Computational and systems biology in trauma and sepsis: current state and future perspectives

Abstract

Trauma, often accompanied by hemorrhage, is a leading cause of death worldwide, often leading to inflammation-related late complications that include sepsis and multiple organ failure. These secondary complications are a manifestation of the complexity of biological responses elicited by trauma/hemorrhage, responses that span most, if not all, cell types, tissues, and organ systems. This daunting complexity at the patient level is manifest by the near total dearth of available therapeutics, and we suggest that this dire condition is due in large part to the lack of a rational, systems-oriented framework for drug development, clinical trial design, in-hospital diagnostics, and post-hospital care. We have further suggested that mechanistic computational modeling can form the basis of such a rational framework, given the maturity of systems biology/computational biology. Herein, we briefly summarize the state of the art of these approaches, and highlight the biological insights and novel hypotheses derived from these approaches. We propose a rational framework for transitioning through the currently fragmented process from identification of biological networks that are potential therapeutic targets, through clinical trial design, to personalized diagnosis and care. Insights derived from systems and computational biology in trauma and sepsis include the centrality of Damage-Associated Molecular Pattern molecules as drivers of both beneficial and detrimental inflammation, along with a novel view of multiple organ dysfunction as a cascade of containment failures with distinct implications for therapy. Finally, we suggest how these insights might be best implemented to drive transformational change in the fields of trauma and sepsis.

Keywords: Trauma; computational biology; mathematical modeling; sespsis; systems biology.

Figure 1

The process from data to knowledge, needed to drive novel therapies for sepsis and trauma. In order to obtain mechanistic, therapeutically-relevant knowledge from high-content data, the data need to describe the dynamics of the biological process, as well as accounting for subject-to-subject variability. Such data are amenable to data-driven analyses such as Principal Component Analysis and Dynamic Network Analysis, methods that can give quasi-mechanistic insights into the biological process and hence may suggest testable hypotheses. In order to increase the likelihood of deriving true therapeutically actionable knowledge from high-content data, insights from data-driven analysis and modeling techniques should be used to create mechanistic computational simulations that, in turn, will yield more refined hypotheses.

Figure 2

Multi-scale control structure of inflammation. This figure demonstrates the tiered scales of biological organization. Control mechanisms (such as inflammation) attempt to balance insults/perturbations that threaten the health state (abstractly represented as the purple circle). Balance occurs at multiple tiers, and the multi-scale nature of the control mechanisms allow for considerable robustness of the system. Note that the control mechanisms themselves have a complex structure that can shift the balance as well, but these mechanisms have been abstracted for clarity.

Figure 3

Tipping Points and Cascading Systems Failure. If there is control failure at one component level of the schematic illustrated in Figure 2 due to the feed-forward loop of inflammation à damage à inflammation, the “tipping” of that system past a point of no return can lead to an override of control mechanisms at the higher level, leading to a cascading effect that culminates in systemic dysfunction (Red Arrows). Importantly, spillover to the next component not only affects that component, but also affects the prior one, propagating the feed-forward behavior of inflammation back across components and compartments (Blue Arrows). Note that these components can be defined by scale of organization, but also by the spatial containment inherent to an organ’s anatomical structure and relationship to other organ systems.

Figure 4

Effects of current and potential therapeutic interventions for trauma/hemorrhage and sepsis. Current therapies for trauma/hemorrhage and sepsis are based on organ-level physiological support (Orange Block Arrows). These strategies temporize the tipping effects on the higher-level system (e.g. organism-wide consequences), but currently do not feedback on the dysfunction at the lower level. The result is a fragile “meta-stable” state, in which organ support attempts to prevent global system failure while waiting for the lower-level systems to “come back on line”. The difficulty with this approach, however, lies in the known potentially detrimental consequences of organ support (such as barotrauma from ventilators or hemodynamic instability from hemodialysis), which can delay or compromise the restitution of cellular-molecular homeostasis. Alternatively, we propose that modulation strategies directed at lower level control structures (i.e. cytokines) at an appropriate point in the failure sequence (i.e. prior to complete lost of containment and tipping to the next level) may help reset the underlying control mechanisms, limit spill-over effects and bolster maintenance of compartmental containment (Purple Block Arrows). All these points of interdiction can forestall the propagation of cascading systems failure if the intervention can be applied at the appropriate time, and due to the feed-forward behavior of the inflammatory response.