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Using Computational Synthetic Biology Tools to Modulate Gene Expression Within a Microbiome

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Comparative Genomics (RECOMB-CG 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13234))

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Abstract

The microbiome is an interconnected network of microorganisms, which exist and influence a wide array of natural and synthetic environments. Genetic information is constantly spread across the members of the microbial community in a process called horizontal gene transfer, causing exposure of genetic alterations and modifications to all members of the community.

In order to accurately and effectively engineer microbiomes, genetic modifications must be introduced to certain species, as selectivity is a key factor in creating and fixing functional abilities within microbial environments. Moreover, introduction of genes into unwanted hosts may cause unprecedented ecological impacts, posing a major biosafety issue. Technologies in the field are usually experimentally developed for a specific host or environment, and the lack of automization and generalization limit them to a specific microbiome. Additionally, they only deal with the transformation process itself at best and do not modulate the different elements of the genetic material, neglecting considerations related to the interactions between the new genetic material and the population.

This work presents a set of computational models that automatically design a microbiome-specific plasmid that is selectively expressed in certain parts of the bacterial population. The underlying algorithm fine-tunes genetic information to be optimally expressed in the wanted hosts of the plasmid, while simultaneously impairing expression in unwanted hosts. We take into account and selectively optimize the main elements linked to gene expression and heredity. In addition, we have provided both in-silico and in-vitro analysis supporting our claim. This study was part of the TAU IGEM 2021 project (https://2021.igem.org/Team:TAU_Israel).

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Acknowledgments

This work was continuously guided by the following researchers: Prof. Martin Kupiec, Professor Uri Gophna, and Professor Itai Benhar for valuable feedback and suggestions. Prof. Hagit Eldar-Finkelman and her lab members, which fully supported us with physical research facilities. Prof. Avigdor Eldar and PhD candidate Tasneem Bareia for both bacterial strains and plasmids, and in general advice. The authors thank the Faculties of Engineering and Life Science at Tel Aviv University for funding this research. This study was part of the TAU IGEM 2021 project (https://2021.igem.org/Team:TAU_Israel) and was also supported by Edmond J. Safra Center for Bioinformatics at Tel Aviv University.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel

    Liyam Chitayat Levi, Moran Ben Tulila, Rotem Galron & Tamir Tuller

  2. The Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv, Israel

    Tamir Tuller

  3. Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel

    Ido Rippin

Authors
  1. Liyam Chitayat Levi
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  2. Ido Rippin
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  3. Moran Ben Tulila
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  5. Tamir Tuller
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Corresponding author

Correspondence to Tamir Tuller .

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Editors and Affiliations

  1. University of Saskatchewan, Saskatoon, SK, Canada

    Lingling Jin

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Dannie Durand

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Chitayat Levi, L., Rippin, I., Ben Tulila, M., Galron, R., Tuller, T. (2022). Using Computational Synthetic Biology Tools to Modulate Gene Expression Within a Microbiome. In: Jin, L., Durand, D. (eds) Comparative Genomics. RECOMB-CG 2022. Lecture Notes in Computer Science(), vol 13234. Springer, Cham. https://doi.org/10.1007/978-3-031-06220-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-06220-9_14

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