(364d) Mathematical Modeling of Lipid Metabolism Using Cybernetic Framework (Kinetic Modeling Technique) and Novel Information-Theoretic Approaches

Regulation of complex biological processes aims to achieve goals essential for an organism's survival or to exhibit specific phenotypes in response to stimuli. This regulation can occur at several levels, such as cellular metabolism, signaling pathways, gene transcription, mRNA translation into proteins, and post-translational modifications. Systems biology approaches can facilitate integrating mechanistic knowledge and high-throughput omics data to develop quantitative models that can help improve our understanding of regulations at various levels. However, computational modeling of biological processes is challenging due to the vast details of the control processes with unknown details. The cybernetic modeling approach accounts for unknown control mechanisms by defining a biological goal that the system aims to optimize, and subsequently mathematically formulates the cybernetic goal.

We developed a mathematical framework that integrates a cybernetic model with novel information-theoretic methods to study the inflammatory response in mammalian macrophage cells. We applied the framework to model pro-inflammatory eicosanoid metabolism in the presence of cytokine TNFα. We predicted causal connections between AA and cytokines using time series analysis as mechanistic link is unknown, and developed Time Delay Renyi Symbolic Transfer Entropy (TDRSTE), a novel model-free information-theoretic method that utilizes high-throughput omics measurements to quantify causal delays in bivariate non-stationary time series. The causality result predicted Arachidonic acid (AA) being the cause of TNF secretion, encouraging future investigations with extensive data. 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 also 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. The cybernetic framework with information theory explains pro/anti-lipid mediator metabolism and predicts causality between AA and cytokines, enabling predictions of inflammatory system’s response to perturbations and facilitating network redesign for novel drug properties.