Dissecting innate immune responses with the tools of systems biology

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Systems biology strives to derive accurate predictive descriptions of complex systems such as innate immunity. The innate immune system is essential for host defense, yet the resulting inflammatory response must be tightly regulated. Current understanding indicates that this system is controlled by complex regulatory networks, which maintain homoeostasis while accurately distinguishing pathogenic infections from harmless exposures. Recent studies have used high throughput technologies and computational techniques that presage predictive models and will be the foundation of a systems level understanding of innate immunity.

Introduction

Systems biology is concerned with understanding the dynamic outcome of molecular interactions between biomolecules (e.g. DNA, RNA, proteins, lipids and metabolites) at the pathway, organelle, cell, organ and organism levels (Figure 1). The methodology of systems biology is an iterative process that begins with the identification of component parts and their respective interactions. This information is then integrated into a predictive (usually mathematical) model of system behavior, which is in essence a hypothesis that can be tested experimentally. The experimental results lead to the refinement of the model and, hence, new hypotheses.

Because of its complex dynamic behavior, its wealth of intricate intra- and inter-cellular interactions, and its medical significance, the immune system offers an ideal focus for systems biology. The innate immune response is highly context dependent, suggesting unplumbed complexity. Depending on the context, host and pathogen factors can be both protective and injurious. For example, Toll-like receptor (TLR)-2 and myeloid differentiation antigen 88 (MyD88) protect against systemic spread of Group B Streptococcus during low dose challenge; by contrast, TLR2 and MyD88 promote lethality to high doses of the same pathogen [1]. Moreover, all infections and inflammatory disorders are not created equal; TNF antagonism is an efficacious therapeutic target in rheumatoid arthritis [2], but can exacerbate systemic lupus erythematosus [3] and mycobacterial infections [4].

Thus, interactions, feedback, dynamic behavior, complexity and context are widespread in, and essential to, innate immunity. These are exactly the issues that systems biology aims to address; raising hopes for early and accurate diagnoses, the instigation of preventative strategies, and the development specific treatments for infectious and inflammatory diseases. Systems biology also presents the opportunity to personalize diagnoses and therapies, taking into account the genetic and environmental factors that make each individual unique.

The rest of this review is divided into two sections reviewing the current technologies and computational tools of systems biology with particular emphasis on applicability to innate immune studies. Because of our backgrounds, we have focused on studies in the biology of TLRs as a representative facet of innate immunity.

Section snippets

Genomic technologies

Genomic technologies aim to define both static and dynamic aspects of genomes within biological systems, including sequence, sequence variation and gene expression. In addition, genomic tools can be used to manipulate genetic sequence and gene expression to help us to better understand biological systems.

Conclusions

The systems biology of innate immunity is in its infancy, with many of the efforts still directed at defining the parts lists and connectivity for the regulatory networks that control these responses. The promise of personalized genomics combined with high throughput diagnostics will revolutionize our understanding of innate immunity and the treatment of its disorders. These advances are predicated on further developments in technologies that quantitatively define biological systems, and of

References and recommended reading

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We thank Adrian Ozinsky, Erica Andersen-Nissen, Colleen Sheridan and Alan Aderem for helpful comments. KS and HB are supported by grants from the National Institutes of Health (U54AI54523 [HB] and R01AI052286 [KS]).

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