Research Highlight: Finn McMillan ’24
他的毕业论文, bbin娱乐平台 senior Finn McMillan ‘24 is working in partnership with 波士顿大学’s Daniel Segrè Lab to investigate the optimization of plant growth for the sustainable and affordable production of consumer biofuels.
Finn was first introduced to the Segrè lab when he toured it as part of the 阀杆 Seminar in his junior year. 对团队的工作很感兴趣, which focuses on 生物信息学 and metabolic networks in living systems, Finn reached out to Professor Ilija Dukovski, a 研究er in the Segrè lab, and arranged to spend his summer conducting 研究 in the lab. Finn’s – and the Segrè Lab’s – work focuses on a small piece of a much larger, multi-institutional project called the Microbial Community Analysis & Functional Evaluation in Soils project, 或m-CAFEs, “一个协作, coordinated and integrated mission-driven proposal that interrogates the function of the soil and rhizosphere microbiome, which has immense implications for carbon cycling, carbon sequestration and plant productivity in natural and agricultural ecosystems.用芬恩的话来说, “m-CAFEs seeks to identify the interactions that influence carbon flow in particular; combined with CRISPR-Cas and RNAi community editing, the goal of the 研究 is to artificially optimize plant growth for the maximum yield of biomass.”
In order to optimize plant biomass, 研究ers need to understand which bacterial colonies promote plants’ growth, 抑制它. As Finn explains in his 论文, one method of plant microbiome analysis utilized in the m-CAFEs study is “performed through Computation Of Microbial Ecosystems in Time and Space (COMETS), a multi-scale modeling framework that computes group dynamics through metabolic stoichiometry, separated from any prior assumptions of how species interact. First becoming publicly available in 2014, COMETS was founded through a collaboration between 研究ers at 波士顿大学, Yale University and the University of Minnesota. Rather than utilizing classical kinetic models in community analysis that require large-scale kinetic parameters and differential equations, COMETS employs both stoichiometric and environmental modeling in accurately predicting metabolic activity at the genome-scale and community level.”
作为他研究的一部分, Finn was tasked with the job of ensuring that the COMETS simulation matched the experimental data for plant microbiome analysis. 为了做到这一点, Finn developed a machine-learning algorithm using a technique known as simulated annealing, “which is beneficial in its ability to identify global minimums and maximums.” Using the code he wrote, Finn perfected a simulation of the bacterium 相似假单胞菌(P. simiae), “by finding ideal Vmax and Km values for the simulation.” Finn then compared the simulated and experimental data of P. simiae in order to measure the kinetic parameters that dictate how this particular bacterium grows.
Finn’s 研究 and the work of the larger mCAFEs project has the potential to revolutionize the biofuels industry by “enabling biofuels such as ethanol to become more sustainable and feasible for consumers.” Finn will present his findings as part of bbin娱乐平台’s Senior Thesis Symposium on May 13, 2024.