Date8, Dec., 2021, (Wed.), 10:00~
PlaceZoom (If you would like to join, please send an email to sosuke.ito(at)
SpeakerArtemy Kolchinsky (Santa Fe Institute) 
TitleThermodynamic threshold for Darwinian evolution
AbstractUnderstanding the thermodynamics of Darwinian evolution has important implications for biophysics, evolutionary biology, and the study of the origin of life. We show that in a population of nonequilibrium autocatalytic replicators, the critical selection coefficient (i.e., the minimal fitness difference visible to selection) is lower bounded by the free energy dissipated per replication event. This bound represents a fundamental thermodynamic threshold for Darwinian evolution, analogous to selection thresholds that arise from finite population sizes or large mutation rates. Our results apply to a large class of molecular replicators, including many types of multistep autocatalytic reaction mechanisms and autocatalytic sets. We illustrate our approach on a thermodynamically-consistent model of simple replicators in a chemostat.
Date16, Jun., 2020, (Tue.), 15:00~
PlaceZoom (If you would like to join, please send an email to sosuke.ito(at)
SpeakerTan Van Vu (The University of Tokyo)
TitleGeometrical bounds of the irreversibility in Markovian systems
AbstractWe derive geometrical bounds on the irreversibility for both classical and open quantum Markovian systems that satisfy the detailed balance conditions. Using the information geometry, we prove that the irreversible entropy production is bounded from below by a modified Wasserstein distance between the initial and final states, thus generalizing the Clausius inequality. The modified Wasserstein metric can be regarded as a discrete-state generalization of the Wasserstein metric, which plays an important role in the optimal transport theory. Notably, the derived bounds can be interpreted as classical and quantum speed limits, implying that the associated entropy production constrains the minimum time required to transform a system state. We illustrate the results on several systems and demonstrate that a tighter bound on the efficiency of quantum heat engines can be obtained. 
Date23, Jan., 2020, (Thu.), 15:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerSeth Fraden (Physics, Brandeise University)
TitleProgrammable self-assembly of DNA origami capsids based on the principles of virus structure
AbstractWe provide a general and modular solution for building synthetic icosahedral shells on the scale of 100 nm, motivated by the 1962 Caspar and Klug theory of virus structure. Strategies were explored for controlling the pathways, kinetics, and the yield by which subunits arrange themselves into icosahedral symmetry. The methods of DNA origami were employed to produce accurately-designed and rigid building blocks. We created multiple large virus-like capsids and validated the structures using cryo electron microscopy and studied the capsid assembly process experimentally and with a computational model to elucidate how the kinetics and yield of target structures depends on control parameters. Our capsid building blocks represent a near-ideal manifestation of patchy particles whose geometry and interactions can be designed with sub-nanometer and kBT precision, thus achieving a long sought after goal in soft matter physics. Applications range from drug delivery to a generalized antiviral agent, which is demonstrated for hepatitis B. 

Date21, Jan., 2020, (Tue.), 13:00~
PlaceRoom 413 in Faculty of Science Building No.1, The University of Tokyo
SpeakerYan Jiawei (Harvard Medical School)
TitleKinetic uncertainty relations for the control of stochastic reaction systems
AbstractNon-equilibrium stochastic reaction networks are commonly found in both biological and non-biological systems, but have remained hard to analyze because small differences in rate functions or topology can change the dynamics drastically. Here we conjecture exact quantitative inequalities that relate the extent of fluctuations in connected components, for various network topologies. Specifically, we find that regardless of how two components affect each other’s production rates, it is impossible to suppress fluctuations below the uncontrolled equivalents for both components: one must increase its fluctuations for the other to be suppressed. For systems in which components control each other in ring-like structures, it appears that fluctuations can only be suppressed in one component if all other components instead increase fluctuations, compared to the case without control. Even the general N-component system, with arbitrary connections and parameters, must have at least one component with increased fluctuations to reduce fluctuations in others. In connected reaction networks it thus appears impossible to reduce the statistical uncertainty in all components, regardless of the control mechanisms or energy dissipation. 

Date9, Dec., 2019, (Thu.), 15:00~
PlaceRoom 1320 in Faculty of Science Building No. 4, The University of Tokyo
SpeakerDavid Wolpert (Santa Fe Institute)
TitleThe stochastic thermodynamics of computation
AbstractThis seminar is mainly organized by Sagawa Lab.. One of the central concerns of computer science is how the resources needed to perform a given computation depend on that computation. Moreover, one of the major resource requirements of computers—ranging from biological cells to human brains to high-performance (engineered) computers—is the energy used to run them, i.e. the thermodynamic costs of running them. Those thermodynamic costs of performing a computation have been a long-standing focus of research in physics, going back (at least) to the early work of Landauer and colleagues. However, one of the most prominent aspects of computers is that they are inherently non-equilibrium systems. Unfortunately, the research by Landauer and co-workers on the thermodynamics of computation was done when non-equilibrium statistical physics was still in its infancy, severely limiting the scope and formal detail of their analyses. The recent breakthroughs in non-equilibrium statistical physics hold the promise of allowing us to go beyond those limitations. Here I present some initial results along these lines, concerning the entropic costs of running (loop-free) digital circuits and Turing machines. These results reveal new, challenging engineering problems for how to design computers to have minimal thermodynamic costs. They also allow us to start to combine computer science theory and stochastic thermodynamics at a foundational level, thereby expanding both. 

Date31, Oct, 2019, (Thu.), 14:00~
PlaceRoom 413 in Faculty of Science Building No. 1, The University of Tokyo
SpeakerSreekanth K. Manikandan (Stockholm University)
TitleInferring entropy production from short experiments
AbstractThis seminar is mainly organized by Sagawa Lab.. We provide a strategy for an exact inference of the average as well as the fluctuations of the entropy production in non-equilibrium systems in the steady state, from the measurements of arbitrary current fluctuations. Our results are built upon the finite time generalization of the thermodynamic uncertainty relation, and require only very short time series data from experiments. We illustrate our results with exact and numerical solutions for two colloidal heat engines.Arxiv link: 

Date27-29, May, 2019, (Mon.-Wed.)
PlaceHongo Campus, the University of Tokyo
WorkshopData analysis and machine learning in dynamical systems (website)
AbstractThe goal of this workshop is to bring together researchers from data analysis, machine learning, and dynamical systems to discuss recent progress in data analysis of complex phenomena in dynamical systems with large degrees of freedom, and to fill the gap between theories in these fields.