(188dk) Application of Cybernetic Control Variables in the Modeling of Lipid Metabolism in Mammalian Systems | AIChE

(188dk) Application of Cybernetic Control Variables in the Modeling of Lipid Metabolism in Mammalian Systems


Aboulmouna, L. - Presenter, Purdue University
Gupta, S., University of California, San Diego
DeVilbiss, F. T., Purdue University
Subramaniam, S., University of California, San Diego
Ramkrishna, D., Purdue University

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 builds on the
perspective that metabolic regulation is organized towards achieving goals
relevant to an organism’s survival or performing specific biological functions. The goal-oriented control
policies of cybernetic models have been used to predict metabolic phenomena
ranging from complex substrate uptake patterns [1] and dynamic metabolic flux
distributions to the behavior of gene knockout strains [2]. The primary logic
to this approach is to understand the dynamics of how a system evolves in a
regulatory manner towards an objective. While goal-oriented control has
successfully yielded the prediction of numerous metabolic phenomena in
bacterial systems, the control policy itself has not been implemented in
mammalian systems.

In this work, we
expand on work done by Ramkrishna et al. in using cybernetic control variables to
regulate the glycolytic metabolic fluxes in bacterial systems and apply that to
lipid metabolism in mammalian systems. We model the dynamic behavior of prostaglandin
formation (PG) from arachidonic acid (AA) in the mouse bone marrow derived
macrophage (BMDM) cells stimulated by ATP resulting in inflammation. Genomic,
proteomic, and metabolomic data for prostaglandin formation was obtained from
the LIPID MAPS database. Prostaglandins are a well characterized set of
inflammatory lipids derived from arachidonic acid. They are widely studied due
to their role in inflammation and related functions. 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 starting with an increase in AA and resulting in the inflammatory outcome represented
by TNF-alpha.

To describe the time-dependent
formation of PGs, a cybernetic model is generated. This description
approximates the conversion of AA into downstream products (Figure 1A). In using cybernetic arguments to model PG
formation (Figure 1B), we are assuming that
these products are formed in varying amounts related to their ability to help
the cell achieve its inflammatory objective in producing TNF-alpha. The production
of PGs that have a stronger relationship with the goal of the system will be
upregulated while the pathways for those PGs which have a lesser relationship
with the objective function will be downregulated.

After fitting parameters to two
independent conditions (control and ATP stimulated), it is evident that the
model correctly explains the evolution of the metabolite concentrations
involved in the fit. We
further validated
our model by predicting the effects of lipopolysaccharide primed and ATP
stimulated BMDM cells using a third independent data set. Cybernetic models
are a robust description of metabolite formation and can be used to predict
perturbations to metabolism via various effectors, including drugs. This work,
for the first time, develops the idea that cybernetic metabolic objectives can
be used to describe the regulation of signaling systems in mammalian
metabolism. It yielded a model describing PG synthesis that is capable of
predicting metabolite levels under basal and drug stimulated conditions
for pursuits in translational research.


1. S.Song and D.
Ramkrishna. Biotechnology and Bioengineering, 106(2):271–284, 2010.

2. D.
Kompala, D. Ramkrishna, N. Jansen, and G. Tsao. Biotechnology and
Bioengineering, 28:1044–1055, 1986.

3. S.
Gupta, M. Maurya, D. Stephens, E. Dennis, and S. Subramaniam. Biophysical
journal, 96:4542–4551, 2009.

4. Y.
Kihara, S. Gupta, M. Maurya, A. Armando, I. Shah, O. Quehenberger, C. Glass, E.
Dennis, and S. Subramaniam. Biophysical Journal, 106(4):966–975, 2014.


This work is supported by the Center for
Science of Information (CSoI), a National Science Foundation Science and
Technology Center, under grant agreement CCF-0939370.