Innate Immune Response Agent-based Model (IIRABM)

What is being modeled?
Systemic Inflammation represented at the endothelial-blood interface
Description & purpose of resource

The Innate Immune Response Agent-Based Model (IIRABM) is a two-dimensional abstract representation of the human endothelial-blood interface.  This abstraction is designed to model the endothelial-blood interface for a traumatic (in the medical sense) injury, and does so by representing this interface as the unwrapped internal vascular surface of a 2D projection of the terminus for a branch of the arterial vascular network. The IIRABM operates by simulating multiple cell types and their interactions, including endothelial cells, macrophages, neutrophils, TH0, TH1, and TH2 cells as well as their associated precursor cells.  The simulated system dies when total damage (defined as aggregate endothelial cell damage) exceeds a pre-defined threshold.

Spatial scales
molecular
cellular
tissue
whole organism
Temporal scales
1 - 103 s
hours
days
weeks to months
This resource is currently
mature and useful in ongoing research
a demonstration or a framework to be built upon (perhaps with a sample implementation)
Has this resource been validated?
Yes
How has the resource been validated?

Validated against published clinical and experimental data

Link to Resource Credibility Assessment
Agent-based Models to address the Crisis of Reproducibility and Precision Medicine
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
No
Key publications (e.g. describing or using resource)

  1. An G. In-silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Critical Care Medicine 2004; 32(10): 2050-2060. PMID: 15483414

  2. Cockrell C, An G. Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model. J Theor Biol. 2017 Jul 18;430:157-168. doi: 10.1016/j.jtbi.2017.07.016. [Epub ahead of print]: PMID:28728997

  3. Cockrell C, An, G. Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. PLoS Comput Biol 2018 2018 Feb 15; 14(2): e1005876. https://doi.org/10.1371/journal.pcbi.1005876

  4. Petersen BK, Yang, J, Grathwohl WS, Cockrell C, Santiago C, An G and Faissol DM. Deep Reinforcement Learning and Simulation as Path Towards Precision Medicine. Journal of Computational Biology, 25 Jan 2019 Published Online. Doi: 10.1089?cmb.2018.0168

  5. Cockrell C, Ozik J, Collier N, An G. Nested Active Learning for Efficient Model Contextualization and Parameterization: Pathway to generating simulated populations using multi-scale computational models. Simulation. 2019 May 21:0037549720975075.

  6. Cockrell C and An G: Using Genetic Algorithms to reproduce the heterogeneity of clinical data through model refinement and rule discovery in a high-dimensional agent-based model of systemic inflammation. Frontiers in Physiology: Computational Physiology and Medicine. Accepted for Publication April 27, 2021

DOI link to publication describing this resource

DOI: 10.1016/j.jtbi.2017.07.016

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Model type
agent-based
Data type
systemic

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

Submitted by kirschne on

Introduction

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed.

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

Submitted by kirschne on

Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Mathematical and computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response to infection, including the formation of lung granulomas.

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

Submitted by kirschne on

Granulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions.

Data and Model Sharing Group

Description & purpose of resource

Data and Model Sharing Group

Science is a social activity requiring transparency, collaboration, and critical evaluation of published research. The bulk of research today is still communicated via peer-reviewed publications, and journals represent the primary gateway by which research is disseminated. One of the core aspects of the scientific method is the need to reproduce results. This is one of the primary distinguishing features that makes the scientific method so successful.

However, there is today significant pressure by research insitutions for researches to publish in volume and more rapidly. One of the consequences of this is that the reproducibility of published research becomes secondary. The lack of reproducibiltiy diminishes the stature of science as a reliable methodology and can affect policymakers in government and industry and instill a lack of trust by the general population. One approach to remedy this situation is to encourage journals to make sure that published work is reproducible. This means developing policies and a more open culture that encourages sharing of empirical data, and models alongside the publication. The data and model sharing group therefore enourages members of the IMAG community and beyond to make every effort to ensure their models, data and computations are publcally avaialsble and reproducible. There are three simple rules that can greatly improve the situation, they include:

1. Stating the software used in the study, including the particular version used.
2. Providing machine readable code in supplements or uploaded to established repositories,
3. Asking a third-party to test that your methods section is free from error and of sufficient detail to reproduce the 
results presented in the paper.  

The last rule can be surprisingly effective and often eliminates many of the most obvious issues when sharing data, models and code.

Data- and model-sharing

To make it easier to conduct reproducible biochemical modeling, the Center for Reproducible Biomedical Modeling is developing tools that simplify model building, annotation, simulation, and visualization. However, if the model, results, and documentation associated with published modeling studies are not accessible, and the modeling workflow is not transparent, reproducing the model and its simulation results remains difficult and reuse is impossible.

Therefore, we recommend that all model artifacts produced during the modeling workflow be publicly shared to facilitate reproducibility and reuse, as emphasized by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Following the FAIR principles will ensure that models can be downloaded and manipulated by independent research groups, allowing published results to be validated, and will allow complex models to be built by integrating previously-published work on components of the system. We encourage modelers to disseminate packages of artifacts alongside publications with an open-source license and to deposit these packages in version-controlled public repositories.

Alignment with the NIH Strategic Plan for Data Science

 

Spatial scales
molecular
cellular
tissue
organ
whole organism
Temporal scales
<10-6 s (chemical reactions)
10-6 - 10-3 s
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
N/A
Key publications (e.g. describing or using resource)

Center for Reproducibility

DOI link to publication describing this resource

Center for Reproducibility

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Why Are CD8 T Cell Epitopes of Human Influenza A Virus Conserved?

Submitted by Veronika Zarnitsyna on

The high degree of conservation of CD8 T cell epitopes of influenza A virus (IAV) may allow for the development of T cell-inducing vaccines that provide protection across different strains and subtypes. This conservation is not fully explained by functional constraint, since an additional mutation(s) can compensate for the replicative fitness loss of IAV escape variants. Here, we propose three additional mechanisms that contribute to the conservation of CD8 T cell epitopes of IAV.

Multiscale Modeling of Silk and Silk-Based Biomaterials—A Review

Submitted by davebra on

Silk embodies outstanding material properties and biologically relevant functions achieved through a delicate hierarchical structure. It can be used to create high-performance, multifunctional, and biocompatible materials through mild processes and careful rational material designs. To achieve this goal, computational modeling has proven to be a powerful platform to unravel the causes of the excellent mechanical properties of silk, to predict the properties of the biomaterials derived thereof, and to assist in devising new manufacturing strategies.

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