Department of Pharmacological Sciences and Institute for Systems Biomedicine, Systems Biology Center, Icahn School of Medicine at Mount Sinai, New York -USA
Lecture: Integrated models of cell shape and function in tissue organization
In this talk, I will describe the usefulness of integrating multiple types of computational models to obtain a deeper understanding of the mechanisms underlying the role of cell shape in controlling physiological functions at the cellular and tissue levels and for drug therapy.
The shape of cells plays an important role in the organization of tissues in health and disease. Pathology often uses tissue morphology, which is dependent on cell shape to identify and classify diseases. We have found that cell shape is an independent repository and source of information. The study of how information in cell shape affects cell phenotype requires a combination of control theory models with dynamical models to demonstrate that cell shape information is transduced into altered phenotype of cells using intracellular signaling pathways that also transduce biomechanical signals.
Additionally, mathematical analyses and numerical simulations show that cell shape modulates transduction of biochemical signals at the plasma membrane. Mathematical analyses indicate that the surface to volume relationships control mechanical, chemical and shape signals to regulate phenotype at different scales of organization. The general physico-chemical principles underlying scalable cell and tissue organization in structure function relationships will be discussed.
We have integrated bioinformatics models with dynamical models to understand how multiple subcellular processes come together to enable change in cell shape and state as exemplified by neurite elongation and axonal regeneration after injury. These models indicate that many distributed and deep subcellular processes contribute to this change in whole cell phenotype. The dynamical models indicate that balance between the top-level subcellular processes is essential for steady state neuronal growth and violations of this balance will lead to altered morphology. These computational predictions have been experimentally verified. The dynamical models can also be used to predict and understand combination drug therapy that enable in vivo regeneration of the optic nerve after injury. The utility of such computational models for repurposing drug and discovery of new drug targets will be discussed.
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
Modeling of cell signaling Spatial
G-protein mediated intracellular signaling in neurons
Spatiotemporal organization of cellular networks