(334t) Paving the Way for Cybernetic Modeling of Biological Processes in Mammalian Systems

Our bodies are well equipped to address and respond to external injury. The body has a two-line defense system against invading pathogens: the skin which acts as a physical barrier and the immune system which triggers an inflammatory response to fight off pathogens. Significant variation in the inflammatory response can lead to complications in certain disease states (e.g., cytokine storm in Covid-19 disease). Macrophages are a versatile immune cell which play a crucial role in inflammation. These cells have the ability to identify a foreign stimulus and trigger a cascade of cellular signaling events. Developing a working knowledge of these cellular events and a description of signaling pathways using mathematical models is imperative for studying immune-modulating drugs and their impact on disease outcome.

Regulation of metabolism in mammalian cells is achieved through a complex interplay between cellular signaling, metabolic reactions, and transcriptional changes. These complex interactions can be modeled through implicit accounting of regulation using a cybernetic framework, as seen in the modeling of prostaglandin metabolism in inflamed macrophage cells [L. Aboulmouna et al., Processes. 2018]. The premise of the cybernetic framework is that the regulatory processes affecting metabolism can be mathematically formulated through variables that constrain the network to achieve a specified “goal”.

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 for induction of enzyme synthesis (u) and modulation of enzyme activity (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 use regulatory goals like maximizing growth rate [1] or carbon uptake rate [2] to describe various biological phenomena in bacterial and yeast systems, such as diauxic growth. The cybernetic goal for mammalian cells may not be based solely on survival or growth but on specific context dependent cellular responses.

This work adapts 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 [5]. Using a defined cybernetic 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.

Research Interests: From the cell to whole animal model—Understanding dysregulation of systems at multiple levels

My previous work has blended mathematical modeling with experimental techniques to develop an understanding of complex biological phenomena. A coupled approach to addressing biological phenomena and understanding how crucial process become dysregulated in certain disease states is required when developing therapeutics. Here, I highlight previous research initiatives.

Novel isotope tracer method for quantifying glucose metabolism in type-2 diabetes

Accurately assessing in vivo rates of glucose turnover, glycogen breakdown, gluconeogenesis, and futile cycling in the liver are crucial to understanding the control of whole-body glucose metabolism and how it becomes dysregulated in conditions such as diabetes and obesity. Currently, these rates are estimated using methods that do not account for the full complexity of intracellular biochemical reaction networks, and, therefore, often lead to inconsistent results. In prior work, we conducted metabolic flux analysis (MFA) studies using a unique formulation of 13C- and 2H-labeled glucose tracers and Gas Chromatography-Mass Spectrometry (GC-MS) profiling in order to understand the regulation of liver glucose fluxes in both fasted and fed Sprague-Dolly rats. By analyzing GC-MS labeling data of plasma glucose and lactate using our custom MFA software tools, we developed strategies to quantify whole-body glucose metabolism using an integrated experimental and computational approach.

Single-cell-based investigation of HL60 differentiation using a microwell array

The average cell population response to a range of stimuli inaccurately reflects individual cell response due to heterogeneity in previously assumed homogeneous cell populations. It is hypothesized that this contributes to metastatic potential in tumors, with the most invasive cells being molecularly and behaviorally distinct from the bulk. In prior work, we designed and optimized a microwell platform using standard photolithography techniques for use in high throughput single cell studies to address heterogeneity in tumor populations. The acute myeloid leukemia cell line HL60 was used as a model uncommitted precursor cell line, and we began investigating population variance by inducing differentiation along the granulocytic lineage with all-trans retinoic acid.

Future Work

I am especially interested in continuing to advance my understanding of the human system and the role inflammation plays in disrupting pathogen invasion in our bodies. As I look to pursue future research initiatives, I am excited at the opportunities available to further understand our collective understanding of what a healthy body looks like at the molecular level leading to the organ level and, ultimately, the system as a whole in order to optimize individuals’ health and well-being.