Mechanistic Computational Model Research Articles

Abstract 4139141: Predicted efficacy of inclisiran on cardiovascular outcomes in lower extremity peripheral artery disease: results of the In Silico Sirius study

Introduction: Symptomatic lower extremity peripheral artery disease (PAD) patients have the highest risk of cardiovascular (CV) outcomes among patients with established atherosclerotic cardiovascular disease (ASCVD). Inclisiran, an siRNA targeting PCSK9 mRNA, reduces LDL-C levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower CV events in ASCVD patients. Research question/Hypothesis: SIRIUS in silico study aims to predict the efficacy of inclisiran on CV outcomes in subgroups of patients with or without PAD. Methods: The SIRIUS in silico trial was conducted using a knowledge-based mechanistic computational model of ASCVD applied to a virtual ASCVD population with LDL-C ≥ 70 mg/dL. Each virtual patient is its own control. This model was previously calibrated and validated before running the trial. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statins with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal ischemic stroke (IS) over 5 years. Occurrence of major acute limb events (MALE) was also individually assessed in time-to-first-event analyses. Results: Among 204, 691 virtual SIRIUS ASCVD patients, 28,072 (13.7%) had PAD. At 5 years, the mean predicted percentage reduction in LDL-C with inclisiran as compared to placebo was -49.3% and -49.8% in patients with or without PAD, respectively. The predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm in patients with PAD (17.62% vs 22.88%; Hazard Ratio (HR): 0.75 medium uncertainty) and in patients without PAD (10.33% vs 13.64%; HR: 0.75 low uncertainty). Compared to placebo, inclisiran was also predicted to consistently reduce MALE in patients with or without PAD (2.71% vs 4.11%; HR: 0.65 medium uncertainty and 0.19% vs 0.29%; HR: 0.66 high uncertainty respectively). Conclusion: Pending the results of ORION-4 and VICTORION-2-P, this first In Silico trial in virtual PAD patients predicted a potential effect of inclisiran on 3P-MACE and MALE reductions over 5 years follow-up.

Abstract 4134364: Predicted Low-Density Lipoprotein Cholesterol and Cardiovascular Outcomes Lowering With Inclisiran in Patients With or Without stroke: Insights from SIRIUS in silico trial.

Introduction: Inclisiran, an siRNA, targeting PCSK9 mRNA, reduces LDL-c levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower cardiovascular (CV) events in virtual patients with atherosclerotic cardiovascular disease (ASCVD). Research question: This analysis predicted the potential efficacy of inclisiran on CV outcomes in virtual patients with or without prior ischemic stroke (IS). Methods: The SIRIUS trial was conducted using a calibrated and validated knowledge-based mechanistic computational model of ASCVD applied to a virtual population with LDL-C ≥ 70 mg/dL. Each virtual patient was its own control. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statin with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal IS over 5 years in patients with or without prior IS. Occurrence of fatal and non-fatal (IS) was also individually assessed in time-to-first-event analyses. Results: Among 204,691 virtual SIRIUS ASCVD patients, 39 371 (19%) had prior IS. At 5 years, the predicted mean percentage reduction in LDL-C with inclisiran as compared to placebo was –49.17% and –49.88% in patients with or without prior IS respectively. Patients with prior IS were at higher risk of 3P-MACE than patients without IS both with placebo and inclisiran (17.01% vs 14.41% with placebo and 13.44% vs 10.83% with inclisiran). However, the predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm for both prior IS and no prior IS (HR 0.78 medium uncertainty and 0.74 low uncertainty respectively). Compared to placebo, inclisiran was also predicted to consistently reduce fatal and non-fatal IS in patients with or without prior IS (5.45% vs 7.22%; HR: 0.75 medium uncertainty and 1.87% vs 2.54%; HR: 0.73 medium uncertainty respectively). Conclusion: SIRIUS provides insights into the potential efficacy of inclisiran on CV events suggesting a substantial 3P-MACE and fatal and non-fatal IS reduction in ASCVD patients including those with prior IS, several years before the availability of results from ongoing outcomes trials (ORION-4, VICTORION-2P).

Computational biomechanics for a standing human body: Modal analysis and simulation.

We develop computational mechanical modeling and methods for the analysis and simulation of the motions of a human body. This type of work is crucial in many aspects of human life, ranging from comfort in riding, the motion of aged persons, sports performance and injuries, and many ergonomic issues. A prevailing approach for human motion studies is through lumped parameter models containing discrete masses for the parts of the human body with empirically determined spring, mass, damping coefficients. Such models have been effective to some extent; however, a much more faithful modeling method is to model the human body as it is, namely, as a continuum. We present this approach, and for comparison, we choose two digital CAD models of mannequins for a standing human body, one from the versatile software package LS-DYNA and another from open resources with some of our own adaptations. Our basic view in this paper is to regard human motion as a perturbation and vibration from an equilibrium position which is upright standing. A linear elastodynamic model is chosen for modal analysis, but a full nonlinear viscoelastoplastic extension is possible for full-body simulation. The motion and vibration of these two mannequin models is analyzed by modal analysis, where the normal vibration modes are determined. LS-DYNA is used as the supercomputing and simulation platform. Four sets of low-frequency modes are tabulated, discussed, visualized, and compared. Higher frequency modes are also selectively displayed. We have found that these modes of motion and vibration form intrinsic basic modes of biomechanical motion of the human body. This view is supported by our finding of the upright walking motion as a low-frequency mode in modal analysis. Such a "walking mode" shows the in-phase and out-of-phase movements between the legs and arms on the left and right sides of a human body, implying that this walking motion is spontaneous, likely not requiring any directives from the brain. Dynamic motions of CAD mannequins are also simulated by drop tests for comparisons and the validity of the models is discussed through Fourier frequency analysis. All computed modes of motion are collected in several sets of video animations for ease of visualization. Samples of LS-DYNA computer codes are also included for possible use by other researchers.

Mechanistic modeling of cell viability assays with in silico lineage tracing.

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) that the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment all impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability assay datasets and better understanding determinants of cellular heterogeneity in drug response.

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

This study presents a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.

Tailored nano-pillar structures on surfaces: facile formation and multifunctional properties

Nano-pillar structured surfaces have prized for exceptional optoelectronic properties and biocompatibility but commonly complex, expensive, and inflexible in production. A systematic investigation of the formation, mechanisms, and properties of nanostructures induced by nano-capillaries remains unexplored. Through this study, the facile formation of nano-pillared surfaces induced by designable nano-capillaries on AAO template demonstrated significant improvements in antibacterial effect, spectral structural color and anti-reflection characteristics. Reverse molding of nano-pillar arrays with aspect ratios of up to 1.2 on polydimethylsiloxane (PDMS) films yielded a super-hydrophobic surface of 150° contact angle (CA). Through the utilization of computational fluid mechanics model, a direct correlation was demonstrated between the aspect ratio of nano-pillars and the viscosity of PDMS. The nano-pillared surfaces had remarkable antibacterial rates of 91% for Escherichia coli and 89% for Staphylococcus aureus. In addition, the structured surfaces exhibited color spanning from light blue to light red by tuning nano-pillars dimensions, incident light angle, and/or adding reflective metallic coating, which offered unique visualization effects for anti-counterfeiting. Moreover, the superior light-trapping and anti-reflection of the nano-pillared surface significantly increased the power conversion efficiency (PCE) 19% from the base performance of the polysilicon solar cells.

Spleen Tyrosine Kinase (SYK) negatively regulates ITAM-mediated human NK cell signaling and CD19-CAR NK cell efficacy.

NK cells express activating receptors that signal through ITAM-bearing adapter proteins. The phosphorylation of each ITAM creates binding sites for SYK and ZAP70 protein tyrosine kinases to propagate downstream signaling including the induction of Ca 2+ influx. While all immature and mature human NK cells co-express SYK and ZAP70, clonally driven memory or adaptive NK cells can methylate SYK genes and signaling is mediated exclusively using ZAP70. Here, we examined the role of SYK and ZAP70 in a clonal human NK cell line KHYG1 by CRISPR-based deletion using a combination of experiments and mechanistic computational modeling. Elimination of SYK resulted in more robust Ca ++ influx after cross-linking of the CD16 and NKp30 receptors and enhanced phosphorylation of downstream proteins, whereas ZAP70 deletion diminished these responses. By contrast, ZAP70 depletion increased proliferation of the NK cells. As immature T cells express both SYK and ZAP70 but mature T cells often express only ZAP70, we transduced the human Jurkat cell line with SYK and found that expression of SYK increased proliferation but diminished TCR-induced Ca 2+ flux and activation. We performed transcriptional analysis of the matched sets of variant Jurkat and KHYG1 cells and observed profound alterations caused by SYK expression. As depletion of SYK in NK cells increased their activation, primary human NK cells were transduced with a CD19-targeting CAR and were CRISPR edited to ablate SYK or ZAP70 . Deletion of SYK resulted in more robust cytotoxic activity and cytokine production, providing a new therapeutic strategy of NK cell engineering for cancer immunotherapy.

Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors.

The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.

Efficient two-way fluid–structure interaction simulation for performance prediction of pressure-compensating emitter

Drip irrigation using a high-performance pressure-compensating (PC) emitter is one of the essential components for precision agriculture, and it is necessary to accurately predict its performance prior to design. In this study, an efficient two-way fluid–structure interaction (FSI) simulation model was developed and verified through an enlarged model experiment. The computational fluid dynamics (CFD) and computational solid mechanics (CSM) models of the FSI simulation were systematically verified, and a calibration method for the overestimated flow rate in the re-rising range was applied. The CFD model was determined to be the shear stress transport turbulence model, and the CSM model was determined to be the Ogden hyperelastic model for the PC emitter. The minimum prediction error for the flow rate was 7.93%, which was within 10% for all cases. The simulation model demonstrated its efficiency by analysing the performance of a single PC emitter with an average total analysis time of 18.6 h. In addition, by comparing various cases according to the design parameters, it is considered that the hardness of the diaphragm has a significant impact on the design of low-pressure PC emitters. The simulation model of this study can accurately predict the performance of PC emitter under specific conditions, yet improvement of simulation model is required to be applied in design optimisation. Future studies may benefit from combining an improved FSI simulation with a surrogate model to further enhance optimisation efforts.

Prospective validation of in silico clinical trials prediction using an EGFR-mutated NSCLC mechanistic computational disease model.

8614 Background: Advanced lung adenocarcinoma (aLUAD) is divided into multiple molecularly defined subsets, each with specific biological characteristics and responses to targeted therapy. Consequently, conducting practice-changing and cost-effective randomized trials is challenging. In silico clinical trials, utilizing mechanistic computational models, offer a potential solution, yet their ability to predict clinical trial results has not been proven. Methods: We updated a previously published version of the computational mechanistic model of EGFR-mutated aLUAD, notably by incorporating metastases. Disease model was integrated with physiologically based pharmacokinetic (PBPK) models of cisplatin, pemetrexed, and osimertinib, offering a more comprehensive understanding of the disease's dynamics. These models are founded on causal connections between biological entities and phenomena, establishing a link between the specific biological behaviors of EGFR-mutated aLUAD and the evolution of tumor size and progression over time. We have independently and prospectively simulated the outcomes of the FLAURA2 and MARIPOSA randomized trials dedicated to EGFR-mutated aLUAD. This was achieved by replicating the experimental protocols and generating cohorts of virtual twins of patients using publicly available data. Results: In silico results were released before the actual outcomes of the FLAURA2 and MARIPOSA trials were publicly available. The simulations yielded predictions with overlapping confidence intervals, similar hazard ratios, median survivals, and shapes of curves compared to the actual trial data. Bootstrapped weighted log-rank tests were used to compare the simulated and observed Kaplan-Meier curves for the two arms of FLAURA2 and for the osimertinib arm of MARIPOSA. Between 94 and 98% of the tests were statistically non-significant (α=0.05), confirming the accuracy of the predictions. Conclusions: This first-of-its-kind prediction of clinical trial outcomes demonstrates that in silico trials, when based on robust mechanistic models, can be a reliable tool for enhancing the design of actual trials, particularly for EGFR-mutated aLUAD. They offer a promising avenue for overcoming the challenges of comparator arms in single-arm trials and may serve as a new standard for formulating statistical hypotheses in future studies.

Digital Twins of Acute Hypoxemic Respiratory Failure Patients Suggest a Mechanistic Basis for Success and Failure of Noninvasive Ventilation.

To clarify the mechanistic basis for the success or failure of noninvasive ventilation (NIV) in acute hypoxemic respiratory failure (AHRF). We created digital twins based on mechanistic computational models of individual patients with AHRF. Interdisciplinary Collaboration in Systems Medicine Research Network. We used individual patient data from 30 moderate-to-severe AHRF patients who had failed high-flow nasal cannula (HFNC) therapy and subsequently underwent a trial of NIV. Using the digital twins, we evaluated lung mechanics, quantified the separate contributions of external support and patient respiratory effort to lung injury indices, and investigated their relative impact on NIV success or failure. In digital twins of patients who successfully completed/failed NIV, after 2 hours of the trial the mean (sd) of the change in total lung stress was -10.9 (6.2)/-0.35 (3.38) cm H2O, mechanical power -13.4 (12.2)/-1.0 (5.4) J/min, and total lung strain 0.02 (0.24)/0.16 (0.30). In the digital twins, positive end-expiratory pressure (PEEP) produced by HFNC was similar to that set during NIV. In digital twins of patients who failed NIV vs. those who succeeded, intrinsic PEEP was 3.5 (0.6) vs. 2.3 (0.8) cm H2O, inspiratory pressure support was 8.3 (5.9) vs. 22.3 (7.2) cm H2O, and tidal volume was 10.9 (1.2) vs. 9.4 (1.8) mL/kg. In digital twins, successful NIV increased respiratory system compliance +25.0 (16.4) mL/cm H2O, lowered inspiratory muscle pressure -9.7 (9.6) cm H2O, and reduced the contribution of patient spontaneous breathing to total driving pressure by 57.0%. In digital twins of AHRF patients, successful NIV improved lung mechanics, lowering respiratory effort and indices associated with lung injury. NIV failed in patients for whom only low levels of positive inspiratory pressure support could be applied without risking patient self-inflicted lung injury due to excessive tidal volumes.

Multiphysics performance evaluation of thermoelectric generator arrays

Thermoelectric generators (TEGs) are widely used nowadays in heat recovery and power generation applications. TEGs are connected in series and/or parallel configurations in an array to meet the voltage and/or current requirements of electric load. Therefore, it is necessary to examine the electrical performance of different TEG array configurations employed in TEG systems. In the present study, an experimentally validated hydro-thermoelectric multiphysics computational model is employed by integrating the computational fluid dynamics (CFD) approach with the TEG model to test the electrical performance of various TEG array configurations and determined load voltage, current, associated power, and conversion efficiency. Each TEGs array configuration’s performance is evaluated by varying the hot water temperature from 27 °C to 42 °C as well as hot and cold water flowrate from 0.5 L/min to 2 L/min. Various configurations including series and a combination of series and parallel connections were designed. The findings show that the series configuration of the TEGs array generates higher electric power than series and parallel combined configurations whereas the latter achieves the maximum power point at a higher electric current than the series configuration. The comparative analysis of CFD model with the experimental results indicates a maximum discrepancy of 7 %. For thermoelectric power-generating systems, the proposed model might serve as a benchmark for optimizing TEGs array configurations according to the electric load requirements.

Digital twins for cardiac electrophysiology: state of the art and future challenges.

Cardiac arrhythmias remain amajor cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as apromising strategy to realize the vision of precision cardiology. While there is no uniform definition of adigital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides ashort overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable asignificant precision medicine-based advancement in cardiac arrhythmia management.

Evolution of stress field and plastic failure characteristics non-isobaric narrow gas storage spaces

The uncertainty of stress distribution and plastic damage threatens the safety of underground gas storage facilities. The bi-directional computational mechanics model is established first, the analytical solution for stress calculation and the implicit equation of plastic zone boundaries are derived. Then, the distribution of stress field in non-isobaric narrow gas storage spaces are studied, and the distribution law of plastic zone is revealed. The results show that: (1) The analytical solution of the surrounding rock of the non-isobaric gas storage roadway is derived, and the implicit equation of the boundary of the plastic zone is obtained; (2) The maximum principal stress exhibits an axisymmetric distribution, gradually decreasing with the increase of the distance; (3) As the stress P0 increases, the pressure equilibrium point increases; as λ increases, the pressure equilibrium point decreases first and then increases; (4) λ is more sensitive to roof failure than the two gangs; When λ is less than 1, the “elliptical” plastic zone appears at the roof, manifested as tensile and shear failure; when λ is 1, the “circular” plastic zone appear, manifested as shear failure; when λ is greater than 1, the elliptical plastic zone appears at the both sides, manifested as shear failure.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.

Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.

Introduction: Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between endothelial cells under normoxia and hypoxia and their response to different stimuli can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. Methods: We calibrate and fit the model parameters based on well-established modeling techniques that include structural and practical parameter identifiability, uncertainty quantification, and global sensitivity. Results: Our results indicate that the main pathways involved in patterning tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia interferes with how different stimuli affect patterning. Additionally, our simulations indicate that Notch signaling might regulate vascular permeability and establish different Nitric Oxide release patterns for tip/stalk cells. Following simulations with various stimuli, our model suggests that factors such as time under hypoxia and oxygen availability must be considered for EC pattern control. Discussion: This project provides insights into the signaling and patterning of endothelial cells under various oxygen levels and stimulation by VEGFA and is our first integrative approach toward achieving EC control as a method for improving angiogenesis. Overall, our model provides a computational framework that can be built on to test angiogenesis-related therapies by modulation of different pathways, such as the Notch pathway.

A computational mechanical constitutive modeling method based on thermally-activated microstructural evolution and strengthening mechanisms

A mechanical constitutive relationship is the basis for the design and application of structural materials, and the establishment of a mechanical constitutive relationship generally relies on experimental data. Herein a computational mechanical constitutive modeling method is proposed, in which all required parameters possess unambiguous physical interpretations and/or can be obtained from accurate numerical simulations. With the inclusion of interaction between dislocations and crystal defects, the dislocation density and grain size evolution with strain for polycrystalline metallic materials are mathematically modeled, and the corresponding contribution to strength is analyzed, further a computational mechanical constitutive relationship can be obtained. Its effectiveness is verified by comparing it with experimental stress-strain relationships for Cu, Al-Mg, and Cu-W alloys. This method might be a powerful tool for the design of structural materials with desirable mechanical properties.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

A computational mechanics model for producing molecular assembly using molecularly woven pantographs

A computational mechanics model for producing molecular assembly using molecularly woven pantographs