Building biological foundries for next-generation synthetic biology

Design and Construction of Artificial Biological Systems for One-Carbon Utilization

The third-generation (3G) biorefinery aims to use microbial cell factories or enzymatic systems to synthesize value-added chemicals from one-carbon (C1) sources, such as CO2, formate, and methanol, fueled by renewable energies like light and electricity. This promising technology represents an important step toward sustainable development, which can help address some of the most pressing environmental challenges faced by modern society. However, to establish processes competitive with the petroleum industry, it is crucial to determine the most viable pathways for C1 utilization and productivity and yield of the target products. In this review, we discuss the progresses that have been made in constructing artificial biological systems for 3G biorefineries in the last 10 years. Specifically, we highlight the representative works on the engineering of artificial autotrophic microorganisms, tandem enzymatic systems, and chemo-bio hybrid systems for C1 utilization. We also prospect the revolutionary impact of these developments on biotechnology. By harnessing the power of 3G biorefinery, scientists are establishing a new frontier that could potentially revolutionize our approach to industrial production and pave the way for a more sustainable future.

Automated, self-resistance gene-guided, and high-throughput genome mining of bioactive natural products from Streptomyces

Natural products (NPs) produced by bacteria, fungi and plants are a major source of drug leads. Streptomyces species are particularly important in this regard as they produce numerous natural products with prominent bioactivities. Here we report a fully a utomated, s calable and high-throughput platform for discovery of bioactive n atural p roducts in S treptomyces (FAST-NPS). This platform comprises computational prediction and prioritization of target biosynthetic gene clusters (BGCs) guided by self-resistance genes, highly efficient and automated direct cloning and heterologous expression of BGCs, followed by high-throughput fermentation and product extraction from Streptomyces strains. As a proof of concept, we applied this platform to clone 105 BGCs ranging from 10 to 100 kb that contain potential self-resistance genes from 11 Streptomyces strains with a success rate of 95%. Heterologous expression of all successfully cloned BGCs in Streptomyces lividans TK24 led to the discovery of 23 natural products from 12 BGCs. We selected 5 of these 12 BGCs for further characterization and found each of them could produce at least one natural product with antibacterial and/or anti-tumor activity, which resulted in a total of 8 bioactive natural products. Overall, this work would greatly accelerate the discovery of bioactive natural products for biomedical and biotechnological applications.

Graphic Abstracts

A scalable platform to discover antimicrobials of ribosomal origin

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a promising source of new antimicrobials in the face of rising antibiotic resistance. Here, we report a scalable platform that combines high-throughput bioinformatics with automated biosynthetic gene cluster refactoring for rapid evaluation of uncharacterized gene clusters. As a proof of concept, 96 RiPP gene clusters that originate from diverse bacterial phyla involving 383 biosynthetic genes are refactored in a high-throughput manner using a biological foundry with a success rate of 86%. Heterologous expression of all successfully refactored gene clusters in Escherichia coli enables the discovery of 30 compounds covering six RiPP classes: lanthipeptides, lasso peptides, graspetides, glycocins, linear azol(in)e-containing peptides, and thioamitides. A subset of the discovered lanthipeptides exhibit antibiotic activity, with one class II lanthipeptide showing low µM activity against Klebsiella pneumoniae, an ESKAPE pathogen. Overall, this work provides a robust platform for rapidly discovering RiPPs.

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable

Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a "digital bioconverter"-a tool to connect the digital aspects of the design-build-learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

Towards a fully automated algorithm driven platform for biosystems design

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

Existing efforts have been focused on one of the elements in the automation of the design, build, test, and learn (DBTL) cycle for biosystems design. Here, the authors integrate a robotic system with machine learning algorithms to fully automate the DBTL cycle and apply it in optimizing the lycopene biosynthetic pathway.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

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.

Improvement of oxytetracycline production mediated via cooperation of resistance genes in Streptomyces rimosus

Increasing the self-resistance levels of Streptomyces is an effective strategy to improve the production of antibiotics. To increase the oxytetracycline (OTC) production in Streptomyces rimosus, we investigated the cooperative effect of three co-overexpressing OTC resistance genes: one gene encodes a ribosomal protection protein (otrA) and the other two express efflux proteins (otrB and otrC). Results indicated that combinational overexpression of otrA, otrB, and otrC (MKABC) exerted a synergetic effect. OTC production increased by 179% in the recombinant strain compared with that of the wild-type strain M4018. The resistance level to OTC was increased by approximately two-fold relative to the parental strain, thereby indicating that applying the cooperative effect of self-resistance genes is useful to improve OTC production. Furthermore, the previously identified cluster-situated activator OtcR was overexpressed in MKABC in constructing the recombinant strain MKRABC; such strain can produce OTC of approximately 7.49 g L^-1, which represents an increase of 19% in comparison with that of the OtcR-overexpressing strain alone. Our work showed that the cooperative overexpression of self-resistance genes is a promising strategy to enhance the antibiotics production in Streptomyces.

Engineering biological systems using automated biofoundries

Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.

Synthetic biology for CO2 fixation

Recycling of carbon dioxide (CO₂) into fuels and chemicals is a potential approach to reduce CO₂ emission and fossil-fuel consumption. Autotrophic microbes can utilize energy from light, hydrogen, or sulfur to assimilate atmospheric CO₂ into organic compounds at ambient temperature and pressure. This provides a feasible way for biological production of fuels and chemicals from CO₂ under normal conditions. Recently great progress has been made in this research area, and dozens of CO₂-derived fuels and chemicals have been reported to be synthesized by autotrophic microbes. This is accompanied by investigations into natural CO₂-fixation pathways and the rapid development of new technologies in synthetic biology. This review first summarizes the six natural CO₂-fixation pathways reported to date, followed by an overview of recent progress in the design and engineering of CO₂-fixation pathways as well as energy supply patterns using the concept and tools of synthetic biology. Finally, we will discuss future prospects in biological fixation of CO₂.

Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle

Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.

Mapping the patent landscape of synthetic biology for fine chemical production pathways

A goal of synthetic biology bio‐foundries is to innovate through an iterative design/build/test/learn pipeline. In assessing the value of new chemical production routes, the intellectual property (IP) novelty of the pathway is important. Exploratory studies can be carried using knowledge of the patent/IP landscape for synthetic biology and metabolic engineering. In this paper, we perform an assessment of pathways as potential targets for chemical production across the full catalogue of reachable chemicals in the extended metabolic space of chassis organisms, as computed by the retrosynthesis‐based algorithm RetroPath. Our database for reactions processed by sequences in heterologous pathways was screened against the PatSeq database, a comprehensive collection of more than 150M sequences present in patent grants and applications. We also examine related patent families using Derwent Innovations. This large‐scale computational study provides useful insights into the IP landscape of synthetic biology for fine and specialty chemicals production.

Accelerated genome engineering through multiplexing

Throughout the biological sciences, the past 15 years have seen a push toward the analysis and engineering of biological systems at the organism level. Given the complexity of even the simplest organisms, though, to elicit a phenotype of interest often requires genotypic manipulation of several loci. By traditional means, sequential editing of genomic targets requires a significant investment of time and labor, as the desired editing event typically occurs at a very low frequency against an overwhelming unedited background. In recent years, the development of a suite of new techniques has greatly increased editing efficiency, opening up the possibility for multiple editing events to occur in parallel. Termed as multiplexed genome engineering, this approach to genome editing has greatly expanded the scope of possible genome manipulations in diverse hosts, ranging from bacteria to human cells. The enabling technologies for multiplexed genome engineering include oligonucleotide-based and nuclease-based methodologies, and their application has led to the great breadth of successful examples described in this review. While many technical challenges remain, there also exists a multiplicity of opportunities in this rapidly expanding field.