Translational systems approaches to the biology of inflammation and healing

Abstract

Inflammation is a complex, non-linear process central to many of the diseases that affect both developed and emerging nations. A systems-based understanding of inflammation, coupled to translational applications, is therefore necessary for efficient development of drugs and devices, for streamlining analyses at the level of populations, and for the implementation of personalized medicine. We have carried out an iterative and ongoing program of literature analysis, generation of prospective data, data analysis, and computational modeling in various experimental and clinical inflammatory disease settings. These simulations have been used to gain basic insights into the inflammatory response under baseline, gene-knockout, and drug-treated experimental animals for in silico studies associated with the clinical settings of sepsis, trauma, acute liver failure, and wound healing to create patient-specific simulations in polytrauma, traumatic brain injury, and vocal fold inflammation; and to gain insight into host-pathogen interactions in malaria, necrotizing enterocolitis, and sepsis. These simulations have converged with other systems biology approaches (e.g., functional genomics) to aid in the design of new drugs or devices geared towards modulating inflammation. Since they include both circulating and tissue-level inflammatory mediators, these simulations transcend typical cytokine networks by associating inflammatory processes with tissue/organ impacts via tissue damage/dysfunction. This framework has now allowed us to suggest how to modulate acute inflammation in a rational, individually optimized fashion. This plethora of computational and intertwined experimental/engineering approaches is the cornerstone of Translational Systems Biology approaches for inflammatory diseases.

Figure 1

Evidence-based modeling. Initial model components are determined from experimental data using Principal Component Analysis. Subsequently, model building follows an iterative process involving calibration from existing or new data, and validation from prediction of data. This process identifies both areas where a model is correct and where it is deficient relative to data and therefore must be corrected.

Figure 2

Dynamic Profiling and its application to traumatic brain injury. Panel A: Dynamic Profiling is a novel technique that involves clustering subjects into disjoint groups based on initial and time-dependent changes in subject-characteristics, in order to predict outcomes of individuals. A cluster is a subset of subjects that share similar characteristics. In the specific case of traumatic brain injury (TBI), we utilize patient demographics (age, gender, severity of injury) along with the sequential change in cytokines over time in order to cluster patients. Panel B: application of Dynamic Profiling to predict outcomes in individual TBI patients. The probability of mortality as a function of clustering stage was predicted using Dynamic Profiling for Patients 11 and 14 (see text for details), which had similar initial cluster placements but for whom subsequent clustering suggested divergent trajectories with regard to probability of survival.

Figure 3

Self-regulating, individualized inflammation-modulating bioreactor. Plasmids consisting of cytokine-sensitive promoters upstream of endogenous cytokine inhibitors have been constructed using standard molecular biology techniques. The plasmids have been transfected into hepatocyte cell lines. The cells are seeded into bioreactors. In the future, such bioreactors could be connected extracorporeally in order to modulate inflammation in a disease- and patient-specific manner.