Computational Biology Projects

Investigating Chemotherapy Dose-dependent Metabolic Changes in Cancer Cells through Computational Modeling and Experimental Validation

CBIO042 - Modeling Chemo's Impact on Cancer MetabolismA key drawback of traditional chemotherapy drugs like cisplatin is that they target both healthy and cancerous cells and are highly toxic in high or medium doses. The Warburg Effect states that cancerous cells prefer to generate energy via aerobic glycolysis over Oxidative Phosphorylation (OxPhos). Glycolysis is 100x faster than OxPhos and produces key metabolic precursors for rapid cell proliferation, a key characteristic of cancerous cells. Low doses of cisplatin have shown potential to increase cellular respiration for a short period of time before apoptosis, or programmed cell death, occurs. For this research, I developed a computational model (using the programming language R) for four cancer lines: two being cisplatin-sensitive and two being cisplatin-resistant. I implemented a dose-dependent cisplatin diffusion partial differential equation and my model proved that after the application of low-dose cisplatin in HT-29 and HeLa cancer cells, lactate production decreases and oxygen consumption increases, while ATP synthesis due to glycolysis decreases and ATP synthesis due to OxPhos increases. Through in-vitro experimentation, I validated my model’s findings that the Warburg Effect is reversed by analyzing pre and post cisplatin ATP production and oxygen consumption via both CellTiter Promega Luminescent and SeaHorse XF Pro Assays. My computational model can be used by scientists and doctors in hopes of treating patients in a less invasive yet effective manner.

Awards/Accolades

@Regeneron ISEF 2023

Grand Award Winner - 4th place, Computational Biology and Bioinformatics

@ScienceMontgomery 2023

International Science and Engineering Fair (ISEF) Finalist
1st place winner in Biology ($100 Cash Award)
1st place winner in Metro Bethesda Rotary Club ($500 Cash Award)
1st place winner of MIT Club Award ($75 Cash Award)
1st place US Nuclear Regulatory Commission Award
Society for In Vitro Biology Award
2nd place Educational Systems Federal Credit Union award

Computational Modeling of Mitochondrial Matrix Ca2+ Regulation and Ca2+ Dynamics

Nanomolar levels of free calcium increase oxidative phosphorylation in mitochondria. However, high levels of free calcium cause mitochondrial dysfunction via the opening of the mitochondrial permeability transition pore (MTP). Thus, it is important to understand how calcium is regulated as well as how calcium regulates mitochondrial processes within mitochondria. A previous computational model gave the flux of a Complex VI reaction but only in terms of oxygen and hydrogen, without considering the potential effects of Ca2+. By integrating a separate model of Ca2+ and looking at its effects on mitochondria with the above-stated model, we can quantitatively understand calcium dynamics within the mitochondrial matrix. Using an empirical equation to describe the state 3 JC4(complex 4) -Ca2+ free relationship in terms of Vo and Vmax and the [Ca2+] concentration, we can characterize the biphasic behavior of mitochondrial O2 consumption rates about the calcium concentration. This empirical equation was integrated into the aforementioned equation and modeled in MATLAB to understand calcium regulation on mitochondrial processes.