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SeminarPhysics of LifeRecording

Growing in flows: from evolutionary dynamics to microbial jets

Severine Atis
University of Chicago
Sep 27, 2021

Biological systems can self-organize in complex structures, able to evolve and adapt to widely varying environmental conditions. Despite the importance of fluid flow for transporting and organizing populations, few laboratory systems exist to systematically investigate the impact of advection on their spatial evolutionary dynamics. In this talk, I will discuss how we can address this problem by studying the morphology and genetic spatial structure of microbial colonies growing on the surface of a viscous substrate. When grown on a liquid, I will show that S. cerevisiae (baker’s yeast) can behave like “active matter” and collectively generate a fluid flow many times larger than the unperturbed colony expansion speed, which in turn produces mechanical stresses and fragmentation of the initial colony. Combining laboratory experiments with numerical modeling, I will demonstrate that the coupling between metabolic activity and hydrodynamic flows can produce positive feedbacks and drive preferential growth phenomena leading to the formation of microbial jets. Our work provides rich opportunities to explore the interplay between hydrodynamics, growth and competition within a versatile system.

SeminarPhysics of LifeRecording

(What) can soft matter physics teach us about biological function?

Workshop, Multiple Speakers
Emory University
Jul 31, 2020

The “soft, active, and living matter” community has grown tremendously in recent years, conducting exciting research at the interface between soft matter and biological systems. But are all living systems also soft matter systems? Do the ideas of function (or purpose) in biological systems require us to introduce deep new ideas into the framework of soft matter theories? Does the (often) qualitatively different character of data in biological experiments require us to change the types of experiments we conduct and the goals of our theoretical treatments? Eight speakers will anchor the workshop, exploring these questions across a range of biological system scales. Each speaker will deliver a 10-minute talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing both the nature of the theoretical tools and experimental techniques we have at present and also those we think we will ultimately need to answer deep questions at the interface of soft matter and biology.

SeminarPhysics of LifeRecording

Can machine learning learn new physics, or do we need to put it in by hand?"\

Workshop, Multiple Speakers
Emory University
Jun 4, 2020

There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.

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