Mini-Workshop on
Frontiers in Biophysics
July 1st, 2024
Room 233, Faculty of Science Bldg.1, Hongo campus,
The University of Tokyo
Supported by Universal Biology InstituteProgram
13:00-13:05 Opening
13:05-13:45 Anne-Florence Bitbol (EPFL)“Optimization and historical contingency in protein
sequences
13:45-14:25 Yuichi Wakamoto (Univ. Tokyo)“Physiological adaptation and homeostasis against
environmental and genetic perturbations”
14:25-15:05 Arvind Murugan (Univ. Chicago)“Neural computation through learned phase diagrams”
15:05-15:20 Coffee break
15:20-16:00 Kenshi Suzuki (Univ. Tokyo)
TBA
16:00-16:40 Namiko Mitarai (Niels Bohr Institute)“The spatiotemporal dynamics of phage predation andcompetition”
16:40-17:20 Tetsuhiro Hatakeyama (Tokyo Tech.)“Evolvability of the toxin-adaptation system”

Abstract
Optimization and historical contingency in protein sequences
Anne-Florence Bitbol (EPFL)
Abstract: Protein sequences are shaped by functional optimization on the one handand by evolutionary history, i.e. phylogeny, on the other hand. A multiple sequencealignment of homologous proteins contains sequences which evolved from the sameancestral sequence and have similar structure and function. In such an alignment,correlations in amino-acid usage at different sites can arise from structural andfunctional constraints due to coevolution, but also from historical contingency.
Correlations arising from phylogeny often confound coevolution signal fromfunctional or structural optimization, impairing the inference of structural contacts fromsequences. I will show that inferred Potts models are more robust than local statistics
to these effects, which may explain their success. I will argue that phylogenetic
correlations can also provide useful information for some inference tasks, especially toinfer interaction partners from sequences among the paralogs of two protein families. In
this case, signal from phylogeny and signal from constraints combine constructively.
Protein language models have recently been applied to sequence data, greatly
advancing structure, function and mutational effect prediction. Language models
trained on multiple sequence alignments capture coevolution and structural contacts,
but also phylogenetic relationships. I will discuss a method we recently proposed thatleverages these models to predict which proteins interact among the paralogs of twoprotein families, and improves the prediction of the structure of some protein
complexes. Finally, I will show that these models have promising generative properties.

Physiological adaptation and homeostasis against environmental andgenetic perturbations
Yuichi Wakamoto (Univ. Tokyo)
Abstract: Exposing biological cells to environmental and genetic stresses can exert
global changes in gene expression and metabolic profiles. These changes can varyeven among genetically identical cells, and a fraction of cells eventually adapt andreach the states that permit cells long-term survival or even stable proliferation. To
understand the mechanisms of cellular adaptation, we need to answer the following
crucial questions: (1) What factors determine which cells adapt and which do not? (2)Are adapted states to an identical stress solitary or plural? (3) Which parts of themolecular profile in cells are altered during adaptation? In this workshop, we present
experimental results on the adaptation of Escherichia coli against environmental andgenetic perturbations. We show the time-dependent significance of the expression
noise of the resistant protein for survival and adaptation against antibiotic stress, thecommonality and diversity of the adapted states, and the role of stoichiometry recoveryof core components in adaptation. Finally, we present the characteristic stoichiometryconserving architecture in gene expression profiles preserved from bacteria to humancells and discuss its role in simultaneous realization of adaptability and homeostasis.

Neural computation through learned phase diagrams
Arvind Murugan (Univ. Chicago)
Abstract: Our model for learning is based on neural networks, i.e., networks of linear
threshold devices. Even physical realizations of neural computation, such as molecularor electrical circuits, effectively mimic these network architectures at an element-byelement level. Here, we explore an alternative paradigm for neural computation inevitable collective physical processes that can learn in Hebbian-inspired ways without
being designed to mimic a neural network element-by-element. Through theory andexperiment, we show how nucleation in molecular systems can learn to recognize
complex patterns in chemical or mechanical stimuli. Our work suggests that exploiting
ubiquitous physical phenomena, such as nucleation, is an underexplored powerfulroute to neural computation without neurons.

The spatiotemporal dynamics of phage predation and competition
Namiko Mitarai (Niels Bohr Institute)
Abstract: Phages and bacteria manage to coexist and sustain ecosystems, despite
limited resources for bacteria growth and heavy predation by various phages. In thisseminar, we focus on the role of spatial structure in phage attack on bacterial
population. We discuss the dynamics of phages infecting a dense bacterial colony
outside, focusing on how much superinfection of the bacteria on the surface canprotect the bacteria close to the centre of the colony. We then discuss how the T-evenphage's ability to respond to secondary infection, called lysis inhibition, provides
competitive advantages to the phage, and how it can affect the phage epidemic in alawn of bacteria.
References:
Eriksen, Rasmus Skytte, et al. "The dynamics of phage predation on amicrocolony." Biophysical Journal 123.2 (2024): 147-156.
Hvid, Ulrik, and Namiko Mitarai. "Competitive advantages of secondary adsorptiontriggered lysis inhibition for T-even phages." bioRxiv (2024): 2024-02.

Evolvability of the toxin-adaptation system
Tetsuhiro Hatakeyama (Tokyo Tech.)
Abstract: In microbial communities, many cooperative interactions have been reported,
working among different species and clonal cells. Besides, competitive interactions
working among cells with different genotypes have mainly been reported, and thoseamong clonal cells would be difficult to evolve, in general. However, we found an
elaborate response of the yeast community against glucose depletion, in which the first
adapted cells kill the latecomer cells. During glucose depletion, yeast cells releaseautotoxins, which can even kill the clonal cells of the ones producing them. Althoughthese autotoxins were likely to induce mass suicide, some cells differentiated to adapt
to the autotoxins without genetic changes. If nondifferentiated latecomers tried to
invade the habitat, autotoxins damaged or killed the latecomers, but the differentiatedcells could selectively survive.

Furthermore, we found distant yeast species maintain this toxin-adaptation
system with the same autotoxins; this is puzzling because the well-known toxinimmunity system is generally weak in cheaters, which do not produce toxins but benefit
from those produced by others. In this case, different toxins continue to evolve, as in anarms race, so that different species use different toxins. To address the question of thestability of the toxin-adaptation system, we analyze the evolutionary stability of thesystem using population dynamics. Under constant conditions, the toxin-adaptation
system cannot evolve; under continuous starvation conditions, cheaters outcompete
adaptive cells with the toxin-adaptation system. In continuous nutrient-rich conditions,the adaptive cells cannot beat sensitive cells that have neither toxin nor immunity.
However, the toxin-adaptation system is stably maintained when the environment
switches stochastically between starvation and nutrient-rich conditions, with theaverage duration of nutrient-rich conditions being much longer than that of starvation.
This suggests that the same toxin-adaptation system can evolve and be stably
maintained in distant species under fluctuating environments with frequent starvation
but will decline when the environment is stable.