Starting up in September 2020, we're proud to introduce our monthly Physics of Life Seminar Series. We'll be hosting top speakers from across the globe, presenting on all topics from fundamental theory to cutting edge experiments. At least initially, seminars will be hosted remotely and everyone is welcome, whether you are a member of College or not. If you're interested, please sign up to our network mailing list for regular updates on speakers; our full schedule is shown below, and you can find details about how to watch live by clicking on the event links.  Recordings of previous talks can be found in the video playlist below. 

If you'd like to suggest a speaker, please do so using this form.

Recorded Seminars

Prof Margaret Gardel

Mechanical Homeostasis in the Actin Cytoskeleton

My lab studies the design principles of cytoskeletal materials the drive cellular morphogenesis, with a focus on contractile machinery in adherent cells. In addition to force generation, a key feature of these materials are distributed force sensors which allow for rapid assembly, adaptation, repair and disintegration. Here I will describe how optogenetic control of RhoA GTPase is a powerful and versatile force spectroscopy approach of cytoskeletal assemblies and its recent use to probe repair response in actomyosin stress fibers. I will also describe our recent identification of 18 proteins from the zyxin, paxillin, Tes and Enigma families with mechanosensitive LIM (Lin11, Isl- 1 & Mec-3) domains that bind exclusively to mechanically stressed actin filaments. Our results suggest that the evolutionary emergence of contractile F-actin machinery coincided with, or required, proteins that could report on the stresses present there to maintain homeostasis of actively stressed networks.

Mechanical Homeostasis in the Actin Cytoskeleton

Prof Margaret Gardel

Mechanical Homeostasis in the Actin Cytoskeleton

Mechanical Homeostasis in the Actin Cytoskeleton

My lab studies the design principles of cytoskeletal materials the drive cellular morphogenesis, with a focus on contractile machinery in adherent cells. In addition to force generation, a key feature of these materials are distributed force sensors which allow for rapid assembly, adaptation, repair and disintegration. Here I will describe how optogenetic control of RhoA GTPase is a powerful and versatile force spectroscopy approach of cytoskeletal assemblies and its recent use to probe repair response in actomyosin stress fibers. I will also describe our recent identification of 18 proteins from the zyxin, paxillin, Tes and Enigma families with mechanosensitive LIM (Lin11, Isl- 1 & Mec-3) domains that bind exclusively to mechanically stressed actin filaments. Our results suggest that the evolutionary emergence of contractile F-actin machinery coincided with, or required, proteins that could report on the stresses present there to maintain homeostasis of actively stressed networks.

RNA-driven phase separation from cells to SARS

Prof Amy Gladfelter

RNA-driven phase separation from cells to SARS

RNA-driven phase separation from cells to SARS

Biomolecular condensation is a mechanism for controlling cell organization. Many condensates are rich in nuclei acids such as RNA. The role of specific RNA sequences and structures in promoting the molecular identity of condensates formed for cell polarity and division and by the SARS CoV-2 virus will be discussed.

Is there Universality in Biology?

Prof Nigel Goldenfeld

Is there Universality in Biology?

Is there Universality in Biology?

It is sometimes said that there are two reasons why physics is so successful as a science. One is that it deals with very simple problems. The other is that it attempts to account only for universal aspects of systems at a desired level of description, with lower level phenomena subsumed into a small number of adjustable parameters. It is a widespread belief that this approach seems unlikely to be useful in biology, which is intimidatingly complex, where “everything has an exception”, and where there are a huge number of undetermined parameters.

I will try to argue, nonetheless, that there are important, experimentally-testable aspects of biology that exhibit universality, and should be amenable to being tackled from a physics perspective. My suggestion is that this can lead to useful new insights into the existence and universal characteristics of living systems. I will try to justify this point of view by contrasting the goals and practices of the field of condensed matter physics with materials science, and then by extension, the goals and practices of the newly emerging field of “Physics of Living Systems” with biology.

Anatomical decision-making by cellular collectives

Prof Michael Levin

Anatomical decision-making by cellular collectives

Anatomical decision-making by cellular collectives: Bioelectrical pattern memories, regeneration, and synthetic living organisms

It is sometimes said that there are two reasons why physics is so successful as a science. One is that it deals with very simple problems. The other is that it attempts to account only for universal aspects of systems at a desired level of description, with lower level phenomena subsumed into a small number of adjustable parameters. It is a widespread belief that this approach seems unlikely to be useful in biology, which is intimidatingly complex, where “everything has an exception”, and where there are a huge number of undetermined parameters.

I will try to argue, nonetheless, that there are important, experimentally-testable aspects of biology that exhibit universality, and should be amenable to being tackled from a physics perspective. My suggestion is that this can lead to useful new insights into the existence and universal characteristics of living systems. I will try to justify this point of view by contrasting the goals and practices of the field of condensed matter physics with materials science, and then by extension, the goals and practices of the newly emerging field of “Physics of Living Systems” with biology.

Neural network like collective dynamics in molecules

Prof Arvind Murugan

Neural network like collective dynamics in molecules

Neural network like collective dynamics in molecules

Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the pattern recognition task solved, time taken and accuracy. This work shows that high dimensional pattern recognition and learning are not special to software algorithms but can be achieved by the collective dynamics of sufficiently disordered molecular systems.

Theory, reimagined

Prof Greg Stephens

Theory, reimagined

Theory, reimagined

Physics offers countless examples for which theoretical predictions are astonishingly powerful. But it’s hard to imagine a similar precision in complex systems where the number and interdependencies between components simply prohibits a first-principles approach, look no further than the challenge of the billions of neurons and trillions of connections within our own brains. In such settings how do we even identify the important theoretical questions? We describe a systems-scale perspective in which we integrate information theory, dynamical systems and statistical physics to extract understanding directly from measurements. We demonstrate our approach with a reconstructed state space of the behavior of the nematode C. elegans, revealing a chaotic attractor with symmetric Lyapunov spectrum and a novel perspective of motor control. We then outline a maximally predictive coarse-graining in which nonlinear dynamics are subsumed into a linear, ensemble evolution to obtain a simple yet accurate model on multiple scales. With this coarse-graining we identify long timescales and collective states in the Langevin dynamics of a double-well potential, the Lorenz system and in worm behavior. We suggest that such an “inverse’’ approach offers an emergent, quantitative framework in which to seek rather than impose effective organizing principles of complex systems.

Magic numbers in protein phase transitions

Prof Ned Wingreen

Magic numbers in protein phase transitions

Magic numbers in protein phase transitions

Biologists have recently come to appreciate that eukaryotic cells are home to a multiplicity of non-membrane bound compartments, many of which form and dissolve as needed for the cell to function. These dynamical “condensates” enable many central cellular functions – from ribosome assembly, to RNA regulation and storage, to signaling and metabolism. While it is clear that these compartments represent a type of separated phase, what controls their formation, how specific biological components are included or excluded, and how these structures influence physiological and biochemical processes remain largely mysterious. I will discuss recent experiments on phase separated condensates both in vitro and in vivo, and will present theoretical results that highlight a novel “magic number” effect relevant to the formation and control of two-component phase separated condensates.