As a part of the Center for Science of Information Spring 2020 Seminar Series (online), Lina Aboulmouna, Ph.D. Candidate, and Rubesh Raja, Postdoctoral Scholar, Center for Science of Information, Chemical Engineering, Purdue University will present a seminar based on collaborations between the Ramkrishna Lab (Purdue) and Subramaniam Lab (UC San Diego) "Cybernetic Modeling of the Eicosanoid Pathway in the Macrophage Cells".
Inflammation, perceived as redness, heat, swelling, and pain, is the human body’s response to remove harmful stimuli and begin the healing process. Any significant variation in the inflammatory response can lead to complications in certain diseases (e.g., cytokine storm in Covid-19 disease). Macrophages, a versatile immune cell type, play a key role in the inflammatory response by identifying a foreign stimulus and responding with key cellular signaling events. Understanding these signaling pathways using mathematical models can aid in the use of immune-modulating drugs to improve disease outcome. Here, we model a network in macrophage cells, the eicosanoid pathway, using a cybernetic modeling framework.
Cybernetic models, developed by Ramkrishna’s group, assume that the kinetic parameters of the mechanistic fluxes are not constants and can vary with time. The kinetic parameters are represented as the product of unregulated rate constants and cybernetic control variables “u” or “v”. The cybernetic control variables can be intuitively formulated based on the goal of the system. This model framework describes gene regulation by attributing metabolic preferences to a suitable survival goal of the organism. Previous cybernetic models using regulatory goals like maximizing growth rate  or carbon uptake rate  explain various behavior in bacterial systems including the diauxic growth.
In this work, we adapt the cybernetic framework to model the eicosanoid pathway— production of prostaglandins (COX-branch) and leukotrienes (LOX-branch) from arachidonic acid— in bone-marrow derived macrophage (BMDM) cells using the in vitro data across four different experimental scenarios. Several models of the eicosanoid pathway precede this work [3, 4], but none consider the regulatory phenomena . Using a goal to maximize system inflammation, we developed a weighted formulation of cybernetic control variables by correlating the lipidomic and transcriptomic data. The model effectively describes the experimental data and provides insights into the complex regulatory patterns present in the macrophage.
In future studies, we will model the outcomes of perturbations in this model system. For example, the Nonsteroidal anti-inflammatory drugs (NSAIDs which include aspirin or ibuprofen) directly target the COX-branch and can perturb the eicosanoid pathway resulting in an impact on disease outcome.
Lina Aboulmouna is a CSoI student member and Ph.D. candidate in the Department of Chemical Engineering at Purdue University, anticipating her graduating at the end of the summer session. Lina has served as a mentor to undergraduates throughout her graduate career and also to young people through the Big Brothers Big Sisters program.
Rubesh Raja, Ph.D., is a Postdoctoral Research Associate in the Ramkrishna Lab with the Department of Chemical Engineering at Purdue University, and a member of the CSoI.
Both Lina and Rubesh are visiting scholars at UC San Diego working on a collaborative project bridging the labs of CSoI faculty D. Ramkrishna at Purdue and S. Subramaniam at UCSD.