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@article{2396578,
author = {Martin, Hector Garcia and Mazurenko, Stanislav and Zhao, Huimin},
article_number = {2},
doi = {http://dx.doi.org/10.1021/acssynbio.3c00760},
keywords = {Genetics; Kinetic modeling; Optimization; Peptides and proteins; Synthetic biology},
language = {eng},
issn = {2161-5063},
journal = {ACS Synthetic Biology},
title = {Special Issue on Artificial Intelligence for Synthetic Biology},
url = {https://pubs.acs.org/doi/10.1021/acssynbio.3c00760},
volume = {13},
year = {2024}
}
TY - JFULL
ID - 2396578
AU - Martin, Hector Garcia - Mazurenko, Stanislav - Zhao, Huimin
PY - 2024
TI - Special Issue on Artificial Intelligence for Synthetic Biology
JF - ACS Synthetic Biology
VL - 13
IS - 2
SP - 408-410
EP - 408-410
PB - American Chemical Society
SN - 21615063
KW - Genetics
KW - Kinetic modeling
KW - Optimization
KW - Peptides and proteins
KW - Synthetic biology
UR - https://pubs.acs.org/doi/10.1021/acssynbio.3c00760
N2 - Synthetic biology presents significant prospects of helping scientists tackle important societal problems. However, a significant hurdle in this endeavor is our inability to predict biological systems as accurately as we predict and simulate physical or chemical ones. This limitation has important fundamental and practical implications: from the practical point of view, we are unable to design biological systems (e.g., proteins, pathways, cells) to a specification (e.g., bind to this molecule with this binding affinity or produce this chemical at this titer, rate, and yield); from the fundamental point of view, we lack an understanding of the underlying mechanisms that produce observed phenotypes. Artificial intelligence (AI) and machine learning (ML) show promise in providing the predictive power that synthetic biology needs and can be applied in all parts of the synthetic biology process (Figure 1).
ER -
MARTIN, Hector Garcia, Stanislav MAZURENKO and Huimin ZHAO. Special Issue on Artificial Intelligence for Synthetic Biology. \textit{ACS Synthetic Biology}. American Chemical Society, 2024, vol.~13, No~2, p.~408-410. ISSN~2161-5063. Available from: https://dx.doi.org/10.1021/acssynbio.3c00760.
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