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Thursday, November 21, 2024

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BMS Seminar: Beyond steady state: Cardiac contraction and remodeling in non-stationary signal environments, Dr Daniel Koch, Max Planck Institute
MTC Lecture Theatre

Abstract: To meet physiological demands, the rhythm and strength of cardiac contractions continuously adapt across different time scales, guided by extracellular biochemical signals sensed via cell surface receptors. Increasingly, it is recognized that cellular responses are influenced not only by the molecular identity and concentration of these signals but also by the information encoded in the varying temporal patterns of the signals encountered by the cell. In this talk, I will discuss the theoretical challenges of studying cellular signaling in non-stationary environments and explore how these challenges can be addressed using concepts from dynamical systems, particularly those related to long transients and criticality. I propose that cardiac cells are significantly influenced by the temporal characteristics of incoming biochemical signals, such as (nor-)adrenaline, angiotensin II, and growth factors. By examining cardiac cells dynamic environments, we can gain a deeper understanding of contractile regulation and remodeling processes (including hypertrophy, apoptosis, and fibrosis) and identify new therapeutic concepts for cardiac arrhythmias and heart failure.

Daniel KochBiography: Following his undergraduate studies in medicine and philosophy at the University of Duesseldorf (Germany), Dr Koch received his PhD in cardiac cell biology from King's College London (UK) in 2021. His research mainly focuses on how cells coordinate their responses to biochemical signals from their environment and how these processes are disrupted in human disease, using a combination of experimental and theoretical approaches. Currently, Dr. Koch is a postdoctoral fellow in the Cellular Computations & Learning group at the Max Planck Institute for Neurobiology of Behavior (Bonn, Germany), where he applies concepts from non-linear dynamics and complex systems to develop new frameworks for understanding how biological systems—from cells to brains—process information and adapt to their surroundings.

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