Imperial College London

DrAndreaBernardi

Faculty of EngineeringDepartment of Chemical Engineering

Research Fellow
 
 
 
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Contact

 

a.bernardi13

 
 
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Location

 

RODH 501Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Perin:2017:10.1016/j.ymben.2017.11.002,
author = {Perin, G and Bernardi, A and Bellan, A and Bezzo, F and Morosinotto, T},
doi = {10.1016/j.ymben.2017.11.002},
journal = {Metabolic Engineering},
title = {A Mathematical model to guide Genetic Engineering of Photosynthetic Metabolism},
url = {http://dx.doi.org/10.1016/j.ymben.2017.11.002},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The optimization of algae biomass productivity in industrial cultivation systems requires genetic improvement of wild type strains isolated from nature. One of the main factors affecting algae productivity is their efficiency in converting light into chemical energy and this has been a major target of recent genetic efforts. However, photosynthetic productivity in algae cultures depends on many environmental parameters, making the identification of advantageous genotypes complex and the achievement of concrete improvements slow.In this work, we developed a mathematical model to describe the key factors influencing algae photosynthetic productivity in a photobioreactor, using experimental measurements for the WT strain of Nannochloropsis gaditana. The model was then exploited to predict the effect of potential genetic modifications on algae performances in an industrial context, showing the ability to predict the productivity of mutants with specific photosynthetic phenotypes. These results show that a quantitative model can be exploited to identify the genetic modifications with the highest impact on productivity taking into full account the complex influence of environmental conditions, efficiently guiding engineering efforts.
AU - Perin,G
AU - Bernardi,A
AU - Bellan,A
AU - Bezzo,F
AU - Morosinotto,T
DO - 10.1016/j.ymben.2017.11.002
PY - 2017///
SN - 1096-7176
TI - A Mathematical model to guide Genetic Engineering of Photosynthetic Metabolism
T2 - Metabolic Engineering
UR - http://dx.doi.org/10.1016/j.ymben.2017.11.002
ER -