Time & Location
17 Feb 2021, 12:00 pm AEDT
Theory of Living Systems Webinar
About the Event
The idea that genotype maps directly and deterministically into the phenotype has dictated evolution biology as well as cancer biology where cancer progression is understood as driven by a somatic Darwinian evolution of cells that accumulate mutations and undergo selection. In reality, one genome can produce thousands of stable and inherited phenotypes, most prosaically manifest in the many natural cell types of the metazoan body – which sit in the valleys in Waddington’s epigenetic landscape. Waddington’s landscape metaphor explains phenotypic variability (“plasticity”) in the absence of genetic alterations and can be grounded in the mathematical principles of gene regulatory network dynamics. Herein, a given stable cell type is a high-dimensional attractor state. But then, how do cells switch phenotypes, as in development, if cell types are so robust? We proposed that cell fate decisions and ensuing state transitions are bifurcation events in which attractor states are destabilized, manifest as critical transitions. The associated phenotype instability (given the same genome) has profound consequences on cancer treatment, offering an explanation, without invoking natural selection, for the near-inevitable, rapid emergence of therapy resistance after treatment. In this talk I will introduce the mathematical basis of epigenetic landscapes and present single-cell transcriptomics data that are consistent with predictions by the theory.
About the speaker
Sui Huang, MD, PhD, obtained his doctorates in medicine and in molecular biology at the University of Zurich in 1995. He was a postdoctoral fellow and a faculty at Harvard Medical School in Boston, where he studied cell mechanics, cell cycle and cellular differentiation. Sui Huang then was recruited to the University of Calgary in 2007 to work alongside Stuart Kauffman on gene regulatory networks and cancer differentiation therapy (=exiting the cancer attractor!) before joining the Institute for Systems Biology in Seattle in 2011. Having demonstrated that cell types are attractor states in high-dimensional gene expression space, he also showed that cell fate decisions represent bifurcation events in which cells states become unstable before settling in new attractors. His current laboratory at ISB uses single-cell omics technologies and combines theory of non-linear dynamical systems with “big data” to better understand the intrinsic limitations of cancer treatment: Why does cancer treatment so often backfire?