Direct and indirect selection in a proofreading polymerase.

The traits that affect evolvability are subject to indirect selection, as these traits affect the course of evolution over many generations rather than the direct replicative fitness of an individual. However, the evolution of evolvability-determining traits is often difficult to study because putative evolvability alleles often have confounding direct fitness effects of unknown origin and size. Here, we study theoretically and experimentally the evolution of mutation rates in proofreading polymerases with orthogonal control of direct and indirect selection.

Limits on the computational expressivity of non-equilibrium biophysical processes.

Many biological decision-making processes can be viewed as performing a classification task over a set of inputs, using various chemical and physical processes as "biological hardware." In this context, it is important to understand the inherent limitations on the computational expressivity of classification functions instantiated in biophysical media. Here, we model biochemical networks as Markov jump processes and train them to perform classification tasks, allowing us to investigate their computational expressivity.

Training physical matter to matter.

Biological systems offer a great many examples of how sophisticated, highly adapted behavior can emerge from training. Here we discuss how training might be used to impart similarly adaptive properties in physical matter. As a special form of materials processing, training differs in important ways from standard approaches of obtaining sought after material properties.

A minimal scenario for the origin of non-equilibrium order.

Extant life contains numerous non-equilibrium mechanisms to create order not achievable at equilibrium; it is generally assumed that these mechanisms evolved because the resulting order was sufficiently beneficial to overcome associated costs of time and energy. Here, we identify a broad range of conditions under which non-equilibrium order-creating mechanisms will evolve as an inevitable consequence of self-replication, even if the order is not directly functional. We show that models of polymerases, when expanded to include known stalling effects, can evolve kinetic proofreading through selection for fast replication alone, consistent with data from recent mutational screens.

Dynamic coexistence driven by physiological transitions in microbial communities.

Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes often modify their environments so strongly that they are forced out of the exponential state into stressed or non-growing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory.

Dynamic coexistence driven by physiological transitions in microbial communities.

Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes often modify their environments so strongly that they are forced out of the exponential state into stressed or non-growing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory.

Contrastive learning through non-equilibrium memory.

Learning algorithms based on backpropagation have enabled transformative technological advances but alternatives based on local energy-based rules offer benefits in terms of biological plausibility and decentralized training. A broad class of such local learning rules involve \textit{contrasting} a clamped configuration with the free, spontaneous behavior of the system. However, comparisons of clamped and free configurations require explicit memory or switching between Hebbian and anti-Hebbian modes.

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.

Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation.

Closed ecosystems extract energy through self-organized nutrient cycles.

Our planet is a self-sustaining ecosystem powered by light energy from the sun, but roughly closed to matter. Many ecosystems on Earth are also approximately closed to matter and recycle nutrients by self-organizing stable nutrient cycles, e.g.

Learning to learn by using nonequilibrium training protocols for adaptable materials.

Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters.

Epithelial tissue confinement inhibits cell growth and leads to volume-reducing divisions.

Cell proliferation is a central process in tissue development, homeostasis, and disease, yet how proliferation is regulated in the tissue context remains poorly understood. Here, we introduce a quantitative framework to elucidate how tissue growth dynamics regulate cell proliferation. Using MDCK epithelial monolayers, we show that a limiting rate of tissue expansion creates confinement that suppresses cell growth; however, this confinement does not directly affect the cell cycle.

Learning to self-fold at a bifurcation.

Disordered mechanical systems can deform along a network of pathways that branch and recombine at special configurations called bifurcation points. Multiple pathways are accessible from these bifurcation points; consequently, computer-aided design algorithms have been sought to achieve a specific structure of pathways at bifurcations by rationally designing the geometry and material properties of these systems. Here, we explore an alternative physical training framework in which the topology of folding pathways in a disordered sheet is changed in a desired manner due to changes in crease stiffnesses induced by prior folding.

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks.

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network's various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions.

Ligand-receptor promiscuity enables cellular addressing.

In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture.

NF-κB responds to absolute differences in cytokine concentrations.

Cells receive a wide range of dynamic signaling inputs during immune regulation, but how gene regulatory networks measure such dynamic inputs is not well understood. Here, we used microfluidic single-cell analysis and mathematical modeling to study how the NF-κB pathway responds to immune inputs that vary over time such as increasing, decreasing, or fluctuating cytokine signals. We found that NF-κB activity responded to the absolute difference in cytokine concentration and not to the concentration itself.

Roadmap on biology in time varying environments.

Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response.

Proofreading through spatial gradients.

Key enzymatic processes use the nonequilibrium error correction mechanism called kinetic proofreading to enhance their specificity. The applicability of traditional proofreading schemes, however, is limited because they typically require dedicated structural features in the enzyme, such as a nucleotide hydrolysis site or multiple intermediate conformations. Here, we explore an alternative conceptual mechanism that achieves error correction by having substrate binding and subsequent product formation occur at distinct physical locations.

Supervised learning through physical changes in a mechanical system.

Mechanical metamaterials are usually designed to show desired responses to prescribed forces. In some applications, the desired force-response relationship is hard to specify exactly, but examples of forces and desired responses are easily available. Here, we propose a framework for supervised learning in thin, creased sheets that learn the desired force-response behavior by physically experiencing training examples and then, crucially, respond correctly (generalize) to previously unseen test forces.

Tuning environmental timescales to evolve and maintain generalists.

Natural environments can present diverse challenges, but some genotypes remain fit across many environments. Such "generalists" can be hard to evolve, outcompeted by specialists fitter in any particular environment. Here, inspired by the search for broadly neutralizing antibodies during B cell affinity maturation, we demonstrate that environmental changes on an intermediate timescale can reliably evolve generalists, even when faster or slower environmental changes are unable to do so.

Physical Constraints on Epistasis.

Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis.

Nonequilibrium Statistical Mechanics of Continuous Attractors.

Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which the emergent bump of neural activity in such networks can be manipulated by space and time-dependent external sensory or motor signals are not understood. Here, we find fundamental limits on how rapidly internal representations encoded along continuous attractors can be updated by an external signal.

Kalman-like Self-Tuned Sensitivity in Biophysical Sensing.

Living organisms need to be sensitive to a changing environment while also ignoring uninformative environmental fluctuations. Here, we argue that living cells can navigate these conflicting demands by dynamically tuning their environmental sensitivity. We analyze the circadian clock in Synechococcus elongatus, showing that clock-metabolism coupling can detect mismatch between clock predictions and the day-night light cycle, temporarily raise the clock's sensitivity to light changes, and thus re-entraining faster.

Temporal Pattern Recognition through Analog Molecular Computation.

Living cells communicate information about physiological conditions by producing signaling molecules in a specific timed manner. Different conditions can result in the same total amount of a signaling molecule, differing only in the pattern of the molecular concentration over time. Such temporally coded information can be completely invisible to even state-of-the-art molecular sensors with high chemical specificity that respond only to the total amount of the signaling molecule.

Shaping the topology of folding pathways in mechanical systems.

Disordered mechanical systems, when strongly deformed, have complex configuration spaces with multiple stable states and pathways connecting them. The topology of such pathways determines which states are smoothly accessible from any part of configuration space. Controlling this topology would allow us to limit access to undesired states and select desired behaviors in metamaterials.