(120c) Metabolic Modeling of Eicosapentaenoic Acid (EPA) and Arachidonic Acid (AA) Fatty Acids Using a Cybernetic Framework

This research concentrates on studying the inflammatory response of the murine macrophage-like RAW cells upon external perturbation, such as a cut or an infection. In response to a disturbance from the environment, prostaglandins (PG2 series) arise from the cyclooxygenase (COX) oxidation of Arachidonic acid (AA), an ω-6 polyunsaturated fatty acid. They are the pain mediators, and are responsible for regulating the induction of inflammation. However, the pro-inflammatory PG2’s heightened concentration contributes to widespread chronic inflammatory diseases, including asthma, and impaired wound healing. Non-steroidal anti-inflammatory drugs (NSAIDs), which work by inhibiting COX, are among the most consumed pharmacotherapeutic agents to treat these diseases. They exhibit anti-inflammatory roles and are beneficial in relieving and blocking acute inflammation but ineffective at terminating inflammation or promoting resolution and tissue repair.

Instead of controlling AA metabolism by COX inhibition, an alternate strategy is to activate the competitive substrate that binds COX, thereby inhibiting the PG2 series formation. The ω-3 fatty acid eicosapentaenoic acid (EPA) is a promising substitutable substrate. EPA competes with AA to bind with COX during the body's inflammatory response. The metabolism of EPA results in the PG3 series formation, thus reducing the levels of PG2-series metabolites. PG3 series is known to exhibit anti-inflammatory attributes and partake in inflammation resolution. Moreover, studies have confirmed that EPA-containing diets, fish oil being the primary source, assist prevent cardiovascular and other diseases.

Metabolic regulation forms an integral part of a biological system. All organisms control the reaction mechanism to achieve a biological goal, such as survival during a dreadful situation in unicellular organisms. Biological systems are challenging to model because of the vast intricacy of the regulatory processes due to unknown details. The cybernetic model is a mathematical framework for studying biochemical networks [1]. A distinguishing feature of the cybernetic model is that it successfully accounts for complicated metabolic regulation. Unlike other biological models that do not study regulation or can best incorporate known control, the cybernetic model excels in capturing known and unknown exhaustive control attributes. Thus, the cybernetic model has been remarkably successful in expounding the dynamics of a complex biological system.

Development of the Cybernetic Model

This work focuses on developing a cybernetic framework to study the AA and EPA metabolism in the presence of inflammatory markers, such as TNFα and IL-6. We model the metabolic network adopted by Gupta et al. [2], Fig. 1. Studies focused on modeling the competitive metabolism of AA and EPA are emerging; however, few studies are available. Gupta et al. [2] successfully captured the underlying mechanism. However, they accounted for transcriptomic regulation only and did not include cytokines in their system. In this work, we incorporate cytokines, well-known markers of inflammation, that exhibit a relationship with AA and EPA networks. Consequently, this study aims to gain insight into the fundamental mechanism ruling the competitive metabolism of AA and EPA in the presence of cytokines.

The cybernetic framework is based on defining a cybernetic goal and correspondingly computing the cybernetic control variables. For the network shown [1], an enzyme can bind with both substrates S₁ and S₂ to form products P_i and Pj as per S₁ + e_i → P_i and S₂ + e_i → Pj. The rate of formation of P_i and e_i follow eq. 1.

The cybernetic control variable u_Pi controls the enzyme synthesis rate; hence it appears in the enzyme synthesis rate formation equation. In eq. 1(b), α denotes the constitutive enzyme synthesis rate, β is the degradation rate and the second term denotes the enzyme synthesis due to Michaelis-Menten kinetics. The cybernetic control variable v_Pi controls the enzyme activity; thus, it appears with the P_i rate formation in 1(a), and γ_Pi is the decay rate of the product. Defining u_Pi and v_Pi requires formulating the cybernetic goal F. We define the cybernetic goal as eq. 1(c), which is the weighted combination of the inflammatory marker levels C₁ (TNFα) and C₂ (IL-6). The cybernetic goal is to maximize the production rate of the markers C₁ and C₂ which are associated with the AA and EPA branches, respectively. We will capture these associations using a data-driven approach. We define the control variables u_Pi and v_Pi using the cybernetic goal as shown in eq. 2(a) and 2(b).

Study outcomes

We lay the groundwork for studying the competitive metabolism of AA and EPA using the cybernetic model while including their interactions with different inflammatory markers. This study will provide insight into the underlying mechanism of AA and EPA formation under the influence of markers and ways to control their competition. Additionally, we expect to better understand how to modulate competitive metabolism of AA and EPA by tackling their marker levels.

Reference:

1. Aboulmouna, L., Raja, R., Khanum, S., Gupta, S., Maurya, M.R., Grama, A., Subramaniam, S. and Ramkrishna, D., 2020. Cybernetic modeling of biological processes in mammalian systems. Current Opinion in Chemical Engineering, 30, pp.120-127

2. Gupta, S., Kihara, Y., Maurya, M.R., Norris, P.C., Dennis, E.A. and Subramaniam, S., 2016. Computational modeling of competitive metabolism between ω3-and ω6-polyunsaturated fatty acids in inflammatory macrophages. The journal of physical chemistry B, 120(33), pp.8346-8353.