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CASTLE: A database of synthetic lethal sets predicted from genome-scale metabolic networks

Vimaladhasan Senthamizhan, Sunanda Subramaniam, Arjun Raghavan, View ORCID ProfileKarthik Raman
doi: https://doi.org/10.1101/2021.02.08.430024
Vimaladhasan Senthamizhan
1Initiative for Biological Systems Engineering, IIT Madras, Chennai - 600 036, India
2Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai - 600 036, India
3Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai - 600 036, India
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Sunanda Subramaniam
1Initiative for Biological Systems Engineering, IIT Madras, Chennai - 600 036, India
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Arjun Raghavan
3Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai - 600 036, India
4Department of Biomedical Engineering, McMaster University, Hamilton, ON, Canada. Present address: University of Manitoba, Winnipeg, Canada
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Karthik Raman
1Initiative for Biological Systems Engineering, IIT Madras, Chennai - 600 036, India
2Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai - 600 036, India
3Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai - 600 036, India
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  • ORCID record for Karthik Raman
  • For correspondence: kraman@iitm.ac.in
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Abstract

Summary Genome-scale metabolic networks have been reconstructed for hundreds of organisms over the last two decades, with wide-ranging applications, including the identification of drug targets. Constraint-based approaches such as flux balance analysis have been effectively used to predict single and combinatorial drug targets in a variety of metabolic networks. We have previously developed Fast-SL, an efficient algorithm to rapidly enumerate all possible synthetic lethals from metabolic networks. Here, we introduce CASTLE, an online standalone database, which contains synthetic lethals predicted from the metabolic networks of over 130 organisms. These targets include single, double or triple lethal set of genes and reactions, and have been predicted using the Fast-SL algorithm. The workflow used for building CASTLE can be easily applied to other pathogenic models and used to identify novel therapeutic targets.

Availability CASTLE is available at https://ramanlab.github.io/CASTLE/

Contact kraman{at}iitm.ac.in

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 08, 2021.
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CASTLE: A database of synthetic lethal sets predicted from genome-scale metabolic networks
Vimaladhasan Senthamizhan, Sunanda Subramaniam, Arjun Raghavan, Karthik Raman
bioRxiv 2021.02.08.430024; doi: https://doi.org/10.1101/2021.02.08.430024
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CASTLE: A database of synthetic lethal sets predicted from genome-scale metabolic networks
Vimaladhasan Senthamizhan, Sunanda Subramaniam, Arjun Raghavan, Karthik Raman
bioRxiv 2021.02.08.430024; doi: https://doi.org/10.1101/2021.02.08.430024

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