Using Computational Modeling with Mathematical Theory
Keisuke Inomura, Assistant Professor of Oceanography
By Janine Weisman
Open any microbiology textbook and see illustrations of cells and the nucleic acids, proteins, carbohydrates and lipids inside them. Advancements in research have increased our understanding of how these biomolecules are produced, stored and degraded. But what is the story behind how these processes happen inside each cell?
That’s why quantitative microbiologist Keisuke Inomura is asking lots of questions about numbers.
“Do we know how much protein exists within the cell?” he asks. “How much carbohydrate? Do these quantities change depending on the conditions? If there’s more nutrients, do they have more proteins or more carbohydrates?”
To find answers, Inomura uses computer models based on data and mathematical theories. By focusing on cell growth, he is able to predict the impact microorganisms can have on global biogeochemistry.
While earning his doctorate in climate physics and chemistry at the Massachusetts Institute of Technology, Inomura developed a Cell Flux Model to explain how nutrients are allocated within cells. He used the model to predict the rate of nitrogen fixation in the ocean. The model relies on data, often through collaborations with other scientists or publicly available online. Inomura then creates code to extrapolate data to fill in knowledge gaps.
“There is a great deal of data available in the literature, in scientific papers, but what’s missing is computational modeling with mathematical theory that connects these available data,” Inomura says.
Born in Japan, Inomura earned his undergraduate and master’s degrees in agricultural science at Kyushu University in Fukuoka. Prior to joining the faculty at the University of Rhode Island Graduate School of Oceanography, he was a research associate at the University of Washington for three years.
Inomura teaches a Python programming language coding course at GSO focused on carbon cycling. His goal is to help students develop their quantitative intuition as they run their models over and over again to predict global carbon cycling and the factors that control it.
“If you change something, how many other things will change?” he says. “You can repeatedly run the model and get some sense of that so that’s a benefit of the class.”