Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-beta1

A mathematical model for wound healing in the reef-building coral Pocillopora damicornis

Coral reefs, among the most diverse ecosystems on Earth, currently face major threats from pollution, unsustainable fishing practices , and perturbations in environmental parameters brought on by climate change. Corals also sustain regular wounding from other sea life and human activity. Recent reef restoration practices have even involved intentional wounding by systematically breaking coral fragments and relocating them to revitalize damaged reefs, a practice known as microfragmentation. Despite its importance, very little research has explored the inner mechanisms of wound healing in corals. Some reef-building corals have been observed to initiate an immunological response to wounding similar to that observed in mammalian species. Utilizing prior models of wound healing in mammalian species as the mathematical basis, we formulated a mechanistic model of wound healing, including observations of the immune response and tissue repair in scleractinian corals for the species Pocillopora damicornis. The model consists of four differential equations which track changes in remaining wound debris, number of cells involved in inflammation, number of cells involved in proliferation, and amount of wound closure through re-epithelialization. The model is fit to experimental wound size data from linear and circular shaped wounds on a live coral fragment. Mathematical methods, including numerical simulations and local sensitivity analysis, were used to analyze the resulting model. The parameter space was also explored to investigate drivers of other possible wound outcomes. This model serves as a first step in generating mathematical models for wound healing in corals that will not only aid in the understanding of wound healing as a whole, but also help optimize reef restoration practices and predict recovery behavior after major wounding events.

Hybrid Hydrogels for Immunoregulation and Proangiogenesis through Mild Heat Stimulation to Accelerate Whole-Process Diabetic Wound Healing

Intense and persistent oxidative stress, excessive inflammation, and impaired angiogenesis severely hinder diabetic wound healing. Bioactive hydrogel dressings with immunoregulatory and proangiogenic properties have great promise in treating diabetic wounds. However, the therapeutic effects of dressings always depend on drugs with side effects, expensive cytokines, and cell therapies. Herein, a novel dynamic borate-bonds crosslinked hybrid multifunctional hydrogel dressings with photothermal properties are developed to regulate the microenvironment of diabetic wound sites and accelerate the whole process of its healing without additional medication. The hydrogel is composed of phenylboronic acid-modified chitosan and hyaluronic acid (HA) crosslinked by tannic acid (TA) through borate bonds and Prussian blue nanoparticles (PBNPs) with photothermal response characteristics are embedded in the polymer networks. The results indicate hydrogels show inherent broad-spectrum antioxidative activities through the integrated interaction of borate bonds, TA, and PBNPs. Meanwhile, combined with the regulation of macrophage phenotype by HA, the inflammatory microenvironment of diabetic wounds is transformed. Moreover, the angiogenesis is then enhanced by the mild photothermal effect of PBNPs, followed by promoted epithelialization and collagen deposition. In summary, this hybrid hydrogel system accelerates all stages of wound repair through antioxidative stress, immunomodulation, and proangiogenesis, showing great potential applications in diabetic wound management.

Modelling Keloids Dynamics: A Brief Review and New Mathematical Perspectives

Keloids are fibroproliferative disorders described by excessive growth of fibrotic tissue, which also invades adjacent areas (beyond the original wound borders). Since these disorders are specific to humans (no other animal species naturally develop keloid-like tissue), experimental in vivo/in vitro research has not led to significant advances in this field. One possible approach could be to combine in vitro human models with calibrated in silico mathematical approaches (i.e., models and simulations) to generate new testable biological hypotheses related to biological mechanisms and improved treatments. Because these combined approaches do not really exist for keloid disorders, in this brief review we start by summarising the biology of these disorders, then present various types of mathematical and computational approaches used for related disorders (i.e., wound healing and solid tumours), followed by a discussion of the very few mathematical and computational models published so far to study various inflammatory and mechanical aspects of keloids. We conclude this review by discussing some open problems and mathematical opportunities offered in the context of keloid disorders by such combined in vitro/in silico approaches, and the need for multi-disciplinary research to enable clinical progress.

Modelling the Effect of Matrix Metalloproteinases in Dermal Wound Healing

With over 2 million people in the UK suffering from chronic wounds, understanding the biochemistry and pharmacology that underpins these wounds and wound healing is of high importance. Chronic wounds are characterised by high levels of matrix metalloproteinases (MMPs), which are necessary for the modification of healthy tissue in the healing process. Overexposure of MMPs, however, adversely affects healing of the wound by causing further destruction of the surrounding extracellular matrix. In this work, we propose a mathematical model that focuses on the interaction of MMPs with dermal cells using a system of partial differential equations. Using biologically realistic parameter values, this model gives rise to travelling waves corresponding to a front of healthy cells invading a wound. From the arising travelling wave analysis, we observe that deregulated apoptosis results in the emergence of chronic wounds, characterised by elevated MMP concentrations. We also observe hysteresis effects when both the apoptotic rate and MMP production rate are varied, providing further insight into the management (and potential reversal) of chronic wounds.

Extracorporeal Shockwave Therapy (ESWT) in the Management of Diabetic Foot Ulcer: A Prospective Randomized Clinical Trial

Diabetic foot ulcer (DFU) is the most common cause of prolonged hospitalization with a high cost of care due to unsatisfactory outcomes with the current mode of therapy. Extracorporeal shockwave therapy (ESWT) is a new technology in the care of nonhealing wounds. The study's main objective was to compare the healing parameters of DFUs between patients undergoing the standard of care (SOC) alone and ESWT + SOC. The secondary objective was to assess inflammatory markers in both study groups. The study was designed as a single-center, randomized trial to provide evidence on the effects of ESWT on DFU healing. Informed consent was obtained from all participants before enrolment. Forty-eight participants were recruited, enrolled, and randomly allocated into the 2 study groups. Twenty-five patients were allocated to the ESWT + SOC group, and 23 patients were allocated into the SOC-only group for a treatment period of 6 weeks. The univariate binary analysis showed more patients with healed DFU in the ESWT + SOC group than the SOC-only group at 6 weeks, though the difference did not reach statistical significance (OR = 3.2, p = .07). The adjusted multivariate binary analysis confirmed this finding; however, the effect size did not reach statistical significance at 6 weeks (OR = 3.9, p = .08). The level of circulating inflammatory markers was similar in both groups of patients. It is the author's opinion that there is a potential benefit of ESWT on diabetic wound healing with further research warranted to determine its role in treatment of DFU. A larger trial with a more extended treatment period is, however, needed to substantiate our findings.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Inhibition of TGFβ1/Smad pathway by NF-κB induces inflammation leading to poor wound healing in high glucose

This study mainly analyzed the relationship between nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and transforming growth factor-β (TGFβ1)/Smad under high glucose environment and its influence on wound healing. Fibroblast NIH-3T3 was used to analyze the effect of high concentration glucose (20 nmol/mL) on cell viability, migration ability, inflammation level and NF-κB pathway. Pyrrolidinedithiocarbamate (PDTC) was used to inhibit NF-κB for rescue experiments. Diabetic mice were used to construct wound healing models. Recombinant TGF-β1 was used to promote wound healing in diabetic mice. FSL-1 was applied to activate NF-κB to verify the mechanism. High glucose inhibited cell viability and migration ability, promoted the expression of TNF-α, IL-6 and IL-1β, induced the activation of NF-κB pathway in fibroblasts. Inhibition of NF-κB not only blocked the decrease in cell viability and migration ability induced by high glucose, but also relieved the release of inflammatory factors. TGF-β1 activated the TGF-β1/Smad pathway and promoted wound healing in diabetic mice. Activating the NF-κB pathway not only inhibited the activation of the TGF-β1/Smad pathway, but also alleviated the promoting effect of TGF-β1 on wound healing. In a high glucose environment, the activation of NF-κB may inhibit the function of fibroblasts by inhibiting the TGF-β1/Smad pathway, resulting in poor wound healing.

Role of Skin Stretch on Local Vascular Permeability in Murine and Cell Culture Models

Excessive mechanical forces, particularly skin stretch, have been implicated in pathological cutaneous scarring. We hypothesize that this reflects, in part, stretch-induced vessel leakage that provokes prolonged wound/scar inflammation. However, this has never been observed directly. Here, a mouse model was used to examine the effect of skin flap stretching on vascular permeability. An in vitro model with pseudocapillaries grown in a stretchable chamber was also used to determine the effect of stretching on endothelial cell morphology and ion channel activity.

Improvement of Skin Wound Healing for Diabetic Mice with Thermosensitive Hydrogel Combined with Insulin Injection

Chronic skin wound caused by diabetic disease is very common worldwide. Moreover, there is a shortage of effective curing technology in clinic. In this work, we developed a novel technology using thermosensitive hydrogel on wound top combined with insulin injection. The efficiency and mechanism of this technology were investigated in a diabetic mouse model. Dorsal-paired 8-10 mm diameter wounds were created in 12 mice. The wound healing rate was determined over a 28-day interval in healthy control (Control), control with diabetes (DControl), poloxamer treatment (Pox), and poloxamer plus insulin injection (Poxin) mice. Histological specimens were observed in all samples. Real-time quantitative polymerase chain reaction (qRT-PCR) was performed to measure the relative expression of α-smooth muscle actin (α-SMA) and transforming growth factor beta 1 (TGF-β1) in wound tissues at 7, 14, and 28 days. Compared with DControl animals, those treated with Poxin showed accelerated wound closure and healing rate (p < 0.05); expression of both α-SMA and TGF-β1 was significantly higher than that of the DControl and Pox animals during the first 7 days postoperation, but a significant decrease at day 14. Therefore, we concluded that hydrogel combined with insulin accelerated wound healing. Controlling the glucose level via insulin injection is more beneficial than hydrogel alone for healing chronic wounds, potentially through the increase of α-SMA and TGF-β1 expression in early phase.

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

Towards in silico Models of the Inflammatory Response in Bone Fracture Healing

In silico modeling is a powerful strategy to investigate the biological events occurring at tissue, cellular and subcellular level during bone fracture healing. However, most current models do not consider the impact of the inflammatory response on the later stages of bone repair. Indeed, as initiator of the healing process, this early phase can alter the regenerative outcome: if the inflammatory response is too strongly down- or upregulated, the fracture can result in a non-union. This review covers the fundamental information on fracture healing, in silico modeling and experimental validation. It starts with a description of the biology of fracture healing, paying particular attention to the inflammatory phase and its cellular and subcellular components. We then discuss the current state-of-the-art regarding in silico models of the immune response in different tissues as well as the bone regeneration process at the later stages of fracture healing. Combining the aforementioned biological and computational state-of-the-art, continuous, discrete and hybrid modeling technologies are discussed in light of their suitability to capture adequately the multiscale course of the inflammatory phase and its overall role in the healing outcome. Both in the establishment of models as in their validation step, experimental data is required. Hence, this review provides an overview of the different in vitro and in vivo set-ups that can be used to quantify cell- and tissue-scale properties and provide necessary input for model credibility assessment. In conclusion, this review aims to provide hands-on guidance for scientists interested in building in silico models as an additional tool to investigate the critical role of the inflammatory phase in bone regeneration.

Diabetic Wound-Healing Science

Diabetes mellitus is an increasingly prevalent chronic metabolic disease characterized by prolonged hyperglycemia that leads to long-term health consequences. It is estimated that impaired healing of diabetic wounds affects approximately 25% of all patients with diabetes mellitus, often resulting in lower limb amputation, with subsequent high economic and psychosocial costs. The hyperglycemic environment promotes the formation of biofilms and makes diabetic wounds difficult to treat. In this review, we present updates regarding recent advances in our understanding of the pathophysiology of diabetic wounds focusing on impaired angiogenesis, neuropathy, sub-optimal chronic inflammatory response, barrier disruption, and subsequent polymicrobial infection, followed by current and future treatment strategies designed to tackle the various pathologies associated with diabetic wounds. Given the alarming increase in the prevalence of diabetes, and subsequently diabetic wounds, it is imperative that future treatment strategies target multiple causes of impaired healing in diabetic wounds.

Impact of microRNA-210 on wound healing among the patients with diabetic foot ulcer

Aim

Diabetic foot ulcer (DFU) is a major concern in diabetes and its control requires in-depth molecular investigation. The present study aimed to screen the expression of microRNA-210 (miR-210) and its association in hypoxic pathway in DFU patients.

Methods

The study consists of 3 groups of circulation samples (50 in each group of: healthy volunteers, T2DM and T2DM with DFU) and 2 groups of tissue samples (10 in each group of: control and T2DM with DFU). Expression of miR-210 and hypoxia inducible factor-1 alpha (HIF-1α), and its responsive genes such as VEGF, TNF-α, IL-6, BCl2, Bax and Caspase 3 were analyzed by RT-PCR, Western blot and ELISA analyses.

Results

The HIF-1α expression decreased in DFU patients with increased miR-210 expression in both circulation and tissue biopsies. The circulatory IL-6 and inflammatory gene TNF-α expression was increased in DFU compared to healthy controls and T2DM subjects. Further, we found there was no alteration in the angiogenic marker, VEGF expression. In comparison, anti-apoptotic BCl2 was decreased and Bax and Caspase 3 was increased in DFU tissues relative to control.

Conclusions

The study showed that there was an inverse relationship between miR-210 and HIF-1α expression in patients with DFU, indicating that miR-210 may regulate the expression of the hypoxic gene.

Agent-based model provides insight into the mechanisms behind failed regeneration following volumetric muscle loss injury

Skeletal muscle possesses a remarkable capacity for repair and regeneration following a variety of injuries. When successful, this highly orchestrated regenerative process requires the contribution of several muscle resident cell populations including satellite stem cells (SSCs), fibroblasts, macrophages and vascular cells. However, volumetric muscle loss injuries (VML) involve simultaneous destruction of multiple tissue components (e.g., as a result of battlefield injuries or vehicular accidents) and are so extensive that they exceed the intrinsic capability for scarless wound healing and result in permanent cosmetic and functional deficits. In this scenario, the regenerative process fails and is dominated by an unproductive inflammatory response and accompanying fibrosis. The failure of current regenerative therapeutics to completely restore functional muscle tissue is not surprising considering the incomplete understanding of the cellular mechanisms that drive the regeneration response in the setting of VML injury. To begin to address this profound knowledge gap, we developed an agent-based model to predict the tissue remodeling response following surgical creation of a VML injury. Once the model was able to recapitulate key aspects of the tissue remodeling response in the absence of repair, we validated the model by simulating the tissue remodeling response to VML injury following implantation of either a decellularized extracellular matrix scaffold or a minced muscle graft. The model suggested that the SSC microenvironment and absence of pro-differentiation SSC signals were the most important aspects of failed muscle regeneration in VML injuries. The major implication of this work is that agent-based models may provide a much-needed predictive tool to optimize the design of new therapies, and thereby, accelerate the clinical translation of regenerative therapeutics for VML injuries.

Mesenchymal Stem Cell Exosomes as a New Strategy for the Treatment of Diabetes Complications

Diabetes mellitus (DM) is a metabolic disease, now prevalent worldwide, which is characterized by a relative or absolute lack of insulin secretion leading to chronically increased blood glucose levels. Diabetic patients are often accompanied by multiple macrovascular complications, such as coronary heart disease, hypertension, macrovascular arteriosclerosis, and microvascular complications. Microvascular complications include diabetic kidney injury, diabetic encephalopathy, and diabetic foot, which reduce the quality of life and survival status of patients. Mesenchymal stem cell exosomes (MSC-Exos) possess repair functions similar to MSCs, low immunogenicity, and ease of storage and transport. MSC-Exos have been proven to possess excellent repair effects in repairing various organ damages. This study reviews the application of MSC-Exos in the treatment of DM and its common complications. MSC-Exos may be used as an effective treatment for DM and its complications.

Lack of lymphocytes impairs macrophage polarization and angiogenesis in diabetic wound healing

AIMS

This study aimed to investigate the effect of lymphocytes in wound healing and the underlying mechanisms, in diabetic and non-diabetic mice, using Balb/c recombination activating gene (Rag)-2 and interleukin 2 receptor gamma (IL-2Rγ) double knockout (KO) (RAG2^-/- IL-2Rγ^-/-) mice.

MAIN METHODS

Wound healing in vivo was performed in control and STZ-induced diabetic mice, in both KO and WT mice. Inflammation and ROS production were evaluated by immunofluorescence microscopy analysis, antioxidant enzymes and angiogenesis were evaluated by quantitative PCR and immunofluorescence microscopy analysis, and wound closure kinetics evolution was evaluated by measurement of acetate tracing of the wound area.

KEY FINDINGS

Wound closure was significantly delayed in KO mice, where the M1/M2 macrophage ratio and basal ROS levels were significantly increased, while antioxidant defenses and angiogenesis were significantly decreased. Moreover, the expected increase in matrix metallopeptidase (MMP)-9 protein levels in diabetic conditions was not observed in KO mice, suggesting that the mechanisms leading to the increase in MMP-9 observed in diabetic wounds may in part be lymphocyte-dependent.

SIGNIFICANCE

Our results indicate that lack of lymphocytes compromises wound healing independent of diabetes. The lack of these cells, even in non-diabetic mice, mimics the phenotype observed in wounds under diabetic conditions. Moreover, the combination of diabetes and the lack of lymphocytes, further impair the wound healing conditions, indicating that when the innate regulatory function is lost in these KO mice, excessive M1 polarization, poor angiogenesis and impaired wound healing are worsen.

In silico disease model: from simple networks to complex diseases

In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.

Identifying important parameters in the inflammatory process with a mathematical model of immune cell influx and macrophage polarization

In an inflammatory setting, macrophages can be polarized to an inflammatory M1 phenotype or to an anti-inflammatory M2 phenotype, as well as existing on a spectrum between these two extremes. Dysfunction of this phenotypic switch can result in a population imbalance that leads to chronic wounds or disease due to unresolved inflammation. Therapeutic interventions that target macrophages have therefore been proposed and implemented in diseases that feature chronic inflammation such as diabetes mellitus and atherosclerosis. We have developed a model for the sequential influx of immune cells in the peritoneal cavity in response to a bacterial stimulus that includes macrophage polarization, with the simplifying assumption that macrophages can be classified as M1 or M2. With this model, we were able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then fit this model to in vivo experimental data obtained from a mouse peritonitis model of inflammation, which is widely used to evaluate endogenous processes in response to an inflammatory stimulus. Model robustness is explored with local structural and practical identifiability of the proposed model a posteriori. Additionally, we perform sensitivity analysis that identifies the population of apoptotic neutrophils as a key driver of the inflammatory process. Finally, we simulate a selection of proposed therapies including points of intervention in the case of delayed neutrophil apoptosis, which our model predicts will result in a sustained inflammatory response. Our model can therefore provide hypothesis testing for therapeutic interventions that target macrophage phenotype and predict outcomes to be validated by subsequent experimentation.

Using experimental data and mathematical analysis, we develop a model for the inflammatory response that includes macrophage polarization between M1 and M2 phenotypes. Dysfunction of this phenotypic switch can disrupt the timely influx and egress of immune cells during the healing process and lead to chronic wounds or disease. The modulation of macrophage population has been suggested as a strategy to dampen inflammation in diseases that feature chronic inflammation, such as diabetes and atherosclerosis. It is therefore important that we learn more about which components of the system drive the population level switch in phenotype. Our model is able to reproduce the expected timing of sequential influx of neutrophils and macrophages in response to an inflammatory stimulus. Model parameters were estimated with weighted least squares fitting to in vivo experimental data from a mouse model of peritonitis while considering identifiability of parameter sets. We perform sensitivity analysis that identifies primary drivers of the system, and predict the effects of variations in these key parameters on immune cell populations.

The role of microRNAs in the healing of diabetic ulcers

MicroRNAs (miRNAs) are small protected molecules with a length of 18 to 25 nucleotides. Many studies have recently been conducted on miRNAs, illustrating their role in regulating many biological, physiological, and pathological activities, such as maintaining cellular signalling and regulating cellular pathways. The main role of miRNAs is to regulate the expression of genes after translation, which can lead to the destruction or suppression of translation by binding to mRNAs. As any change in the regulation of miRNAs is associated with several physiological abnormalities, such as type 2 diabetes and its complications, these molecules can be used for therapeutic purposes or as biomarkers for the diagnosis of diseases such as diabetes and its complications. In this review article, we will discuss important findings about the miRNAs and the role of these molecules in different phases of the wound-healing process of chronic wounds, especially diabetic ulcer.

Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling

OBJECTIVES

There have been recent calls for the application of the complex systems approach in sports injury research. However, beyond theoretical description and static models of complexity, little progress has been made towards formalising this approach in way that is practical to sports injury scientists and clinicians. Therefore, our objective was to use a computational modelling method and develop a dynamic simulation in sports injury research.

METHODS

Agent-based modelling (ABM) was used to model the occurrence of sports injury in a synthetic athlete population. The ABM was developed based on sports injury causal frameworks and was applied in the context of distance running-related injury (RRI). Using the acute:chronic workload ratio (ACWR), we simulated the dynamic relationship between changes in weekly running distance and RRI through the manipulation of various 'athlete management tools'.

RESULTS

The findings confirmed that building weekly running distances over time, even within the reported ACWR 'sweet spot', will eventually result in RRI as athletes reach and surpass their individual physical workload limits. Introducing training-related error into the simulation and the modelling of a 'hard ceiling' dynamic resulted in a higher RRI incidence proportion across the population at higher absolute workloads.

CONCLUSIONS

The presented simulation offers a practical starting point to further apply more sophisticated computational models that can account for the complex nature of sports injury aetiology. Alongside traditional forms of scientific inquiry, the use of ABM and other simulation-based techniques could be considered as a complementary and alternative methodological approach in sports injury research.

A mathematical model of the use of supplemental oxygen to combat surgical site infection

Infections are a common complication of any surgery, often requiring a recovery period in hospital. Supplemental oxygen therapy administered during and immediately after surgery is thought to enhance the immune response to bacterial contamination. However, aerobic bacteria thrive in oxygen-rich environments, and so it is unclear whether oxygen has a net positive effect on recovery. Here, we develop a mathematical model of post-surgery infection to investigate the efficacy of supplemental oxygen therapy on surgical-site infections. A 4-species, coupled, set of non-linear partial differential equations that describes the space-time dependence of neutrophils, bacteria, chemoattractant and oxygen is developed and analysed to determine its underlying properties. Through numerical solutions, we quantify the efficacy of different supplemental oxygen regimes on the treatment of surgical site infections in wounds of different initial bacterial load. A sensitivity analysis is performed to investigate the robustness of the predictions to changes in the model parameters. The numerical results are in good agreement with analyses of the associated well-mixed model. Our model findings provide insight into how the nature of the contaminant and its initial density influence bacterial infection dynamics in the surgical wound.

EPO protects mesenchymal stem cells from hyperglycaemic injury via activation of the Akt/FoxO3a pathway

INTRODUCTION

Mesenchymal stem cell (MSC)-based therapies have demonstrated positive outcomes for treating cardiovascular disease. However, the proliferative ability of MSCs decreases during chronic exposure to hyperglycaemia; their ability to contribute to endogenous injury repair is thus reduced. Erythropoietin (EPO) was recently reported to protect against hyperglycaemia-related injury in various cells and may be a good candidate for enhancing MSC functions under hyperglycaemic conditions.

METHODS

Bone marrow-derived MSCs were isolated from male donor rats weighing 60-80 g. The roles of EPO in regulating cell viability, senescence, angiogenesis and inflammation were investigated using the Cell Counting Kit-8 (CCK-8) assay and 5-ethynyl-2'-deoxyuridine (EdU) assays; senescence-associated β-galactosidase (SA-β-gal) staining; VEGF, HGF, IGF, bFGF ELISAs and TNF-α ELISA, respectively. ROS production was measured by flow cytometry. The expression levels of Akt, forkhead box class O3a (FoxO3a) and VEGF proteins in MSCs were analysed by western blotting. Matrigel was used for tube formation assays.

RESULTS

The results of the current study showed that EPO has beneficial effects on MSCs exposed to hyperglycaemia by promoting proliferation, inhibiting senescence and the release of pro-inflammatory factors, increasing the secretion of proangiogenic cytokines, and enhancing the ability of MSCs to stimulate tube formation among human umbilical vein endothelial cells (HUVECs). In addition, the beneficial effects of EPO may result from the activation of the Akt/FoxO3a signalling pathway.

CONCLUSIONS

Our study demonstrates for the first time that EPO protects MSCs from hyperglycaemia-induced damage by targeting the Akt/FoxO3a signalling pathway.

Immune aging in diabetes and its implications in wound healing

Immune systems have evolved to recognize and eliminate pathogens and damaged cells. In humans, it is estimated to recognize 109 epitopes and natural selection ensures that clonally expanded cells replace unstimulated cells and overall immune cell numbers remain stationary. But, with age, it faces continuous repertoire restriction and concomitant accumulation of primed cells. Changes shaping the aging immune system have bitter consequences because, as inflammatory responses gain intensity and duration, tissue-damaging immunity and inflammatory disease arise. During inflammation, the glycolytic flux cannot cope with increasing ATP demands, limiting the immune response's extent. In diabetes, higher glucose availability stretches the glycolytic limit, dysregulating proteostasis and increasing T-cell expansion. Long-term hyperglycemia exerts an accumulating effect, leading to higher inflammatory cytokine levels and increased cytotoxic mediator secretion upon infection, a phenomenon known as diabetic chronic inflammation. Here we review the etiology of diabetic chronic inflammation and its consequences on wound healing.

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

Diagnosis and Management of Polytraumatized Patients With Severe Extremity Trauma

Multiply injured patients with severe extremity trauma are at risk of acute systemic complications and are at high risk of developing longer term orthopaedic complications including soft-tissue infection, osteomyelitis, posttraumatic osteoarthritis, and nonunion. It is becoming increasingly recognized that injury magnitude and response to injury have major jurisdiction pertaining to patient outcomes and complications. The complexities of injury and injury response that affect outcomes present opportunities to apply precision approaches to understand and quantify injury magnitude and injury response on a patient-specific basis. Here, we present novel approaches to measure injury magnitude by adopting methods that quantify both mechanical and ischemic tissue injury specific to each patient. We also present evolving computational approaches that have provided new insight into the complexities of inflammation and immunologic response to injury specific to each patient. These precision approaches are on the forefront of understanding how to stratify individualized injury and injury response in an effort to optimize titrated orthopaedic surgical interventions, which invariably involve most of the multiply injured patients. Finally, we present novel methods directed at mangled limbs with severe soft-tissue injury that comprise severely injured patients. Specifically, methods being developed to treat mangled limbs with volumetric muscle loss have the potential to improve limb outcomes and also mitigate uncompensated inflammation that occurs in these patients.

Microbiota of Chronic Diabetic Wounds: Ecology, Impact, and Potential for Innovative Treatment Strategies

World Health Organization considered diabetes as one of the 20th century epidemics, estimating that over 10% of the world population is diabetic or at high risk. Self-assessment studies indicate that diabetic patients consider chronic wounds to affect their quality of life more dramatically than vision loss or renal failure. In addition to being the main reason for diabetic patients' hospitalization, the economic burden of diabetic chronic wounds is close to 1% of United Kingdom and United States health systems budgets, which exceeds the funds allocated to the treatment of some types of cancer in both countries. Among the factors preceding the emergence of chronic diabetic wounds, also designated diabetic foot ulcers (DFUs), hygiene and pressure in specific areas are under patient control, while others are still far from being understood. A triple impairment in the innervation, immune responses, and vascularization associated to DFU has been extensively studied by the scientific community. However, the skin natural microbiota has only recently emerged as having a tremendous impact on DFU emergence and evolution to chronicity. Despite the great inter- and intra-variability of microbial colonizers, ongoing efforts are now focused on deciphering the impact of commensal and pathogenic microbiota on DFU etiology, as well as the mechanisms of interkingdom microbial-host communication. This review summarizes recent work in this context and offers new microbiological perspectives that may hold potential in the prevention and treatment of chronic diabetic wounds.

Diabetic Foot Ulcers and Epidermal Growth Factor: Revisiting the Local Delivery Route for a Successful Outcome

Soon after epidermal growth factor (EGF) discovery, some in vivo models appeared demonstrating its property to enhance cutaneous wound healing. EGF was the first growth factor (GF) introduced in the clinical arena as a healing enhancer, exerting its mitogenic effects on epithelial, fibroblastoid, and endothelial cells via a tyrosine kinase membrane receptor. Compelling evidences from the 90s documented that, for EGF, locally prolonged bioavailability and hourly interaction with the receptor were necessary for a successful tissue response. Eventually, the enthusiasm on the clinical use of EGF to steer the healing process was wiped out as the topical route to deliver proteins started to be questioned. The simultaneous in vivo experiments, emphasizing the impact of the parenterally administered EGF on epithelial and nonepithelial organs in terms of mitogenesis and cytoprotection, rendered the theoretical fundamentals for the injectable use of EGF and shaped the hypothesis that locally infiltrating the diabetic ulcers would lead to an effective healing. Although the diabetic chronic wounds microenvironment is hostile for local GFs bioavailability, EGF local infiltration circumvented the limitations of its topical application, thus expanding its therapeutic prospect. Our clinical pharmacovigilance and basic studies attest the significance of the GF local infiltration for chronic wounds healing.

MicroRNA-132 promotes fibroblast migration via regulating RAS p21 protein activator 1 in skin wound healing

MicroRNA (miR)-132 has been identified as a top up-regulated miRNA during skin wound healing and its inhibition impairs wound repair. In a human in vivo surgical wound model, we showed that miR-132 was induced in epidermal as well as in dermal wound-edge compartments during healing. Moreover, in a panel of cells isolated from human skin wounds, miR-132 was found highly expressed in human dermal fibroblasts (HDFs). In HDFs, miR-132 expression was upregulated by TGF-β1. By overexpression or inhibition of miR-132, we showed that miR-132 promoted HDF migration. Mechanistically, global transcriptome analysis revealed that RAS signaling pathway was regulated by miR-132 in HDFs. We found that RAS p21 protein activator 1 (RASA1), a known target of miR-132, was downregulated in HDFs upon miR-132 overexpression. Silencing of RASA1 phenocopied the pro-migratory effect of miR-132. Collectively, our study reveals an important role for miR-132 in HDFs during wound healing and indicates a therapeutic potential of miR-132 in hard-to-heal skin wounds.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Computational Modeling of Muscle Regeneration and Adaptation to Advance Muscle Tissue Regeneration Strategies

Skeletal muscle has an exceptional ability to regenerate and adapt following injury. Tissue engineering approaches (e.g. cell therapy, scaffolds, and pharmaceutics) aimed at enhancing or promoting muscle regeneration from severe injuries are a promising and active field of research. Computational models are beginning to advance the field by providing insight into regeneration mechanisms and therapies. In this paper, we summarize the contributions computational models have made to understanding muscle remodeling and the functional implications thereof. Next, we describe a new agent-based computational model of skeletal muscle inflammation and regeneration following acute muscle injury. Our computational model simulates the recruitment and cellular behaviors of key inflammatory cells (e.g. neutrophils and M1 and M2 macrophages) and their interactions with native muscle cells (muscle fibers, satellite stem cells, and fibroblasts) that result in the clearance of necrotic tissue and muscle fiber regeneration. We demonstrate the ability of the model to track key regeneration metrics during both unencumbered regeneration and in the case of impaired macrophage function. We also use the model to simulate regeneration enhancement when muscle is primed with inflammatory cells prior to injury, which is a putative therapeutic intervention that has not yet been investigated experimentally. Computational modeling of muscle regeneration, pursued in combination with experimental analyses, provides a quantitative framework for evaluating and predicting muscle regeneration and enables the rational design of therapeutic strategies for muscle recovery.

Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Erythropoietin-activated mesenchymal stem cells promote healing ulcers by improving microenvironment

BACKGROUND

Mesenchymal stem cell (MSC) transplantation is an effective treatment therapy for ischemic ulcers. However, in high-glucose microenvironment, the original inflammation-inhibiting function of MSCs leads to turns into secreting large amounts of inflammatory mediators, such as tumor necrosis factor alpha, for example, which decreases their capacity and becomes poor quality stem cells over inflammation cells for diabetic foot ulcers repair in the healing of diabetic foot ulcers. Erythropoietin (EPO) is an anti-inflammatory, proangiogenic cytokine. It is unclear whether EPO-activated MSCs with biomaterials can promote the effective healing of diabetic foot ulcers.

METHODS

Cultivated MSCs in MSC-L, MSC-H, EPO-G, Akt-G, and mTOR-G, then separated the supernatant-conditioned medium of these groups to stimulate human umbilical vein endothelial cells on proliferation and migration experiments; a new type of biomaterial planted with the EPO-activated MSCs was applied to the diabetic foot ulcers of the C57 mice.

RESULTS

In vitro experiments showed that EPO could stimulate MSCs to secrete vascular endothelial growth factor in high-glucose microenvironment. More importantly, EPO could reduce the damage to MSCs by high-glucose microenvironment, promote their proliferation and migration functions, and inhibit the high-glucose-induced MSCs from secreting the inflammatory mediator tumor necrosis factor alpha. In vivo experiments showed greater angiogenesis in EPO-MSC group than in control group, ulcer healing in EPO-MSC group was significantly better than that in control group, and MSCs partially differentiated into endothelial cells. EPO-activated MSCs could inhibit the monocyte invasion of localized diabetic foot ulcers.

CONCLUSIONS

Our results indicate that EPO-activated MSCs can promote the effective healing of diabetic foot ulcers. The mechanism is that EPO can change stem cells from excessive inflammation into general inflammation and improved diabetic foot ulcers inflammatory microenvironment.

Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis

Pulmonary fibrosis is pathologic remodeling of lung tissue that can result in difficulty breathing, reduced quality of life, and a poor prognosis for patients. Fibrosis occurs as a result of insult to lung tissue, though mechanisms of this response are not well-characterized. The disease is driven in part by dysregulation of fibroblast proliferation and differentiation into myofibroblast cells, as well as pro-fibrotic mediator-driven epithelial cell apoptosis. The most well-characterized pro-fibrotic mediator associated with pulmonary fibrosis is TGF-β1. Excessive synthesis of, and sensitivity to, pro-fibrotic mediators as well as insufficient production of and sensitivity to anti-fibrotic mediators has been credited with enabling fibroblast accumulation. Available treatments neither halt nor reverse lung damage. In this study we have two aims: to identify molecular and cellular scale mechanisms driving fibroblast proliferation and differentiation as well as epithelial cell survival in the context of fibrosis, and to predict therapeutic targets and strategies. We combine in vitro studies with a multi-scale hybrid agent-based computational model that describes fibroblasts and epithelial cells in co-culture. Within this model TGF-β1 represents a pro-fibrotic mediator and we include detailed dynamics of TGF-β1 receptor ligand signaling in fibroblasts. PGE2 represents an anti-fibrotic mediator. Using uncertainty and sensitivity analysis we identify TGF-β1 synthesis, TGF-β1 activation, and PGE2 synthesis among the key mechanisms contributing to fibrotic outcomes. We further demonstrate that intervention strategies combining potential therapeutics targeting both fibroblast regulation and epithelial cell survival can promote healthy tissue repair better than individual strategies. Combinations of existing drugs and compounds may provide significant improvements to the current standard of care for pulmonary fibrosis. Thus, a two-hit therapeutic intervention strategy may prove necessary to halt and reverse disease dynamics.

Examining the Relationship between Pre-Malignant Breast Lesions, Carcinogenesis and Tumor Evolution in the Mammary Epithelium Using an Agent-Based Model

INTRODUCTION

Breast cancer, the product of numerous rare mutational events that occur over an extended time period, presents numerous challenges to investigators interested in studying the transformation from normal breast epithelium to malignancy using traditional laboratory methods, particularly with respect to characterizing transitional and pre-malignant states. Dynamic computational modeling can provide insight into these pathophysiological dynamics, and as such we use a previously validated agent-based computational model of the mammary epithelium (the DEABM) to investigate the probabilistic mechanisms by which normal populations of ductal cells could transform into states replicating features of both pre-malignant breast lesions and a diverse set of breast cancer subtypes.

METHODS

The DEABM consists of simulated cellular populations governed by algorithms based on accepted and previously published cellular mechanisms. Cells respond to hormones, undergo mitosis, apoptosis and cellular differentiation. Heritable mutations to 12 genes prominently implicated in breast cancer are acquired via a probabilistic mechanism. 3000 simulations of the 40-year period of menstrual cycling were run in wild-type (WT) and BRCA1-mutated groups. Simulations were analyzed by development of hyperplastic states, incidence of malignancy, hormone receptor and HER-2 status, frequency of mutation to particular genes, and whether mutations were early events in carcinogenesis.

RESULTS

Cancer incidence in WT (2.6%) and BRCA1-mutated (45.9%) populations closely matched published epidemiologic rates. Hormone receptor expression profiles in both WT and BRCA groups also closely matched epidemiologic data. Hyperplastic populations carried more mutations than normal populations and mutations were similar to early mutations found in ER+ tumors (telomerase, E-cadherin, TGFB, RUNX3, p < .01). ER- tumors carried significantly more mutations and carried more early mutations in BRCA1, c-MYC and genes associated with epithelial-mesenchymal transition.

CONCLUSIONS

The DEABM generates diverse tumors that express tumor markers consistent with epidemiologic data. The DEABM also generates non-invasive, hyperplastic populations, analogous to atypia or ductal carcinoma in situ (DCIS), via mutations to genes known to be present in hyperplastic lesions and as early mutations in breast cancers. The results demonstrate that agent-based models are well-suited to studying tumor evolution through stages of carcinogenesis and have the potential to be used to develop prevention and treatment strategies.

Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.

Impaired T-cell differentiation in diabetic foot ulceration

Foot ulceration is one of the most debilitating complications associated with diabetes, but its cause remains poorly understood. Several studies have been undertaken to understand healing kinetics or find possible therapies to enhance healing. However, few studies have been directed at understanding the immunological alterations that could influence wound healing in diabetes. In this study, we analysed the T-cell receptor (TCR) repertoire diversity in TCR-αβ⁺ T cells. We also analysed the distribution and phenotype of T cells obtained from the peripheral blood of healthy controls and diabetic individuals with or without foot ulcers. Our results showed that diabetic individuals, especially those with foot ulcers, have a significantly lower naive T-cell number and a poorer TCR-Vβ repertoire diversity. We also showed that the reduced TCR-Vβ repertoire diversity in diabetic individuals was mainly owing to the accumulation of effector T cells, the major source of tumour necrosis factor-α production, which was even more pronounced in patients with acute foot ulceration. Moreover, the expression of several inflammatory chemokine receptors was significantly reduced in diabetic patients. In conclusion, effector T-cell accumulation and TCR repertoire diversity reduction appear to precede the development of foot ulcers. This finding may open new immunological therapeutic possibilities and provide a new prognostic tool in diabetic wound care.

Trauma in silico: Individual-specific mathematical models and virtual clinical populations

Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1β inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.

Atypical Diabetic Foot Ulcer Keratinocyte Protein Signaling Correlates with Impaired Wound Healing

Diabetes mellitus is associated with chronic diabetic foot ulcers (DFUs) and wound infections often resulting in lower extremity amputations. The protein signaling architecture of the mechanisms responsible for impaired DFU healing has not been characterized. In this preliminary clinical study, the intracellular levels of proteins involved in signal transduction networks relevant to wound healing were non-biasedly measured using reverse-phase protein arrays (RPPA) in keratinocytes isolated from DFU wound biopsies. RPPA allows for the simultaneous documentation and assessment of the signaling pathways active in each DFU. Thus, RPPA provides for the accurate mapping of wound healing pathways associated with apoptosis, proliferation, senescence, survival, and angiogenesis. From the study data, we have identified potential diagnostic, or predictive, biomarkers for DFU wound healing derived from the ratios of quantified signaling protein expressions within interconnected pathways. These biomarkers may allow physicians to personalize therapeutic strategies for DFU management on an individual basis based upon the signaling architecture present in each wound. Additionally, we have identified altered, interconnected signaling pathways within DFU keratinocytes that may help guide the development of therapeutics to modulate these dysregulated pathways, many of which parallel the therapeutic targets which are the hallmarks of molecular therapies for treating cancer.

Reverse Engineering the Inflammatory "Clock": From Computational Modeling to Rational Resetting

Properly-regulated inflammation is central to homeostasis. Traumatic injury, hemorrhagic shock, septic shock, and other injury-related processes such as wound healing are associated with dysregulated inflammation. Like many biological processes, inflammation is a dynamic, complex system whose function, like that of an analog clock, cannot be discerned simply from a laundry list of its parts (data). The advent of multiplexed platforms for gathering biological data, while providing an unprecedented level of detailed information about the inflammatory response, has paradoxically also proven to be overwhelming. This problem is especially acute when the datasets involve time courses, since typical statistical analyses and data-driven modeling are geared towards single time points. Various groups have addressed this problem using dynamic approaches to data-driven and mechanistic computational modeling. These modeling tools can be thought of as the "gears" and "hands" of the "clock," and have led to insights regarding principal drivers, dynamic networks, feedbacks, and regulatory switches that characterize and perhaps regulate the inflammatory response. In parallel, mechanistic computational models have given an abstracted sense of how the inflammatory "clock" works, leading to in silico models of critically ill individuals and populations. Integrating data-driven and mechanistic modeling may point the way to a rational "resetting" of inflammation via model-driven precision medicine.

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.

A recent approach to quantitatively characterize the timing and intensity of the inflammatory response relies on the use of four quantities termed inflammation indices. The values of the inflammation indices may reflect the differences between normal and pathological inflammation, and may be used to gauge the effects of therapeutic interventions aimed to control inflammation. Yet, the specific inflammatory mechanisms that can be targeted to selectively control these indices remain unknown. Here, we developed and applied a computational strategy to identify potential target mechanisms to regulate such indices. We used our recently developed model of local inflammation to simulate thousands of inflammatory scenarios. We then subjected the corresponding inflammation index values to sensitivity and correlation analysis. We found that the inflammation indices may be significantly influenced by the macrophage influx and efflux rates, as well as by the degradation rates of three specific molecular mediators. These results suggested that the indices can be effectively regulated by individual or combined inhibition of those molecular mediators, which we confirmed by computational experiments. Taken together, our results highlight possible targets of therapeutic intervention that can be used to control both the timing and the intensity of the inflammatory response.

Introduction of a Framework for Dynamic Knowledge Representation of the Control Structure of Transplant Immunology: Employing the Power of Abstraction with a Solid Organ Transplant Agent-Based Model

Agent-based modeling has been used to characterize the nested control loops and non-linear dynamics associated with inflammatory and immune responses, particularly as a means of visualizing putative mechanistic hypotheses. This process is termed dynamic knowledge representation and serves a critical role in facilitating the ability to test and potentially falsify hypotheses in the current data- and hypothesis-rich biomedical research environment. Importantly, dynamic computational modeling aids in identifying useful abstractions, a fundamental scientific principle that pervades the physical sciences. Recognizing the critical scientific role of abstraction provides an intellectual and methodological counterweight to the tendency in biology to emphasize comprehensive description as the primary manifestation of biological knowledge. Transplant immunology represents yet another example of the challenge of identifying sufficient understanding of the inflammatory/immune response in order to develop and refine clinically effective interventions. Advances in immunosuppressive therapies have greatly improved solid organ transplant (SOT) outcomes, most notably by reducing and treating acute rejection. The end goal of these transplant immune strategies is to facilitate effective control of the balance between regulatory T cells and the effector/cytotoxic T-cell populations in order to generate, and ideally maintain, a tolerant phenotype. Characterizing the dynamics of immune cell populations and the interactive feedback loops that lead to graft rejection or tolerance is extremely challenging, but is necessary if rational modulation to induce transplant tolerance is to be accomplished. Herein is presented the solid organ agent-based model (SOTABM) as an initial example of an agent-based model (ABM) that abstractly reproduces the cellular and molecular components of the immune response to SOT. Despite its abstract nature, the SOTABM is able to qualitatively reproduce acute rejection and the suppression of acute rejection by immunosuppression to generate transplant tolerance. The SOTABM is intended as an initial example of how ABMs can be used to dynamically represent mechanistic knowledge concerning transplant immunology in a scalable and expandable form and can thus potentially serve as useful adjuncts to the investigation and development of control strategies to induce transplant tolerance.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

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.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC)

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.