Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

Abstract

The complexity of the systemic inflammatory response and the lack of a treatment breakthrough in the treatment of pathogenic infection demand that advanced tools be brought to bear in the treatment of severe sepsis and trauma. Systems medicine, the translational science counterpart to basic science's systems biology, is the interface at which these tools may be constructed. Rapid initial strides in improving sepsis treatment are possible through the use of phenomenological modelling and optimization tools for process understanding and device design. Higher impact, and more generalizable, treatment designs are based on mechanistic understanding developed through the use of physiologically based models, characterization of population variability, and the use of control-theoretic systems engineering concepts. In this review we introduce acute inflammation and sepsis as an example of just one area that is currently underserved by the systems medicine community, and, therefore, an area in which contributions of all types can be made.

Fast and slow domino regimes in transient network dynamics

“…We consider an idealization of behaviour that has been seen in a variety of applications: this includes (a) signal propagation by sequential switching between asymmetric stable states (observed experimentally in chains of bistable electronic circuits [28] or in cases where the bistability is noise-induced [38]) (b) waves along unidirectionally coupled chains (or lattices) of bistable nodes with forcing at one end [27] (c) photoinduced phase transitions in spincrossover materials with bistable dynamic potentials [8,32,37] (d) avalanches of gene activation in gene regulatory pathways to drive cell differentiation/development/cancer [19,36] (e) cell fate in biofilm formation [10]. Other applications that could benefit from a better un-derstanding of similar transient dynamics induced by noise include (a) the contagion of bank defaults in a system of financial institutions interconnected by mutual loans [13,18,20,35], (b) interconnections between "tipping elements" [1,26], (c) the role of spreading of abnormal large-amplitude oscillators in modelling onset of epileptic seizures [3,22] (d) multiple organ failure [33] or (e) cascading failures in power systems [15].…”

A chemical engineer's perspective on health and disease

“…Such computational models are not, however, intrinsically useful in a clinical context, and therefore they must be structured in a manner that allows them to both leverage clinically obtainable data and ultimately produce clinically useful predictions (Vodovotz, 2010; Vodovotz et al, 2007). Systems-based translational research considers physiological conditions as dynamically evolving, networked systems with clearly identified boundaries and rules that define their response, providing a systematic framework for translating biological information into organism-level insights (An et al, 2008; Foteinou et al, 2009; McGuire et al, 2011; Parker & Clermont, 2010; Vodovotz, 2010). …”

Immune therapeutic strategies using optimal controls with L 1 and L 2 type objectives

“…Using computational tools and methodologies is one means by which to explore this and offer insight. In particular, applying automatic control for biomedical therapeutic intervention has recently gained interest in such areas as glucose control for diabetes or in critically ill patients and for anesthesia depth control, as seen for example, in [5], [6], [7], [8], and the references therein. The use of optimal control theory for biomedical applications can be seen in [9], [10], and [11] for example; and, more recently, the use of model predictive control (MPC) in [12] and [13] which used the same mathematical model as used herein.…”