Dominique Bouthinon
Papers
1
Total Citations
32
H-Index
1
About
Dominique Bouthinon is a researcher whose work sits at the intersection of systems biology and automated experimentation, with a particular focus on yeast metabolism and the diauxic shift. Their most notable contribution is a landmark 2019 study that pioneered a closed-loop framework integrating experiment design, execution, and machine learning to accelerate model development. In this work, Bouthinon demonstrated a powerful three-cycle approach: first building a model that outperformed the best previous diauxic shift models using bioinformatics; then refining it through automatically planned experiments; and finally validating it with hypothesis-driven tests. This methodology has garnered 32 citations and represents a significant advance in how computational and experimental biology can be synergized. Bouthinon’s research is central to the growing field of autonomous laboratories, where AI-driven cycles of experimentation and learning replace traditional trial-and-error approaches. Their work not only deepens our understanding of yeast metabolic regulation but also provides a template for accelerating systems biology in other organisms, making them a key figure in the push toward more efficient, data-driven biological discovery.
Research Focus
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Top Papers
- 1