Application of combinatorial optimization strategies in synthetic biology

Abstract

In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.

Fig. 1. Schematic workflow for microbial factory optimization.

Libraries of pathway elements such as promoters (bent arrow), RBSs (chord), coding sequences (arrow), terminators (“T”) are assembled to generate a combinatorial library, in which the microbial members produce different levels of the target metabolite. High-throughput techniques screen the library for the optimized pathway variant. Consequently, the best producer is used for large-scale production.

Fig. 2. Applying synthetic biology tools toward optimized production of chemicals.

Synthetic biology speeds up combinatorial optimization. DNA modification tools in the synthetic biology toolbox provide combinatorial optimization methods with various tools e.g. regulators and genome editing tools (black arrow). Barcoding allows tracking of combinatorial library members through screening steps (gray arrow). Biosensors paired with high-throughput monitoring techniques, such as flow cytometry, improve selection of library members to isolate (blue arrow).

Fig. 3. Schematic workflow to generate complex combinatorial library.

Construction of a combinatorial library relies on iterative engineering cycles of one-pot assembly reactions, and amplification of assembled products in microbial cells. At the level of the assembly reaction, the reaction cocktail contains libraries of genetic elements such as promoters (blue arrow), genes (green arrow), and terminators (orange “T”). Combinatorial assembly allows assembly of all standard elements (e.g. promoters, genes, and terminators) in different combination in a single cloning step. To do this, homology sequences (for homology-based cloning method) or sequences that consist of a restriction enzyme cleavage site (for classical digestion/ligation method) at the ends of the fragments to assemble are required: X0 and X1 are segments upstream (left) and downstream (right) of the cloning in plasmid 1, respectively; segment Z0 represents the 3′ region of the promoter and overlaps with the sequence upstream (left) of the gene; segment Y0 represents the 3′ region of the gene and overlaps with the sequence upstream (left) of the terminator. Thereafter, the multiple groups of gene modules of may be integrated into multi-locus of the host genome. A first combinatorial reaction cocktail is used for assembly of gene module, while a second reaction is used for generation of two-gene module from individual gene module in plasmid 2. X2 and X3 are segments upstream (left) and downstream (right) of the cloning in plasmid 2, respectively; and segment Z1 represents the 3′ region of the first gene module and overlaps with the sequence upstream (left) of the second gene module. After establishing a plasmid library containing the entire pathway gene modules, the plasmid library can be directly transformed into the host or can be integrated into the genome of the host to generate stable combinatorial library variants.

Fig. 4. Diverse biosensors used for screening combinatorial libraries.

a The conformation of transcription factor (TF, orange oval) changes to active form upon binding the target ligand (blue octagon). When activated, the TF binds to its binding site (light orange square), upstream of a fluorescent reporter gene, to induce production of a fluorescent reporter protein (green oval) that is detected by flow cytometry. b FRET sensors comprised of a donor-acceptor fluorophore pair. Ligand is sandwiched between the two donor (orange cylinder) and acceptor fluorophores (green cylinder). Therefore, a conformation of FRET is changed that allows detecting the fluorescent signal by flow cytometry. c Correctly folded aptamer structure of riboswitch (orange–gray structure) allows transcription of fluorescent reporter gene (green arrow). The production of fluorescent protein (green oval) is detected by flow cytometry. In presence of ligand (blue octagon), the secondary structure of riboswitch device is changed. Consequently, transcription of its fluorescent reporter gene is inhibited.

Fig. 5. Schematic overview of computational design and evaluation to achieve optimal performance.

Synthetic biology tools are used to establish combinatorial optimization methods. The generated library is profiled using barcoding tools and biosensors allow to screen top producers within the library (gray area). Nature is a vital source of identified nutrients and pharmaceuticals. Metabolic engineering applies synthetic biology tools to produce compounds of the characterized biosynthetic pathway in a desired host (blue area). The production of certain compounds can be optimized using combinatorial optimization strategies. The data obtained from combinatorial library and its pre-characterized modules are computationally integrated to establish mathematical models to support the early design steps for chosen host on the basis of genome-scale metabolic modelling. The computational data suggest which synthetic pathways are the most promising in a given target organism and which host pathway genes need to be upregulated or be silenced based on knowledge of how different cellular subsystems work together. The best producers in combinatorial libraries can provide detailed information to feed into models that aim to uncover principles of how synthetic circuits behave in host systems. Blue arrows, regulators. Orange squares, CDSs. Brown “T”, terminators.

Fig. 6. Application of combinatorial optimization strategies.

One main aim of synthetic biology is development of microbial strains able to optimize and maximize yield and productivity of target chemicals, e.g. biofuels, biomaterials and, medicines, or multi-subunit cellular complexes that can be facilitated by applying combinatorial optimization approaches. Another interesting goal of synthetic biology is engineering sophisticated GRNs to expose the genetic architecture of complex traits and diseases. Smartly designed combinatorial libraries can generate huge number of GRN variants, where the optimal expression level of regulators of networks can be monitored. To overcome limitations regarding the transferability and expression of all involved systems in one chassis, a promising alternative solution is to focus on parallel optimization of metabolic pathways divided among different cells in synthetic microbial consortia^,.