Seminars

May 29 2018

12:00 158 HH

This seminar is sponsored by QCB and IBP

Eric Deeds

Crosstalk, heterogeneity and the physiology of intracellular communication

Summary

Eric Deeds
Center for Computational Biology
Department of Molecular Biosciences
University of Kansas

Sponsored by the Department of Integrative Biology and Physiology and the Institute for Quantitative and Computational Biosciences

Human cells have the remarkable capacity to self-organize their behavior in order to generate and maintain complex tissues and organ systems. Cells accomplish this largely through intercellular communication, using a combination of endocrine, paracrine, autocrine and contact signaling to coordinately regulate their activities. A major thrust of research in my lab is aimed at understanding the function of the cell signaling networks that process and interpret intracellular cues. One striking characteristic of human cell signaling networks is their complexity: in particular, they tend to exhibit a massive degree of crosstalk between canonical signaling pathways. We have recently shown that high levels of crosstalk have likely evolved to allow different cell types within a particular tissue microenvironment to respond in different ways to the same perturbation. It has also been found that, even within isogenic populations of the same cell type, different cells within the population often respond very differently to the same stimulus. This high degree of heterogeneity has been shown to limit the amount of information that individual cells can have about their environment. Using the framework of information theory, we recently demonstrated that high levels of heterogeneity can actually be highly advantageous, allowing tissues to exert precise control over processes like differentiation or cell death at the population level. In addition to providing a framework for rationalizing the high degree of crosstalk and heterogeneity observed in human cell signaling, our work indicates that consideration of these effects is likely to be crucial for attempts to target cell signaling in the treatment of complex diseases. Our current and future work in this area is focused on developing more detailed kinetic models of cell signaling networks and using those models to understand how communication between populations of highly heterogeneous cells results in the robust behaviors observed in human development and tissue homeostasis.

Tuesday, May 29, 2018
12:00 noon
Hershey Hall Grand Salon, Room 158



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



this is idtest: