A description of the model, the workflow of model building, and associated data.

An overview of invasive pulmonary aspergillosis.

The average human inhales hundreds of spores of the A. fumigatus fungus on a daily basis. Despite of this, the vast majority of immunocompetent patients are able to clear the fungus without any seeming effects. A small number of spores are capable of reaching the alveolar sac, where resident macrophages and epithelial cells quickly mount an immune response recruiting various other cells from the circulation such as dendritic cells (DCs), monocyte derived macrophages, and neutrophils. A weakened immune system (e.g. neutropenia) presents a window of opportunity for the fungus to start germinating and breach the epithelial layer, getting into the circulation and causing a systemic infection. Thus, the immune system must act quickly and efficiently to clear the fungus. See e.g. (missing reference).

Nutritional Lung Immunity

Focusing on nutritional immunity

Although the response of the innate immune system to A. fumigatus is multifaceted, the sequestration of iron (in turn starving the fungus from its needed iron) by the host’s immune cells is a crucial response to reduce fungal burden. The A. fumigatus mould is equipped with a sideorophore system that attempts to scavenge iron from its environment. The host immune system is equipped with various mechanisms of iron retention and scavenging. We thus focus on this battle over iron that occurs in the alveolar space between the host and the fungus.

Our approach

Multiscale modeling

The progression of invasive pulmonary aspergillosis is dependent on processes that occur at various spatiotemporal scales. For example, after the spore is inhaled into the alveolar space (tissue scale), receptors on immune cells detect the pathogen and initiate intracellular signaling processes (intracelullar scales) that result in the translation of various cytokines and chemokines. Some of these cytokines then get into the circulation and recruit other immune cells to the site of infection. For example, the cytokine interleukin-6 signals the liver (whole-body scale) to produce the hormone hepcidin which works to reduce iron export from host cells, arresting iron from pathogens. These biophysical processes take effect in ranges from milliseconds to hours. As such, the area of multiscale modeling is becoming increasingly popular for the modeling of diseases that affect multiple scales. Multiscale modeling seeks to model system behavior by modeling the events occurring at various scales and bridging them appropriately.

Agent-based modeling

We are building a 3D agent-based model of the innate immune response to invasive pulmonary aspergillosis with a focus on the “battle over iron” that occurs in the alveolar space between the host and the fungus. Agent-based modeling aims to model how autonomous individuals interact with each other and their computational environment. For us, the individual agents are:

  1. Alveolar macrophages.
  2. Monocyte derived macrophages.
  3. Monocyte derived dendritic cells (DCs).
  4. Neutrophils.
  5. Epithelial cells.
  6. A. fumigatus
  7. Liver cells

Agents behave according to a set of rules that are derived from literature and de novo experiments. For example, the monocyte derived macrophages respond to the sensing of A. fumigatus and respond accordingly to the output of an intracellular Generalized Boolean Network (GBN). The GBN is built from a combination of literature results and analysis of a transcriptomics dataset generated as part of this project.

Calibrating the model with experimental data

In order to build the 3D agent based model, we have performed several in vivo and in vitro experiments.

For example, we co-cultured monocyte derived macrophages with A. fumigatus in a time-series and extracted mRNA for RNA sequencing. We used the transcriptomic data for the calibration of the intracellular model of macrophages. We also performed other protein-level assays such as ELISA and blot assays to calibrate parameters related to extracellular behavior. For the purpose of transparency, all experimental data that was generated with the goal of calibrating the 3D model can be found on this website.

How we build features of the model

We provide an example from our own project to describe how the research process proceeds within our interdisciplinary research group.

Example Goal: Implement a model of the iron-handling behavior of an immune cell in response to A. fumigatus

To answer this question, the following steps could be taken:

  1. An experimentalist might perform a co-cultured experiment of an immune cell type with A. fumigatus.
  2. Various experiments including transcriptomics and ELISA are used to measure transcriptional and protein-level changes.
  3. After sequencing, the data is preprocessed and analyzed by a computational biologist.
  4. Such analysis (such as differential expression analysis, pathway analysis, etc.),together with the protein-level data, is then used to enrich a preliminary intracellular mathematical model built from literature knowledge.
  5. The implementation of this individual-cell model is then incorporated into the tissue-level 3D model.

From this example alone, we see the various data and artifacts that were required and/or generated:

  1. Experimental notebooks describing the protocol of the experiment.
  2. Raw data (e.g. RNA seq fastq files).
  3. Computational scripts analyzing the generated data.
  4. Versions of different software dependencies used in the analysis.
  5. Literature results.
  6. Implementation of mathematical model.


Overview of innate immunity to invasive pulmonary aspergillosis

Acknowledgements of figures.

Many of the individual figures were generated by the scientific illustration team at The Jackson Laboratory.