David Alexander Sivak

Geometric ideas play a role in areas such as stochastic thermodynamics, spectral graph theory, and optimal transport. In this Letter, the authors link a thermodynamic friction metric that governs dissipation in slowly driven systems with two graph-theoretic geometries, the commute-time and resistance distances.#TechnicalAdvancement #AdvancingField

Biological communities are connected through dispersal, which regulates diversity across local and regional scales. However, dispersal is difficult to measure directly, limiting what is known about dispersal’s impact on species composition in complex communities. One method to measure dispersal employs the Neutral Community Model (NCM) to quantify how a local community is influenced by the immigration of individuals from a larger source community. Conveniently, the immigration rate N _T m of the NCM can be fit from biological sequence abundance datasets, which are plentiful. Yet it is neither known if these estimated values reflect the ground truth, nor what sampling effort is required to yield accurate estimates. In this study, we introduce two inference methods, a variance-based and a Dirichlet-multinomial log-likelihood (DM-LL) method, to complement the established occupancy-based inference method. In simulations of communities that resemble activated sludge microbiomes, all inference methods were capable of estimating N _T m within 10% of ground-truth, with the variance-based and DM-LL methods requiring less sampling effort. Accurate inferences require read depths greater than N _T m in each sample. The three methods agree in their inferred N _T m in simulations of communities experiencing weak non-neutral effects (e.g., selection) and in applications to abundance datasets from wastewater activated sludge, tropical trees, and coral reefs. Based on these findings, we propose practical sampling and methodological guidelines for quantifying immigration between highly diverse, complex communities using the NCM.

The framework of bipartite stochastic thermodynamics is a powerful tool to analyze a composite system's internal thermodynamics. It has been used to study the components of different molecular machines such as ATP synthase. However, this approach has not yet been used to describe ion-transporting proteins despite their high-level functional similarity. Here, we study the bipartite thermodynamics of the sodium-potassium pump in the nonequilibrium steady state. Using a physically intuitive partition between the ATP-consuming subsystem and the ion-transporting subsystem, we find considerable information flow comparable to other molecular machines, and Maxwell-demon behavior in the ATP-consuming subsystem. We vary ion concentrations and transmembrane voltage in a range including the neuronal action potential, and find that the information flow inverts during depolarization.

We study the optimal performance of an information engine consisting of an overdamped Brownian bead confined in a controllable, d-dimensional harmonic trap and additionally subjected to gravity. The trap's center is updated dynamically via a feedback protocol designed such that no external work is done by the trap on the bead while maximizing the extraction of gravitational potential energy and achieving directed motion. We show that performance improves when thermal fluctuations in directions perpendicular to gravity are harnessed. This improvement arises from feedback cooling of these transverse degrees of freedom, along which all heat is extracted. Strikingly, engines based on a single transverse degree of freedom already outperform engines based solely on vertical (z) measurements. This engine design modularizes the functions of harnessing fluctuations and storing free energy, drawing a close analogy to the Szilard engine.

F 1 -ATPase ( F 1 ) is central to cellular energy transduction. Forcibly rotated by another motor F o , F 1 catalyzes adenosine triphosphate (ATP) synthesis by converting mechanical work into chemical free energy stored in the molecule ATP. The details of how F o drives F 1 are not fully understood; however, evaluating efficient ways to rotate F 1 could provide fruitful insights into this driving since there is a selective pressure to improve efficiency. Here, we show that rotating F 1 with an angle clamp is significantly more efficient than a constant torque. Our experiments, combined with theory and simulation, indicate that the angle clamp significantly suppresses the nonequilibrium variation that contributes to the futile dissipation of input work.

Biological molecular machines convert free energy between different forms in cells, often at high efficiency. Optimal control theory provides a framework to elucidate design principles governing energetically efficient driving. Here, we use linear-response theory to design efficient protocols exercising dynamic control of trap center and stiffness in a model of driven F_{1}-ATPase. We find that the key design principles of an efficient protocol can be satisfied either by dynamic control of both parameters or by dynamic control of a single parameter and a good static choice for the second. These results illustrate that accessing a new degree of dynamic control provides varying performance improvements in different systems.

F$_1$-ATPase (F$_1$) is central to cellular energy transduction. Forcibly rotated by another motor F$_\mathrm{o}$, F$_1$ catalyzes ATP synthesis by converting mechanical work into chemical free energy stored in the molecule ATP. The details of how F$_\mathrm{o}$ drives F$_1$ are not fully understood; however, evaluating efficient ways to rotate F$_1$ could provide fruitful insights into this driving since there is a selective pressure to improve efficiency. Here, we show that rotating F$_1$ with an angle clamp is significantly more efficient than a constant torque. Our experiments, combined with theory and simulation, indicate that the angle clamp significantly suppresses the nonequilibrium variation that contributes to the futile dissipation of input work.

Molecular machines transduce free energy between different forms throughout all living organisms. Unlike their macroscopic counterparts, molecular machines are characterized by stochastic fluctuations, overdamped dynamics, and soft components, and operate far from thermodynamic equilibrium. In addition, information is a relevant free energy resource for molecular machines, leading to new modes of operation for nanoscale engines. Toward the objective of engineering synthetic nanomachines, an important goal is to understand how molecular machines transduce free energy to perform their functions in biological systems. In this review, we discuss the nonequilibrium thermodynamics of free energy transduction within molecular machines, with a focus on quantifying energy and information flows between their components. We review results from theory, modeling, and inference from experiments that shed light on the internal thermodynamics of molecular machines, and ultimately explore what we can learn from considering these interactions.

Biological communities are connected through dispersal, which regulates diversity across local and regional scales. However, dispersal is difficult to measure directly, limiting what is known about dispersal’s impact on species composition in complex communities. One method to measure dispersal employs the Neutral Community Model (NCM) to quantify how a local community is influenced by the immigration of individuals from a larger source community. Conveniently, the immigration rate N _T m of the NCM can be fit from biological sequence abundance datasets, which are plentiful. Yet it is neither known if these estimated values reflect the ground truth, nor what sampling effort is required to yield accurate estimates. In this study we introduce two inference methods, a variance-based and a Dirichlet-Multinomial Log-Likelihood (DM-LL) method, to complement the established occupancy-based inference method. In simulations of communities that resemble activated sludge microbiomes, all inference methods were capable of estimating N _T m within 10% of ground-truth, with the variance-based and DM-LL methods requiring less sampling effort. Accurate inferences require read depths greater than N _T m in each sample. The three methods agree in their inferred N _T m in simulations of communities experiencing weak non-neutral effects (e.g., selection), and in applications to an empirical dataset from wastewater activated sludge. Based on these findings, we propose practical sampling and methodological guidelines for quantifying immigration between highly diverse, complex communities using the NCM.

Soft and active condensed matter represent a class of fascinating materials that we encounter in our everyday lives -- and constitute life itself. Control signals interact with the dynamics of these systems, and this influence is formalized in control theory and optimal control. Recent advances have employed various control-theoretical methods to design desired dynamics, properties, and functionality. Here we provide an introduction to optimal control aimed at physicists working with soft and active matter. We describe two main categories of control, feedforward control and feedback control, and their corresponding optimal control methods. We emphasize their parallels to Lagrangian and Hamiltonian mechanics, and provide a worked example problem. Finally, we review recent studies of control in soft, active, and related systems. Applying control theory to soft, active, and living systems will lead to an improved understanding of the signal processing, information flows, and actuation that underlie the physics of life.