Math Modeling to Understand Immune Response
When thousands of diverse health threats exist to attack our immune system, how does our body respond appropriately?
As a graduate student researcher in the Signaling Systems Laboratory advised by Dr. Alexander Hoffmann at UCLA, I use math modeling to study immune response.
On the molecular biology level, health threats are recognized as pathogen associated molecular patterns (PAMPs) which converge on a small subset of signaling pathways to elicit appropriate response. My research focuses on NFκB, a transcription factor responsible for transcribing countless genes in innate and adaptive immune response.
How does NFκB elicit the appropriate gene expression for a given health threat?
Research suggests that dynamic features of the nuclear NFκB temporal profile carries information about a stimulus. The Hoffmann lab has developed a time lapse fluorescent microscopy pipeline to quantitate nuclear NFκB over time at the single cell level. Figure Caption: Taylor et al 2020 bioRxiv.
Nuclear NFkB over time forms a 'dynamic code'
This ‘dynamic code’ hypothesis is indeed illustrated by the contrast in single cell NFκB response between two different immune response stimuli: inflammatory cytokine, tumor necrosis factor (TNF), and lipopolysaccharide (LPS), a component to the bacterial cell wall.
Figure Caption: Each row is the nuclear NFκB activity for a single cell. Taylor et al 2020 bioRxiv.
I use ordinary differential equation (ODE) math models to tackle the following questions:
What molecular components lead to stimulus specific NFκB response?
What are the sources and consequences of biological heterogeneity in NFκB response?
Figure Caption: Taylor et al 2020 bioRxiv.