Further Informed Design Process of the Model and Experiments

Creating a representation of our model was one facet, but ensuring the harmony, or composability, of parts through runs was informed through an empirical experimentation methodology of its own. SimBiology provides tools such as Parameter Sweep to allow the iteration of equation parameters and sequential data collection through runs. While much of this data was transient and highly variable, the different simulation settings we used can be viewed in the full model, as linked above. 

Repeated trials and iterations of parameters thus ultimately informed the design of our sensor specifications, network topology, and graded responses needed for treatment. 

For example, at first pass, we found our system was unstable, in which the concentration of output marker protein continued to rise exponentially after any observable period of time. Knowing from our circuit design that our ERNs had differential cutting specificities/efficiencies meant that we expected the inputting of different parameters in their respective ODEs to have differential effects upon target substrates. In an attempt to correct for this, we continued to modulate these parameters until the efficiencies were much above those observed within the PERSIST system. However, the concentration of system output continued to increase exponentially. After consulting with Dr. Weiss, we realized we weren’t constraining our system to naturally available resources within the cell. To remediate this, we introduced resources at transcription step reactions: a finite number of RNA polymerases, the central piece of the intracellular schematic. 

To demonstrate our proof of concept, ELK-1 was sufficient as our observed output of cytokine sequestration as its level was representative of if our cytokine modulator was able to act as a negative feedback loop, reducing its own inputs.  Additionally, in our model, it is produced at a scaled, otherwise identical, rate to NF-KB. By targeting the output protein, scFv, to each cytokine, we could understand the behavior of each cytokine with regards to both the intracellular system but also the severity of the cytokine storm.


(2020). MathWorks: SimBiology Documentation. Mathworks.Com.

Acharya, D., Liu, G., & Gack, M. U. (2020). Dysregulation of type I interferon responses in COVID-19. Nat Rev Immunol 20, 397–398 doi:10.1038/s41577-020-0346-x

Yiu, H. H., Graham, A. L., & Stengel, R. F. (2012). Dynamics of a cytokine storm. PloS one7(10). doi:10.1371/journal.pone.0045027

This page was written by Sangita Vasikaran, Ethan Levy, Rachel Shen, and Erin Shin