Browsing by Author "Hirning, Andrew J."

Now showing 1 - 4 of 4
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    Emergent genetic oscillations in a synthetic microbial consortium
    (American Association for the Advancement of Science, 2015) Chen, Ye; Kim, Jae Kyoung; Hirning, Andrew J.; Josić, Krešimir; Bennett, Matthew R.; Institute of Biosciences and Bioengineering
    A challenge of synthetic biology is the creation of cooperative microbial systems that exhibit population-level behaviors. Such systems use cellular signaling mechanisms to regulate gene expression across multiple cell types. We describe the construction of a synthetic microbial consortium consisting of two distinct cell types—an "activator" strain and a "repressor" strain. These strains produced two orthogonal cell-signaling molecules that regulate gene expression within a synthetic circuit spanning both strains. The two strains generated emergent, population-level oscillations only when cultured together. Certain network topologies of the two-strain circuit were better at maintaining robust oscillations than others. The ability to program population-level dynamics through the genetic engineering of multiple cooperative strains points the way toward engineering complex synthetic tissues and organs with multiple cell types.
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    Indirect Enrichment of Desirable, but Less Fit Phenotypes, from a Synthetic Microbial Community Using Microdroplet Confinement
    (American Chemical Society, 2023) Prabhakar, Ramya Ganiga; Fan, Gaoyang; Alnahhas, Razan N.; Hirning, Andrew J.; Bennett, Matthew R.; Shamoo, Yousif
    Spatial structure within microbial communities can provide nearly limitless opportunities for social interactions and are an important driver for evolution. As metabolites are often molecular signals, metabolite diffusion within microbial communities can affect the composition and dynamics of the community in a manner that can be challenging to deconstruct. We used encapsulation of a synthetic microbial community within microdroplets to investigate the effects of spatial structure and metabolite diffusion on population dynamics and to examine the effects of cheating by one member of the community. The synthetic community was composed of three strains: a “Producer” that makes the diffusible quorum sensing molecule (N-(3-oxododecanoyl)-l-homoserine lactone, C12-oxo-HSL) or AHL; a “Receiver” that is killed by AHL; and a Non-Producer or “cheater” that benefits from the extinction of the Receivers, but without the costs associated with the AHL synthesis. We demonstrate that despite rapid diffusion of AHL between microdroplets, the spatial structure imposed by the microdroplets allows a more efficient but transient enrichment of more rare and slower-growing Producer subpopulations. Eventually, the Non-Producer population drove the Producers to extinction. By including fluorescence-activated microdroplet sorting and providing sustained competition by the Receiver strain, we demonstrate a strategy for indirect enrichment of a rare and unlabeled Producer. The ability to screen and enrich metabolite Producers from a much larger population under conditions of rapid diffusion provides an important framework for the development of applications in synthetic ecology and biotechnology.
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    Sources of Variability in a Synthetic Gene Oscillator
    (Public Library of Science, 2015) Veliz-Cuba, Alan; Hirning, Andrew J.; Atanas, Adam A.; Hussain, Faiza; Vancia, Flavia; Josić, Krešimir; Bennett, Matthew R.
    Synthetic gene oscillators are small, engineered genetic circuits that produce periodic variations in target protein expression. Like other gene circuits, synthetic gene oscillators are noisy and exhibit fluctuations in amplitude and period. Understanding the origins of such variability is key to building predictive models that can guide the rational design of synthetic circuits. Here, we developed a method for determining the impact of different sources of noise in genetic oscillators by measuring the variability in oscillation amplitude and correlations between sister cells. We first used a combination of microfluidic devices and time-lapse fluorescence microscopy to track oscillations in cell lineages across many generations. We found that oscillation amplitude exhibited high cell-to-cell variability, while sister cells remained strongly correlated for many minutes after cell division. To understand how such variability arises, we constructed a computational model that identified the impact of various noise sources across the lineage of an initial cell. When each source of noise was appropriately tuned the model reproduced the experimentally observed amplitude variability and correlations, and accurately predicted outcomes under novel experimental conditions. Our combination of computational modeling and time-lapse data analysis provides a general way to examine the sources of variability in dynamic gene circuits.
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    The Timing of Transcriptional Regulation in Synthetic Gene Circuits
    (American Chemical Society, 2017) Cheng, Yu-Yu; Hirning, Andrew J.; Josić, Krešimir; Bennett, Matthew R.
    Transcription factors and their target promoters are central to synthetic biology. By arranging these components into novel gene regulatory circuits, synthetic biologists have been able to create a wide variety of phenotypes, including bistable switches, oscillators, and logic gates. However, transcription factors (TFs) do not instantaneously regulate downstream targets. After the gene encoding a TF is turned on, the gene must first be transcribed, the transcripts must be translated, and sufficient TF must accumulate in order to bind operator sites of the target promoter. The time to complete this process, here called the “signaling time,” is a critical aspect in the design of dynamic regulatory networks, yet it remains poorly characterized. In this work, we measured the signaling time of two TFs in Escherichia coli commonly used in synthetic biology: the activator AraC and the repressor LacI. We found that signaling times can range from a few to tens of minutes, and are affected by the expression rate of the TF. Our single-cell data also show that the variability of the signaling time increases with its mean. To validate these signaling time measurements, we constructed a two-step genetic cascade, and showed that the signaling time of the full cascade can be predicted from those of its constituent steps. These results provide concrete estimates for the time scales of transcriptional regulation in living cells, which are important for understanding the dynamics of synthetic transcriptional gene circuits.