We construct neuron models from data by transferring information from an observed time series to the state variables and parameters of Hodgkin-Huxley models. When the learning period completes, the model will predict additional observations and its parameters uniquely characterize the complement of ion channels. However, the assimilation of biological data, as opposed to model data, is complicated by the lack of knowledge of the true neuron equations. Reliance on guessed conductance models is plagued with multivalued parameter solutions. Here, we report on the distributions of parameters and currents predicted with intentionally erroneous models, overspecified models, and an approximate model fitting hippocampal neuron data. We introduce a recursive piecewise data assimilation algorithm that converges with near-perfect reliability when the model is known. When the model is unknown, we show model error introduces correlations between certain parameters. The ionic current waveforms reconstructed from these parameters are excellent predictors of true currents and carry a higher degree of confidence, greater than $95.5\%$, than underlying parameters, which is $53\%$. Unexpressed ionic currents are correctly filtered out even in the presence of mild model error. When the model is unknown, the covariance eigenvalues of parameter estimates are found to be a good gauge of model error. Our results suggest that biological information may be retrieved from data by focusing on current estimates rather than parameters.

Computational biology holds immense promise as a domain that can leverage quantum advantages due to its involvement in a wide range of challenging computational tasks. Researchers have recently explored the applications of quantum computing in genome assembly implementation. However, the issue of repetitive sequences remains unresolved. In this paper, we propose a hybrid assembly quantum algorithm using high-accuracy short reads and error-prone long reads to deal with sequencing errors and repetitive sequences. The proposed algorithm builds upon the variational quantum eigensolver and utilizes divide-and-conquer strategies to approximate the ground state of larger Hamiltonian while conserving quantum resources. Using simulations of ten-qubit quantum computers, we address problems as large as 140 qubits, yielding optimal assembly results. The convergence speed is significantly improved via the problem-inspired Ansatz based on the known information about the assembly problem. In addition, we qualitatively verify that entanglement within quantum circuits may accelerate the assembly path optimization.

The exponential growth of protein sequences in the post-genomic era has revolutionized the application of generative sequence models for pivotal tasks such as contact prediction, protein design, alignment, and homology search. Despite remarkable progress in these areas, the interpretability of the modeled pairwise parameters remains limited due to complexities arising from coevolution, phylogeny, and entropy. While post-correction methods for contact prediction have been developed to eliminate entropy-related contributions from predicted contact maps, there is currently no direct approach to correct entropy in other applications reliant on raw parameters. In this paper, we investigate the sources of entropy signal and propose a novel spectral regularizer, LH (an abbreviation of Henri Lebesgue), to mitigate its impact during model fitting. By incorporating this regularizer into the GREMLIN framework (utilizing a Markov random field or Potts model), we enable the accurate inference of sparse contact maps while simultaneously improving interpretability and addressing overfitting concerns critical for sequence evaluation and design. To validate the efficacy of our approach, we design multiple protein sequences based on GREMLIN with both L2 and LH regularizers, and subsequently experimentally measure their using cDNA display proteolysis. Our findings demonstrate that proteins designed using the LH regularizer exhibit increased diversity and enhanced folding stability.

Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex data sets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DiSTNet2D, a new deep neural network architecture for two-dimensional (2D) cell segmentation and tracking that leverages both mid- and long-term temporal information. DiSTNet2D considers seven frames at the input and uses a postprocessing procedure that exploits information from the entire video to correct segmentation errors. DiSTNet2D outperforms two recent methods on two experimental data sets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DiSTNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.

The bacterial colonization of surfaces is a ubiquitous process that shapes nature and profoundly affects human health. While much is known about the biology of this process, the pivotal interplay between physical environment and active bacterial micromechanics remains poorly understood. In fact, strong adhesion and high motility, both of which are essential for surface colonization, are two apparently contradictory goals, as they mutually obstruct each other. Here, we investigate how the human pathogen Pseudomonas aeruginosa optimizes its behavior for colonization of surfaces under flow. From the analysis of the dynamics of fluorescently labeled type-IV pili, we construct a mathematical model that quantitatively connects individual motor dynamics with whole-cell motility and migration. The data analysis also reveals that cells upregulate the number of visible pili on surface contact, although individual pili do not display a measurable sensory response to surfaces. When applying shear flow, we unexpectedly find that robust sticking to a surface requires passive surface adhesion rather than pilus activity. Instead, pilus activity actually promotes cell detachment while enabling migration. Using genetic perturbations of the pilus apparatus, it is shown that wild-type cells achieve a trade-off between adhesion and migration by limiting the number of pili. Simulations reveal a generic underlying trait space, where, depending on the interplay of active and passive forces, adhesion and migration are either compatible or a trade-off is required for efficient bacterial surface colonization. The discovered adhesion-migration problem is paradigmatic of a broad class of piliated bacteria and may also have implications for other cells.

Microbial communities often exhibit complex spatial structures that are important for their functioning, ecology, and evolution. While the role of biochemical interactions is extensively studied, how physical interactions contribute to structuring typically multispecies and phenotypically diverse bacterial communities is unclear. Here, we examined how motility, a major bacterial trait, affects the organization of model binary mixtures of motile and nonmotile Escherichia coli bacteria, revealing a novel type of active self-organization. Motile bacteria induce large-scale, fluctuating density patterns in nonmotile bacteria across a wide range of physiologically relevant cell densities. Combining experiments and quantitative modeling, we demonstrate that this pattern formation solely results from physical interactions and relies on two key ingredients: By swimming in circles at surfaces, the motile bacteria generate recirculating hydrodynamic flows that advect nonmotile cells, and the breaking of vertical symmetry by gravity permits local density accumulation. The density patterns fluctuate as the advection landscape slowly rearrange with motile cells configurations on the surfaces. This new nonequilibrium mechanism for pattern formation in bacteria belongs to a different class compared to previous models for self-organization in self-propelled systems, which rely on localized traffic jamming in two dimensions. It is instead similar to fluctuation-dominated phase ordering in active nematics. The behavior also drives the formation of large scale and highly structured aggregates of nonmotile cells that express adhesins in the mixture, and it showcases how motile species activity can shape the spatial organization of complex microbial communities.

Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales, which hampers quantitative reasoning and the identification of general principles. Here, we combine data analysis and theory to investigate the relationship between behavioral plasticity and heavy-tailed statistics often observed in animal behavior. Specifically, we first leverage high-resolution recordings of Caenorhabditis elegans locomotion to show that stochastic transitions among long-lived behaviors exhibit heavy-tailed first-passage-time distributions and correlation functions. Such heavy tails can be explained by slow adaptation of behavior over time. This particular result motivates our second step of introducing a general model where we separate fast dynamics on a quasistationary multiwell potential, from nonergodic, slowly varying modes. We then show that heavy tails generically emerge in such a model, and we provide a theoretical derivation of the resulting functional form, which can become a power law with exponents that depend on the strength of the fluctuations. Finally, we provide direct support for the generality of our findings by testing them in a C. elegans mutant where adaptation is suppressed and heavy tails thus disappear, and recordings of larval zebrafish swimming behavior where heavy tails are again prevalent.

In a developing embryo, information about the position of cells is encoded in the concentrations of morphogen molecules. In the fruit fly, the local concentrations of just a handful of proteins encoded by the gap genes are sufficient to specify position with a precision comparable to the spacing between cells along the anterior-posterior axis. This matches the precision of downstream events such as the striped patterns of expression in the pair-rule genes, but is not quite sufficient to define unique identities for individual cells. We demonstrate theoretically that this information gap can be bridged if positional errors are spatially correlated, with correlation lengths approximately $20\%$ of the embryo length. We then show experimentally that these correlations are present, with the required strength, in the fluctuating positions of the pair-rule stripes, and this can be traced back to the gap genes. Taking account of these correlations, the available information matches the information needed for unique cellular specification, within error bars of approximately $2\%$. These observation support a precisionist view of information flow through the underlying genetic networks, in which accurate signals are available from the start and preserved as they are transformed into the final spatial patterns.

RNA in extant biological systems is homochiral; it consists exclusively of d-ribonucleotides rather than l-ribonucleotides. How the homochirality of RNA emerged is not known. Here we use stochastic simulations to quantitatively explore the conditions for RNA homochirality to emerge in the prebiotic scenario of an RNA reactor, in which RNA strands react in a nonequilibrium environment. These reactions include the hybridization, dehybridization, template-directed ligation, and cleavage of RNA strands. The RNA reactor is either closed, with a finite pool of ribonucleotide monomers of both chiralities (d and l), or the reactor is open, with a constant inflow of a racemic mixture of monomers. For the closed reactor, we also consider the interconversion between d and l monomers via a racemization reaction. We first show that template-free polymerization is unable to reach a high degree of homochirality, due to the lack of autocatalytic amplification. In contrast, in the presence of template-directed ligation, with base pairing and stacking between bases of the same chirality thermodynamically favored, a high degree of homochirality can arise and be maintained provided the nonequilibrium environment overcomes product inhibition, for instance, via temperature cycling. Indeed, if the experimentally observed kinetic stalling of ligation after chiral mismatches is also incorporated, the RNA reactor can evolve towards a fully homochiral state, in which one chirality is entirely lost. This is possible because the kinetic stalling after chiral mismatches effectively implements a chiral cross-inhibition process. Taken together, our model supports a scenario where the emergence of homochirality is assisted by template-directed ligation and polymerization in a nonequilibrium RNA reactor.

How diversity is maintained in natural ecosystems is a long-standing question in Theoretical Ecology. By studying a system that combines ecological dynamics, heterogeneous interactions, and spatial structure, we uncover a new mechanism for the survival of diversity-rich ecosystems in the presence of demographic fluctuations. For a single species, one finds a continuous phase transition between an extinction and a survival state, that falls into the universality class of Directed Percolation. Here we show that the case of many species with heterogeneous interactions is different and richer. By merging theory and simulations, we demonstrate that with sufficiently strong demographic noise, the system exhibits behavior akin to the single-species case, undergoing a continuous transition. Conversely, at low demographic noise, we observe unique features indicative of the ecosystem's complexity. The combined effects of the heterogeneity in the interaction network and migration enable the community to thrive, even in situations where demographic noise would lead to the extinction of isolated species. The emergence of mutualism induces the development of global bistability, accompanied by sudden tipping points. We present a way to predict the catastrophic shift from high diversity to extinction by probing responses to perturbations as an early warning signal.

Recent advancements in measurement techniques have resulted in an increasing amount of data on neural activities recorded in parallel, revealing largely heterogeneous correlation patterns across neurons. Yet, the mechanistic origin of this heterogeneity is largely unknown because existing theoretical approaches linking structure and dynamics in neural circuits are restricted to population-averaged connectivity and activity. Here we present a systematic inclusion of heterogeneity in network connectivity to derive quantitative predictions for neuron-resolved covariances and their statistics in spiking neural networks. Our study shows that the heterogeneity in covariances is not a result of variability in single-neuron firing statistics but stems from the ubiquitously observed sparsity and variability of connections in brain networks. Linear-response theory maps these features to the effective connectivity between neurons, which in turn determines neuronal covariances. Beyond-mean-field tools reveal that synaptic heterogeneity modulates the variability of covariances and thus the complexity of neuronal coordination across many orders of magnitude.

Understanding the mechanisms by which single-stranded RNA viruses regulate capsid assembly around their RNA genomes has become increasingly important for the development of both antiviral treatments and drug delivery systems. In this study, we investigate the effects of RNA-induced allostery in a single-stranded RNA virus—Levivirus bacteriophage MS2 assembly—using the computational methods of the Dynamic Flexibility Index and the Dynamic Coupling Index. We demonstrate that not only does asymmetric binding of RNA to a symmetric MS2 coat protein dimer increase the flexibility of the distant FG-loop, inducing a conformational change to an asymmetric dimer, but also RNA binding reorganizes long-distance communications, making all the other positions extremely sensitive to the fluctuation of the ordered FG-loop. Additionally, we find that a point mutation in the FG-loop, W82R, leads to the loss of this asymmetry in communications, likely being a leading cause for assembly-deficient dimers. Lastly, this dominant communication that enhances its dynamic coupling with all the distal positions is not only a property of the dimer but is also exhibited by all the observed capsid intermediates. This strong dynamic coupling allows for unidirectional signal transduction that drives the formation of the experimentally observed capsid intermediates and fully assembled capsid.

The adaptive immune response relies on T cells that combine phenotypic specialization with diversity of T-cell receptors (TCRs) to recognize a wide range of pathogens. TCRs are acquired and selected during T-cell maturation in the thymus. Characterizing TCR repertoires across individuals and T-cell maturation stages is important for better understanding adaptive immune responses and for developing new diagnostics and therapies. Analyzing a dataset of human TCR repertoires from thymocyte subsets, we find that the variability between individuals generated during the TCR V(D)J recombination is maintained through all stages of T-cell maturation and differentiation. The interindividual variability of repertoires of the same cell type is of comparable magnitude to the variability across cell types within the same individual. To zoom in on smaller scales than whole repertoires, we defined a distance measuring the relative overlap of locally similar sequences in repertoires. We find that the whole repertoire models correctly predict local similarity networks, suggesting a lack of forbidden T-cell receptor sequences. The local measure correlates well with distances calculated using whole repertoire traits and carries information about cell types.

To optimize their fitness, cells face the crucial task of efficiently responding to various stresses. This necessitates striking a balance between conserving resources for survival and allocating resources for growth and division. The fundamental principles governing these tradeoffs is an outstanding challenge in the physics of living systems. In this study, we introduce a coarse-grained theoretical framework for bacterial physiology that establishes a connection between the physiological state of cells and their survival outcomes in dynamic environments, particularly in the context of antibiotic exposure. Predicting bacterial survival responses to varying antibiotic doses proves challenging due to the profound influence of the physiological state on critical parameters, such as the minimum inhibitory concentration (MIC) and killing rates, even within an isogenic cell population. Our proposed theoretical model bridges the gap by linking extracellular antibiotic concentration and nutrient quality to intracellular damage accumulation and gene expression. This framework allows us to predict and explain the control of cellular growth rate, death rate, MIC, and survival fraction in a wide range of time-varying environments. Surprisingly, our model reveals that cell death is rarely due to antibiotic levels being above the maximum physiological limit, but instead survival is limited by the inability to alter gene expression sufficiently quickly to transition to a less susceptible physiological state. Moreover, bacteria tend to overexpress stress response genes at the expense of reduced growth, conferring greater protection against further antibiotic exposure. This strategy is in contrast to those employed in different nutrient environments, in which bacteria allocate resources to maximize growth rate. This highlights an important tradeoff between the cellular capacity for growth and the ability to survive antibiotic exposure.

The kinetoplast DNA (kDNA) is the archetype of a two-dimensional Olympic network, composed of thousands of DNA minicircles and found in the mitochondrion of certain parasites. The evolution, replication, and self-assembly of this structure are fascinating open questions in biology that can also inform us how to realize synthetic Olympic networks in vitro. To obtain a deeper understanding of the structure and assembly of kDNA networks, we sequenced the Crithidia fasciculata kDNA genome and performed high-resolution atomic force microscopy and analysis of kDNA networks that had been partially digested by selected restriction enzymes. We discovered that these topological perturbations lead to networks with significantly different geometrical features and morphologies with respect to the unperturbed kDNA, and that these changes are strongly dependent on the class of DNA circles targeted by the restriction enzymes. Specifically, cleaving maxicircles leads to a dramatic reduction in network size once adsorbed onto the surface, while cleaving both maxicircles and a minor class of minicircles yields noncircular and deformed structures. We argue that our results are a consequence of a precise positioning of the maxicircles at the boundary of the network, and we discuss our findings in the context of kDNA biogenesis, design of artificial Olympic networks, and detection of in vivo perturbations.

Animals must constantly make decisions on the move, such as when choosing among multiple options, or “targets,” in space. Recent evidence suggests that this results from a recursive feedback between the (vectorial) neural representation of the targets and the resulting motion defined by this consensus, which then changes the egocentric neural representation of the options, and so on. Here we employ a simple model of this process both to explore how its dynamics accounts for the experimentally observed abruptly branching trajectories exhibited by animals during spatial decision-making, and to provide new insights into spatiotemporal computation. Essential neural dynamics, notably local excitation and long-range inhibition, are captured in our model via spin-system dynamics, with groups of Ising-spins representing neural “activity bumps” corresponding to target directions (as in a neural ring-attractor network, for example). Analysis, employing a novel “mean-field trajectory” approach, reveals the nature of the spontaneous symmetry breaking bifurcations in the model that result in literal bifurcations in trajectory space—and how it results in new geometric principles for spatiotemporal decision-making. We find that all bifurcation points, beyond the very first, fall on a small number of “bifurcation curves.” It is the spatial organization of these curves that is shown to be key to determining the shape of the trajectories, such as self-similar or space filling, exhibited during decision-making, irrespective of the trajectory's starting point. Furthermore, we find that a non-Euclidean (neural) representation of space (effectively an elliptic geometry) considerably reduces the number of bifurcation points in many geometrical configurations (including from an infinite number to only three), preventing endless indecision and promoting effective spatial decision-making. This suggests that a non-Euclidean neural representation of space may be expected to have evolved across species in order to facilitate spatial decision-making.

Much progress has been made in elucidating the inner workings of voltage-gated ion channels, but less understood is the influence of lipid rafts on gating kinetics. Here we propose that state-dependent channel affinity for different lipid species provides a unified explanation for the experimentally observed behaviors of clustering, cooperativity, and hysteresis. We develop models of diffusing lipids and channels engaged in Ising-like interactions to investigate the collective behaviors driven by raft formation in critical membranes close to the demixing transition. The model channels demonstrate lipid-mediated long-range interactions, activation curve steepening, and long-term memory in ionic currents. These behaviors likely play a role in channel-mediated cellular signaling and suggest a universal mechanism for self-organization of biomolecular assemblies.

Recent advances chromatin capture, imaging techniques, and polymer modeling have dramatically enhanced quantitative understanding of chromosomal folding. However, the dynamism inherent in genome architectures due to physical and biochemical forces and their impact on nuclear architecture and cellular functions remains elusive. While imaging of chromatin in four dimensions is becoming more common, there is a conspicuous lack of physics-based computational tools appropriate for revealing the forces that shape nuclear architecture and dynamics. To this end, we have developed a multiphase liquid model of the nucleus, which can resolve chromosomal territories, compartments, and nuclear lamina using a physics-based and data-informed free-energy function. The model enables rapid hypothesis-driven prototyping of nuclear dynamics in four dimensions, thereby facilitating comparison with whole nucleus imaging experiments. As an application, we model the Drosophila nucleus and map phase diagram of various possible nuclear morphologies. We shed light on the interplay of adhesive and cohesive interactions which give rise to distinct radial organization seen in conventional, inverted, and senescent nuclear architectures. The results also show the highly dynamic nature of the radial organization, the disruption of which leads to significant variability in domain coarsening dynamics and consequently variability of chromatin architecture. The model also highlights the impact of oblate nuclear geometry and heterochromatin-subtype interactions on the global chromatin architecture and local asymmetry of chromatin compartments.

Within the last decades, it was often assumed that in hydrogen-rich disordered samples, such as proteins in solution or hydrated powder form, incoherent neutron scattering from hydrogen nuclei dominated the scattering signal to an extent that allowed all other contributions to be neglected. As incoherent scattering arises solely from self-correlations, it further justified such a choice. Consequently, heavy water was often used as a contrast tool to highlight molecular motions in live samples. Coherent scattering, which implies the scattering from many nuclei and therefore collective processes, is another non-negligible contribution to neutron scattering where cross sections of other nuclei than hydrogen are significant. The recent advent of instrumentation based on polarization of neutron beams and the analysis of their polarization state after scattering for dynamical studies allows us to separate and shed light on the two contributions. In the present study we reveal that, unexpectedly, the isotopic exchange of water in the hydration shell of proteins arises on a much faster timescale than assumed so far. Moreover, the collective and local D-bond network relaxation of hydration water contributes to a high extent to the coherent scattering signal, at odds with usual “static” approaches used to estimate the relative impacts of dynamics in the sample. Hence, hydration water necessarily contaminates nonpolarized standard experiments. The findings are of paramount importance for all neutron scattering experiments where partial or full deuteration is used.