Amir Akbari

This paper presents a multi-scale systems approach to design biomanufacturing systems for CO2 fixation, showing the reductive glycine pathway can fix CO2 at higher rates than photosynthetic organisms.

This paper introduces a computational framework to decompose biochemical reaction networks into timescales, validating it on kinetic models and glycolytic pathways to reveal biologically interpretable dynamics.

This study uses machine learning to predict E. coli transcriptional regulatory networks from DNA sequences, finding that motif strength and DNA shape are key predictors.

The paper formulates abiotic constraints that shape the quantitative metabolome and validates them using Escherichia coli data.