DESHARKY: automatic design of metabolic pathways for optimal cell growth

Computer-aided design for metabolic engineering

The development and application of biotechnology-based strategies has had a great socio-economical impact and is likely to play a crucial role in the foundation of more sustainable and efficient industrial processes. Within biotechnology, metabolic engineering aims at the directed improvement of cellular properties, often with the goal of synthesizing a target chemical compound. The use of computer-aided design (CAD) tools, along with the continuously emerging advanced genetic engineering techniques have allowed metabolic engineering to broaden and streamline the process of heterologous compound-production. In this work, we review the CAD tools available for metabolic engineering with an emphasis, on retrosynthesis methodologies. Recent advances in genetic engineering strategies for pathway implementation and optimization are also reviewed as well as a range of bionalytical tools to validate in silico predictions. A case study applying retrosynthesis is presented as an experimental verification of the output from Retropath, the first complete automated computational pipeline applicable to metabolic engineering. Applying this CAD pipeline, together with genetic reassembly and optimization of culture conditions led to improved production of the plant flavonoid pinocembrin. Coupling CAD tools with advanced genetic engineering strategies and bioprocess optimization is crucial for enhanced product yields and will be of great value for the development of non-natural products through sustainable biotechnological processes.

Computational tools for modeling xenometabolism of the human gut microbiota

The gut microbiota is increasingly being recognized as a key site of metabolism for drugs and other xenobiotic compounds that are relevant to human health. The molecular complexity of the gut microbiota revealed by recent metagenomics studies has highlighted the need as well as the challenges for system-level modeling of xenobiotic metabolism in the gut. Here, we outline the possible pathways through which the gut microbiota can modify xenobiotics and review the available computational tools towards modeling complex xenometabolic processes. We put these diverse computational tools and relevant experimental findings into a unified perspective towards building holistic models of xenobiotic metabolism in the gut.

Advances in metabolic pathway and strain engineering paving the way for sustainable production of chemical building blocks

Bio-based production of chemical building blocks from renewable resources is an attractive alternative to petroleum-based platform chemicals. Metabolic pathway and strain engineering is the key element in constructing robust microbial chemical factories within the constraints of cost effective production. Here we discuss how the development of computational algorithms, novel modules and methods, omics-based techniques combined with modeling refinement are enabling reduction in development time and thus advance the field of industrial biotechnology. We further discuss how recent technological developments contribute to the development of novel cell factories for the production of the building block chemicals: adipic acid, succinic acid and 3-hydroxypropionic acid.

Microbial engineering strategies to improve cell viability for biochemical production

Efficient production of biochemicals using engineered microbes as whole-cell biocatalysts requires robust cell viability. Robust viability leads to high productivity and improved bioprocesses by allowing repeated cell recycling. However, cell viability is negatively affected by a plethora of stresses, namely chemical toxicity and metabolic imbalances, primarily resulting from bio-synthesis pathways. Chemical toxicity is caused by substrates, intermediates, products, and/or by-products, and these compounds often interfere with important metabolic processes and damage cellular infrastructures such as cell membrane, leading to poor cell viability. Further, stresses on engineered cells are accentuated by metabolic imbalances, which are generated by heavy metabolic resource consumption due to enzyme overexpression, redistribution of metabolic fluxes, and impaired intracellular redox state by co-factor imbalance. To address these challenges, herein, we discuss a range of key microbial engineering strategies, substantiated by recent advances, to improve cell viability for commercially sustainable production of biochemicals from renewable resources.

Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis

Continued advances in metabolic engineering are increasing the number of small molecules being targeted for microbial production. Pathway yields and productivities, however, are often suboptimal, and strain improvement remains a persistent challenge given that the majority of small molecules are difficult to screen for and their biosynthesis does not improve host fitness. In this work, we have developed a generalized approach to screen or select for improved small-molecule biosynthesis using transcription factor-based biosensors. Using a tetracycline resistance gene 3' of a small-molecule inducible promoter, host antibiotic resistance, and hence growth rate, was coupled to either small-molecule concentration in the growth medium or a small-molecule production phenotype. Biosensors were constructed for two important chemical classes, dicarboxylic acids and alcohols, using transcription factor-promoter pairs derived from Pseudomonas putida, Thauera butanivorans, or E. coli. Transcription factors were selected for specific activation by either succinate, adipate, or 1-butanol, and we demonstrate product-dependent growth in E. coli using all three compounds. The 1-butanol biosensor was applied in a proof-of-principle liquid culture screen to optimize 1-butanol biosynthesis in engineered E. coli, identifying a pathway variant yielding a 35% increase in 1-butanol specific productivity through optimization of enzyme expression levels. Lastly, to demonstrate the capacity to select for enzymatic activity, the 1-butanol biosensor was applied as synthetic selection, coupling in vivo 1-butanol biosynthesis to E. coli fitness, and an 120-fold enrichment for a 1-butanol production phenotype was observed following a single round of positive selection.

Computational tools for guided discovery and engineering of metabolic pathways

With a high demand for increasingly diverse chemicals, as well as sustainable synthesis for many existing chemicals, the chemical industry is increasingly looking to biosynthesis. The majority of biosynthesis examples of useful chemicals are either native metabolites made by an organism or the heterologous expression of known metabolic pathways into a more amenable host. For chemicals that no known biosynthetic route exists, engineers are increasingly relying on automated computational algorithms, as described here, to identify potential metabolic pathways. In this chapter, we review a broad range of approaches to predict novel metabolic pathways. Broadly, these can rely on biochemical databases to assemble known reactions into a new pathway or rely on generalized biochemical rules to predict unobserved enzymatic reactions that are likely feasible. Many programs are freely available and immediately useable by non-computationally experienced scientists.

Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.

From measurement to implementation of metabolic fluxes

The intracellular reaction rates (fluxes) are the ultimate outcome of the activities of the complete inventory (from DNA to metabolite) and in their sum determine the cellular phenotype. The genotype-phenotype relationship is fundamental in such different fields as cancer research and biotechnology. Here, we summarize the developments in determining metabolic fluxes, inferring major pathways from the DNA-sequence, estimating optimal flux distributions, and how these flux distributions can be achieved in vivo. The technical advances to intervene with the many levels of the cellular architecture allow the implementation of new strategies in for example Metabolic Engineering.

An in silico platform for the design of heterologous pathways in nonnative metabolite production

Background

Microorganisms are used as cell factories to produce valuable compounds in pharmaceuticals, biofuels, and other industrial processes. Incorporating heterologous metabolic pathways into well-characterized hosts is a major strategy for obtaining these target metabolites and improving productivity. However, selecting appropriate heterologous metabolic pathways for a host microorganism remains difficult owing to the complexity of metabolic networks. Hence, metabolic network design could benefit greatly from the availability of an in silico platform for heterologous pathway searching.

Results

We developed an algorithm for finding feasible heterologous pathways by which nonnative target metabolites are produced by host microorganisms, using Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae as templates. Using this algorithm, we screened heterologous pathways for the production of all possible nonnative target metabolites contained within databases. We then assessed the feasibility of the target productions using flux balance analysis, by which we could identify target metabolites associated with maximum cellular growth rate.

Conclusions

This in silico platform, designed for targeted searching of heterologous metabolic reactions, provides essential information for cell factory improvement.

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods

Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype-phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.

Computational tools for the synthetic design of biochemical pathways

As the field of synthetic biology is developing, the prospects for de novo design of biosynthetic pathways are becoming more and more realistic. Hence, there is an increasing need for computational tools that can support these efforts. A range of algorithms has been developed that can be used to identify all possible metabolic pathways and their corresponding enzymatic parts. These can then be ranked according to various properties and modelled in an organism-specific context. Finally, design software can aid the biologist in the integration of a selected pathway into smartly regulated transcriptional units. Here, we review key existing tools and offer suggestions for how informatics can help to shape the future of synthetic microbiology.

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

BACKGROUND

Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.

RESULTS

In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.

CONCLUSIONS

We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.

Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate

Synthetic biology uses modeling to facilitate the design of new genetic constructions. In particular, it is of utmost importance to model the reaction of the cellular chassis when expressing heterologous systems. We constructed a mathematical model for the response of a bacterial cell chassis under heterologous expression. For this, we relied on previous characterization of the growth-rate dependence on cellular resource availability (in this case, DNA and RNA polymerases and ribosomes). Accordingly, we estimated the maximum capacities of the cell for heterologous expression to be 46% of the total RNA and the 33% of the total protein. To experimentally validate our model, we engineered two genetic constructions that involved the constitutive expression of a fluorescent reporter in a vector with a tunable origin of replication. We performed fluorescent measurements using population and single-cell fluorescent measurements. Our model predicted cell growth for several heterologous constructions under five different culture conditions and various plasmid copy numbers with significant accuracy, and confirmed that ribosomes act as the limiting resource. Our study also confirmed that the bacterial response to synthetic gene expression could be understood in terms of the requirement for cellular resources and could be predicted from relevant cellular parameters.

The logicome of environmental bacteria: merging catabolic and regulatory events with Boolean formalisms

The regulatory and metabolic networks that rule biodegradation of pollutants by environmental bacteria are wired to the rest of the cellular physiology through both transcriptional factors and intermediary signal molecules. In this review, we examine some formalisms for describing catalytic/regulatory circuits of this sort and advocate the adoption of Boolean logic for combining transcriptional and enzymatic occurrences in the same biological system. As an example, we show how known regulatory and metabolic actions that bring about biodegradation of m-xylene by Pseudomonas putida mt-2 can be represented as clusters of binary operations and then reconstructed as a digital network. Despite the many simplifications, Boolean tools still capture the gross behaviour of the system even in the absence of kinetic constants determined experimentally. On this basis, we argue that still with a limited volume of data binary formalisms allow us to penetrate the raison d'être of extant regulatory and metabolic architectures.

Prediction of novel synthetic pathways for the production of desired chemicals

BACKGROUND

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism.

RESULTS

In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates.

CONCLUSIONS

It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

Chemical synthesis using synthetic biology

An immense array of naturally occurring biological systems have evolved that convert simple substrates into the products that cells need for growth and persistence. Through the careful application of metabolic engineering and synthetic biology, this biotransformation potential can be harnessed to produce chemicals that address unmet clinical and industrial needs. Developing the capacity to utilize biology to perform chemistry is a matter of increasing control over both the function of synthetic biological systems and the engineering of those systems. Recent efforts have improved general techniques and yielded successes in the use of synthetic biology for the production of drugs, bulk chemicals, and fuels in microbial platform hosts. Synthetic promoter systems and novel RNA-based, or riboregulator, mechanisms give more control over gene expression. Improved methods for isolating, engineering, and evolving enzymes give more control over substrate and product specificity and better catalysis inside the cell. New computational tools and methods for high-throughput system assembly and analysis may lead to more rapid forward engineering. We highlight research that reduces reliance upon natural biological components and point to future work that may enable more rational design and assembly of synthetic biological systems for synthetic chemistry.

Evolution of efficient pathways for degradation of anthropogenic chemicals

Anthropogenic compounds used as pesticides, solvents and explosives often persist in the environment and can cause toxicity to humans and wildlife. The persistence of anthropogenic compounds is due to their recent introduction into the environment; microbes in soil and water have had relatively little time to evolve efficient mechanisms for degradation of these new compounds. Some anthropogenic compounds are easily degraded, whereas others are degraded very slowly or only partially, leading to accumulation of toxic products. This review examines the factors that affect the ability of microbes to degrade anthropogenic compounds and the mechanisms by which new pathways emerge in nature. New approaches for engineering microbes with enhanced degradative abilities include assembly of pathways using enzymes from multiple organisms, directed evolution of inefficient enzymes, and genome shuffling to improve microbial fitness under the challenging conditions posed by contaminated environments.

Towards the automated engineering of a synthetic genome

The development of the technology to synthesize new genomes and to introduce them into hosts with inactivated wild-type chromosome opens the door to new horizons in synthetic biology. Here it is of outmost importance to harness the ability of using computational design to predict and optimize a synthetic genome before attempting its synthesis. The methodology to computationally design a genome is based on an optimization that computationally mimics genome evolution. The biggest bottleneck lies on the use of an appropriate fitness function. This fitness function, usually cell growth, relies on the ability to quantitatively model the biochemical networks of the cell at the genome scale using parameters inferred from high-throughput data. Computational methods integrating such models in a common multilayer design platform can be used to automatically engineer synthetic genomes under physiological specifications. We describe the current state-of-the-art on automated methods for engineering or re-engineering synthetic genomes. We restrict ourselves to global models of metabolism, transcription and DNA structure. Although we are still far from the de novo computational genome design, it is important to collect all relevant work towards this goal. Finally, we discuss future perspectives about the practicability of an automated methodology for such computational design of synthetic genomes.

Synthetic metabolism: engineering biology at the protein and pathway scales

Biocatalysis has become a powerful tool for the synthesis of high-value compounds, particularly so in the case of highly functionalized and/or stereoactive products. Nature has supplied thousands of enzymes and assembled them into numerous metabolic pathways. Although these native pathways can be use to produce natural bioproducts, there are many valuable and useful compounds that have no known natural biochemical route. Consequently, there is a need for both unnatural metabolic pathways and novel enzymatic activities upon which these pathways can be built. Here, we review the theoretical and experimental strategies for engineering synthetic metabolic pathways at the protein and pathway scales, and highlight the challenges that this subfield of synthetic biology currently faces.