulating information processing within signaling networks. For these studies at the experimental level we are using approaches to observe and quantify biochemical signaling reactions in live cells. We are also using patterned surfaces at the nano and microscale as to obtain cells of specific shapes that can be imaged. To decipher the information content in cell shape we are analyzing signaling networks using partial differential equations. We are developing spatially realistic models of signaling networks to understand the origins and dynamics of microdomains of signaling components. We are also interested in understanding the dynamics underlying tissue integrity. We are developing multi-scale models as well as experimentally engineered systems to determine if a dynamic loop that integrates signaling networks in multiple cell types forms the basis for tissue integrity.

We are developing systems level approaches to understanding drug action at a genome-wide level. We are constructing large scale networks to capture all of the known protein-protein interactions in the human genome to computationally identify selective regions (disease neighborhood) within the interaction space associated with specific diseases. We are analyzing the relationship between drug targets and other cellular components to understand the relationship between disease neighborhood and targets of drugs used to treat the disease. From such analyses we are attempting to predict adverse events and explain adverse events reports in the FDA–AERS database.

For more information, visit the Iyengar Laboratory website.

Bromberg KD, Ma'ayan A, Neves SR, Iyengar R. Design logic of a cannabinoid receptor signaling network that triggers neurite outgrowth. Science 2008 May 16; 320(5878): 903-909.

Neves SR, Tsokas P, Sarkar A, Grace EA, Rangamani P, Taubenfeld SM, Alberini CM, Schaff JC, Blitzer RD, Moraru II, Iyengar R. Cell shape and negative links in regulatory motifs together control spatial information flow in signaling networks. Cell 2008 May 16; 133(4): 666-680.

Iyengar R, Diverse-Pierluissi MA, Jenkins SL, Chan AM, Devi LA, Sobie EA, Ting AT, Weinstein DC. Inquiry learning. Integrating content detail and critical reasoning by peer review. Science 2008 Feb 29; 319(5867): 1189-1190.

Eungdamrong NJ, Iyengar R. Compartment-specific feedback loop and regulated trafficking can result in sustained activation of Ras at the Golgi. Biophys J 2007 Feb 1; 92(3): 808-815.

Ma'ayan A, Blitzer RD, Iyengar R. Toward predictive models of mammalian cells. Annu Rev Biophys Biomol Struct 2005; 34: 319-349.

Ma'ayan A, Jenkins SL, Neves S, Hasseldine A, Grace E, Dubin-Thaler B, Eungdamrong NJ, Weng G, Ram PT, Rice JJ, Kershenbaum A, Stolovitzky GA, Blitzer RD, Iyengar R. Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 2005 Aug 12; 309(5737): 1078-1083.

Bhalla US, Ram PT, Iyengar R, . MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science 2002 Aug 9; 297(5583): 1018-1023.

Jordan JD, Landau EM, Iyengar R. Signaling networks: the origins of cellular multitasking. Cell 2000 Oct 13; 103(2): 193-200.

Ram P, Iyengar R. Stat3-mediated transformation of NIH-3T3 cells by the constitutively active Q205L Galphao protein. Science 2000 Jan 7; 287(5450): 142-144.

Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science 1999 Jan 15; 283(5400): 381-387.