Mathematical modeling of energy consumption in the acute inflammatory response. (7th January 2019)

• Record Type: Journal Article
• Authors: Ramirez-Zuniga, Ivan; Rubin, Jonathan E.; Swigon, David; Clermont, Gilles
• Is Part Of: Journal of theoretical biology. Volume 460(2019)
• Page Start: 101
• Page End: 114
• Publication Date: 2019-01-07
• Subjects: Immunology -- Sepsis -- Bioenergetics -- Bifurcation analysis -- Basins of attraction; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.08.033
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: A computational model accounts for the energy required by the immune system to fight an infection. The model exhibits three biologically significant equilibrium points: healthy, aseptic and septic. Bifurcation analysis yields bistability and tristability among equilibria with varying pathogen growth rate. Low levels of ATP, nitric oxide over-production and lactate accumulation are related to sepsis. Basins of attraction of equilibria and survival analysis reveal effects of hyperglycemia, hypoglycemia, and hypoxia. Abstract: When a pathogen invades the body, an acute inflammatory response is activated to eliminate the intruder. In some patients, runaway activation of the immune system may lead to collateral tissue damage and, in the extreme, organ failure and death. Experimental studies have found an association between severe infections and depletion in levels of adenosine triphosphate (ATP), increase in nitric oxide production, and accumulation of lactate, suggesting that tissue energetics is compromised. In this work we present a differential equations model that incorporates the dynamics of ATP, nitric oxide, and lactate accompanying an acute inflammatory response and employ this model to explore their roles in shaping this response. The bifurcation diagram of the model system with respect to the pathogen growth rate reveals three equilibrium states characterizing the health, aseptic and septic conditions. We explore the domains of attraction of these states to inform the instantiation of heterogeneous virtual patient populations utilized in a survival analysis. We then apply the model to study alterations in the inflammatory response and survival outcomes in metabolically altered conditions such as hypoglycemia, hyperglycemia, and hypoxia.