Abstract
Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.
Highlights
In a time of moon shooting projects to cure cancer (Nature Editorial, 2016), the reader may wonder why it remains interesting to deploy a “systemic approach” to deepen our understanding of bacterial lung infections
A fundamental limitation of ordinary differential equations (ODE) crucial for some biological systems and transcriptional circuits is that predictions based on ODE models may fail for systems with low copy numbers for the molecules or the cells involved in the interactions, in which randomness in their dynamical behavior emerges
We have great expectations in terms of what mathematical modeling can contribute in the coming decade to the understanding of lung infection pathophysiology
Summary
In a time of moon shooting projects to cure cancer (Nature Editorial, 2016), the reader may wonder why it remains interesting to deploy a “systemic approach” to deepen our understanding of bacterial lung infections. In line with this example but in the context of lung infection, Anderson et al (2016) studied human dendritic cell response against the influenza H1N1, a virus that can co-infect with several types of bacteria to produce pneumonia (Joseph et al, 2013) To this end, they derived a biochemical network with 13 nodes corresponding to genes and transcription factors playing a role in antiviral response (e.g., NF-κB, STAT1 and IRF1), and 42 edges representing the activation of key immune pathways during the infection. A fundamental limitation of ODE crucial for some biological systems and transcriptional circuits is that predictions based on ODE models may fail for systems with low copy numbers for the molecules or the cells involved in the interactions, in which randomness in their dynamical behavior emerges These special features are better represented by stochastic models, which are discussed in the coming section. We think that ABMs will be an interesting alternative in the coming future for modeling bacterial lung infection
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