Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Q04: Non-equilibrium Thermodynamics: From Chemical Reaction Networks to Natural Selection IIFocus Recordings Available
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Sponsoring Units: DBIO Chair: John Baez, Topos Institute Room: McCormick Place W-176C |
Wednesday, March 16, 2022 3:00PM - 3:12PM |
Q04.00001: Free energy transduction in chemical reaction networks from enzymes to metabolism Massimiliano Esposito I will rigorously define energy transduction in open chemical reaction networks (CRNs). The method is based on the stoichiometric matrix and the chemostatted species to identify the fundamental set of thermodynamic forces and fluxes contributing to the CRN dissipation at steady state. Transduction arises when some fluxes flow against their force thus creating negative contributions to the dissipation. This is possible because other fluxes power transduction by being aligned with their force and ensuring the overall positivity of the dissipation. Transduction is an emergent phenomenon arising at the network level because fluxes of elementary reactions are always aligned with their force. I will apply our method to study the efficiency of metabolic pathways in central metabolism. Our method generalizes to arbitrary (nonlinear) CRNs the work by Terrell L. Hill on free energy transduction in pseudo first order (linear) CRNs. |
Wednesday, March 16, 2022 3:12PM - 3:48PM |
Q04.00002: Performance bounds, trade-offs, and scaling for collective motor-driven transport Invited Speaker: David A Sivak Motor-driven intracellular transport of organelles, vesicles, and other cargo is accomplished by teams of transport motors, with numbers ranging from only a few to several hundred. Using stochastic thermodynamics, we derive a series of bounds that constrain the performance (including velocity, precision, and efficiency) of a broad class of these collective transport systems for any number of motors. We explore a simple stochastic collective-transport model that exactly saturates the derived Pareto frontiers. This model is analytically tractable, giving simple functional forms for the performance metrics as a function of motor number. The resulting trade-offs point to design principles governing functional collections of transport motors in different contexts. |
Wednesday, March 16, 2022 3:48PM - 4:00PM |
Q04.00003: Learning Regulation from the Ground Up: Combining Natural Selection, Thermodynamics and Data Mark Alber, William R Cannon, Sam Britton, Jolene Britton Modeling cells has many challenges: data is sparse, noisy, and measured over a population instead of over individuals or cell compartments. Moreover, parameters needed to build kinetic and thermodynamic models are extremely labor intensive to obtain. This makes model building a physics-based model a very hard problem. We address this challenge by taking advantage of the fact that natural selection selects for the most optimal individuals out of all solutions. We formulate fitness from a thermodynamic perspective to obtain the most likely model parameters, and then use data to constrain the solution space. Rate parameters that are statistically the most likely can be inferred in this way. Then we predict regulation of the cellular system using one of two approaches: Assuming that we have an optimal control problem and using control theory to infer regulation, or widely sample the solution space for regulation using reinforcement learning. The result is a model with reasonable parameters and predicts regulation for central metabolism that agrees with the literature. |
Wednesday, March 16, 2022 4:00PM - 4:12PM |
Q04.00004: Scaling relations of energy dissipation rate in nonequilibrium reaction systems Qiwei Yu, Dongliang Zhang, Yuhai Tu Many biochemical systems operate in nonequilibrium steady states that carry out certain biological functions. These systems constantly dissipate energy, and the dissipation rate could be determined by the underlying reaction network. For complex systems with numerous microscopic states, however, the system could only be measured at a coarse-grained level, and such a coarse-grained description leads to underestimation of the dissipation rate. To quantify how energy dissipation is associated across scales, we develop a coarse-graining process in the state space and a corresponding renormalization procedure for reaction rates, an approach conceptually inspired by the real space renormalization group. We find that the energy dissipation rate has an inverse power-law dependence on the number of microscopic states in a coarse-grained state, with an exponent that depends on both the network structure and the probability flux correlation. We demonstrate the existence of the scaling relation in realistic biochemical models such as biochemical oscillators and microtubule-kinesin active flow systems and discuss its relation with the Kolmogorov cascade in Turbulence. Finally, we report on the exact solution of the scaling exponent in square lattice and regular lattices of higher dimensions. |
Wednesday, March 16, 2022 4:12PM - 4:24PM |
Q04.00005: Algebra for large macromolecular complexes Rebecca J Rousseau, Justin B Kinney Large macromolecular complexes play central roles in biophysics and biochemistry. Their combinatorial complexity, however, has hindered their theoretical study using the standard methods of statistical physics. To overcome this barrier, we introduce an algebraic formalism for describing classical multi-particle complexes. Using a Fock space comprised of hard-core bosons, this framework allows pre-existing particles to be joined together into large complexes based on algebraically defined assembly rules. Physically interesting quantities, such as Gibbs free energy, can then be computed based on the contributions from individual component particles, pairwise interactions between these particles, and so on. We also introduce diagrammatic techniques that make this algebra visually intuitive and facilitate analytical calculations through a (somewhat surprising) realization of Wick's theorem. Finally, we show how this algebra unifies seemingly distinct notions of coarse graining, a fact we illustrate in the context of a biophysical model of transcriptional regulation. We expect that our Fock space formalism will be useful for mathematical and computational studies of a wide range of combinatorially complex systems in biophysics and biochemistry. |
Wednesday, March 16, 2022 4:24PM - 4:36PM |
Q04.00006: Decomposing the local arrow of time in interacting systems Christopher W Lynn, Caroline Holmes, William S Bialek, David J Schwab Living systems are fundamentally irreversible, breaking detailed balance and establishing an arrow of time. But how does the evident arrow of time for a whole system arise from the interactions among its multiple elements? We show that the local evidence for the arrow of time, which is the entropy production for thermodynamic systems, can be decomposed. First, it can be split into two components: an independent term reflecting the dynamics of individual elements and an interaction term driven by the dependencies among elements. Adapting tools from non-equilibrium physics, we further decompose the interaction term into contributions from pairs of elements, triplets, and higher-order terms. We illustrate our methods on models of cellular sensing and logical computations, as well as on patterns of neural activity in the retina as it responds to visual inputs. We find that neural activity can define the arrow of time even when the visual inputs do not, and that the dominant contribution to this breaking of detailed balance comes from interactions among pairs of neurons. |
Wednesday, March 16, 2022 4:36PM - 4:48PM |
Q04.00007: Information Transmission by Heterogeneous Cell Populations Andrew D Goetz, Hoda Akl, Purushottam Dixit Reliable signal transduction through biological networks is crucial for accurate interpretation of environmental cues and downstream cellular decision making. Information theory provides a natural framework to quantify how much does the cell know about its extracellular environment; the so-called channel capacity represents the upper limit of the information transmitted through any signaling network channel. Surprisingly, previous work on several mammalian cell signaling networks found very low channel capacities. However, mammalian cells are known to respond precisely to several levels of an environmental signal, which indicates the level of information gained from the environment is higher than previous results would indicate. To address this discrepancy, we develop a new theoretical framework that separately accounts for intrinsic stochastic noise in signaling networks and extrinsic cell-to-cell variability when quantifying channel capacity. We estimate the channel capacity of two important mammalian pathways, the epidermal growth factor pathway, and the insulin-like growth factor pathway. We find that by treating each cell as a separate channel and by explicitly accounting for extrinsic cell-to-cell differences, our method leads to conceptually clearer and significantly higher estimates of channel capacities. We discuss the consequences for downstream cellular decision making. |
Wednesday, March 16, 2022 4:48PM - 5:00PM |
Q04.00008: Functional universality in slow-growing microbial communities arises from thermodynamic constraints Ashish B George, Tong Wang, Sergei Maslov The dynamics of microbial communities is incredibly complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network. The central ingredient of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accumulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network—independent of species composition and biochemical details—providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial environments. Furthermore, we discovered that the structure of the community metabolic network is governed by the thermodynamic principle of maximum heat dissipation. Overall, the work demonstrates how universal thermodynamic constraints may constrain community metabolism and explain observed functional convergence in microbial communities. |
Wednesday, March 16, 2022 5:00PM - 5:12PM |
Q04.00009: Life without oxygen: the energy saving mechanisms of anaerobic gut bacteria. Vadim Patsalo, Brian R Taylor, Hiroyuki Okano, Zhongge Zhang, James Williamson, Terence T Hwa When faced with limited availability of a certain resource, an effective strategy is to use that resource more efficiently. In the human gut, thousands of species of bacteria compete for limited carbon sources. However, without oxygen, metabolism can be 10 times less efficient at extracting energy from these carbon sources. We present a study on the energy saving mechanisms of Bacteroides thetaiotaomicron, a representative gut organism which is surprisingly energy efficient for an anaerobe. Through proteomic and metabolomic analysis, we find the molecular mechanisms that underlie this organism's ability to grow efficiently in such a competitive environment. This strategy revolves around the efficient use of pyrophosphate, an ATP analogue that aerobes typically disposed of as waste. Ultimately, use of this molecule comes with the cost of reduced protein synthesis rate, a cost which cells must consider. Given the widespread presence of pyrophosphate-utilizing enzymes in anaerobic genomes, this tradeoff in energy saving is common throughout the anaerobic world. |
Wednesday, March 16, 2022 5:12PM - 5:24PM |
Q04.00010: 3D diffusion in live Escherichia coli cells Diana S Mendez, Benjamin P Bratton, Joseph P Sheehan, Liam J Holt, Zemer Gitai, Joshua W Shaevitz The bacterial cytoplasm is a crowded and polydisperse environment which leads to interesting anomalous diffusion of intracellular macromolecules. Using a combination of biplane microscopy and single particle tracking, we reconstruct the 3D motion of Genetically Encoded Multimeric nanoparticles (GEMs) inside Escherichia coli cells to study the rheology of the bacterial cytoplasm. We use GEMs ranging in size from 20 to 50 nm, similar in scale to ribosomes and other complexes in the cell. The motion of larger particles is confined to cylindrical shells around the nucleoid, with several diffusive regimes. We modify the charge of GEMs using fluorescent proteins with charges from -18 to +36e and find that positive charged particles move less than their negative counterparts. Drug treatment and metabolic perturbation allows us to study the dynamic restructuring of the nucleoid and its effect on the 3D diffusion of particles inside the bacterial cell. |
Wednesday, March 16, 2022 5:24PM - 5:36PM |
Q04.00011: In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density Franco Tavella, Qiong Yang, Minjun Jin, Shiyuan Wang Cells control the properties of the cytoplasm to ensure the proper functioning of biochemical processes. Recent studies showed that the density of the cytoplasm varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here we study how a cell cycle oscillator comprising cyclin-dependent kinase (CDK1) responds to cytoplasmic density changes by systematically diluting or concentrating a cycling Xenopus egg cytoplasm in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density by as low as 0.2x to more than 1.22x. A further dilution or concentration from these values will arrest the system in a low or high steady-state of CDK1 activity, respectively. Interestingly, diluting a concentrated arrested cytoplasm recovers its oscillatory behavior but requires a significantly lower concentration than 1.22x. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of density tuning, forming a hysteresis loop. We recapitulated these observations by a mathematical model. The model predicted that Wee1 and Cdc25 positive feedback do not contribute to the observed robustness, confirmed by experiments. Nevertheless, modulating these feedback strengths and cytoplasmic density changes the total number of cycles, revealing a new role of Wee1 and Cdc25 in controlling the cycle number of early embryonic extracts. Our system can be applied to study how cytoplasmic density affects other cellular processes. |
Wednesday, March 16, 2022 5:36PM - 5:48PM |
Q04.00012: Ornstein-Uhlenbeck models of phenotypic selection and cellular evolution Gabor Balazsi, Amr Ibrahim, Yiming Wan The century-old Ornstein-Uhlenbeck (OU) process describes stochastic diffusion processes with a linear restoring force. The OU process can model a Brownian particle on a spring, or electrical current fluctuations in an RL circuit. Here we generalize the OU process to model stochastic protein level fluctuations in single cells that can divide or die. If each cell's probabilities of division and death per unit time depend on the cell's current protein level, we obtain models of phenotypic selection and evolution. We investigate how such models can predict or interpret the results of cellular evolution experiments and phenotypic selection experiments. |
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