A chemical engineer's perspective on health and disease

Abstract

Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

The future of computational biomedicine: Complex systems thinking

“…As early as 1920"s, in an insightful work that mirrored the technical and cultural state of development of Germany during the Weimar Republic, Fritz Kahn envisioned the human being in the form of an industriepalast (industrial palace) [63]. At that time, the novel idea of realizing a complex physiological system as an industrial chemical plant conveyed a simple, nevertheless paradigm-breaking message: the human body is a collection of connected processes [1] ( Figure 1, panels A and B). network structures (see [102]) -hub oligarchy -these systems are unusually organized, optimized and robust (see [40,101]).…”

A systematic framework for the design, simulation and optimization of personalized healthcare: Making and healing blood

“…Ultimately, applying such a tool could inform not only optimal timing and type of personalized treatment for improved outcomes, but also provide a platform for pre-clinical assessment of novel targeted therapies for leukemia and other cancers (Heitzig et al, 2014). Here, we review our efforts in simulating and optimizing chemotherapy treatment, from the time the drug is given to the patient, followed by the circulation in the body until it reaches its target (the bone marrow where leukemia resides) and finally the calculation of its effect according to cellular susceptibility (defined by the proliferative state) (Pefani et al, 2013;2014). Detailed building blocks are illustrated in blue, Figure 1.…”

“…The RBCs produced comply with the in vivo specifications in terms of oxygen-carrying capacity, surface markers, and shape (Macedo, 2011 Colijn et al (2005). The model is implemented in GAMS 24.1 and solved using the MINLP solver ANTIGONE 1.1 (Misener and Floudas, 2013;2014).…”

“…For the latter to be accomplished it is essential to develop appropriate experimental setups that reproduce in vitro the biological characteristics and behavior of the in vivo system. In silico techniques may bridge the gap between the in vitro and in vivo scales, through simulating the patient response (Androulakis, 2014;Chen et al, 2012;Harrold and Parker, 2009;Ho et al, 2013). The study of normal and abnormal blood production faces these challenges and many others related to the complexity of the underlying biological system and the heterogeneity observed in hematological malignancies.…”

Liver function as an engineering system

“…[4][5][6][7][8] give some further examples and perspectives from the Process Systems Engineering community. Many others have contributed to the area often known more broadly as Systems Biology, and Systems Medicine when clinical objectives are to the forefront.…”