Srinivas (Ravi) Iyengar, PhD
- DIRECTOR, MOUNT SINAI INSTITUTE FOR SYSTEMS BIOMEDICINE
- DIRECTOR SYSTEMS BIOLOGY CENTER NEW YORK
- PROFESSOR | Pharmacological Sciences
Research Topics:Addiction, Axonal Growth and Degeneration, Bioinformatics, Cell Adhesion, Cell Biology, Cell Motility, Cell Transformation, Computational Biology, Computer Simulation, Cytoskeleton, Dendritic Cells, Drug Design and Discovery, Hippocampus, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Membranes, Memory, Nanotechnology, Phosphorylation, Protein Kinases, Protein Phosphatases, Proteomics, Receptors, Signal Transduction, Synaptic Plasticity, Systems Biology, Theoretical Biology, Transcription Factors, cAMP
Dr. Iyengar is a Dorothy H. and Lewis Rosenstiel Professor in the Department of Pharmacology and Systems Therapeutics and the 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.
Systems pharmacology and systems biology
Computational cell biology
Cellular signaling networks
Spatial modeling of cell signaling
G-protein mediated intracellular signaling in neurons
Spatiotemporal organization of cellular networks
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Multi-Disciplinary Training AreasNeuroscience [NEU], Pharmacology and Therapeutics Discovery [PTD]
BS, Bombay University
MSc, Bombay University
MS, University of Houston
PhD, University of Houston
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, please visit the Iyengar Laboratory, Systems Pharmacology and Systems Biology website.