(613e) Mathematical Modeling of Lipid Metabolism in Bone Marrow-Derived Macrophage Cells Using Cybernetic Control Variables

Metabolism is regulated by a number of factors in the cell. The concerted action of metabolism and regulation gives rise to the cellular phenotype or cellular outcome behavior. The cybernetic approach developed by our group assumes a goal and evolves the dynamics of the system under regulation to determine how each of the variables, e.g., metabolite concentrations, evolve over time, and how the metabolite fluxes are regulated.

The key advantage of cybernetic descriptions of cellular regulation is that they capture the molecular phenomena that control metabolic fluxes in the form of an intuitive regulatory principle. From the cybernetic perspective, regulatory mechanisms at the molecular level are not isolated events. Regulation is a cooperative cascade of molecular incidents that are coordinated to enhance a cell’s survival. Regulatory goals, such as maximizing growth [1] or carbon uptake rate [2], provide a causality driven basis for the regulation of individual chemical events. In the absence of high resolution, dynamic data for all cellular events that modulate metabolism, cybernetic assumptions of regulation offer a significant advantage in that they are simple and can robustly predict metabolic phenomena given an appropriate objective function.

While cybernetic models have focused on bacterial systems in the past, we adapt this framework to model the dynamic behavior of prostaglandin (PG) formation in a mammalian cell line, bone marrow-derived macrophage (BMDM) cells. Several kinetic descriptions of PG formation precede this work [3, 4], but none take into account the regulatory phenomena present in PG formation. Our application of cybernetics to macrophages provides a quantitative model of eicosanoid metabolism initiated with the input of arachidonic acid (AA) and using inflammation as the system objective. The cybernetic model provides a robust description of metabolite formation and can be used to predict perturbations to metabolism [5].

Cybernetic models are a robust description of metabolite formation and can be used to predict perturbations to metabolism via various effectors including drugs. Having a more reliable description of PG formation is useful in that it can provide a more predictive description of the action of inhibitory drugs. We show that cybernetic control theory can be broadened to describe objective functions in complex multi-cellular systems and have applications in predicting the response of metabolic networks to drugs.

References

1. Kompala, D. Ramkrishna, N. Jansen, and G. Tsao. Biotechnology and Bioengineering, 28:1044–1055, 1986.
2. Song and D. Ramkrishna. Biotechnology and Bioengineering, 106(2):271–284, 2010.
3. Gupta, M. Maurya, D. Stephens, E. Dennis, and S. Subramaniam. Biophysical journal, 96:4542–4551, 2009.
4. Kihara, S. Gupta, M. Maurya, A. Armando, I. Shah, O. Quehenberger, C. Glass, E. Dennis, and S. Subramaniam. Biophysical Journal, 106(4):966–975, February 2014.
5. Aboulmouna, S. Gupta, M.R. Maurya, F. DeVilbiss, S. Subramaniam, D. Ramkrishna. A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells. Processes. 6(8):126, August 2018.