Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model.

INTRODUCTION: B-cells are essential components of the immune system that neutralize infectious agents through the generation of antigen-specific antibodies and through the phagocytic functions of naïve and memory B-cells. However, the B-cell response can become compromised by a variety of conditions that alter the overall inflammatory milieu, be that due to substantial, acute insults as seen in sepsis, or due to those that produce low-level, smoldering background inflammation such as diabetes, obesity, or advanced age. This B-cell dysfunction, mediated by the inflammatory cytokines Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), increases the susceptibility of late-stage sepsis patients to nosocomial infections and increases the incidence or severity of recurrent infections, such as SARS-CoV-2, in those with chronic conditions. We propose that modeling B-cell dynamics can aid the investigation of their responses to different levels and patterns of systemic inflammation. METHODS: The B-cell Immunity Agent-based Model (BCIABM) was developed by integrating knowledge regarding naïve B-cells, short-lived plasma cells, long-lived plasma cells, memory B-cells, and regulatory B-cells, along with their various differentiation pathways and cytokines/mediators. The BCIABM was calibrated to reflect physiologic behaviors in response to: 1) mild antigen stimuli expected to result in immune sensitization through the generation of effective immune memory, and 2) severe antigen challenges representing the acute substantial inflammation seen during sepsis, previously documented in studies on B-cell behavior in septic patients. Once calibrated, the BCIABM was used to simulate the B-cell response to repeat antigen stimuli during states of low, chronic background inflammation, implemented as low background levels of IL-6 and TNF-α often seen in patients with conditions such as diabetes, obesity, or advanced age. The levels of immune responsiveness were evaluated and validated by comparing to a Veteran's Administration (VA) patient cohort with COVID-19 infection known to have a higher incidence of such comorbidities. RESULTS: The BCIABM was successfully able to reproduce the expected appropriate development of immune memory to mild antigen exposure, as well as the immunoparalysis seen in septic patients. Simulation experiments then revealed significantly decreased B-cell responsiveness as levels of background chronic inflammation increased, reproducing the different COVID-19 infection data seen in a VA population. CONCLUSION: The BCIABM proved useful in dynamically representing known mechanisms of B-cell function and reproduced immune memory responses across a range of different antigen exposures and inflammatory statuses. These results elucidate previous studies demonstrating a similar negative correlation between the B-cell response and background inflammation by positing an established and conserved mechanism that explains B-cell dysfunction across a wide range of phenotypic presentations.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds.

INTRODUCTION: The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). METHODS: Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy. RESULTS: The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted. CONCLUSIONS: These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

Defining Risk and Risk Factors for Unplanned ICU Admission of Trauma Patients.

BACKGROUND: Unplanned ICU admissions (up-ICUad) are associated with poor outcomes. It is difficult to identify who is at risk for up-ICUad in trauma patients. This study aimed to identify injury patterns and comorbidities associated with up-ICUad and develop a predictive tool for who is at risk. METHODS: A retrospective study compared trauma patients admitted to the floor who experienced an up-ICUad to similar patients without an up-ICUad. Univariate analysis and multivariate logistic regression identified independent risk factors associated with up-ICUad. Based on those factors, a Risk Score (RS) was created and compared between the two groups. RESULTS: 2.15% of the 7206 patients experienced an up-ICUad. The up-ICUad group was older, experienced longer length of stay, and had higher mortality. Age, congestive heart failure, COPD, peptic ulcer disease, mild liver disease, CKD, and significant injuries to the thorax, spine, and lower extremities were independently associated with up-ICUad. A RS equation was created and was used for each patient. CONCLUSIONS: Trauma patients are at increased risk for up-ICUad based on specific factors. These factors can be used to calculate a RS to determine who is at greatest risk for an up-ICUad which may be helpful for preventing up-ICUad.

Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds.

BACKGROUND: The clinical characterization of the biological status of complex wounds remains a considerable challenge. Digital photography provides a non-invasive means of obtaining wound information and is currently employed to assess wounds qualitatively. Advances in machine learning (ML) image processing provide a means of identifying "hidden" features in pictures. This pilot study trains a convolutional neural network (CNN) to predict gene expression based on digital photographs of wounds in a canine model of volumetric muscle loss (VML). MATERIALS AND METHODS: Images of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. A CNN was trained to regress gene expression values as a function of the extracted image segment (color and spatial distribution). Performance of the CNN was assessed in a held-back test set of images using Mean Absolute Percentage Error (MAPE). RESULTS: The CNN was able to predict the gene expression of certain genes based on digital images, with a MAPE ranging from ∼10% to ∼30%, indicating the presence and identification of distinct, and identifiable patterns in gene expression throughout the wound. CONCLUSIONS: These initial results suggest promise for further research regarding this novel use of ML regression on medical images. Specifically, the use of CNNs to determine the mechanistic biological state of a VML wound could aid both the design of future mechanistic interventions and the design of trials to test those therapies. Future work will expand the CNN training and/or test set, with potential expansion to predicting functional gene modules.

Frequency and Predictors of Trauma Transfer Futility to a Rural Level I Trauma Center.

INTRODUCTION: Transfer of trauma patients whose injuries are deemed unsurvivable, often results in early death or transition to comfort care and could be considered misuse of health care resources. This is particularly true where tertiary care resources are limited. Identifying riskfactors for and predicting futile transfers could reduce this impact and help to optimize triage and management. METHODS: A retrospective study of interfacility trauma transfers to a single rural Level I rauma center from 2014 to 2019. Futility was defined as death, hospice, or declaration of comfort measures within 48 h of transfer without procedural or radiographic intervention at the accepting center. Multiple logistic regressions identified independent predictors of futile transfers. The predictive power of Mechanism,Glasgow coma scale, Age, and Arterial pressure (MGAP), an injury severity score based on Mechanism, Glasgow coma scale, Age, and systolic blood Pressure, were evaluated. RESULTS: Of the 3368 trauma transfers, 37 (1.1%) met criteria as futile. Futile transfers occurred among patients who were significantly older with falls as the most common mechanism. Age, Glasgow coma scale, systolic blood Pressure and Injury Severity Score were significant (P < 0.05) independent predictors of futile transfer. MGAP had a high predictive power area under the receiver operating characteristic (AUROC 0.864, 95% confidence interval 0.803-0.925) for futility. CONCLUSIONS: A small proportion (1.1%) of transfers to a rural Level I trauma center met criteria for futility. Predictive tools, such as MGAP scoring, can provide objective criteria for evaluation of transfer necessity and prompt care pathways that involve pre-transfer communications, telemedicine, and/or patient centered goals of care discussions. Such tools could be used in conjunction with a more granular assessment regarding potential operational barriers to reduce futile transfers and to enhance optimization of resource utilization in low-resource service areas.

Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype.

Artificial intelligence (AI) has made increasing inroads in clinical medicine. In surgery, machine learning-based algorithms are being studied for use as decision aids in risk prediction and even for intraoperative applications, including image recognition and video analysis. While AI has great promise in surgery, these algorithms come with a series of potential pitfalls that cannot be ignored as hospital systems and surgeons consider implementing these technologies. The aim of this review is to discuss the progress, promise, and pitfalls of AI in surgery.

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models.

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model's rule set. The model's internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers.

The Acute Care Surgery model has been widely adopted by hospitals across the United States, with Acute Care Surgery services managing Emergency General Surgery patients that were previously being treated by General Surgery. In this analysis, we evaluate the impact of an Acute Care Surgery service model on General Surgery at the University of Vermont Medical Center using three metrics: under-utilized time, spillover time, and a financial ratio of work Relative Value Units over clinical Full Time Equivalents. These metrics are evaluated and used to identify three-dimensional Pareto optimality of General Surgery prior to and after the October 2015 tactical allocation to the Acute Care Surgery model. Our analysis was further substantiated using a Markov Chain Monte Carlo model for Bayesian Inference. We applied multi-objective Pareto and Bayesian breakpoint analysis to three operating room metrics to assess the impact of new operating room management decisions. In the two-dimensional space of Fig. 2, panel a), the post-tactical allocation front lies closer to the origin representing more optimal solutions for productivity and under-utilized time. The post-tactical allocation front is also closer to the origin for productivity and spillover time as shown in the two-dimensional space of Fig. 2, panel b). The results of the three-dimensional multi-objective analysis of Fig. 3 illustrate that the GS post-tactical allocation Pareto-surface is contained within a much smaller volume of space than the GS pre-tactical allocation Pareto-surface. The post-tactical allocation Pareto-surface is slightly lower along the z-axis, representing lower productivity than the pre-tactical allocation surface. This methodology might contribute to the external benchmarking and monitoring of perioperative services by visualizing the operational implications following tactical decisions in operating room management.

A System Dynamics Model of Violent Trauma and the Role of Violence Intervention Programs.

BACKGROUND: Violence intervention programs (VIPs) can reduce interpersonal violence (IPV); however, optimizing the implementation of VIPs is challenging, given the complex dynamics of IPV. System dynamics models (SDMs) provide a means of visualizing dynamic and causal relationships in such complex systems. We use the IPVSDM to characterize and examine the relationship between IPV, VIPs, and the social determinants of health (SDH). MATERIALS AND METHODS: The simulation model was created from a diagram that links putative causal relationships between VIPs, SDH, and IPV events. Simulation rules are then used to calculate a risk of violence parameter based on the SDH, which drives the transition from low-risk to high-risk populations and in turn influences IPV event rates. A qualitative relational approach was used to evaluate long-term effects of VIP on IPV events. RESULTS: The model produced qualitatively plausible behavior with respect to IPV events, population transitions, and relative overall VIP effect. Simulation runs converged to stable steady states with an exponential benefit of VIP on reducing IPV that is best appreciated after 1-2 y. The VIP functioned in a recognizable fashion by slowing the shift from low-risk to high-risk populations. CONCLUSIONS: This initial implementation of the IPVSDM produced recognizable baseline behavior while incorporating the possible effects of a VIP. The model allows causality and counterfactual testing, which is impractical in vivo. Community-level VIP efforts should show benefit particularly after a couple years. Future work will emphasize adding complexity to the IPVSDM and identifying real-world metrics to aid in testing, validation, and prediction of the model.

Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation.

Sepsis, a manifestation of the body's inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical "sepsis," and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true "precision control" of sepsis.

Application of an external fixator vascular compressor (EFVC) in the critically injured trauma patient: a novel damage control technique.

Methods of controlling hemorrhage in penetrating abdominal injuries are varied, ranging from electrocautery, ligation, laparotomy sponge packing, angiography, hemostatic agents, and direct manual pressure. Unfortunately, traditional methods are sometimes unsuccessful due to the location or nature of the hemorrhage, and manual pressure cannot be held indefinitely. We describe a novel damage control technique for hemorrhage control in these situations, followed by three cases where an external fixator vascular compressor (EFVC) was used to hold continual pressure. Three patients are presented to a Level 1 trauma center following multiple ballistic injuries, all requiring emergent exploratory laparotomy. The first had a two-pin iliac crest EFVC placed during repeat exploratory laparotomy to control bleeding. The second patient had a supra-acetabular EFVC placed during initial exploratory laparotomy after emergent embolization failed to control bleeding from the L3 vertebral body. The third patient had a two-pin iliac crest EFVC placed at initial exploratory laparotomy due to uncontrollable bleeding from the sacral venous plexus and internal iliac veins. Of the three patients, two stabilized and survived, while one passed away due to multi-organ failure. We describe a novel damage control technique that may be a useful means of temporarily stemming intraabdominal bleeding that is otherwise recalcitrant to traditional hemostatic methods. Additionally, we provided a limited case series of patients who have undergone this technique to illustrate its utility and versatility. This technique is simple, fast, effective, and adaptable to a variety of circumstances that may be encountered in patients with intraabdominal bleeding recalcitrant to conventional hemorrhage control.

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow.

BACKGROUND: Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies-one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization-can provide a computational means for high-throughput hypothesis testing, and eventually, optimization. RESULTS: In this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization. CONCLUSIONS: While key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

The Crisis of Reproducibility, the Denominator Problem and the Scientific Role of Multi-scale Modeling.

The "Crisis of Reproducibility" has received considerable attention both within the scientific community and without. While factors associated with scientific culture and practical practice are most often invoked, I propose that the Crisis of Reproducibility is ultimately a failure of generalization with a fundamental scientific basis in the methods used for biomedical research. The Denominator Problem describes how limitations intrinsic to the two primary approaches of biomedical research, clinical studies and preclinical experimental biology, lead to an inability to effectively characterize the full extent of biological heterogeneity, which compromises the task of generalizing acquired knowledge. Drawing on the example of the unifying role of theory in the physical sciences, I propose that multi-scale mathematical and dynamic computational models, when mapped to the modular structure of biological systems, can serve a unifying role as formal representations of what is conserved and similar from one biological context to another. This ability to explicitly describe the generation of heterogeneity from similarity addresses the Denominator Problem and provides a scientific response to the Crisis of Reproducibility.

Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model.

OBJECTIVES: Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28-50% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary conditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation. DESIGN: Current predictive models for sepsis use correlative methods that are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods. RESULTS: The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days in a sweep across the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic regions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis. CONCLUSIONS: HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish "boundaries of futility" with respect to existing investigatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. The issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the "Crisis of Reproducibility" at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time).

Insights into the pathogenesis of ulcerative colitis from a murine model of stasis-induced dysbiosis, colonic metaplasia, and genetic susceptibility.

Gut dysbiosis, host genetics, and environmental triggers are implicated as causative factors in inflammatory bowel disease (IBD), yet mechanistic insights are lacking. Longitudinal analysis of ulcerative colitis (UC) patients following total colectomy with ileal anal anastomosis (IPAA) where >50% develop pouchitis offers a unique setting to examine cause vs. effect. To recapitulate human IPAA, we employed a mouse model of surgically created blind self-filling (SFL) and self-emptying (SEL) ileal loops using wild-type (WT), IL-10 knockout (KO) (IL-10), TLR4 KO (T4), and IL-10/T4 double KO mice. After 5 wk, loop histology, host gene/protein expression, and bacterial 16s rRNA profiles were examined. SFL exhibit fecal stasis due to directional motility oriented toward the loop end, whereas SEL remain empty. In WT mice, SFL, but not SEL, develop pouchlike microbial communities without accompanying active inflammation. However, in genetically susceptible IL-10-deficient mice, SFL, but not SEL, exhibit severe inflammation and mucosal transcriptomes resembling human pouchitis. The inflammation associated with IL-10 required TLR4, as animals lacking both pathways displayed little disease. Furthermore, germ-free IL-10 mice conventionalized with SFL, but not SEL, microbiota populations develop severe colitis. These data support essential roles of stasis-induced, colon-like microbiota, TLR4-mediated colonic metaplasia, and genetic susceptibility in the development of pouchitis and possibly UC. However, these factors by themselves are not sufficient. Similarities between this model and human UC/pouchitis provide opportunities for gaining insights into the mechanistic basis of IBD and for identification of targets for novel preventative and therapeutic interventions.

A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury.

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to "better" vs. "worse" outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU.

Investigation of inflammation and tissue patterning in the gut using a Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT).

The mucosa of the intestinal tract represents a finely tuned system where tissue structure strongly influences, and is turn influenced by, its function as both an absorptive surface and a defensive barrier. Mucosal architecture and histology plays a key role in the diagnosis, characterization and pathophysiology of a host of gastrointestinal diseases. Inflammation is a significant factor in the pathogenesis in many gastrointestinal diseases, and is perhaps the most clinically significant control factor governing the maintenance of the mucosal architecture by morphogenic pathways. We propose that appropriate characterization of the role of inflammation as a controller of enteric mucosal tissue patterning requires understanding the underlying cellular and molecular dynamics that determine the epithelial crypt-villus architecture across a range of conditions from health to disease. Towards this end we have developed the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) to dynamically represent existing knowledge of the behavior of enteric epithelial tissue as influenced by inflammation with the ability to generate a variety of pathophysiological processes within a common platform and from a common knowledge base. In addition to reproducing healthy ileal mucosal dynamics as well as a series of morphogen knock-out/inhibition experiments, SEGMEnT provides insight into a range of clinically relevant cellular-molecular mechanisms, such as a putative role for Phosphotase and tensin homolog/phosphoinositide 3-kinase (PTEN/PI3K) as a key point of crosstalk between inflammation and morphogenesis, the protective role of enterocyte sloughing in enteric ischemia-reperfusion and chronic low level inflammation as a driver for colonic metaplasia. These results suggest that SEGMEnT can serve as an integrating platform for the study of inflammation in gastrointestinal disease.

Investigation of the essential role of platelet-tumor cell interactions in metastasis progression using an agent-based model.

BACKGROUND: Metastatic tumors are a major source of morbidity and mortality for most cancers. Interaction of circulating tumor cells with endothelium, platelets and neutrophils play an important role in the early stages of metastasis formation. These complex dynamics have proven difficult to study in experimental models. Prior computational models of metastases have focused on tumor cell growth in a host environment, or prediction of metastasis formation from clinical data. We used agent-based modeling (ABM) to dynamically represent hypotheses of essential steps involved in circulating tumor cell adhesion and interaction with other circulating cells, examine their functional constraints, and predict effects of inhibiting specific mechanisms. METHODS: We developed an ABM of Early Metastasis (ABMEM), a descriptive semi-mechanistic model that replicates experimentally observed behaviors of populations of circulating tumor cells, neutrophils, platelets and endothelial cells while incorporating representations of known surface receptor, autocrine and paracrine interactions. Essential downstream cellular processes were incorporated to simulate activation in response to stimuli, and calibrated with experimental data. The ABMEM was used to identify potential points of interdiction through examination of dynamic outcomes such as rate of tumor cell binding after inhibition of specific platelet or tumor receptors. RESULTS: The ABMEM reproduced experimental data concerning neutrophil rolling over endothelial cells, inflammation-induced binding between neutrophils and platelets, and tumor cell interactions with these cells. Simulated platelet inhibition with anti-platelet drugs produced unstable aggregates with frequent detachment and re-binding. The ABMEM replicates findings from experimental models of circulating tumor cell adhesion, and suggests platelets play a critical role in this pre-requisite for metastasis formation. Similar effects were observed with inhibition of tumor integrin αV/ß3. These findings suggest that anti-platelet or anti-integrin therapies may decrease metastasis by preventing stable circulating tumor cell adhesion. CONCLUSION: Circulating tumor cell adhesion is a complex, dynamic process involving multiple cell-cell interactions. The ABMEM successfully captures the essential interactions necessary for this process, and allows for in-silico iterative characterization and invalidation of proposed hypotheses regarding this process in conjunction with in-vitro and in-vivo models. Our results suggest that anti-platelet therapies and anti-integrin therapies may play a promising role in inhibiting metastasis formation.

Agent-based model of fecal microbial transplant effect on bile acid metabolism on suppressing Clostridium difficile infection: an example of agent-based modeling of intestinal bacterial infection.

Agent-based modeling is a computational modeling method that represents system-level behavior as arising from multiple interactions between the multiple components that make up a system. Biological systems are thus readily described using agent-based models (ABMs), as multi-cellular organisms can be viewed as populations of interacting cells, and microbial systems manifest as colonies of individual microbes. Intersections between these two domains underlie an increasing number of pathophysiological processes, and the intestinal tract represents one of the most significant locations for these inter-domain interactions, so much so that it can be considered an internal ecology of varying robustness and function. Intestinal infections represent significant disturbances of this internal ecology, and one of the most clinically relevant intestinal infections is Clostridium difficile infection (CDI). CDI is precipitated by the use of broad-spectrum antibiotics, involves the depletion of commensal microbiota, and alterations in bile acid composition in the intestinal lumen. We present an example ABM of CDI (the C. difficile Infection ABM, or CDIABM) to examine fundamental dynamics of the pathogenesis of CDI and its response to treatment with anti-CDI antibiotics and a newer treatment therapy, fecal microbial transplant. The CDIABM focuses on one specific mechanism of potential CDI suppression: commensal modulation of bile acid composition. Even given its abstraction, the CDIABM reproduces essential dynamics of CDI and its response to therapy, and identifies a paradoxical zone of behavior that provides insight into the role of intestinal nutritional status and the efficacy of anti-CDI therapies. It is hoped that this use case example of the CDIABM can demonstrate the usefulness of both agent-based modeling and the application of abstract functional representation as the biomedical community seeks to address the challenges of increasingly complex diseases with the goal of personalized medicine.

A design specification for Critical Illness Digital Twins (CIDTs) to cure sepsis: responding to the National Academies of Sciences, Engineering and Medicine Report "Foundational Research Gaps and Future Directions for Digital Twins".

On December 15, 2023, The National Academies of Sciences, Engineering and Medicine (NASEM) released a report entitled: "Foundational Research Gaps and Future Directions for Digital Twins." The ostensible purpose of this report was to bring some structure to the burgeoning field of digital twins by providing a working definition and a series of research challenges that need to be addressed to allow this technology to fulfill its full potential. In the work presented herein we focus on five specific findings from the NASEM Report: 1) definition of a Digital Twin, 2) using "fit-for-purpose" guidance, 3) developing novel approaches to Verification, Validation and Uncertainty Quantification (VVUQ) of Digital Twins, 4) incorporating control as an explicit purpose for a Digital Twin and 5) using a Digital Twin to guide data collection and sensor development, and describe how these findings are addressed through the design specifications for a Critical Illness Digital Twin (CIDT) aimed at curing sepsis.