Computational modeling of heterogeneity and function of CD4+ T cells

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Computational modeling of complex bioenergetic mechanisms that modulate CD4+ T cell effector and regulatory functions

We built a computational model of complex mechanisms at the intersection of immunity and metabolism that regulate CD4+ T cell effector and regulatory functions by using coupled ordinary differential equations. The model provides an improved understanding of how CD4+ T cells are shaping the immune response during Clostridioides difficile infection (CDI), and how they may be targeted pharmacologically to produce a more robust regulatory (Treg) response, which is associated with improved disease outcomes during CDI and other diseases. LANCL2 activation during CDI decreased the effector response, increased regulatory response, and elicited metabolic changes that favored Treg. Interestingly, LANCL2 activation provided greater immune and metabolic modulation compared to the addition of exogenous IL-2. Additionally, we identified gluconeogenesis via PEPCK-M as potentially responsible for increased immunosuppressive behavior in Treg cells. The model can perturb immune signaling and metabolism within a CD4+ T cell and obtain clinically relevant outcomes that help identify novel drug targets for infectious, autoimmune, metabolic, and neurodegenerative diseases.

The Pathogenicity and Synergistic Action of Th1 and Th17 Cells in Inflammatory Bowel Diseases

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, are characterized by chronic idiopathic inflammation of gastrointestinal tract. Although the pathogenesis of IBD remains unknown, intestinal immune dysfunction has been considered as the core pathogenesis. In the intestinal immune system, T helper 1 (Th1) and Th17 cells are indispensable for intestine homeostasis via preventing pathogenic bacteria invasion, regulating metabolism and functions of intestinal epithelial cells (IECs), and promoting IEC self-renewal. However, during the development of IBD, Th1 and Th17 cells acquire the pathogenicity and change from the maintainer of intestinal homeostasis to the destroyer of intestinal mucosa. Because of coexpressing interferon-γ and interleukin-17A, Th17 cells with pathogenicity are named as pathogenic Th17 cells. In disease states, Th1 cells impair IEC programs by inducing IEC apoptosis, recruiting immune cells, promoting adhesion molecules expression of IECs, and differentiating to epithelial cell adhesion molecule-specific interferon γ-positive Th1 cells. Pathogenic Th17 cells induce IEC injury by triggering IBD susceptibility genes expression of IECs and specifically killing IECs. In addition, Th1 and pathogenic Th17 cells could cooperate to induce colitis. The evidences from IBD patients and animal models demonstrate that synergistic action of Th1 and pathogenic Th17 cells occurs in the diseases development and aggravates the mucosal inflammation. In this review, we focused on Th1 and Th17 cell programs in homeostasis and intestine inflammation and specifically discussed the impact of Th1 and Th17 cell pathogenicity and their synergistic action on the onset and the development of IBD. We hoped to provide some clues for treating IBD.

Effect of LncRNA XIST on Immune Cells of Primary Biliary Cholangitis

Objective

Primary biliary cholangitis (PBC) is an autoimmune disease with significant gender difference. X chromosome inactivation (XCI) plays important roles in susceptibility to diseases between genders. This work focuses on the differences of LncRNA XIST in several defined immune cells populations as well as its effects on naive CD4+ T cells proliferation and differentiation in patients with PBC.

Methods

NKs, B cells, CD4+ T, and CD8+ T cells were separated by MicroBeads from peripheral blood mononuclear cells (PBMCs) of PBC patients and healthy control (HC). The expression levels of LncRNA XIST in these immune cells were quantified by qRT-PCR and their subcellular localized analyzed by FISH. Lentivirus were used to interfere the expression of LncRNA XIST, and CCK8 was used to detect the proliferation of naive CD4+ T cells in PBC patients. Finally, naive CD4+ T cells were co-cultured with the bile duct epithelial cells (BECs), and the effects of LncRNA XIST on the typing of naive CD4+ T cells and related cytokines were determined by qRT-PCR and ELISA.

Results

The expression levels of LncRNA XIST in NKs and CD4+ T cells in PBC patients were significantly higher than those in HC, and were primarily located at the nucleus. LncRNA XIST could promote the proliferation of naive CD4+ T cells. When naive CD4+ T cells were co-cultured with BECs, the expressions of IFN-γ, IL-17, T-bet and RORγt in naive CD4+ T cells were decreased.

Conclusion

LncRNA XIST was associated with lymphocyte abnormalities in patients with PBC. The high expression of LncRNA XIST could stimulate proliferation and differentiation of naive CD4+ T cells, which might account for the high occurrence of PBC in female.

Mathematical Modeling of Proliferative Immune Response Initiated by Interactions Between Classical Antigen-Presenting Cells Under Joint Antagonistic IL-2 and IL-4 Signaling

During an adaptive immune response from pathogen invasion, multiple cytokines are produced by various immune cells interacting jointly at the cellular level to mediate several processes. For example, studies have shown that regulation of interleukin-4 (IL-4) correlates with interleukin-2 (IL-2) induced lymphocyte proliferation. This motivates the need to better understand and model the mechanisms driving the dynamic interplay of proliferation of lymphocytes with the complex interaction effects of cytokines during an immune response. To address this challenge, we adopt a hybrid computational approach comprising of continuous, discrete and stochastic non-linear model formulations to predict a system-level immune response as a function of multiple dependent signals and interacting agents including cytokines and targeted immune cells. We propose a hybrid ordinary differential equation-based (ODE) multicellular model system with a stochastic component of antigen microscopic states denoted as Multiscale Multicellular Quantitative Evaluator (MMQE) implemented using MATLAB. MMQE combines well-defined immune response network-based rules and ODE models to capture the complex dynamic interactions between the proliferation levels of different types of communicating lymphocyte agents mediated by joint regulation of IL-2 and IL-4 to predict the emergent global behavior of the system during an immune response. We model the activation of the immune system in terms of different activation protocols of helper T cells by the interplay of independent biological agents of classic antigen-presenting cells (APCs) and their joint activation which is confounded by the exposure time to external pathogens. MMQE quantifies the dynamics of lymphocyte proliferation during pathogen invasion as bivariate distributions of IL-2 and IL-4 concentration levels. Specifically, by varying activation agents such as dendritic cells (DC), B cells and their joint mechanism of activation, we quantify how lymphocyte activation and differentiation protocols boost the immune response against pathogen invasion mediated by a joint downregulation of IL-4 and upregulation of IL-2. We further compare our in-silico results to in-vivo and in-vitro experimental studies for validation. In general, MMQE combines intracellular and extracellular effects from multiple interacting systems into simpler dynamic behaviors for better interpretability. It can be used to aid engineering of anti-infection drugs or optimizing drug combination therapies against several diseases.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment

Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.

Immune Responses and Risk of Triple-negative Breast Cancer: Implications for Higher Rates among African American Women

The etiology of triple-negative breast cancers (TNBC) is poorly understood. As many TNBCs develop prior to the initiation of breast cancer screening or at younger ages when the sensitivity of mammography is comparatively low, understanding the etiology of TNBCs is critical for discovering novel prevention approaches for these tumors. Furthermore, the higher incidence rate of estrogen receptor-negative breast cancers, and specifically, of TNBCs, among young African American women (AAW) versus white women is a source of racial disparities in breast cancer mortality. Whereas immune responses to TNBCs have received considerable attention in relation to prognosis and treatment, the concept that dysregulated immune responses may predispose to the development of TNBCs has received limited attention. We present evidence that dysregulated immune responses are critical in the pathogenesis of TNBCs, based on the molecular biology of the cancers and the mechanisms proposed to mediate TNBC risk factors. Furthermore, proposed risk factors for TNBC, especially childbearing without breastfeeding, high parity, and obesity, are more prevalent among AAW than white women. Limited data suggest genetic differences in immune responses by race, which favor a stronger Thr type 2 (Th2) immune response among AAW than white women. Th2 responses contribute to wound-healing processes, which are implicated in the pathogenesis of TNBCs. Accordingly, we review data on the link between immune responses and TNBC risk and consider whether the prevalence of risk factors that result in dysregulated immunity is higher among AAW than white women.

Gene Regulatory Network Modeling of Macrophage Differentiation Corroborates the Continuum Hypothesis of Polarization States

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.

Mathematical modeling identifies Lck as a potential mediator for PD-1 induced inhibition of early TCR signaling

Programmed cell death-1 (PD-1) is an inhibitory immune checkpoint receptor that negatively regulates the functioning of T cell. Although the direct targets of PD-1 were not identified, its inhibitory action on the TCR signaling pathway was known much earlier. Recent experiments suggest that the PD-1 inhibits the TCR and CD28 signaling pathways at a very early stage ─ at the level of phosphorylation of the cytoplasmic domain of TCR and CD28 receptors. Here, we develop a mathematical model to investigate the influence of inhibitory effect of PD-1 on the activation of early TCR and CD28 signaling molecules. Proposed model recaptures several quantitative experimental observations of PD-1 mediated inhibition. Model simulations show that PD-1 imposes a net inhibitory effect on the Lck kinase. Further, the inhibitory effect of PD-1 on the activation of TCR signaling molecules such as Zap70 and SLP76 is significantly enhanced by the PD-1 mediated inhibition of Lck. These results suggest a critical role for Lck as a mediator for PD-1 induced inhibition of TCR signaling network. Multi parametric sensitivity analysis explores the effect of parameter uncertainty on model simulations.

Simulation of Stimulation: Cytokine Dosage and Cell Cycle Crosstalk Driving Timing-Dependent T Cell Differentiation

Triggering an appropriate protective response against invading agents is crucial to the effectiveness of human innate and adaptive immunity. Pathogen recognition and elimination requires integration of a myriad of signals from many different immune cells. For example, T cell functioning is not qualitatively, but quantitatively determined by cellular and humoral signals. Tipping the balance of signals, such that one of these is favored or gains advantage on another one, may impact the plasticity of T cells. This may lead to switching their phenotypes and, ultimately, modulating the balance between proliferating and memory T cells to sustain an appropriate immune response. We hypothesize that, similar to other intracellular processes such as the cell cycle, the process of T cell differentiation is the result of: (i) pleiotropy (pattern) and (ii) magnitude (dosage/concentration) of input signals, as well as (iii) their timing and duration. That is, a flexible, yet robust immune response upon recognition of the pathogen may result from the integration of signals at the right dosage and timing. To investigate and understand how system's properties such as T cell plasticity and T cell-mediated robust response arise from the interplay between these signals, the use of experimental toolboxes that modulate immune proteins may be explored. Currently available methodologies to engineer T cells and a recently devised strategy to measure protein dosage may be employed to precisely determine, for example, the expression of transcription factors responsible for T cell differentiation into various subtypes. Thus, the immune response may be systematically investigated quantitatively. Here, we provide a perspective of how pattern, dosage and timing of specific signals, called interleukins, may influence T cell activation and differentiation during the course of the immune response. We further propose that interleukins alone cannot explain the phenotype variability observed in T cells. Specifically, we provide evidence that the dosage of intercellular components of both the immune system and the cell cycle regulating cell proliferation may contribute to T cell activation, differentiation, as well as T cell memory formation and maintenance. Altogether, we envision that a qualitative (pattern) and quantitative (dosage) crosstalk between the extracellular milieu and intracellular proteins leads to T cell plasticity and robustness. The understanding of this complex interplay is crucial to predict and prevent scenarios where tipping the balance of signals may be compromised, such as in autoimmunity.

Modeling the Mechanisms by Which HIV-Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa

Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.

Mucosal Interactions between Genetics, Diet, and Microbiome in Inflammatory Bowel Disease

Numerous reviews have discussed gut microbiota composition changes during inflammatory bowel diseases (IBD), particularly Crohn’s disease (CD). However, most studies address the observed effects by focusing on studying the univariate connection between disease and dietary-induced alterations to gut microbiota composition. The possibility that these effects may reflect a number of other interconnected (i.e., pantropic) mechanisms, activated in parallel, particularly concerning various bacterial metabolites, is in the process of being elucidated. Progress seems, however, hampered by various difficult-to-study factors interacting at the mucosal level. Here, we highlight some of such factors that merit consideration, namely: (1) the contribution of host genetics and diet in altering gut microbiome, and in turn, the crosstalk among secondary metabolic pathways; (2) the interdependence between the amount of dietary fat, the fatty acid composition, the effects of timing and route of administration on gut microbiota community, and the impact of microbiota-derived fatty acids; (3) the effect of diet on bile acid composition, and the modulator role of bile acids on the gut microbiota; (4) the impact of endogenous and exogenous intestinal micronutrients and metabolites; and (5) the need to consider food associated toxins and chemicals, which can introduce confounding immune modulating elements (e.g., antioxidant and phytochemicals in oils and proteins). These concepts, which are not mutually exclusive, are herein illustrated paying special emphasis on physiologically inter-related processes.

Modeling-Enabled Systems Nutritional Immunology

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through “use cases” centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.

Systems-wide analyses of mucosal immune responses to Helicobacter pylori at the interface between pathogenicity and symbiosis

Helicobacter pylori is the dominant member of the gastric microbiota in over half of the human population of which 5-15% develop gastritis or gastric malignancies. Immune responses to H. pylori are characterized by mixed T helper cell, cytotoxic T cell and NK cell responses. The presence of Tregs is essential for the control of gastritis and together with regulatory CX3CR1+ mononuclear phagocytes and immune-evasion strategies they enable life-long persistence of H. pylori. This H. pylori-induced regulatory environment might contribute to its cross-protective effect in inflammatory bowel disease and obesity. Here we review host-microbe interactions, the development of pro- and anti-inflammatory immune responses and how the latter contribute to H. pylori's role as beneficial member of the gut microbiota. Furthermore, we present the integration of existing and new data into a computational/mathematical model and its use for the investigation of immunological mechanisms underlying initiation, progression and outcomes of H. pylori infection.

Supervised learning methods in modeling of CD4+ T cell heterogeneity

Background

Modeling of the immune system – a highly non-linear and complex system – requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales.

Methods

This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models.

Results

Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF.

Conclusions

Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.

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.

Multiscale modeling of mucosal immune responses

Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.