- Analyzing gene regulatory networks by comparing the dynamics obtained via DSGRN (Dynamic Signatures Generated by Regulatory Networks) and RACIPE (Random Circuit Perturbation)
- Project Year:
2020
- REU Student (s):
Aaron Scheiner | Rutgers University-New Brunswick NJ
- Project Mentor (s) :
Konstantin Mischaikow - Mathematics
- Project Abstract:
Understanding gene regulatory networks is key to targeting diseases with drugs that don't include dangerous risks. In this research project, we studied the analyses generated by Huang et al. using random circuit perturbation (RACIPE). After gaining a comprehensive understanding of Huang's paper, we employed RACIPE to reproduce these results. We then worked to produce analogous results in DSGRN (Dynamic Signatures Generated by Regulatory Networks). Moreover, we found the DSGRN parameter nodes corresponding to the RACIPE data. We added weights to the parameter nodes based on what percentage of the RACIPE models occurred in each parameter node. Our preliminary low dimensional results lead to enticing conjectures. When including weighting, the DSGRN results are very similar to RACIPE for high Hill coefficients. Thus, weighting in DSGRN with sampling from RACIPE yields comparable results to RACIPE with far quicker computations. Hence, with additional work we hope to show that DSGRN can achieve comparable results to RACIPE for a fraction of the computational cost of RACIPE.
