PLOS Computational Biology

Glutamate synaptic vesicles co-release protons, producing a brief acidification of the synaptic cleft that could modulate AMPA receptor (AMPARs) operation. To evaluate the extent of receptor acidification, we develop a diffusion-reaction model that couples vesicle-evoked proton and glutamate transients to AMPAR dynamics. Our simulations reveal that the rapid diffusion of protons and glutamate within the flat-cylindrical synaptic cleft leads to a mixture of protonated, singly glutamate-bound and doubly glutamate-bound AMPARs. We studied four postsynaptic AMPAR distributions - uniform disk, sub-disk, Gaussian cluster, and point-like cluster - and showed a [~] 50% increase in the number of acidified receptors when AMPARs are clustered on the postsynaptic cleft compared to a uniform arrangement. We further explored the impact of pH revealing that at acidic conditions (pH [~] 5), approximately 80-90% of open receptors are non-acidified, whereas under strongly acidic conditions (pH [~] 3), about 80-90% of open receptors exist in the protonated form. Finally, we explored how acidification modulates AMPARs during paired-pulse stimulation, a measure of short-term synaptic depression. While the presence of protons does not markedly alter the overall trends, acidified receptor states reduce the occupancies of their neutral counterparts by roughly 10-15%, indicating a mild redistribution toward protonated receptor conformations. To conclude, our model suggests that AMPAR protonation can influence the synaptic current, and we predict that this effect is determined primarily by the dissociation kinetics of glutamate and protons from AMPAR.

Building a simple model that precisely and functionally characterizes a neuron is a challenging and important task to select the best concise and computationally efficient model. However, this type of work has only been done for subthreshold properties of neurons. Here, we take a different perspective and suggest a method to obtain point-neuron models from morphologically-detailed models with dendrites. To do this, we focus on the functional characterization of the neuron response under in vivo conditions, and compute the transfer function of the detailed model. The parameters of this transfer function, in terms of mean voltage, voltage standard deviation and correlation time, can be used to compute the "best" point-neuron model that generates a transfer function very close to that of the morphologically-detailed model. We illustrate this approach for two very different neuronal morphologies, one from Drosophila larvae and one from mammals. In conclusion, this approach provides a tool to generate point-neuron models from detailed models, based on a functional characterization of the neuron response. Significance StatementThis study provides a new computational method to reduce morphological models into point-neuron models. To do so, we calculate the transfer function parameters, ie the voltage standard deviation, the mean voltage and the correlation time, of the morphological model and fit a point neuron-model onto this data. Here, we successfully apply this approach for two very different neuron morphologies, a drosophila neuron and a rat motoneuron.

Perceptual systems in humans and many other animals are able to segment scenes into regions that are likely to be physically meaningful. This ability depends on having low-level mechanisms that can accurately categorize whether local image patches are samples from the same or different kinds of texture. We find that using spatial proximity as a proxy for same-different ground truth makes it possible to train accurate decision variables and bounds directly from arbitrary natural images with no feedback. We also find that performance can be further improved by using proximity as a ground truth for adjusting the final decision variables and bounds for the current image/scene. These surprising findings result from the simple fact that under a wide range of conditions proximity discrimination (near vs. far) and texture discrimination (same vs. different) have mathematically identical decision bounds if the same image features are used for both tasks. We used the decision variables and bounds trained on natural images as the initial steps in a hierarchical Bayesian observer (HBO) model of texture discrimination [9]. Given the relative simplicity of this HBO model, it did an excellent job of segmenting images having randomly shaped regions containing arbitrary natural textures. We suggest that the proximity proxy is something that natural selection could discover and exploit for any same-different task where the task-relevant stimulus features also vary systematically with distance in space and/or time. For example, natural selection could have created developmental learning/plasticity mechanisms that exploit the proximity proxy.

The mitochondrial fatty acid {beta}-oxidation (mFAO) is an important source of energy when carbohydrate stores are depleted. It is also involved in many diseases, including inherited fatty-acid oxidation deficiencies (mFAODs). Patients with the same genetic variant often present with clinically heterogeneous phenotypes, but the mechanisms contributing to this heterogeneity are poorly understood. To investigate the underlying pathophysiology of different mFAODs, we constructed a computational model of mFAO in human liver, based on experimentally determined enzyme kinetics. A recognised, but seldom addressed challenge in metabolic modelling is the substantial uncertainty about kinetic parameter values. Whereas experimental values of some mFAO parameters are quite reproducible, others vary by up to four orders of magnitude between different reports. To address this, we generated an ensemble of kinetic models, each with the same reaction stoichiometry and rate equations, but different kinetic parameters, sampled from distributions of literature-derived values. We also comprehensively report these values and the arguments based on which they were evaluated. The resulting models were validated against available flux data, yielding a final ensemble of 51 valid models. These models recapitulate recent findings about the accumulation of medium-chain acyl-CoAs and the concomitant depletion of free CoA (CoASH) in medium-chain acyl-CoA dehydrogenase deficiency. We applied the ensemble to a set of known mFAODs, separating them into long-chain (LC-) and short-/medium-chain (S/MC-)mFAODs. The residual activity at which clinical symptoms are known to occur corresponded well with the residual activity in the model at which pathway flux was significantly decreased in LC-mFAODs. Residual activity in S/MC-mFAODs correlated less strongly with pathway flux, but these deficiencies did show a combined flux- and CoASH-reduction effect. This comparison is of importance to researchers and clinicians, as it identifies possible ways in which insights about one mFAOD may be applied to another based on shared biochemical properties. Author SummaryWhen building computer models of metabolic pathways, it is typical to take the "best" experimental data and use that as input into the model. However, especially when working with human cells, ethical and practical constraints often mean that even the "best" experimental data is still subject to substantial uncertainty. We explicitly modelled the uncertainty about the inner workings of fat burning (fatty acid oxidation). The resulting model is known as an "ensemble". The ensemble predicts ranges instead of single outcomes, allowing us to assess the confidence level of our predictions. We assess a set of inherited diseases - enzyme deficiencies - simulating them at different levels of severity with the ensemble. We find that the model does a good job of predicting the severity of the deficiencies at which symptoms will occur. It also allows us to identify a key difference between two subgroups within this group of deficiencies: long-chain and medium-/short-chain, depending on the size of the fats being metabolised. The long-chain variant is predicted to correlate most straightforwardly with the severity of the deficiencies, due to its effect on energy generation. Medium-/short-chain deficiencies, in contrast, have more complex consequences.

Detailed agent-based simulations are increasingly used to support policy decisions, but their computational cost and complex uncertainty structure make systematic scenario analysis challenging. We present a data-driven, uncertainty-aware decision support (DDUADS) workflow for using stochastic simulation models as decision-support tools under limited computational budgets. The approach combines several established techniques--sensitivity screening, Bayesian calibration using simulation-based inference, and multi-surrogate model integration for translational efficiency--into a coherent pipeline that enables uncertainty-aware policy analysis. Rather than producing a single baseline, the calibration stage yields a posterior distribution over plausible model parameterizations, allowing flexible, uncertainty-aware forward projections. We demonstrate the DDUADS workflow on the INFORM-HIV agent-based model of HIV transmission in Chicago to evaluate potential disruptions in antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use. While the specific application is HIV modeling, the challenges and techniques described here arise in other simulation studies and can be applied to decision support in other domains.

Phylodynamics bridges the gap between epidemiology and pathogen genetic data by estimating epidemiological parameters from time-scaled pathogen phylogenies. Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer the average number of secondary infections R and the infection duration d. Moreover, more complex MTBD models add extra parameters, such as the average length of the incubation period or the proportion of superspreaders in the infected population. However, these additional parameters come at an important computational cost: Apart from the simplest, BD, model, MTBD models do not have a closed-form solution and require numerical methods for their likelihood computation. This leads to increased computational times and potential numerical errors. Therefore, the BD model remains the favorite researchers choice for real dataset analyses, and is often applied even in cases where more complex epidemiological aspects are present. We investigated, using simulations, how model misspecification influences inference of R and d in the phylodynamic framework. We showed that the use of models not accounting for various epidemiological aspects leads to bias. In particular the simplest, BD, estimator tends to underestimate R in the presence of super-spreading or incubation, which might be dangerous from the public health prospective. However, deep-learning-based estimators for complex models, which account for multiple epidemiological factors, perform well both on the data where those factors are present and where they are absent. This advocates for the use of complex epidemiologically realistic estimators, whose design has recently become possible thanks to deep learning.

Human walking is intrinsically variable. For example, there is considerable stride to stride variability even when walking speed is constant. This variability is due to uncertainty in the sensorimotor system and the environment, and is shaped by both musculoskeletal dynamics (e.g. joint stiffness and damping originating from muscles) and the control strategy used to mitigate the effects of uncertainty. Yet, insight into how sensorimotor noise shapes walking variability is limited due to a lack of experimental methods to assess sensorimotor noise and control strategies during walking. Simulations that account for uncertainty can elucidate how sensorimotor noise affects movement variability but due to numerical challenges, accounting for sensorimotor noise is not common in simulations of walking. Existing simulations have hugely simplified musculoskeletal dynamics (e.g. no muscles), the control policy (e.g. pre-defined feedback loops), or sensorimotor noise sources (e.g. only motor noise). Here, we performed stochastic optimal control simulations of walking based on a model with 9 degrees of freedom and 18 muscles to study how the level of sensory and motor noise influences walking. We solved for feedforward muscle excitations and full-state time-varying feedback gains that minimised expected effort while generating periodic, and hence stable, gait patterns. To enable these simulations, we approximated the state distribution with a Gaussian and used an unscented transform to propagate the state covariance. Resulting optimisation problems were solved with direct collocation. Sensorimotor noise level had a small effect on the mean kinematics but shaped kinematic and muscle activity variability as well as expected effort. Although simulations underestimated the magnitude of experimental positional variability, they captured its structure. In agreement with experimental results, the control policy prioritised limiting variability of centre of mass kinematics and minimal swing foot clearance over limiting joint angle variability. Hence, our simulations suggest that effort minimisation underlies these observations. Author summaryWhen performing a movement multiple times, each repetition will be slightly different due to random disturbances in the neural signals used to control movement, i.e. sensorimotor noise. Because it is difficult to measure inside the nervous system of a moving person, computer simulations are used to study movement control. They found that both sensorimotor noise and musculoskeletal mechanics determine how people control arm movements and standing. However, there are no simulations of walking that systematically evaluated how sensorimotor noise level influences walking kinematics because they pose computational challenges. Here, we proposed and used an approach for minimal effort simulations of walking in the presence of uncertainty. We imposed forward speed and stability but not kinematics. We found that the level of sensorimotor noise had little effect on the mean movement but a strong effect on the variability and the expected effort. The control strategy prioritised reducing the variability of the centre of mass position and swing foot clearance over reducing the variability of individual joint angles, which is also observed in experiments. Interestingly, strict control of centre of mass position and foot clearance in our simulations emerged from minimising effort.

Patient-specific four-chamber electromechanical models provide a physics-constrained framework for investigating whole-heart cardiac physiology and disease mechanisms. Identifying which model parameters impact whole-heart function is important for understanding cellular-, tissue-, and organ-scale determinants of cardiac performance and for calibrating patient-specific models. However, previous global sensitivity analyses of cardiac electromechanical models have typically been performed on a single heart, and systematic evaluation of how parameter influence compares across anatomically different subjects remains limited. We created four-chamber electromechanical models using cardiac MRI from five healthy subjects (n = 5). The models simulated atrial and ventricular cellular electrophysiology, calcium dynamics, and active contraction, with heterogeneous fibre orientation, transversely isotropic tissue mechanics, pericardial constraint, and a closed-loop cardiovascular system providing physiological boundary conditions. In total, 46 parameters described the integrated model. Using Gaussian process emulators, we performed multi-scale global sensitivity analysis to evaluate the relative contribution of model parameters to left and right atrial and ventricular function. Across all anatomies, the most influential parameters were systemic and pulmonary resistances, ventricular end-diastolic pressures, and the venous reference pressure, highlighting the dominant role of haemodynamic loading conditions in governing pressure- and volume-based outputs. A chamber-level analysis of atrioventricular coupling revealed a phase-dependent pattern. Atrial pressures were predominantly governed by global haemodynamic parameters (> 90% of total sensitivity), atrial filling volumes showed substantial ventricular influence ({approx}40-55% across anatomies), and atrial end-systolic volumes were primarily determined by intrinsic atrial parameters ({approx}60-65%). These patterns were consistent across subjects despite differences in anatomy. We show that, in healthy male subjects, inter-individual anatomical variation does not substantially change the ranking of dominant parameters. This work provides a repeatable modelling and sensitivity analysis framework and establishes a benchmark reference for whole-heart electromechanical modelling in healthy hearts. Author summaryComputational models of the heart can simulate cardiac physiology in unprecedented detail, but these models contain many parameters whose influence on predicted function is not fully understood. We built patient-specific four-chamber heart models from MRI scans of five healthy subjects and used statistical methods to systematically test how 46 model parameters affect simulated cardiac performance. Across all five subjects, we found that the haemodynamic loading parameters, including systemic and pulmonary vascular resistance, ventricular filling pressures, and the venous reference pressure, consistently had the greatest influence on the model outputs, regardless of differences in individual heart anatomy. This finding suggests that in healthy resting conditions, the boundary conditions of the cardiovascular system, rather than individual differences in heart geometry or electrical properties, are the primary drivers of whole-heart function. We also found a structured coupling pattern between the upper and lower heart chambers, where global haemodynamic parameters dominate atrial pressure regulation, ventricular mechanics shape atrial filling, and intrinsic atrial properties control atrial emptying. This work provides a benchmark dataset of five anatomically detailed heart models and a sensitivity analysis framework to guide calibration of future cardiac digital twin models.

Generative diffusion models have demonstrated an ability to produce novel images sampled from the learned underlying data distribution. These models are able to infer system characteristics for parameter combinations that were not seen during training. We investigate the ability of these models to infer trends in biological data from limited samples. Specifically, we consider the response of system scale behaviors such as cortical flow in a simulated actomyosin system as we tune filament turnover rates. We train a diffusion model on coarse grained actin curvature and density heatmap images, and are able to generate images from conditioning variables not seen during training. These images are predictive of nonlinear trends in the system. We also consider characteristics of the system that allows this level of inference, such as the strong linear relationship between average density and filament turnover in the system, and by exploring minimal underlying dynamics with a motor binding model.

Cells release signaling molecules such as neurotransmitters that diffuse through the extracellular space and bind to receptors. These signaling molecules can be detected by fluorescent sensors/probes to provide images of the signaling process. Such images are not equivalent to a concentration because diffusion and sensor kinetics affect (convolute) them. Therefore, computational approaches are necessary to disentangle these contributions and allow interpretation of fluorescent sensor-based images. Here, we present a kinetic Monte Carlo framework (FLuorescence Imaging Kinetic Simulation, FLIKS) that simulates signaling molecules undergoing cellular release, stochastic diffusion and reversible binding to sensors in realistic cellular (2D or 3D) geometries. We apply it to model neurotransmitter (dopamine) release in synaptic clefts and for paracrine signaling by immune cells. We also show how sensor location, sensor kinetics and release location affect fluorescence images. For example, we show how sensor sensitivity depends on the distance from the synaptic cleft and changes when dopamine transporters (DAT) clear dopamine. The approach also allows to compare the performance of membrane bound (genetically encoded) sensors versus artificial sensors such as nanosensors placed outside under or around the cells. As an example, we also demonstrate how the images of catecholamine release by immune cells can be modeled and compared to experimental data to better understand the release pattern. This framework provides a quantitative basis for analyzing and interpreting fluorescent sensor imaging data.

The interplay between inflammation and coagulation is a central driver of thrombotic risk across various diseases. While mathematical models of blood coagulation are well established, there remains a critical gap in quantitative frameworks that capture inflammation-induced hypercoagulability. In this study, we develop a mathematical model that explicitly simulates the interaction between pro-inflammatory cytokines and the coagulation cascade. The model incorporates key mechanisms, including: (i) upregulation of tissue factor (TF) by IL-1{beta}, IL-6, and TNF-; (ii) suppression of natural anticoagulants, namely antithrombin III (ATIII) and tissue factor pathway inhibitor (TFPI), by IL-6 and TNF-; and (iii) feedback amplification of proinflammatory cytokines by thrombin. By encoding the bidirectional feedback between inflammatory and coagulation pathways, the model captures essential features of inflammation-driven hypercoagulability and enables systematic quantification of how variability in inflammatory extent and duration results in heterogeneous thrombin generation (TG) dynamics. To evaluate its effectiveness, we integrate the model with TG assays and apply it to virtual patient cohorts representing four clinically distinct conditions: COVID-19, sickle cell disease (SCD), type 2 diabetes mellitus (T2DM) and Hemophilia A. Model simulations predict that disease-specific inflammatory environments induce distinct shifts in TG dynamics. In COVID-19 and T2DM, elevated cytokine levels lead to shortened lag times and increased thrombin peak, whereas in SCD, shortened lag times are accompanied by a reduced thrombin peak. These effects are strongly modulated by both cytokine concentration and duration of exposure. These results demonstrate that the proposed computational model augments conventional TG assays by mechanistically linking inflammatory signaling to disease-specific coagulation responses. Collectively, the proposed computational framework extends conventional TG assays by considering the interplay between inflammation and coagulation, thereby providing a potential tool for predicting disease progression and identifying disease-specific therapeutic targets to advance personalized management strategies in thrombo-inflammatory disorders.

Decision-making behavior changes over time, exhibiting temporal correlation and nonstationarity. Existing drift diffusion model (DDM) fitting methods either do not provide uncertainty quantification for parameter estimates, or rely on restrictive assumptions that decisions are independent and that parameters remain constant over time, potentially underestimating uncertainty. To address these limitations, we propose a computationally efficient method for estimating analytic uncertainties in DDM parameters that are robust to temporal dependence and unmodeled parameter variability, while explicitly modeling nonstationary variability through covariates. We apply this method to rat decision-making in a two-alternative forced-choice (2AFC) visual task, revealing dynamic decision-making states across multiple timescales. A Python implementation of the method is provided.

Neuronal activity alters extracellular ion concentrations and electric potentials. Ephaptic effects refer to the feedback influence that these extracellular changes can have on neuronal activity. While electric ephaptic effects occur on a fast timescale due to extracellular potential perturbations, ionic ephaptic effects are driven by slower, accumulative changes in ion concentrations. Among the previous computational studies of ephaptic effects, the vast majority have focused exclusively on electric effects, while ionic ephaptic effects have largely been neglected. In this work, we present an electrodiffusive computational framework consisting of two-compartment neurons that interact via a shared extracellular space. By accounting for both electric potentials and ion-concentration dynamics in a self-consistent manner, our framework enables us to explore the relative roles of electric and ionic ephaptic effects. Through numerical experiments, we demonstrate that ionic and electric ephaptic interactions play very different roles. While ionic ephaptic interactions increase population firing rates, electric ephaptic interactions primarily drive subtle shifts in spike timing. Furthermore, we show that these spike shifts cause the phase difference (the distance in spike times between a small collection of neurons) to converge to a stable, unique phase difference, which we coin the ephaptic intrinsic phase preference. Author summaryNeurons predominantly communicate through synapses: specialized contact points where a brief electrical signal, known as a spike or action potential, in one neuron influences another. Neurons generate these spikes by exchanging ions with the surrounding extracellular space. This way, spiking neurons alter extracellular ion concentrations and electric potentials. Since neurons are sensitive to such changes in their environment, they can also influence one another indirectly through the shared extracellular medium. This form of non-synaptic interaction is known as ephaptic coupling. Most computational models of neuronal activity neglect ephaptic interactions, and those that include them typically consider only electric effects while ignoring ionic contributions. As a result, the relative roles of electric and ionic ephaptic effects remain poorly understood. Here, we introduce a computational framework that accounts for both mechanisms in a self-consistent way. Our results show a functional distinction: ionic ephaptic effects act slowly, regulating population firing rates, whereas electric ephaptic effects act on millisecond timescales and subtly shift spike timing. These shifts cause spike-time differences between neurons to converge to a stable value, a phenomenon we call ephaptic intrinsic phase preference.

AO_SCPLOWBSTRACTC_SCPLOWGene interactions play an important role in the development of antimicrobial drug resistance and other evolutionary processes of medical importance. Empirical studies have revealed multiple peaks, inaccessible trajectories, and constraints on mutation order. Higher order epistasis is associated with obstacles in fitness landscapes. However, its significance has been debated in recent years, sometimes through reinterpretations of data from previous publications. We suggest that local higher order interactions may help reconcile these seemingly contradictory findings. Rank order based methods can be informative when other methods fail to detect consequential interactions. In addition to conventional rank order methods, including sign epistasis, we introduce signed bipyramids. A bipyramid interaction compares extreme genotypes against their intermediates, for example a triple mutant and the wild-type against the corresponding single mutants. In general, interactions are signed if they are implied by the rank order alone.

MotivationInformation flow and temporal coding in cortical circuits depend critically on the reliable transmission of precisely timed synchronous spike patterns. Although cortical assemblies achieve such transmission despite pronounced intrinsic heterogeneities and stochastic high-conductance states, the mechanisms underlying effective synchrony propagation under in vivo conditions remain poorly understood. MethodologyIn this study, we address this gap using large-scale, conductance-based models of excitatory and inhibitory neurons organized into feedforward synfire chains operating in noisy, high-conductance regimes. Using independent stochastic search algorithms, we first identified physiologically valid heterogeneous populations of cortical neurons. Both excitatory and inhibitory populations exhibited cellular-scale degeneracy, whereby distinct combinations of biophysically identified molecular components produced signature physiological characteristics. We then constructed synfire chains with varying degrees of heterogeneity using these populations and assessed the propagation of different spike packets across neuronal assemblies. ResultsWe found synchrony propagation to be inherently probabilistic, revealing a stochastic separatrix that separated input patterns that consistently succeeded from those that consistently failed in propagation. The stochastic nature of this separatrix highlighted a critical role for background synaptic fluctuations, defining a regime in which identical inputs alternately propagated or failed across trials solely due to stochastic background activity. Comparing networks with different degrees of intrinsic heterogeneity, we found that increasing heterogeneity did not alter mean propagation efficacy but reduced network-to-network variability, indicating a stabilizing role for intrinsic diversity. Strikingly, when we tested the impact of neuronal intrinsic properties on synchrony propagation, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels emerged as robust enhancers of synchrony propagation across all heterogeneity regimes. Mechanistically, the slow restorative kinetics of HCN conductances narrowed the temporal window for spike initiation, sharpening output synchrony, and improving propagation reliability. This effect was abolished when HCN kinetics were accelerated, underscoring the importance of the slow negative feedback mediated by these channels. ImplicationsTogether, our analyses identify HCN channels as key regulators of synchronous information transfer and reveal strong interactions among intrinsic conductances, input characteristics, neuronal heterogeneity, and stochastic background activity in shaping cortical synchrony propagation. The ability of diverse cellular and network configurations to achieve similar propagation efficacy further highlights degeneracy as a fundamental principle governing robust and flexible neural computation.

Genetic surveillance is increasingly used to track malaria transmission, yet genomic metrics can respond nonlinearly to changes in transmission intensity and depend on the diversity already present in the parasite population. Here, we present a stochastic agent-based model of hu-man-mosquito transmission that integrates SEIS-like epidemiological dynamics with within-host Plasmodium falciparum haplotype dynamics. By varying the maximum mosquito biting rate and the initial parasite diversity, we examine how transmission intensity and standing diversity jointly shape mixed infections, recombination, and long-term population structure across a continuous transmission gradient. Our study revealed a sequential pattern in which increasing biting intensity first increases infection prevalence and multiplicity of infection, then expands opportunities for outcrossing, and only thereafter increases effective recombination and recombinant haplotype generation. These responses are strongest in low- to intermediate transmission and tend to plateau at higher transmission levels. Initial population diversity constrains the amount of diversity that can be maintained and the magnitude of recombination output, while temporal trajectories show that haplotype evenness can pass through transient non-equilibrium phases before stabilizing. Together, these results show that the structure of the parasite population is shaped not by trans-mission intensity alone but by its interaction with standing genetic diversity. Furthermore, this study works to clarify when and how genomic metrics reliably reflect transmission conditions across heterogeneous malaria settings.

Evidence accumulation is a fundamental aspect of human decision-making. However, how the precise temporal structure of evidence shapes the accumulation process has not been systematically studied. As a result, current understanding of evidence accumulation remains largely limited to its time-averaged behavior. We tested human subjects in a visual estimation task in which they inferred the angular position of an unknown source from a noisy stimulus sequence. Introducing systematic temporal perturbations, i.e., breaks of different durations and at different positions in the otherwise regular evidence sequence, revealed that subjects actively compensated for the memory loss endured during the break by dynamically enhancing evidence integration and memory maintenance immediately after the break. We derived a new time-continuous Bayesian updating model that is dynamically constrained by optimal performance-effort trade-offs. With two free parameters determining the overall resource-efficiencies of encoding and memory maintenance, the model accurately predicts the rich dependencies of subjects accumulation behavior on the evidence schedule, including subjects individual tendencies to emphasize either early (primacy) or late (recency) samples in the evidence sequence. Our results suggest that evidence accumulation is a non-stationary, dynamically controlled process that optimally balances the information gained from incoming evidence against the cognitive effort required to acquire and maintain it. The proposed model is general and should apply broadly across many task domains.

Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage-RBC signaling with Dissipative Particle Dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for robust parameter inference. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage-RBC contact, generating spatially resolved trajectories of CD47-SIRP signaling and antibody-receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47-SIRP-SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRP antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis. Significance statementRed blood cells are normally removed from circulation by macrophages through tightly regulated molecular signals. In diseases such as sickle cell disease and Gaucher disease, this clearance process becomes abnormal, contributing to anemia and other complications. However, the mechanisms linking the physical properties of red blood cells to immune signaling remain poorly understood. Here we develop a multiscale computational framework that combines particle-based biophysical simulations, systems biology models, and physics-informed machine learning. This approach provides a quantitative framework to interpret how changes in red blood cell mechanics and surface signaling disrupt the CD47-SIRP inhibitory pathway that normally prevents phagocytosis. The framework provides a predictive platform for studying immune clearance and may help guide therapeutic strategies targeting red blood cell-macrophage interactions.

Parameter estimation in nonlinear biological dynamical systems is a difficult inverse problem because the governing equations are often stiff or oscillatory, the data are sparse and noisy, and the objective landscape is non-convex. Physics-informed neural networks (PINNs) offer an alternative to purely simulation-based calibration by representing state trajectories with neural networks while penalizing violations of the governing equations. This paper studies the empirical reliability of PINNs for recovering the parameters of the repressilator, a synthetic genetic oscillator formed by three cyclically repressive genes. We use synthetic time-series generated from the standard ordinary differential equation model and train inverse PINNs to estimate the production parameter {beta} and the Hill coefficient n. The study varies observation noise, partial observation of repressors, sampling density, sensitivity to initial parameter guesses, and the difference between stable and oscillatory regimes. The results show that PINNs can reconstruct trajectories accurately when the model structure is correct and the three repressors are observed, but parameter recovery is more fragile than trajectory fitting. Noise, sparse sampling, unobserved variables, and unfavorable initial guesses increase the risk of biased estimates. The stable regime is easier to reconstruct, whereas the oscillatory regime provides richer information but also exposes optimization sensitivity. These findings support PINNs as a useful reverse-engineering tool for small gene-regulatory ODE models, while highlighting the need for repeated runs, uncertainty reporting, and experimental designs that improve identifiability.

Metabolic pathways are often hypothesized to benefit from the spatial organization of enzymes, facilitating substrate transfer through mechanisms such as metabolic channeling or metabolon formation. However, it remains unclear whether the spatial proximity of catalytic sites represents a general organizational principle of metabolism or is restricted to specific pathways. Here, we investigate whether consecutive enzymes in metabolic pathways, when physically interacting, exhibit structurally optimized arrangements that minimize distances between their catalytic sites, thereby increasing metabolite transfer efficiency from one enzyme to the next. We first evaluated the ability of current protein-protein interaction prediction methods, including AlphaFold2, AlphaFold3, ESMFold, and HDOCK, to model weak and transient interactions using a benchmark dataset of 112 low-affinity protein dimers from PDBbind. AlphaFold-based approaches performed best in recovering correct interaction geometries, while ESMFold showed limited performance. We further assessed several confidence metrics and identified ipTM, ipSAE, and VoroIF-GNN as the most informative predictors of correct interaction conformations. In addition to simple Euclidean distance metrics, we developed a computational procedure to estimate shortest accessible space paths between catalytic sites in predicted enzyme-enzyme complexes. Applying this framework to 107 consecutive enzyme pairs in E.coli revealed an increased tendency for consecutive enzymes to interact, but no systematic evidence that interacting enzymes position their catalytic sites in spatially optimized configurations. In the predicted complex conformations, catalytic sites tend not to be positioned closer than expected at random. The developed computational workflow provides a general framework for analyzing structural aspects of metabolic organization.