IFAC-PapersOnLine

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.

The increase in size of metabolic network models especially with the advent of single-cell data calls for scalable reconstruction and analysis tools. Such models, often used for drug discovery and the analysis of microbial communities rely on consistency testing and reconstruction algorithms such as FASTCORE and FASTCC. However, with models nowadays comprising hundreds of thousands of reactions, the running times of such algorithms increased from few minutes to hours or days even with high performance computing. Experiments that require multiple reconstructions, such as parameter tuning or cross-validation, are practically infeasible in very large networks. Here we introduce FASTERCC, a new version of FASTCC, that leverages structural information for removing type I and II dead-ends, the orientation of reversible reactions and correcting the reversibility of reactions that are structurally incapable of carrying flux in both directions prior to any feasibility tests. These improvements reduce drastically the running time of FASTERCC by a median 20-fold speedup in comparison to FASTCC for networks with a larger number of block reactions. The model cleaning performed by FASTERCC also reduces the computational time of downstream analyses, notably of FASTCORE up to 50%.

Spectral similarity is often judged with a single metric such as RMSE, yet this can be misleading: physically different errors can produce similar scores. This is a critical limitation for computational biomechanics, where spectral agreement underpins both model validation and machine-learning loss design. Here, we develop a multi-metric framework for objective spectral biofidelity and test whether it better captures meaningful disagreement across complex frequency-domain responses. We evaluated 12 complementary similarity metrics, including CORA and ISO/TS 18571, using controlled spectral perturbations that mimic common real-world deviations such as resonance shifts, localized spikes, and broadband tilts. We then applied the framework to an SBI-tuned finite-element middle-ear model to assess convergence with training dataset size and robustness to measurement noise across repeated stochastic runs. No single metric performed reliably across all distortion types. Shape-based metrics tracked resonance morphology but could miss vertical scaling, whereas MaxError remained important for narrowband anomalies that smoother metrics underweighted. CORA and ISO 18571 did not consistently outperform simpler metrics. Rank aggregation using Borda count provided a robust consensus across metrics, enabling objective identification of training-data saturation and noise thresholds beyond which similarity rankings became unstable. These results show that spectral biofidelity cannot be reduced to a single norm. A multi-metric consensus provides a clearer and more physically meaningful basis for comparing experimental and simulated spectra, and offers a more defensible foundation for data-fidelity terms in physics-informed and simulation-based machine learning.

Clavicle fractures often exhibit markedly different clinical outcomes: some patients recover acceptable function despite shortening or displacement, whereas others with apparently similar deformity develop persistent pain, functional loss, or poor healing. To explain this distinction, we propose a minimal nonlinear mechanical model for prognostic analysis of clavicle fractures. The model describes the interaction between fracture-related shortening and compensatory shoulder-girdle posture through a reduced equilibrium equation incorporating stiffness, geometric nonlinearity, and shortening-posture coupling. Within this framework, we analyze equilibrium branches, local stability, and the emergence of critical thresholds. We show that post-fracture destabilization can be interpreted as a fold bifurcation, while more complex parameter dependence gives rise to cusp-type structures and multistability. These bifurcation mechanisms provide a mathematical explanation for sudden deterioration after injury or treatment, as well as for strong inter-individual variability. We further introduce an optimization principle based on a utility functional to guide treatment planning. The analysis predicts that the optimal safe correction should lie strictly below the bifurcation threshold, thereby generating a natural safety margin. Although the model is simplified and has not yet been calibrated against patient data, it nevertheless provides a theoretical framework for understanding why fracture prognosis may deteriorate abruptly near critical mechanical conditions and offers a dynamical-systems interpretation of empirical treatment thresholds used in clinical practice.

PurposeLarge quantities of human pluripotent stem cells (hPSCs) are required for clinical applications. 3D suspension cultures are suitable for large scale manufacturing of hPSCs but yield, viability and quality are affected by the hydrodynamic environment. This paper characterizes the hydrodynamic environment inside vertical wheel bioreactors (VWBRs) as a function of size and agitation rates, measures its effect on cell aggregation and proliferation, and proposes the use of Lagrangian-based shear stress and energy dissipation rate (EDR) exposures to support scale-up. MethodsIn silico: Transient, 3D, turbulent flow simulations are conducted for two VWBR sizes (100, 500 mL) at five agitation rates between 20 and 80 rpm. Trajectories of cell aggregates of sizes from 200 to 1,000 microns are calculated, and shear stress and EDR exposures are collected along these trajectories. In vitro: ESI-017 hPSCs were cultured in VWBRs for 6 days. Aggregation efficiency and daily fold ratios were calculated based on cell counts and initial inoculation density. ResultsAggregate size, agitation rate and bioreactor size modulate cell aggregate exposures to EDR and shear stress, which significantly depart from maximum or volume average metrics used for scale-up. Combined in vitro/in silico results show EDR affects aggregation efficiency, cell counts and aggregate size, and has a small effect on daily fold ratios but a significant effect on total fold ratio. ConclusionHistory of trajectory-based cell aggregate exposures to EDRs provide a better scale-up basis for VWBRs than volume-averaged EDR. Shear stress does not significantly affect hPSC aggregation, proliferation and expansion in VWBRs under the tested conditions.

Despite its ubiquity in natural flows, the effects of turbulence on fish locomotion and behavior remain poorly understood. The prevailing hypothesis is that these effects depend on the spatial and temporal scales of the turbulence relative to the fishs size and swimming speed. But in conventional facilities, turbulence usually increases with mean flow, which forces higher swimming speeds and can leave these relative scales unchanged. We therefore present a novel experimental facility that leverages a jet array to decouple the turbulence from the mean flow and systematically control its scales. This approach allows the ratio of turbulent to fish inertial scales to be varied over an order of magnitude, providing a controlled framework for quantifying fish-turbulence interactions. The facility also supports experiments probing strategies fish may use to cope with turbulence, including collective behaviors. Insights from this work have broader implications for ecological studies and engineering applications, including the design of effective fishways and bio-inspired underwater vehicles.

Cancer therapeutics are increasingly incorporating engineered receptors due to their ability to detect extracellular ligands and initiate intracellular responses that regulate gene expression. By redesigning these natural signaling systems, synthetic receptors hold great potential for use in novel cell-based therapies. One particularly promising direction is modifying the Notch receptor, a transmembrane protein that naturally mediates ligand-dependent signaling at the cell surface to regulate cell proliferation and differentiation in neurogenesis. Both the intracellular and extracellular domains of Notch can be replaced with alternative domains, creating the family of modified Notch receptors known as synthetic Notch (synNotch). In existing synNotch-activated chimeric antigen receptor (CAR) T-cells, the extracellular domain can be engineered to adjust binding affinity for a specific cancer antigen, enabling precise tuning of therapeutic activity while minimizing off-target effects. To quantify and inform such tuning, we develop differential equations models of synNotch receptor signaling and subsequent gene expression. The mathematical models couple activation dynamics on fast timescales (characteristic of receptor-ligand interactions) and on slow timescales (characteristic of downstream gene expression dynamics). Global Sobol sensitivity analysis of the proposed models highlights parameters that yield the greatest variability in synNotch signal transduction and gene expression, indicating their potential to be engineered for different functions in future cancer therapeutics. For the receptor-ligand interactions in the synNotch model, we find that ligand association and ligand-independent activation are the most sensitive parameters. In the downstream gene expression model, promoter strength and degradation rates of mRNA and gene product are found to be most amenable to engineering.

We describe an approach for analyzing biological networks using rows of the Krylov subspace of the adjacency matrix. Specifically, we explore the scenario where the Krylov subspace matrix is computed via power iteration using a non-random and potentially non-uniform initial vector that captures a specific biological state or perturbation. In this case, the rows the Krylov subspace matrix (i.e., Krylov trajectories) carry important functional information about the network nodes in the biological context represented by the initial vector. We demonstrate the utility of this approach for community detection and perturbation analysis using the C. Elegans neural network.

The environments inhabited by flying insects demand a balance between flight efficiency and flight manoeuvrability. In structural oscillators such as the insect indirect flight motor, efficiency (arising from resonance) and manoeuvrability (arising from kinematic modulation) are typically quid pro quo, with modulation incurring penalties to efficiency. Band-type resonance is a phenomenon that offers, in theory, a strategy to lessen these penalties via careful navigation through a band of efficient kinematic states. However, identifying this band is challenging: no methods exist to identify the complete band in realistic motor models, involving elasticity distributed across thorax and wing. Nor are the effects of elasticity distribution on the band known. In this work, we address both open topics. We present a suite of numerical methods for identifying the complete resonance band in general systems. Applying them to models of the insect flight motor with distributed elasticity--thoracic and wing flexion--reveals that distributed elasticity is moderate-risk but high-reward morphological feature. Well-tuned distributions expand the resonance band over fourfold whereas poorly-tuned distributions completely extinguish the resonance band. These results indicate that distributing elasticity across the insect flight motor can have adaptive value, and motivate broader work identifying distributions across species.

Assessing the risk of pesticides to birds requires models that can extrapolate laboratory data to realistic exposure scenarios. In this work, we propose a new modeling framework BIRDkiss (Bird - Impact on Reproduction via Diet, keep it simple and suitable) that accounts for both a simplified Dynamic Energy Budget (DEBkiss) of organisms and the toxicokinetic-toxicodynamic (TKTD) of chemical substances according to a trait-based approach, thereby reducing the number of parameters to identify and strengthening the statistical robustness of the critical endpoints. The BIRDkiss model describes how food intake and toxicant exposure affect growth and egg production in birds over time. The model is fully embedded within an R package, including routines for calibration, validation and prediction under single-compound scenarios performed via Bayesian inference using standard data from the OECD avian reproduction tests. The BIRDkiss model also allows the simulations of scenarios under both varying food availability and multi-compound exposures based on the two classical mixture-toxicity paradigms: Concentration Addition (CA) and Independent Action (IA). The results of calibration for single compounds show good results matching with observed weights and egg counts. From these calibrations, predictions for new exposure scenarios can be readily generated. For mixtures, the IA algorithm is simpler and does not require to scale variables as in CA. Simulations indicate that high food levels do not further increase egg production (saturation), whereas substantial food reductions markedly decrease reproduction because energy is reallocated to maintenance. Exposure to chemicals combined to low food availability amplify the decline in reproductive output. The ready-to-use mechanistic, open-source BIRDkiss tool enables predicting the impact of pesticides on avian reproduction under realistic dietary exposure profiles. The implementation of CA and IA models is a first step toward mechanistic assessment of chemical mixtures, although validation still requires empirical mixture data. The model highlights the importance of food availability and shows that chemical stress can exacerbate the negative effects of nutritional stress. Integrating such models into regulatory frameworks could improve the ecological relevance of risk assessments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/712277v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@102246dorg.highwire.dtl.DTLVardef@1a58f65org.highwire.dtl.DTLVardef@695cd7org.highwire.dtl.DTLVardef@14e4329_HPS_FORMAT_FIGEXP M_FIG C_FIG

This article presents the development of an advanced modeling and simulation platform for carbon capture systems, with a focus on integrated process analysis from upstream CO2 capture through to bioethanol production. The platform supports the evaluation of CO2 mitigation technology by coupling mathematical bioprocess models with an interactive desktop application. The biological system employs Chlorella vulgaris microalgae to fix CO2 through photosynthesis and generate carbohydrate substrates, which are subsequently converted to bioethanol by Saccharomyces cerevisiae yeast via fermentation. The simulation integrates three established kinetic models--the Monod, Logistic, and Luedeking-Piret models--to predict biomass growth, substrate consumption, and ethanol yield under varying operational conditions. A closed-loop CO2 recycling subsystem captures fermentation off-gases and reintroduces them into the bioreactor, enhancing overall carbon utilization efficiency. Three representative simulation scenarios demonstrated process efficiencies ranging from 1.09% to 93.78% of the theoretical maximum CO2-to-ethanol conversion efficiency, confirming the platforms capacity to evaluate a wide operational envelope. The Electron/React-based desktop application provides real-time visualization, interactive 3D bioreactor models, and a simulation history module, making it accessible to researchers, engineers, and students. The platform serves as a digital twin that bridges rigorous bioprocess mathematics with intuitive user interaction, providing a cost-effective tool for designing and optimizing sustainable carbon capture and biofuel production systems.

Tuberculosis (TB) remains a leading cause of death globally, with early mortality often driven by severe malnutrition and human immuno-deficiency virus (HIV) co-infection. Traditional survival analyses identify risk factors but remain associative, failing to capture the dynamic physiological collapse preceding death. In a novel interdisciplinary adaptation, we applied the Merton jump-diffusion structural framework from quantitative finance to model survival as a state of biological solvency, in which mortality occurs when a stochastic health trajectory crosses a critical failure threshold. We analysed a retrospective cohort of 15,182 TB patients in Cameroon over two decades. Adjusted body mass index (BMI) was conceptualized as a proxy for health capital and modeled using a stochastic process accounting for individual recovery trends, physiological instability, and acute clinical shocks. The study included predominantly young adult males (median age: 33 years) with a median BMI of 20.7 kg/m2. HIV co-infection was present in 35% of patients. The overall mortality rate during the 240 days follow-up period was 7.0%, with 55.1% of deaths occurring within the first 30 days. The model identified a critical failure threshold at BMI 17.329 kg/m2. HIV co-infection emerged as a key driver of metabolic instability, significantly increasing physiological volatility. Statistical validation confirmed that sudden clinical shocks were necessary to explain observed mortality patterns. The resulting Distance-to-Death (DtD) metric slightly outperformed standard associative extended Cox models in predicting survival, achieving a higher discriminative ability in testing set (Harrells C-index: 0.781 vs. 0.772; p = 0.038). Patients stratified into the highest-risk category showed a mortality rate of 16.7%, compared with 1.6% in the most stable group.This study bridges financial engineering and clinical epidemiology, offering a mechanistic understanding of how physiological reserves and metabolic instability determine survival. To support clinical application, we developed an interactive digital triage tool enabling identification of high-risk patients in resource-limited settings. Author summaryTuberculosis remains a major cause of death worldwide, particularly in people with poor nutrition or co-infection with HIV. In this study, we explored a new way to understand why some patients survive while others do not. We adapted a method originally used in finance to track the "health reserves" of patients over time, using body weight and related measures to estimate how close someone is to a critical health threshold. Our approach captures both gradual health decline and sudden medical complications, such as severe infections or rapid deterioration. By applying this method to a large group of patients in Cameroon, we found that a very low body weight is a strong warning sign for impending death and that HIV infection makes health outcomes less predictable. We also created a simple scoring tool that can help doctors identify patients at greatest risk, so that life-saving interventions and closer monitoring can be prioritized. This work bridges mathematical modeling and clinical care, offering a new way to assess patient vulnerability and improve outcomes in resource-limited settings.

Recurrence Quantification Analysis (RQA) is a powerful phenomenological method for characterizing dynamical systems from sequential empirical data, but it is fundamentally limited to continuous signals. Symbolic RQA (sRQA) extends this framework to discrete state sequences, enabling the analysis of both inherently discrete systems and continuous systems where state-based dynamics and motifs are of interest. Despite its promise, accessible and unified software support for sRQA has remained limited. Here we introduce the sRQA package, an open-source R library that consolidates discretization and symbolization, data visualization, and computation of recurrence and cross-recurrence metrics into a single accessible toolset. We validated the method using simulated data with known dynamical properties, confirming that sRQA metrics behaved as theoretically expected. We then demonstrated the utility of sRQA across three real-world applications. First, we applied sRQA to ECG recordings, showing that symbolic recurrence metrics reliably distinguished atrial fibrillation from normal sinus rhythm, with an XGBoost classifier achieving 92% accuracy and an AUC of 0.97. Second, we applied sRQA to fMRI BOLD time series from the dorsal attention network, finding that symbolic and cross-recurrence metrics differentiated movie-viewing from resting-state conditions, revealing greater regularity and inter-subnetwork coordination during task engagement. Third, we applied sRQA to intrinsically symbolized sequences of pauses in speech, identifying valence-specific differences in pause dynamics between truthful and deceptive statements, as well as sex differences in pause structure during negatively-valenced speech. Together, these results demonstrate that sRQA provides a flexible and sensitive framework for characterizing discrete and discretized dynamical systems across biological and behavioral domains. AUTHOR SUMMARYMany biological and behavioral systems are best understood as sequences of discrete states rather than smooth, continuous processes. For example, a heartbeat that shifts between rhythms, a brain that transitions between activity patterns, or a speaker who pauses and resumes in ways that carry meaning. Standard methods for analyzing the dynamics of such systems were not designed with this kind of data in mind. Here, we introduce the sRQA package, an open-source software library that makes it straightforward to apply symbolic recurrence analysis to both discrete and continuous data. We demonstrate the library across four examples: simulated data with known properties, cardiac recordings distinguishing atrial fibrillation from normal heart rhythm, brain imaging data capturing differences between rest and task engagement, and speech recordings where pause patterns differ between truthful and deceptive statements. In each case, sRQA revealed meaningful structure in the data that would be difficult to detect with conventional tools. We hope this library will make symbolic recurrence analysis more accessible to researchers across the biological and behavioral sciences.

Digital volume correlation (DVC) has become the benchmark experimental technique for full-field strain measurement in bone mechanics. In our previous work we developed a novel data-driven image mechanics (D2IM) approach that learns from DVC data and predicts displacement fields directly from undeformed X-ray computed tomography (XCT) images, deriving strain fields from such predictions. However, strain fields derived through numerical differentiation of displacement fields amplify high-frequency noise, and regularization techniques compromise spatial resolution while incurring substantial computational costs. Here we propose the upgrade D2IM-Strain to predict strain fields directly from XCT images of bone. Two prediction strategies were compared: displacement-derived strain and direct strain prediction. The direct strain prediction model significantly improved accuracy particularly for strain magnitudes below 10000{micro}{varepsilon}, taken as a representative threshold value for bone tissue yielding in compression. In addition, the direct approach reduced false-positive high-strain classifications by 75%. By eliminating numerical differentiation, the approach reduces noise amplification while maintaining computational efficiency. These findings represent a critical step toward developing robust data-driven volume correlation methods for hierarchical materials.

A passive device that attaches to the feet, called an exotendon, can reduce the energetic cost of running at moderate speeds, but its efficacy and optimal design parameters at higher speeds are unknown. Identifying optimal parameters at new speeds experimentally would require many experimental trials with different exotendon designs, which is challenging for participants at higher running speeds. We developed a muscle-driven simulation framework to predict the effect of various exotendon designs on the energetic cost of running at an experimentally untested speed (4 m/s). We used these predictions to select four designs, which we evaluated experimentally as users ran at this speed. The framework correctly predicted that an exotendon that reduced energetic cost at 2.7 m/s would also reduce energetic cost at 4 m/s (10% predicted vs. 5.7% measured) and that a short, stiff exotendon and a long, compliant exotendon would not significantly reduce energetic cost. However, exotendon parameters predicted by the simulation to maximize energetic savings did not significantly reduce energetic cost when evaluated experimentally. There was variability between participants in both the magnitude of maximum energy savings and the exotendon condition associated with those savings. In a 5-km time trial performed with and without the exotendon condition that elicited the largest energy savings for each participant during the experiment, we observed a lower average heart rate (-3.9 {+/-} 3.8 beats/min; P=0.03; mean {+/-} standard deviation) and increased cadence (15.9 {+/-} 9.6 steps/min; P=0.002) when participants ran with the exotendon but did not observe a statistically significant difference in finishing time (-13.5 {+/-} 24.6 sec; P=0.3). These results demonstrate exotendons can reduce energetic cost across multiple running speeds and that predictive simulations provide a framework for guiding experiments to evaluate assistive device designs. Author summaryDesigning assistive devices that help people move more efficiently usually requires many experimental trials. These studies can be time-consuming and physically demanding, especially when testing multiple device designs. In this study, we explored whether computer simulations could help guide the design of an assistive device for running called an exotendon. The exotendon is a simple elastic band that connects the feet and can help runners use less energy. Previous experiments showed that the device reduces the energy needed to run at moderate speeds, but it was unclear whether it would also work at faster speeds or which design would lead to energetic savings. We first used simulations of human running to test many possible exotendon designs at a faster speed. These simulations allowed us to identify promising designs before conducting experiments. We then tested a small number of these designs with runners. The experiments confirmed that the exotendon can reduce the energy required to run at faster speeds, although the efficacy of different designs varied between individuals. Our results show that computer simulations can help researchers rapidly evaluate a variety of assistive device ideas and focus experimental testing on the most promising designs.

Agent-based models (ABMs) are widely used to study complex multiscale biological systems, particularly in cancer research. However, their high-dimensional parameter spaces, stochasticity, and computational costs pose significant challenges for uncertainty quantification, calibration, and systematic comparison of competing mechanistic hypotheses. PhysiCell has evolved into a growing ecosystem of open-source tools supporting physics-based multicellular modeling, including model construction, visualization, and data integration. However, despite these advances, systematic support for uncertainty-aware model analysis, scalable parameter exploration, and formal calibration workflows remains limited. Here, we introduce UQ-PhysiCell, an open-source Python package that enables uncertainty quantification, calibration, and model selection for PhysiCell models using a modular and scalable workflow. UQ-PhysiCell acts as a manager of PhysiCell simulation inputs and outputs, including parameters, initial conditions, rules, and MultiCellDS-compliant objects, and provides automated orchestration of large ensembles of simulations. The framework supports multiple levels of parallelism to accelerate the analysis, including the parallel execution of independent simulations, stochastic replicates, and downstream analysis tasks. UQ-PhysiCell integrates seamlessly with established Python libraries for sensitivity analysis, optimization, Bayesian inference, and surrogate modeling, allowing users to construct customized pipelines that match their modeling goals and computational resource requirements. By decoupling model execution from statistical analysis and emphasizing extensibility and reproducibility, UQ-PhysiCell lowers the barrier to applying rigorous uncertainty-aware methodologies to agent-based modeling and supports the systematic evaluation of PhysiCell models in biological and biomedical research. Author summaryWe developed UQ-PhysiCell to address a key challenge in agent-based modeling: the systematic quantification of uncertainty in complex stochastic simulations. PhysiCell is widely used to model multicellular biological systems, particularly in cancer research; however, practical tools for uncertainty analysis, calibration, and model comparison are often developed in an ad hoc manner. This makes the results difficult to reproduce and limits the ability to rigorously evaluate competing biological hypotheses. UQ-PhysiCell provides a flexible Python framework that manages the inputs and outputs of PhysiCell simulations and enables large-scale computational analysis. We designed the software to be modular, allowing users to build their own analysis pipelines and combine different methodologies for sensitivity analysis, calibration, and model selection. Rather than enforcing a single workflow, UQ-PhysiCell supports customization to match specific scientific questions and computational requirements. To make uncertainty-aware analyses feasible for computationally intensive agent-based models, UQ-PhysiCell implements multiple parallelism strategies, enabling the concurrent execution of simulations, stochastic replicates, and downstream analyses. By promoting reproducibility, scalability, and methodological flexibility, UQ-PhysiCell helps researchers move beyond single best-fit simulations toward more reliable and interpretable computational modeling.

Synchrony is a key mechanism that builds up the foundations of human interactions. Quantifying the level of physiological synchronization that occurs during dyadic exchanges is essential to fully comprehend social phenomena. We present a new index to characterize the coupling of complex physiological dynamics: the optimized Multichannel Complexity Index (opMCI). We validated this approach using synthetic time series of two coupled Henon Maps, with four different coupling levels in unidirectional and bidirectional manners. We demonstrated that the opMCI method allows to effectively discern between all coupling levels. Then, we applied the opMCI metric on heart rate variability data collected from 37 parent-infant dyads, during shared reading and playing activities, in the framework of the Shared Emotional Reading (SHER) project, with the aim of assessing the effects of early intervention in preterm babies. Two groups presented preterm infants: an intervention group, who participated in a two-month shared reading program, and a control group, who practiced shared play activities. A full-term group provided additional control data. The opMCI values were significantly higher for the intervention dyads with respect to the other groups during the shared reading task, showing that an early reading intervention program could increase parent-infant synchrony in preterm babies.

Developing approaches to link structure and function is an ongoing challenge in computational and structural biology. Using a systems-level framework, we present here an analysis pipeline in a Python package, mdsa-tools, that constructs network representations of structures in a time series of trajectory frames from molecular dynamics (MD) simulations. Here, we demonstrate its use on a ribosomal subsystem. The subsystem is centered on the CAR interaction surface, a "brake pad" adjacent to the aminoacyl (A-site) decoding center that tunes protein translation rates. We leverage unsupervised learning algorithms to explore the conformational landscape of behaviors visited by two versions of the subsystem (brake-on and brake-off) that differ at the codon 3 adjacent to the A-site codon. Our network representations of MD frames embody H-bond interactions between all pairwise combinations of residues in the system. By utilizing per-frame vector representations of network edges, we can apply standard clustering and dimensionality reduction methods to explore behavioral differences between the brake-on and brake-off versions of the system. K-means clustering of frame vectors revealed a striking separation of the two system versions, consistent with principal components analysis (PCA) embeddings and Uniform Manifold Approximation and Projection (UMAP) embeddings. Dissection of K-means centroids and PCA loadings highlighted H-bond interactions between residue pairs in the ribosomes peptidyl site (P site), suggesting potential allosteric signaling across the subsystem. Author summaryWith the impressive development of computational algorithms to successfully simulate the dynamics of biological molecules over time, the exploration and incorporation of systems modes of analysis is a natural next step to begin to understand the molecular dynamics behaviors that emerge from these experiments. Following the approaches of classical molecular genetics, we used a "computational genetics" paradigm where we introduced changes (mutations) in potentially important residues, changing their identities or modifying their chemical properties, and asked how the dynamic system responded to these changes, viewing the simulations as a series of movie frames of the dynamic structure over time. Starting with network representations of each frames structure, where the nodes are residues, and the edges denote H-bond interactions between the residues, we used several unsupervised machine learning algorithms to uncover behavioral changes in the different mutated versions of the system. Applied to our ribosome neighborhood, this revealed unexpected changes in behavior at the ribosome peptidyl site (P site) in response to mutating mRNA residues on the other side of the aminoacyl site (A site) codon, suggesting long-range allosteric interactions across the neighborhood.

Precise regulation of gene expression in batch bacterial cultures is challenging because the underlying dynamics vary with cellular physiological state over time. Although cell-silicon systems enable rapid, real-time optogenetic control, disturbance rejection remains difficult in batch culture because the plant dynamics shift across growth phases, limiting the effectiveness of fixed-gain controllers designed under constant-growth assumptions. Here, we present a multiscale model-guided feedback control framework for disturbance rejection in batch E. coli cultures. Frequency-response analysis shows that the input-output dynamics of gene expression depend strongly on growth phase, revealing operating-point-dependent limits on the disturbance rejection performance of a fixed-gain PID controller. To address this limitation, we develop two growth-aware control strategies: a gain-scheduled PID (PID-GS) controller that adapts to cellular physiological state, and a gain-scheduled feedback-feedforward controller (PID-GS-FF) that further compensates for growth perturbations. We also introduce a controller evaluation framework that identifies three distinct operating regimes for targeted experimental validation. Together, these results show that accounting for growth-state-dependent dynamics is necessary for robust disturbance rejection in batch culture and provide a control-oriented framework for regulating living systems with shifting operating conditions.

Cancer-associated fibroblasts (CAFs) are a central component of the tumor microenvironment that facilitate a supportive niche for cancer progression and metastasis. Experimental evidence suggests that CAFs can facilitate estrogen-independent tumor growth, thereby reducing the efficacy of anti-hormonal therapies. Understanding and quantifying the complex interactions between tumor cells, hormonal signalling, and the microenvironment are crucial for designing more effective and individualized treatment strategies. We propose a mathematical framework to explore the influence of CAFs on ER+ breast cancer progression and to evaluate strategies to mitigate their impact. We develop a system of nonlinear ordinary differential equations that substantiates the experimental observations by providing a mechanistic basis for the role of CAFs in regulating estrogen-independent growth dynamics. We then employ optimal control theory to evaluate distinct therapeutic approaches involving monotherapy or combinations of: (i) inhibition of tumor-to-CAF signaling, (ii) inhibition of CAF-to-tumor proliferative signaling, and (iii) endocrine therapy. Taken together, our results demonstrate that CAF-targeted strategies can enhance treatment efficacy across various estrogen dosing regimens. Our study provides new insights into the potential of CAF as a therapeutic target that could help to improve existing approaches for endocrine therapies.