dAMN: Predicting Bacterial Growth Dynamics with a Hybrid Neural–Mechanistic Model
dAMN: Predicting Bacterial Growth Dynamics with a Hybrid Neural–Mechanistic Model

25 May 2026

Architecture of dAMN (figure from the original article). The model combines two neural networks — one predicting lag phase parameters, the other metabolic fluxes — within

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↬ Welcome

Our group is interested in synthetic biology in whole-cell and cell-free systems.  We develop computational and wet lab protocols to search, design, and engineer biological pathways and networks. Other activities include retrosynthesis, structure-activity, sequence-function relationships, and the design of experiments using active and reinforcement machine learning methods. The applications of our work include synthetic metabolic pathways and genetic circuits engineering for bioproduction, biosensing, and biocomputing.

 

↬ Open Position(s)

  • We have 2 Research Associate Open Position:
    • Seeking for a Research Associate Position in Synthetic Biology. Learn More.
    • Seeking for a Research Associate Position in Data Science / Systems Biology Biology. Learn More.
  • Interested in working with us? Don’t hesitate to contact us and share your CV!