Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session E17: Stochastic Thermodynamics of Biological and Artificial Information Processing  IFocus Live

Hide Abstracts 
Sponsoring Units: GSNP DCOMP Chair: David Wolpert, Santa Fe Inst 
Tuesday, March 16, 2021 8:00AM  8:36AM Live 
E17.00001: Thermodynamics of computation with chemical and electronic architectures Invited Speaker: Massimiliano Esposito Stochastic thermodynamics offers a powefull framework to study thermodynamics of computation. I will start by briely reviewing some general results on the fundamental limits and on the costspeedaccuracy tradeoffs of computation. I will then present recent results on the cost of computing with realistic architectures such as chemical reaction networks and electronic circuits. 
Tuesday, March 16, 2021 8:36AM  8:48AM Live 
E17.00002: Entropy production and thermodynamics of information under protocol constraints Artemy Kolchinsky, David Wolpert We investigate bounds on the entropy production (EP) and extractable work involved in transforming a system from some initial distribution p to some final distribution p′, given the driving protocol constraint that the dynamical generators belong to some fixed set. We first show that, for any operator φ over distributions that (1) obeys the Pythagorean theorem from information geometry and (2) commutes with the set of available dynamics, the contraction of KL divergence D(p∥φ(p)) − D(p′∥φ(p′)) provides a lower bound on EP. We also derive a bound on extractable work, as well as a decomposition of the nonequilibrium free energy into an “accessible free energy” (which can be extracted as work) and “inaccessible free energy” (which must be dissipated). We use our general results to derive bounds on EP and work that reflect symmetry, modularity, and coarsegraining constraints. We also use our results to decompose the information acquired in a measurement of a system into “accessible information” (which can be used to extract work from the system) and “inaccessible information” (which cannot be used to extract work from the system). 
Tuesday, March 16, 2021 8:48AM  9:00AM Live 
E17.00003: Maximizing fluctuation exploitation in a simple information ratchet Tushar Kanti Saha, Joseph Neil Lucero, Jannik Ehrich, David Sivak, John Bechhoefer At the dawn of statistical mechanics, Maxwell proposed a thought experiment where acquiring information about a system can allow the extraction of work from a heat reservoir, something seemingly forbidden by the Second Law of Thermodynamics. We present an analagous Maxwell Demon system whereby a particle with mass can be lifted by a spring attached to a stage without direct effort by simply observing its motion as it bounces up and down. This device is an information ratchet, using information to convert thermal fluctuations into stored work, which may be later used to power other processes. We find, for this device, that the maximal rate of energy extraction is not limited by the observation rate but rather by the physical parameters of the ratchet: the size of the particle, the stiffness of the spring, the temperature of the surrounding medium, and the friction coefficient associated with the particle motion. 
Tuesday, March 16, 2021 9:00AM  9:12AM Live 
E17.00004: Maximizing the performance of information engines Tushar Kanti Saha, Joseph Neil Lucero, Jannik Ehrich, David Sivak, John Bechhoefer Information engines are the modern realization of the Maxwell demon, a thought experiment that revealed the close connection between thermodynamics and information. We introduce a “textbook” model of an information engine and experimentally study it. The engine is based on an optically trapped heavy colloidal bead in water. The water functions as a thermal bath, whose fluctuation forces can, via a feedback algorithm, ratchet the bead “up”, storing the gravitational energy in a work reservoir (battery). We optimize both the rate of energy storage and the directed velocity and find that big beads store more energy, while small beads go faster. However, increasing trap stiffness improves both criteria, showing the fundamental role of the material parameters of the motor. Our observations agree well with a recently developed theory based on mean firstpassage times. By optimizing the feedback algorithm and trap parameters, we have observed energy storage rates of 1000 k_{B}T/s and directed velocities of 190 µm/s, numbers that exceed previous efforts by an order of magnitude. 
Tuesday, March 16, 2021 9:12AM  9:24AM Live 
E17.00005: Estimating entropy production by machine learning of shorttime fluctuating currents Shun Otsubo, Sosuke Ito, Andreas Dechant, Takahiro Sagawa The entropy production rate is an important quantitative measure of nonequilibrium, and there is a great demand for its estimation solely on the basis of trajectory data from experiments. Meanwhile, thermodynamic uncertainty relations (TURs) are inequalities which give lower bounds on the entropy production rate using only the mean and variance of fluctuating currents. Here, we show that a TUR in the shorttime limit can be used to estimate the exact value, not only a lower bound, of the entropy production rate for Langevin dynamics [1]. Specifically, we formulate the shorttime TUR both for Markov jump processes and Langevin dynamics, and show that the equality is always achievable in Langevin dynamics, while this is not the case in Markov jump processes. On the basis of the results, we develop an efficient estimation algorithm by combining the shorttime TUR with machine learning techniques such as the gradient ascent. We numerically demonstrate that our method performs very well even in nonlinear or highdimensional Langevin dynamics. 
Tuesday, March 16, 2021 9:24AM  9:36AM Live 
E17.00006: Information efficiency of bacterial chemotaxis Henry Mattingly, Keita Kamino, Benjamin B Machta, Thierry Emonet Organisms acquire sensory information to guide behavioral decisions. Past studies have used information theory to understand the maximum amount of information biological sensing systems can transmit, showing that in some cases they can approach the theoretical limits. However, how information constrains the ability of organisms to perform behavioral tasks remains unknown. Here we show that the information a bacterium’s sensory system acquires during navigation sets an upper limit on how fast it can climb a chemical gradient. Then, we quantify how much information E. coli cells acquire by measuring swimming statistics, signal transduction responses, and noise fluctuations in single cells. Finally, measuring their gradientclimbing speeds and comparing to the theoretical limit, we determine how efficiently E. coli use information to navigate. 
Tuesday, March 16, 2021 9:36AM  9:48AM Live 
E17.00007: Functional Thermodynamics of Maxwellian Ratchets: Constructing and Deconstructing Patterns, Randomizing and Derandomizing Behaviors Alexandra Jurgens, James P Crutchfield Maxwellian ratchets are autonomous, finitestate thermodynamic engines that implement inputoutput informational transformations. Previous studies of these "demons" focused on how they exploit environmental resources: randomizing ordered inputs, leveraging increased Shannon entropy to transfer energy from a thermal reservoir to a work reservoir. However, to date, correctly determining such functional thermodynamic operating regimes was restricted to engines for which correlations among their informationbearing degrees of freedom could be calculated exactly and in closed form. Additionally, a key second dimension of ratchet behavior was ignoredratchets do not merely change the randomness of environmental inputs, they construct and deconstruct patterns. To address both dimensions, we adapt recent results from dynamicalsystems and ergodic theories that efficiently and accurately calculate the entropy rates and the rate of statistical complexity divergence of general hidden Markov processes. These methods accurately determine thermodynamic operating regime for finitestate Maxwellian demons with arbitrary numbers of states and transitions. The result is a greatly enhanced perspective on the information processing capabilities of information engines. 
Tuesday, March 16, 2021 9:48AM  10:00AM Live 
E17.00008: Use of Carnot’s Engine and Bernoulli’s Pump to identify efficiency of information processing for computing beyond Moore's Law Sadasivan Shankar In this paper, we illustrate a theretical framework that we have developed for extending Landauertype formalism for elementary information processing to architectures and systems. We evaluate the efficiency of an ideal computing architecture at the limits of scaling. Our methodology is based on a bottomup approach using free energybased open system model to build an ideal computing system from a binary switch using statistical physics, thermodynamics, and quantum analysis. We devised simple computing engines such as Bernoulli’s pump and Carnot's engine to information processing operations to illustrate the premise of ideal computing subject to the physical/thermodynamic laws. 
Tuesday, March 16, 2021 10:00AM  10:12AM On Demand 
E17.00009: Nonequilibrium thermodynamics of circadian oscillations: Interplay between energy dissipation, robustness, and coherence Agnish Behera, Suriyanarayanan Vaikuntanathan Collective oscillations are ubiquitous in nature. They help living organisms time their biological functions like the cell cycle, circadian rhythm, etc. These biological oscillators are inherently stochastic and operate far from equilibrium. They dissipate energy and use various mechanisms that ensure their robustness with respect to perturbations. Previous work by various authors [A. Barato and U. Seifert Phys. Rev. E 95, 062409 (2017), C. del Junco and S. Vaikuntanathan, Phys. Rev. E 101, 012410 (2020)] has shown the significance of energy dissipation in generating coherent and stable oscillations. Here we take a minimal model that includes biophysical mechanisms like differential affinity and ultrasensitivity and look at their contribution towards dissipation and generating oscillations that are robust. A biological system like a cell working with a fixed energy budget needs to allocate its energy to different mechanisms in the oscillatory circuit. Our work aims to explore this question. As a specific example, we shall be studying a minimalist model of the KaiABC system. 
Tuesday, March 16, 2021 10:12AM  10:24AM Live 
E17.00010: Statistical inference of scale dependent biological activity using carbon nanotubes Alexandru Bacanu, James F Pelletier, Yoon Jung, Nikta Fakhri Systems built from energy consuming components often exhibit an intimate relationship between structure and function. In biological systems, irreversible, yet stochastic, molecular interactions form dissipative structures, such as cytoskeletal networks, which mediate scaledependent processes. However, due to a lack of methods able to quantify time reversal asymmetry, their dynamics remain poorly characterized. By measuring time reversal asymmetry encoded in the conformational dynamics of filamentous single walled carbon nanotubes (SWNTs) embedded in the actomyosin network of Xenopus egg extract, we characterize the scale dependence of mechanical activity. Our method is sensitive to distinct perturbations at the molecular level and can thus probe the interplay between microscopic structure and emergence of larger scale nonequilibrium activity. We characterize the dynamics of a semiflexible polymer embedded in a viscoelastic medium to contextualize our results in terms of key physical parameters. Our analysis provides a general tool to characterize steady state nonequilibrium activity in high dimensional spaces. 
Tuesday, March 16, 2021 10:24AM  10:36AM On Demand 
E17.00011: A Phase Transition Between Random (Fragile) and Correlated (Robust) Phases of InputOutput Maps Vaibhav Mohanty, Ard Louis Systems which accept a sequencebased input and produce a nontrivial output appear widely across scientific disciplines. Examples include protein/RNA primary sequences mapping to their folded structures, gene regulatory network interactions mapping to expression cycles, or the set of interactions in a spin glass mapping to the ground state(s), among others. In uncorrelated systems, the robustness to perturbations of the inputs scales as the frequency of obtaining the output. Since there are typically many outputs, this implies that inputoutput maps are fragile. It has been observed, however, that many inputoutput maps exhibit enhanced robustness, which scales as the log of the output frequencies. We present a generalized statistical physics model of discrete inputoutput maps arising from entropy maximization with a single constraint on the global robustness. By mapping to a Potts model on a Hamming graph with fixed state frequencies, we analytically derive the naturally observed scaling laws for robustness and numerically reproduce observed topological properties of subnetworks which map to a common output. We suggest that there is a universal transition between uncorrelated “fragile” and correlated “robust” phases for inputoutput maps. 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2024 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700