(364e) Kinetic Modeling of Eicosanoid Metabolism Using Cybernetic Framework and Novel Information-Theoretic Approaches

My thesis developed a theoretical framework that integrates cybernetic modeling with novel information-theoretic approaches to study inflammatory response in mammalian mouse macrophages. The distinguishing feature of a cybernetic model is that by defining a cybernetic objective, it can account for control at different levels, such as cellular metabolism, signaling pathways, gene transcription, mRNA translation into proteins, and post-translational modifications. The success of cybernetic models for inflammatory systems validates that regulation aims to achieve goals essential for an organism's survival or to exhibit specific phenotypes in response to stimuli.

We applied the framework to model pro-inflammatory eicosanoid metabolism in the presence of cytokine TNFα. The causality result suggests Arachidonic acid (AA) is the cause of TNF secretion. Based on the cybernetic model results, causality analysis, and heuristic reasoning, this study suggests a potential clinical implication that modulating AA levels could reduce TNFα expression, indicating eicosanoids as a promising strategy for managing hyperinflammation associated with elevated cytokines in COVID-19.

We developed a cybernetic model to study anti-inflammatory lipid mediators from eicosapentaenoic acid (EPA) metabolism, which can be beneficial in reducing the severity of diseases such as cancer. The model estimates the ratio of to concentrations required for the switch from pro- to anti-response as 2.2, aligning with the experimental range of 1-5 needed to promote anti-inflammation.

We developed TDRSTE, a novel model-free information-theoretic method that utilizes high-throughput omics measurements to quantify causal delays in bivariate non-stationary time series. We predicted causal connections between AA and cytokines using time series analysis as the mechanistic link is unknown. The causality results indicate a potential causal link between AA and cytokines, encouraging future investigations with extensive data.

The cybernetic framework with information theory explains pro/anti-lipid mediator metabolism and determines causality between AA and cytokines, enabling prediction of the inflammatory system's response to perturbations.