(732c) A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena ranging from complex substrate uptake patterns and dynamic metabolic flux distributions to the behavior of gene knockout strains. The primary logic to this approach is to understand the dynamics of how a system evolves in a regulatory manner towards an objective. Cybernetic theory builds on the perspective that metabolic regulation is organized towards achieving goals relevant to an organism’s survival or performing specific biological functions. For example, in multi-substrate cultures of E. coli, cells invest resources into enzymes for pathways which maximize their growth rate. While goal-oriented control has successfully yielded the prediction of numerous metabolic phenomena, the control policy itself has not been compared with data for metabolic regulation.

In this work, we build on preliminary work done by Ramkrishna et al. in using cybernetic control variables weighed against gene expression data (an indicator of cellular regulation) in bacterial systems and apply that to mammalian systems. We model the dynamic behavior of prostaglandin formation from arachidonic acid in the mouse macrophage RAW 264.7 cells stimulated by lipopolysaccharide resulting in inflammation and/or compactin, a statin drug. Prostoglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. They are widely studied due to their influence on inflammation and related functions. Statin drugs are used by 93% of adults on cholesterol-lowering medication in the United States¹, and while intended to reduce cholesterol levels in human physiology, the consequences to inflammatory metabolism are largely unknown making this system a good case study.

1. Gu Q, Paulose-Ram R, Burt VL, Kit BK (2014) Prescription cholesterol-lowering medication use in adults aged 40 and over: United States, 2003–2012. NCHS Data Brief: 1–8