Search

Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.

Due to sustainability concerns, bio-based production capitalizing on microbes as cell factories is in demand to synthesize valuable products. Nevertheless, the nonhomogenous variations of the extracellular environment in bioprocesses often challenge the biomass growth and the bioproduction yield. To enable a more rational bioprocess optimization, we have established a model-driven approach that systematically integrates experiments with modeling, executed from flask to bioreactor scale, and using ferulic acid to vanillin bioconversion as a case study. The impacts of mass transfer and aeration on the biomass growth and bioproduction performances were examined using minimal small-scale experiments. An integrated model coupling the cell factory kinetics with the three-dimensional computational hydrodynamics of bioreactor was developed to better capture the spatiotemporal distributions of bioproduction. Full-factorial predictions were then performed to identify the desired operating conditions. A bioconversion yield of 94% was achieved, which is one of the highest for recombinant Escherichia coli using ferulic acid as the precursor., (© 2020 Wiley Periodicals LLC.)

A biofoundry provides automation and analytics infrastructure to support the engineering of biological systems. It allows scientists to perform synthetic biology and aligned experimentation on a high-throughput scale, massively increasing the solution space that can be examined for any given problem or question. However, establishing a biofoundry is a challenging undertaking, with numerous technical and operational considerations that must be addressed. Using collated learnings, here we outline several considerations that should be addressed prior to and during establishment. These include drivers for establishment, institutional models, funding and revenue models, personnel, hardware and software, data management, interoperability, client engagement and biosecurity issues. The high cost of establishment and operation means that developing a long-term business model for biofoundry sustainability in the context of funding frameworks, actual and potential client base, and costing structure is critical. Moreover, since biofoundries are leading a conceptual shift in experimental design for bioengineering, sustained outreach and engagement with the research community are needed to grow the client base. Recognition of the significant, long-term financial investment required and an understanding of the complexities of operationalization is critical for a sustainable biofoundry venture. To ensure state-of-the-art technology is integrated into planning, extensive engagement with existing facilities and community groups, such as the Global Biofoundries Alliance, is recommended., (© Crown copyright 2020.)

In the process of constructing and characterizing the whole cell biosensor for Vibrio cholerae detection, two main techniques have been employed-DNA assembly using the Gibson isothermal assembly reaction was used for the assembly of the PCRed plasmid fragments (DNA parts), and microplate fluorescence readings were used for bacterial strain characterization. The general workflow can be summed up as: the in silico designed DNA fragments were assembled by isothermal assembly to be later transformed into Escherichia coli that, in turn, was characterized using the microplate reader. As fine-tuning of the sensor design was required, the process was repeated iteratively until the final strain was created with desired characteristics. This chapter describes in detail this workflow for different constructs which finally led to the creation of the first whole cell biosensor in E. coli for V. cholerae detection.

New strategies to control cholera are urgently needed. This study develops an in vitro proof-of-concept sense-and-kill system in a wild-type Escherichia coli strain to target the causative pathogen Vibrio cholerae using a synthetic biology approach. Our engineered E. coli specifically detects V. cholerae via its quorum-sensing molecule CAI-1 and responds by expressing the lysis protein YebF-Art-085, thereby self-lysing to release the killing protein Art-085 to kill V. cholerae. For this report, we individually characterized YebF-Art-085 and Art-085 expression and their activities when coupled to our previously developed V. cholerae biosensing circuit. We show that, in the presence of V. cholerae supernatant, the final integrated sense-and-kill system in our engineered E. coli can effectively inhibit the growth of V. cholerae cells. This work represents the first step toward a novel probiotic treatment modality that could potentially prevent and treat cholera in the future.

Cholera is a potentially mortal, infectious disease caused by Vibrio cholerae bacterium. Current treatment methods of cholera still have limitations. Beneficial microbes that could sense and kill the V. cholerae could offer potential alternative to preventing and treating cholera. However, such V. cholerae targeting microbe is still not available. This microbe requires a sensing system to be able to detect the presence of V. cholera bacterium. To this end, we designed and created a synthetic genetic sensing system using nonpathogenic Escherichia coli as the host. To achieve the system, we have moved proteins used by V. cholerae for quorum sensing into E. coli. These sensor proteins have been further layered with a genetic inverter based on CRISPRi technology. Our design process was aided by computer models simulating in vivo behavior of the system. Our sensor shows high sensitivity to presence of V. cholerae supernatant with tight control of expression of output GFP protein.