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Agent-Based Modeling of Host-Pathogen Systems: The Successes and Challenges
Amy L. Bauer, Catherine A. A. Beauchemin, Alan S. Perelson
TL;DR
Host-pathogen systems are difficult to study because they involve many interacting components, while conventional models can omit spatial and individual-level behavior. The paper reviews ABMs and their applications across immunology and disease, finding that they support biological insight and experimental engagement while requiring careful attention to validation, calibration, and reproducibility.
Problem
Host-pathogen systems involve complex interactions, and ODE models provide homogeneous mean-field descriptions that may omit spatial variation and deviations from aggregate behavior.
Method
The paper reviews agent-based models used to study host-pathogen interactions, immune dynamics, disease processes, and multiscale biological systems.
Results
The reviewed ABMs contributed to understanding immune and disease dynamics, including spatial infection stability, tumor growth, lymph-node motility, and inflammatory responses.
Takeaways & Limitations
ABMs can connect biological descriptions with experiments and provide insights, predictions, and guidance for future experimental pursuits.
Takeaways & Limitations
ABMs require many parameters, making calibration difficult when experimental assays cannot measure the data needed for model complexity.
Abstract
from arXiv · showhide
Agent-based models have been employed to describe numerous processes in immunology. Simulations based on these types of models have been used to enhance our understanding of immunology and disease pathology. We review various agent-based models relevant to host-pathogen systems and discuss their contributions to our understanding of biological processes. We then point out some limitations and challenges of agent-based models and encourage efforts towards reproducibility and model validation.
I. INTRODUCTION
Host-pathogen systems contain many interacting components and nonlinear interactions, making mathematical modeling and simulation important tools for immunology. The paper introduces ABMs as an alternative to ODE models and reviews their use in host-pathogen research.
- Mathematical modeling and simulation help researchers reason about host-pathogen systems with many interacting components and nonlinear interactions.
- ODE models are simple, analytically tractable, and useful when data lack spatial information, but they assume homogeneous populations and provide mean-field behavior.ODEs generally require fewer parameters than spatial models such as ABMs or partial differential equations.
- ABMs describe stochastic populations of interacting agents through simple behavioral rules.The approach originated from cellular-space models intended to represent biological processes using local rules.
- The review surveys agent-based modeling approaches and their contributions to understanding host-pathogen interactions and disease dynamics.
II. APPLICATIONS OF AGENT-BASED MODELS
ABMs are gaining popularity as experimental knowledge, computational power, and desired model complexity increase. Their agent-level descriptions also resemble the language used to describe biological systems.
- Improved experimental assays and faster, more efficient computers are enabling increasingly complex ABMs of host-pathogen systems.
- ABM implementations describe agents and rules in language that tends to mimic the real biological system.
A. ABMs as Immune System and Disease Simulators
Immune and disease simulators provide frameworks for defining interaction rules and exploring immune or disease dynamics. The review distinguishes general immune simulators, disease simulators, and models focused on particular diseases or mechanisms.
- Immune simulators provide programming frameworks for defining interaction rules and simulating immune reactions.Examples include IMMSIM, SIMMUNE, Reactive Animation, and SIS.
- IMMSIM has been applied to affinity maturation, vaccine design, tolerance to pathologic rheumatoid factors, and pedagogy.
- Disease simulators are general frameworks calibrated to diseases such as tumor growth, tuberculosis, or influenza by changing biological parameters.
- Disease simulators reproduce varied host-pathogen interactions and are typically easier to use and calibrate than immune simulators.
- Specialized ABMs focus on individual diseases or immune mechanisms, including HIV, tuberculosis, Epstein-Barr virus, influenza, cancer vaccination, tumors, angiogenesis, and acute inflammation.
B. The Use of Agent-Based Models by Experimentalists
ABMs have engaged experimentalists because their representations resemble biological systems and can connect theoretical models with experiments. Examples show models built from experimental evidence and applied to inflammatory disease and clinical-trial simulation.
- ABMs have engaged experimentalists and helped bridge theoretical modeling with experiments through biologically familiar system representations.The early IMMSIM collaboration joined computer science and experimental immunology.
- Published experimental results supplied the simulated cell-path restrictions and interaction times.
- Jenkins built a simulation piece by piece from experimental literature rather than calibrating a model designed independently.
- Jenkins’ simulations showed that restricted spatial movement and T-cell proliferation can produce B–T interactions without directed chemotaxis.
- An used ABMs to model systemic inflammatory response syndrome, multiple organ failure, and clinical trials, representing immune cells, receptors, and mediators.
- The inflammatory model reproduced the general behavior of innate inflammatory responses and could help formulate and test treatment strategies before animal or human trials.
C. Studying Localized Spatial Effects
Spatially explicit and stochastic agent-based models reveal infection dynamics that mean-field approaches can miss, including effects of heterogeneity, local dispersal, immune cells, initial conditions, and tissue regeneration.
- Localized spatial effects: ABMs reveal dynamics caused by specific spatial configurations or rare localized events that mean-field models can miss.These features may contribute to variation in infection development and outcomes across individuals or times.
- HIV infection: ODE models overestimated viral infectiousness by more than an order of magnitude compared with a spatial HIV cellular automaton model.The difference arose because infectivity depended on local T-cell concentration and neighbor packing.
- Heterogeneous infection dynamics: Spatial heterogeneity and local dispersal produced source-sink sites and damped oscillations across sites in a virus-immune-cell model.The spatial model’s equalized dynamics were more consistent with experimental and clinical observations than the large oscillations of the homogeneous model.
- Influenza tissue infection: Influenza infection dynamics were sensitive to the initial spatial distribution of infected cells.Cells beginning in infected patches had fewer infectable neighbors, producing a smaller effective infection rate than isolated infected cells.
- Tissue regeneration: A local regeneration rule produced more dead cells but fewer infected cells than a non-spatial global rule under the same infection and death rates.Regeneration occurred only when uninfected cells were locally accessible after immune cells breached the infection wave, and the resulting dead-cell curve resembled experimental observations.
D. Infection of Cell Layers
Agent-based models of cell layers can represent spatially constrained infection and regeneration, but their fidelity depends on matching cellular movement and tissue geometry to the biological system. Applications to influenza and other layered tissues have also supported parameter estimation and model refinement.
- Biological representation: HIV lattice models may generate intricate infection waves that would likely disappear if motile T cells were represented realistically.The models capture tight packing but omit T-cell motility of approximately 11 µm/min.
- Biological representation: Static 2-D lattices may accurately represent tightly packed, non-motile cell layers such as airway epithelia during uncomplicated influenza infection.Influenza virions bud apically, making infection effectively two-dimensional in this setting.
- Regeneration rules: In 2-D tissue simulations, local regeneration yielded more dead cells and fewer infected cells than a non-spatial global rule under identical infection and death rates.The figure compares global replacement, local neighbor-dependent replacement, and regeneration after immune-cell breach of the infection wave.
- Model development: The influenza ABM improved understanding of flu kinetics and supplied parameter values later used in other influenza models.Agreement with limited available data motivated expansion into a more detailed in-vitro model.
- Model development: An expanded influenza ABM was calibrated against experimental time courses for infected-cell fraction, patchiness, and viral concentrations.The model represented lung tissue on a 2-D hexagonal grid with viral concentrations evolving through a discretized diffusion equation.
E. Agent-Based Models in Shape Space
Shape-space agent-based models represent receptor-epitope relationships through generalized spatial distance, enabling simulation of immune recognition, clonal dynamics, and viral antigenic evolution. They have quantitatively connected repeated influenza vaccination outcomes to experimental data and supported antigenic-distance analysis.
- Shape-space representation: Shape-space models use spatial dimensions to represent generalized receptor and epitope shapes, with affinity determined by their distance.Epitope locations stimulate nearby high-affinity lymphocytes, allowing immune-response dynamics to be modeled in shape space.
- Shape-space representation: Shape-space ABMs can represent receptor variability, clonal expansion and contraction, and viral epitope mutation that may evade immune elimination.Their framework is described as flexible for immunological modeling.
- Influenza vaccination: An influenza shape-space ABM quantitatively explained available experimental data on the efficacy of repeated annual vaccination.The model followed broad populations of B cells and antibodies across vaccine epitopes and represented cross-reactivity between vaccinations.
- Influenza vaccination: Monitoring antigenic distance in shape space became a valuable approach for studying influenza evolution and a factor in World Health Organization recommendations.The approach connected model-based antigenic analysis with influenza research and surveillance.
F. The Role of Agent-Based Models in Multiscale Systems
Agent-based models are well suited to multiscale biological systems because they can represent individual components within separate biological hierarchies and link those subsystem models. Hybrid ABM-differential-equation models extend this strategy, with validated tumor models matching experiments and generating experimentally relevant predictions.
- Multiscale modeling: Biological processes span scales from 10^-2 seconds and 10^-9 meters to 10^6 seconds and several meters.Experimental data often focus on isolated scales, creating a need to translate between biological levels.
- Multiscale modeling: ABMs can link biological scales by modeling individual components and developing models for each subsystem or hierarchy.Merging ABMs across scales has translated results and facilitated collaboration between experimental groups.
- Hybrid modeling: Hybrid models couple agent-based dynamics with differential equations to represent processes occurring across different time and length scales.Applications include tumor models combining intracellular Boolean networks, cell agents, and extracellular partial differential equations.
- Validation and prediction: Tumor simulations agreed quantitatively with spheroid measurements of growth curves, proliferating rims, and necrotic cores.The model also predicted environmental conditions and molecular weights of growth regulators associated with tumor-cell viability.
- Validation and prediction: Validated biological models can provide new biological insights, support prediction, and guide future experiments.The paper identifies integration with angiogenesis and immune-response models as a step toward systems-level tumor and cancer-invasion modeling.
G. Improved Experimental Data Fueling Advances in Modeling
Improved spatial measurements have enabled increasingly realistic agent-based models of lymphocyte movement. Comparisons with experimental observations show that these models can reproduce cell velocities and explain motility patterns through tissue anatomy.
- Spatial data requirements: ABMs require spatial data, and high-resolution measurements are important because localized spatial effects can substantially affect system dynamics.New experimental techniques provide unprecedented data, but spatial data remains relatively rare.
- T-cell movement models: A T-cell movement model matched experiments with a mean free path of approximately 38 µm, compared with 17 ± 7 µm between fibroblastic reticular cell intersections.The model represented alternating straight free runs and pauses that allowed the cell to turn.
- T-cell movement models: At fibroblastic reticular cell intersections, T cells turn roughly 50% of the time and continue along their original fiber roughly 50% of the time.This conclusion was derived by comparing simulated movement with experimental measurements.
- Cellular Potts models: A 2-D cellular Potts model produced B- and T-cell velocity distributions that agreed impressively well with experimental velocities.Cells were represented as neighboring lattice sites whose movements preserved cell volume and an overall movement direction.
- Cellular Potts models: A 3-D cellular Potts model found that lymph-node anatomy can explain T-cell velocity fluctuations and organize cells into small, highly dynamic streams.It predicted that dendritic cells could contact approximately 2,000 T cells per hour, whereas T cells could contact roughly 100 dendritic cells per hour.
H. Sensitivity and Uncertainty Analysis in Modeling and Prediction
Sensitivity analysis links uncertain model parameters to tuberculosis granuloma outcomes across infection times. The analysis shows that intracellular bacterial growth can have opposite associations with extracellular bacteria depending on infection stage.
- Sensitivity analysis: The study selected 12 of 27 model parameters and assessed their effects on extracellular bacteria and granuloma size.Latin hypercube sampling mapped the parameter space, and partial rank correlation coefficients quantified parameter sensitivity.
- Results: The intracellular bacterial growth rate was strongly positively correlated with extracellular bacteria early and late after infection, but negatively correlated at intermediate times.The correlation changed sign across infection stages.
- Results: At low bacterial levels, a large intracellular growth rate was critical to infection, whereas a small growth rate appeared necessary to generate larger lasting granulomas.This illustrates how simulation and sensitivity analysis can reveal time-dependent disease dynamics.
III. DISCUSSION
The review presents ABMs as useful but costly tools for studying host-pathogen dynamics, with growing applications beyond immunology. It emphasizes that model choice, reproducibility, calibration, and validation constrain their scientific value.
- Discussion: ABMs have contributed novel modeling approaches and disease- or mechanism-specific knowledge, while their use has expanded to epidemics, cancer, financial markets, and bio-warfare.The survey is explicitly limited rather than exhaustive.
- Model choice: Model selection depends on the research question, assumptions, parameter availability, and computational expense, so ABMs are not universally preferable.ABMs generally require more parameters than ODE and spatial PDE models.
- Reproducibility: ABM implementations can be difficult to reproduce because important details may be lost when many lines of code are translated into prose.The review recommends permanent model repositories and static versions matching the programs used for published results.
- Validation and rigor: Useful ABMs should be calibrated, use physically relevant parameter ranges, and include sensitivity analysis to identify key parameters and consequences of uncertainty.These practices are presented as part of scientific rigor and mechanistic usefulness.