Modeling innate immune response to early mycobacterium infection
- Authors
- Carvalho, R.V., Kleijn, J., Meijer, A.H., and Verbeek, F.J.
- ID
- ZDB-PUB-130211-20
- Date
- 2012
- Source
- Computational and Mathematical Methods in Medicine 2012: 790482 (Journal)
- Registered Authors
- Meijer, Annemarie H., Verbeek, Fons J.
- Keywords
- none
- MeSH Terms
-
- Animals
- Cell Communication
- Computational Biology/methods*
- Host-Pathogen Interactions
- Humans
- Immunity, Innate
- Models, Immunological*
- Mycobacterium Infections/immunology*
- Mycobacterium Infections/physiopathology*
- Mycobacterium tuberculosis/immunology
- Mycobacterium tuberculosis/metabolism*
- Zebrafish/embryology
- Zebrafish/metabolism
- PubMed
- 23365620 Full text @ Comput. Math. Methods Med.
In the study of complex patterns in biology, mathematical and computational models are emerging as important tools. In addition to experimental approaches, these modeling tools have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to mycobacterial infection and tuberculous granuloma formation. We present an approach in which a computational model represents the interaction of the Mycobacterium infection with the innate immune system in zebrafish at a high level of abstraction. We use the Petri Net formalism to model the interaction between the key host elements involved in granuloma formation and infection dissemination. We define a qualitative model for the understanding and description of causal relations in this dynamic process. Complex processes involving cell-cell or cell-bacteria communication can be modeled at smaller scales and incorporated hierarchically into this main model; these are to be included in later elaborations. With the infection mechanism being defined on a higher level, lower-level processes influencing the host-pathogen interaction can be identified, modeled, and tested both quantitatively and qualitatively. This systems biology framework incorporates modeling to generate and test hypotheses, to perform virtual experiments, and to make experimentally verifiable predictions. Thereby it supports the unraveling of the mechanisms of tuberculosis infection.