A model-driven approach towards rational microbial bioprocess optimization.

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.

A mechanistic model of a PDGFRα(+) cell.

A novel platelet-derived growth factor receptor alpha-positive cell (PDGFRα(+)) has recently been identified as part of the purinergic inhibitory neural control mechanism in the gastrointestinal (GI) tract. The mechanism through which PDGFRα(+) cells mediate GI muscle relaxation has been found to be associated with the purine receptors P2Y1 and apamin-sensitive SK3 channels that are highly expressed in these cells. This study aims to develop a mechanistic model elucidating a proposed mechanism through which PDGFRα(+) cells contribute to purinergic inhibitory neuromuscular transmission. In accordance with recent experimental findings, the model describes how the binding of neurotransmitters, released from enteric neurons, triggers the release of Ca(2+) from the endoplasmic reticulum in the PDGFRα(+) cells, and how this subsequently leads to large amplitude transient outward currents, which in turn hyperpolarize the cell. The model has been validated against experimental recordings and good agreement was found under normal and pharmacologically-altered conditions. This model demonstrates the feasibility of the proposed mechanism and provides a basis for understanding the mechanism underlying purinergic control of colonic motility.

Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization.

To enable a more rational optimization approach to drive the transition from lab-scale to large industrial bioprocesses, a systematic framework coupling both experimental design and integrated modeling was established to guide the workflow executed from small flask scale to bioreactor scale. The integrated model relies on the coupling of biotic cell factory kinetics to the abiotic bioreactor hydrodynamics to offer a rational means for an in-depth understanding of two-way spatiotemporal interactions between cell behaviors and environmental variations. This model could serve as a promising tool to inform experimental work with reduced efforts via full-factorial in silico predictions. This chapter thus describes the general workflow involved in designing and applying this modeling approach to drive the experimental design towards rational bioprocess optimization.

A biological camera that captures and stores images directly into DNA.

The increasing integration between biological and digital interfaces has led to heightened interest in utilizing biological materials to store digital data, with the most promising one involving the storage of data within defined sequences of DNA that are created by de novo DNA synthesis. However, there is a lack of methods that can obviate the need for de novo DNA synthesis, which tends to be costly and inefficient. Here, in this work, we detail a method of capturing 2-dimensional light patterns into DNA, by utilizing optogenetic circuits to record light exposure into DNA, encoding spatial locations with barcoding, and retrieving stored images via high-throughput next-generation sequencing. We demonstrate the encoding of multiple images into DNA, totaling 1152 bits, selective image retrieval, as well as robustness to drying, heat and UV. We also demonstrate successful multiplexing using multiple wavelengths of light, capturing 2 different images simultaneously using red and blue light. This work thus establishes a 'living digital camera', paving the way towards integrating biological systems with digital devices.

Thermogenetics: Applications come of age.

Temperature is a ubiquitous physical cue that is non-invasive, penetrative and easy to apply. In the growing field of thermogenetics, through beneficial repurposing of natural thermosensing mechanisms, synthetic biology is bringing new opportunities to design and build robust temperature-sensitive (TS) sensors which forms a thermogenetic toolbox of well characterised biological parts. Recent advancements in technological platforms available have expedited the discovery of novel or de novo thermosensors which are increasingly deployed in many practical temperature-dependent biomedical, industrial and biosafety applications. In all, the review aims to convey both the exhilarating recent technological developments underlying the advancement of thermosensors and the exciting opportunities the nascent thermogenetic field holds for biomedical and biotechnology applications.

Highly Reversible Tunable Thermal-Repressible Split-T7 RNA Polymerases (Thermal-T7RNAPs) for Dynamic Gene Regulation.

Temperature is a physical cue that is easy to apply, allowing cellular behaviors to be controlled in a contactless and dynamic manner via heat-inducible/repressible systems. However, existing heat-repressible systems are limited in number, rely on thermal sensitive mRNA or transcription factors that function at low temperatures, lack tunability, suffer delays, and are overly complex. To provide an alternative mode of thermal regulation, we developed a library of compact, reversible, and tunable thermal-repressible split-T7 RNA polymerase systems (Thermal-T7RNAPs), which fused temperature-sensitive domains of Tlpa protein with split-T7RNAP to enable direct thermal control of the T7RNAP activity between 30 and 42 °C. We generated a large mutant library with varying thermal performances via an automated screening framework to extend temperature tunability. Lastly, using the mutants, novel thermal logic circuitry was implemented to regulate cell growth and achieve active thermal control of the cell proportions within co-cultures. Overall, this technology expanded avenues for thermal control in biotechnology applications.

BMSS2: A Unified Database-Driven Modeling Tool for Systematic Biomodel Selection.

Modeling in synthetic biology constitutes a powerful means in our continuous search for improved performance with a rational Design-Build-Test-Learn approach. Particularly, kinetic models unravel system dynamics and enable system analysis for guiding experimental design. However, a systematic yet modular pipeline that allows one to identify the appropriate model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we develop BMSS2, a unified tool that streamlines and automates model selection by combining information criterion ranking with upstream and parallel analysis algorithms. These include Bayesian parameter inference, a priori and a posteriori identifiability analysis, and global sensitivity analysis. In addition, the database-driven design supports interactive model storage/retrieval to encourage reusability and facilitate automated model selection. This allows ease of model manipulation and deposition for the selection and analysis, thus enabling better utilization of models in guiding experimental design.

An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs.

Constructing a complex functional gene circuit composed of different modular biological parts to achieve the desired performance remains challenging without a proper understanding of how the individual module behaves. To address this, mathematical models serve as an important tool toward better interpretation by quantifying the performance of the overall gene circuit, providing insights, and guiding the experimental designs. As different gene circuits might require exclusively different mathematical representations in the form of ordinary differential equations to capture their transient dynamic behaviors, a recurring challenge in model development is the selection of the appropriate model. Here, we developed an automated biomodel selection system (BMSS) which includes a library of pre-established models with intuitive or unintuitive features derived from a vast array of expression profiles. Selection of models is built upon the Akaike information criteria (AIC). We tested the automated platform using characterization data of routinely used inducible systems, constitutive expression systems, and several different logic gate systems (NOT, AND, and OR gates). The BMSS achieved a good agreement for all the different characterization data sets and managed to select the most appropriate model accordingly. To enable exchange and reproducibility of gene circuit design models, the BMSS platform also generates Synthetic Biology Open Language (SBOL)-compliant gene circuit diagrams and Systems Biology Markup Language (SBML) output files. All aspects of the algorithm were programmed in a modular manner to ease the efforts on model extensions or customizations by users. Taken together, the BMSS which is implemented in Python supports users in deriving the best mathematical model candidate in a fast, efficient, and automated way using part/circuit characterization data.

Programming the Dynamic Control of Bacterial Gene Expression with a Chimeric Ligand- and Light-Based Promoter System.

To program cells in a dynamic manner, synthetic biologists require precise control over the threshold levels and timing of gene expression. However, in practice, modulating gene expression is widely carried out using prototypical ligand-inducible promoters, which have limited tunability and spatiotemporal resolution. Here, we built two dual-input hybrid promoters, each retaining the function of the ligand-inducible promoter while being enhanced with a blue-light-switchable tuning knob. Using the new promoters, we show that both ligand and light inputs can be synchronously modulated to achieve desired amplitude or independently regulated to generate desired frequency at a specific amplitude. We exploit the versatile programmability and orthogonality of the two promoters to build the first reprogrammable logic gene circuit capable of reconfiguring into logic OR and N-IMPLY logic on the fly in both space and time without the need to modify the circuit. Overall, we demonstrate concentration- and time-based combinatorial regulation in live bacterial cells with potential applications in biotechnology and synthetic biology.

Cell-Free Optogenetic Gene Expression System.

Optogenetic tools provide a new and efficient way to dynamically program gene expression with unmatched spatiotemporal precision. To date, their vast potential remains untapped in the field of cell-free synthetic biology, largely due to the lack of simple and efficient light-switchable systems. Here, to bridge the gap between cell-free systems and optogenetics, we studied our previously engineered one component-based blue light-inducible Escherichia coli promoter in a cell-free environment through experimental characterization and mathematical modeling. We achieved >10-fold dynamic expression and demonstrated rapid and reversible activation of the target gene to generate oscillatory response. The deterministic model developed was able to recapitulate the system behavior and helped to provide quantitative insights to optimize dynamic response. This in vitro optogenetic approach could be a powerful new high-throughput screening technology for rapid prototyping of complex biological networks in both space and time without the need for chemical induction.

Modelling Human Colonic Smooth Muscle Cell Electrophysiology.

The colon is a digestive organ that is subject to a wide range of motility disorders. However, our understanding of the etiology of these disorders is far from complete. In this study, a quantitative single cell model has been developed to describe the electrical behaviour of a human colonic smooth muscle cell (hCSMC). This model includes the pertinent ionic channels and intracellular calcium homoeostasis. These components are believed to contribute significantly to the electrical response of the hCSMC during a slow wave. The major ion channels were constructed based on published data recorded from isolated human colonic myocytes. The whole cell model is able to reproduce experimentally recorded slow waves from human colonic muscles. This represents the first biophysically-detailed model of a hCSMC and provides a means to better understand colonic disorders.

Repurposing a Two-Component System-Based Biosensor for the Killing of Vibrio cholerae.

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.