CBEB 001
10:35a - 11:35am
This seminar presents two distinct but complementary vignettes exploring innovative approaches to address the mitigation of climate change. The first vignette introduces the GARF algorithm, a novel computational method that integrates a genetic algorithm with random forest machine learning to discover superior metal-organic frameworks (MOFs) for methane adsorption. By using only basic information about molecular building blocks and crystal structure as input, the GARF algorithm efficiently identifies high-performing MOFs without the need for computationally intensive simulations. When tested on a database of 50,000 MOFs, the algorithm achieved an impressive R2 value of 0.92 for methane adsorption prediction and successfully identified a top-performing MOF that matched the third best in the database. This approach opens up new avenues for rapid materials discovery, enabling the identification of advanced materials for greenhouse gas reduction.
The second vignette unravels the complex interplay between Arctic diatom-cyanobacteria symbiosis and the global carbon cycle using dynamic flux balance analysis (dFBA). By integrating genome-scale metabolic models of diatoms and their cyanobacterial symbionts with environmental data, such as temperature, light intensity, and nutrient levels, we elucidate how climate change impacts the timing, duration, and intensity of diatom blooms in the Arctic Ocean. Our findings suggest that under future climate scenarios, the Arctic Ocean may shift towards a cyanobacteria-dominant, nitrogen-limited state, with potential implications for carbon export and storage. This work highlights the importance of considering metabolic adaptations and species interactions when assessing the impacts of climate change on marine ecosystems and the global carbon cycle.
While these vignettes focus on different aspects of combating climate change, they both demonstrate the power of innovative computational approaches in understanding and addressing this global challenge. By developing efficient algorithms for identifying high-performing materials and unraveling the intricate relationships between climate, microbial metabolism, and biogeochemical cycles, we can inform strategies to alleviate the effects of climate change on global ecosystems and the Earth's atmosphere. These insights can contribute to the development of more accurate global climate models and guide efforts to mitigate the impacts of climate change through advanced materials and a deeper understanding of the role of microbial communities in the Earth's carbon balance.
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