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Quorum sensing (QS) is a molecular signaling modality that mediates molecular-based cell–cell communication. Prevalent in nature, QS networks provide bacteria with a method to gather information from the environment and make decisions based on the intel. With its ability to autonomously facilitate both inter- and intraspecies gene regulation, this process can be rewired to enable autonomously actuated, but molecularly programmed, genetic control. On the one hand, novel QS-based genetic circuits endow cells with smart functions that can be used in many fields of engineering, and on the other, repurposed QS circuitry promotes communication and aids in the development of synthetic microbial consortia. Furthermore, engineered QS systems can probe and intervene in interkingdom signaling between bacteria and their hosts. Lastly, QS is demonstrated to establish conversation with abiotic materials, especially by taking advantage of biological and even electronically induced assembly processes; such QS-incorporated biohybrid devices offer innovative ways to program cell behavior and biological function.
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Assessing electrogenetic activation via a network model of biological signal propagation
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.
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- Award ID(s):
- 2227598
- PAR ID:
- 10633169
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Systems Biology
- Volume:
- 4
- ISSN:
- 2674-0702
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Bacteria utilize cell-cell signaling to coordinate gene expression in populations of cells. Bacterial signal exchange was originally interpreted as a mechanism bacteria use to regulate gene expression in response to changes in cell density, denoted as quorum sensing. Bacterial communication is now known to encompass the exchange of multiple chemical signals between different species of bacteria. Such signal crosstalk within communities of bacteria can have unexpected consequences. Some bacterial species even utilize more than one orthogonal signaling molecule, enabling such species to simultaneously communicate within distinct subsets of species. Such cells utilizing two sets of signals act as a bridge to link gene expression states within the community. Here, a mathematical model was implemented to investigate the consequences of multi-signal communication within heterogeneous bacterial communities. The model was inspired by simple neural networks, with nodes representing bacterial species and directed weights between nodes accounting for the impacts of inter-species signal exchange on gene expression. The activity state of such a network is defined as the gene expression state of each species within the community. Using the model, the stability of the activity states of such networks to changes in signal concentration and population size were quantified. Networks exchanging one set of signals were compared to network exchanging two orthogonal sets of signals. A multilayer neural network model was developed to analyze such networks exchanging orthogonal sets of signals. The model reveals that signal crosstalk increased the activity of the network. These networks were largely resilient to perturbation, however networks were more sensitive to perturbations of the largest population size. Bacterial species utilizing two orthogonal signals, within multilayer networks, had the potential to couple activity states of species that cannot directly communicate. These results give insight into strategies for manipulating signal exchange to predict and control gene expression within bacterial communities.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fsysb.2024.1291293
