Assessing electrogenetic activation via a network model of biological signal propagation

Chun, Kayla; VanArsdale, Eric; May, Elebeoba; Payne, Gregory F.; Bentley, William E.

doi:10.3389/fsysb.2024.1291293

Title: Assessing electrogenetic activation via a network model of biological signal propagation

Abstract

Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control. Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia. Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks. Discussion: We developed thismore »« less

Authors:: Chun, Kayla; VanArsdale, Eric; May, Elebeoba; Payne, Gregory F.; Bentley, William E.

Publication Date:: Fri Mar 01 00:00:00 EST 2024

Sponsoring Org.:: USDOE

OSTI Identifier:: 2317716

Grant/Contract Number:: BER#SCW1710

Resource Type:: Published Article

Journal Name:: Frontiers in Systems Biology

Additional Journal Information:: Journal Name: Frontiers in Systems Biology Journal Volume: 4; Journal ID: ISSN 2674-0702

Publisher:: Frontiers Media SA

Country of Publication:: Switzerland

Language:: English

Citation Formats


                    Chun, Kayla, VanArsdale, Eric, May, Elebeoba, Payne, Gregory F., and Bentley, William E. Assessing electrogenetic activation via a network model of biological signal propagation.  Switzerland: N. p., 2024. 
Web.  doi:10.3389/fsysb.2024.1291293.

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                    Chun, Kayla, VanArsdale, Eric, May, Elebeoba, Payne, Gregory F., & Bentley, William E. Assessing electrogenetic activation via a network model of biological signal propagation.  Switzerland.  https://doi.org/10.3389/fsysb.2024.1291293

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                    Chun, Kayla, VanArsdale, Eric, May, Elebeoba, Payne, Gregory F., and Bentley, William E. Fri .  
"Assessing electrogenetic activation via a network model of biological signal propagation".  Switzerland.  https://doi.org/10.3389/fsysb.2024.1291293.

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@article{osti_2317716,

  title        = {Assessing electrogenetic activation via a network model of biological signal propagation},

  author       = {Chun, Kayla and VanArsdale, Eric and May, Elebeoba and Payne, Gregory F. and Bentley, William E.},

  abstractNote = {Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control. Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia. Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks. Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.},

  doi          = {10.3389/fsysb.2024.1291293},

  journal      = {Frontiers in Systems Biology},

  number       = ,

  volume       = 4,

  place        = {Switzerland},

  year         = {Fri Mar 01 00:00:00 EST 2024},

  month        = {Fri Mar 01 00:00:00 EST 2024}

}

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Title: Assessing electrogenetic activation via a network model of biological signal propagation

Abstract

Citation Formats

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