CASUS Institute Seminar, Prof. Ricardo Martinez-Garcia, PhD, SIMONS-FAPESP Assistant Professor in Biological Physics at the ICTP-South American Institute for Fundamental Research (São Paulo, Brazil) and a SIMONS Associate at the Quantitative Life Science section at the Abdus Salam ICTP (Trieste, Italy)
From microbial colonies to ecosystems extending over continental scales, complex biological systems often feature self-organized patterns. These regular structures cover large portions of the system and emerge from nonlinear interactions among its components. Importantly, because harsh environmental conditions provide a context in which self-organization becomes important for survival, emergent patterns contain crucial information about the physical and biological processes that occur in the systems in which they form. For example, spatial patterns have been hypothesized to inform about the robustness of the water-limited ecosystems in which they form, thus constituting a powerful tool to prevent biodiversity loss.
Due to the long timescales in which patterns emerge, mathematical modeling has been a very powerful tool to explore their origin and to speculate about their possible (eco)system-level consequences. Existing models for pattern formation, however, focus on reproducing the observed shape of the pattern and often avoid a detailed description of its underlying interactions. This approach has recently raised important concerns, since patterns that seem to be identical can emerge in very different contexts and from very different underlying processes, which may lead to contradictory system-level consequences. Therefore, in order to exploit biological patterns and extract meaningful conclusions about their potential implications, it is necessary to develop a new theoretical framework that focuses not only on recovering the observed structures, but also on doing so from the right set of individual-level interactions.
Ricardo Martinez-Garcia will use his ongoing work on self-organizing systems across different spatiotemporal scales to discuss how such new approach can be developed and how, in this context, self-organized patterns provide allow us to integrating processes that occur within and between several scales in complex biological systems.