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Systems biology based metabolic engineering for non-natural chemicals

Alessandra Biz, Scott Proulx, Zhiqing Xu, Kavya Siddartha, Alex

Mulet Indrayanti, Radhakrishnan Mahadevan

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Citation (published version)

Publisher’s Statement

Biz, A., Proulx, S., Xu, Z., Siddartha, K., Indrayanti, A.M. and Mahadevan, R., 2019. Systems biology based metabolic engineering for non-natural chemicals. Biotechnology Advances. The article has been published in final form at [10.1016/j.biotechadv.2019.04.001]

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Accepted Manuscript

Systems biology based metabolic engineering for non-naturalchemicals

Alessandra Biz, Scott Proulx, Zhiqing Xu, Kavya Siddartha, AlexMulet Indrayanti, Radhakrishnan Mahadevan

PII: S0734-9750(19)30060-6DOI: https://doi.org/10.1016/j.biotechadv.2019.04.001Reference: JBA 7379

To appear in: Biotechnology Advances

Received date: 4 December 2018Revised date: 23 February 2019Accepted date: 1 April 2019

Please cite this article as: A. Biz, S. Proulx, Z. Xu, et al., Systems biology based metabolicengineering for non-natural chemicals, Biotechnology Advances, https://doi.org/10.1016/j.biotechadv.2019.04.001

This is a PDF file of an unedited manuscript that has been accepted for publication. Asa service to our customers we are providing this early version of the manuscript. Themanuscript will undergo copyediting, typesetting, and review of the resulting proof beforeit is published in its final form. Please note that during the production process errors maybe discovered which could affect the content, and all legal disclaimers that apply to thejournal pertain.

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Systems Biology Based Metabolic Engineering for Non-natural Chemicals

Alessandra Biz1, Scott Proulx

1, Zhiqing Xu

1, Kavya Siddartha

1, Alex Mulet Indrayanti

1,

Radhakrishnan Mahadevan1,*

krishna.mahadevan@utoronto.ca

1Department of Chemical Engineering and Applied Chemistry, University of Toronto,

Toronto, ON, Canada

*Corresponding Author.

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Abstract

Production of chemicals in microorganisms is no longer restricted to products arising from

native metabolic potential. In this review, we highlight the evolution of metabolic

engineering studies, from the production of natural chemicals fermented from biomass

hydrolysates, to the engineering of microorganisms for the production of non-natural

chemicals. Advances in synthetic biology are accelerating the successful development of

microbial cell factories to directly produce value-added chemicals. Here we outline the

emergence of novel computational tools for the creation of synthetic pathways, for designing

artificial enzymes for non-natural reactions and for re-wiring host metabolism to increase the

metabolic flux to products. We also highlight exciting opportunities for applying directed

evolution of enzymes, dynamic control of growth and production, growth-coupling strategies

as well as decoupled strategies based on orthogonal pathways in the context of non-natural

chemicals.

Keywords: systems biology; non-natural chemicals; in-silico pathway design; directed

evolution; de novo protein engineering; genome-scale models; flux balance analysis; dynamic

control; growth-coupled production; orthogonality

Abbreviations

FBA Flux Balance Analysis

MOMA Minimization of Metabolic Adjustment

FDCA 2,5-Furandicarboxylic acid

5-HMF Hydroxymethylfurfural

PEF polyethylene furanoate

PLA polylactic acid

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BDO butanediol

GSMMs Genome-Scale Metabolic Models

ER enoate reductases

1,3 PDO 1,3 pentanediol

CBM Constraint-Based Modeling

COBRA Constraint-Based Reconstruction and Analysis

CSOMs Computational Strain Optimization Methods

MCS minimal cut sets

EMs elementary flux modes

NCD nicotinamide cytosine dinucleotide

EMA Elementary Mode Analysis

MOVE Metabolic Valve Enumerator

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1. Introduction

In 2004, when the U. S. Department of Energy released the report “Top Value-Added

Chemicals from Biomass” (Werpy and Petersen, 2004) the idea was to screen platform

chemicals that were naturally produced by microorganisms from sugars, which, in turn, could

be chemically transformed into molecules of industrial relevance. In fact, currently there are

several companies producing bulk and fine chemicals from renewable sources, although, in

most cases, via chemical routes (Table 1).

Now, instead of producing these platform chemicals, more studies are being driven by the

idea that any chemical, including non-natural chemicals, could be potentially made directly in

microbial cell factories (Lee et al., 2012). The technology that is accelerating this shift is the

integration of systems biology with metabolic engineering (Jensen and Keasling, 2018).

Non-natural chemicals are molecules, such as, fuels, materials and pharmaceuticals, that

rarely occur in nature and are primarily being made chemically by using petroleum as starting

material (Lee et al., 2012). As they are not made naturally by any organism, there are no

known natural pathways that lead to the production of these compounds. However, recent

advances in systems biology based metabolic engineering are being used to uncover synthetic

pathways and rewire host metabolism for their biochemical production from sugars (Jensen

and Keasling, 2018). These studies, and the insights learned from them, are the focus of this

review.

Table 2 shows that the production of non-natural chemicals is already a reality, with more

than 40 different compounds being produced in different hosts, in particular, Escherichia

coli, Pseudomonas putida, Corynebacterium glutamicum and Saccharomyces cerevisiae. The

major synthetic pathways for producing non-natural compounds are highlighted in Figure 1.

Recently, a few of those compounds have reached the commercial scale. One noteworthy

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example is the development of a process for the production of 1,4 butanediol (1,4 BDO)

directly in E. coli by the company Genomatica (San Diego, CA), which reached the

production scale of 30000 tons/year.

Systems biology is generally defined by the integration of computational techniques and

biological Big Data, acquired by using high-throughput technologies, to unravel the

principles of complex biological systems (Dai and Nielsen, 2015). Recently, there has been a

focus on synthetic biology, which involves the use of engineering fundamentals, such as

systems abstraction, part standardization, characterization and modeling, for the design,

construction and optimization of genetic circuits and synthetic pathways. Thus, systems

biology based metabolic engineering is the field of study that combines systems and synthetic

biology to develop processes for making non-natural chemicals biologically (Lee et al.,

2011). Such strategies are based on, 1) identifying de novo pathways that could provide a

route to produce the proposed chemical, 2) identifying the enzyme candidates that could be

used for the proposed reactions of the pathway, and 3) determining and implementing the

genetic interventions that can be made in the host for increasing flux to products (Lee et al.,

2011). The general outline of how systems biology based metabolic engineering is used to

design microbes for non-natural chemicals production is shown in Figure 2.

In this review, we will briefly present each of these concepts, including the latest

achievements in systems biology based strategies to design microbial cell factories and how

these technologies are being used for producing non-natural chemicals. Important aspects that

will be covered in this review are: de novo pathway design, engineering of enzymes through

directed evolution, model-based designs for both growth-coupled synthetic pathways and

decoupled growth and production, as well as dynamic control and orthogonality concepts

based on metabolic valves.

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2. In-silico pathway design

In-silico pathway design is the preliminary step in the development of novel biosynthetic

pathways. In the past decade, a large number of pathway design tools were developed,

making use of divergent approaches (i.e., different molecule encodings, pathway network

topologies, pruning methods, search algorithms, pathway prioritization metrics etc.) (Wang et

al., 2017; Hadadi and Hatzimanikatis, 2015). However, only a small portion of those

algorithms allow prediction of de novo pathways with non-natural intermediates and

chemicals, while most tools have focused on searching the reaction networks of metabolites

in natural biochemical processes.

A generalized workflow for de novo pathway design involves the following critical steps: (a)

the identification, from a list of starting materials, of all chemically plausible pathways to the

desired target compound on the basis of chemical structure transformation, (b) the pathway

prioritization based on various criteria, including thermodynamic feasibility, number of non-

natural reaction steps, enzyme docking and cellular resources cost etc., and (c) the selection

of the most suitable host organism and designing the synthetic expression constructs

(pathway integration).

In this section, we will briefly discuss how such steps are linked together to build a pipeline

for the discovery of new bio-pathways, as well as the recently developed computational tools

designed for that task, and the main challenges that need to be overcome regarding pathway

prediction.

2.1 Pathway identification

Recent efforts for de novo pathway discovery involve two closely-related approaches to

pathway identification, one of which is the construction of a putative reaction network using

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generalized reaction transformations of promiscuous enzymes, which is often referred to as

“graph-based” pathway finding methods (BNICE, SimPheny, ReactPRED, GEM-path,

RetroPath 2.0) (Wang et al., 2017a). The other approach is the creation of an extended

pathway database (XTMS, MINE, ATLAS of Biochemistry) which allows a fast search for

pathways with non-natural intermediates and reactions (Carbonell et al., 2014). Both

strategies explore generalized reaction transformations of putative promiscuous enzymes that

can act upon structurally similar molecules (Figure 3A).

Different molecular and reaction encoding systems (Figure 3C) have been proposed to solve

the pathway identification in a retrosynthesis context. SimPheny (Yim et al., 2011) and

BNICE (Hatzimanikatis et al., 2005) use the third level EC number to guide their reaction

rules encoding where molecules and reaction operators are represented in the form of BEM

(bond-electron matrix), while GEM-path (Campodonico et al., 2014) and RetroPath2.0

(Delépine et al., 2017) utilize the simpler SMILES/SMARTS and SMIRKS system to encode

the compounds and transformations (Figure 3C). Most of the pathfinding algorithms use in-

house sets of transformation rules, for example, the latest version of BNICE has 361

generalized reaction rules, covering ~90% of KEGG 2014 (with 9000+ reactions).

Retropath2.0 and ReactPRED (Sivakumar et al., 2016) take a more data-driven approach.

ReactPRED automates the generation of transformation rules from user-defined reactions and

RetroPath2.0 systematically extracts thousands of transformation rules (expressed in

SMARTS/SMIRKS format) from 31527 reactions in the MetaNetX database, automatically

identifying the reaction centre and generating generalized reaction operator for each reaction

in the database. These automatically generated rules have specified more neighboring atoms

to the reaction centre and therefore are relatively less “general” as compared to reaction rules

in other methods (i.e., BNICE).

An update of the reaction rules database used in RetroPath2.0, RetroRules (Duigou et

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al.,2018) extract over 400,000 rules from multiple public databases including MetaNetX

(Moretti et al., 2016), Brenda (Placzek et al., 2017), BiGG (King et al., 2015), KEGG

(Kanehisa and Goto, 2000), MetaCyc (Caspi et al., 2016), MetRxn (Kumar et al., 2012) etc.

While most currently used reaction rules include only substructures of the substrates and

products, the recently developed RING (Gupta et al., 2018), sets more constraints in the

transformation rules, for example, upper and lower bounds of size of each substrate, what

substructure must not be included in each reactant, etc.

Various network pruning methods have also been developed to reduce the redundant branches

(reactions) and nodes (substrates) in the pathway network. SimIndex and SimZyme (Pertusi

et al., 2014) screen intermediate compounds based on their structural similarities to the target

compound and the enzyme’s native substrates, respectively. GEM-Path uses estimated

reaction thermodynamics to guide the network search. SimPheny (BioPathway Predictor) sets

a bound for molecule size. Other approaches include calculating a penalty score based on

enzyme sequence consistency in Retropath2.0.

The network search algorithm (Figure 3D) most commonly used by different de novo

pathway prediction tools is breadth-first search (some with pathway finding heuristics)

starting from the target compound (as used in RetroPath 2.0 and Delepine et al., 2017). This

retrosynthetic approach works well for relatively large molecular structures. However, since

transformation rules cannot be used to predict the reactants and products at the same time,

retrosynthesis does not work as well for pathways that consist of decomposition reactions. In

addition to this retrosynthetic approach, breadth-first search starting from the source

compounds, depth-first search (as used in BNICE, Hatzimanikatis et al., 2005) and double

direction search from the both the source and target chemicals (used in Uri Alon

Biopathfinder, Noor et al., 2010) are also used in different pathway identification algorithms.

Breadth-first search starting from the source compounds (as used in ReTrace, Pitkanen et al.,

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2009) is applicable to identify pathways with complex pathway topology. The bottleneck of

this approach is the fast growth of the number of compounds (i.e., combinatorial explosion in

the hypergraph case) and relatively heavier computational burden. Likewise, double-direction

search from both the source and target chemicals (as used in Uri Alon Biopathfinder, Noor et

al., 2010) is a combination of substrates-side breadth-first-search and product-side

retrosynthesis and allows identification of pathways with complicated structures while saving

runtime to a great extent. Finally, depth-first search (as used in BNICE, Hatzimanikatis et al.,

2005) is an alternative method used by some pathway identification algorithms, yet it is

limited to simple graph pathway networks. To our knowledge, there is no existing pathway

finding algorithm that uses depth-first search to explore a hypergraph reaction network.

Several pathfinding webservers extended the search space of existing databases by applying

the graph-based pathway finding techniques, such as XTMS, ATLAS and MINE. XTMS

(Carbonell et al., 2014), for example, collects metabolites and reactions from MetaCyc and

EcoCyc (Caspi et al., 2016, Keseler et al., 2017) and then uses Retropath (Carbonell et al.,

2011; Carbonell et al., 2013) to identify intermediate heterologous reaction steps. On the

other hand, ATLAS (Hadadi et al., 2016) and MINE (Jeffryes et al., 2015) are hypothetical

databases built based on KEGG and BNICE computational frameworks.

One of the main challenges that still remain concerning pathway identification is to enhance

the prediction of linear pathways and to recognize branched pathways (using a hypergraph or

a bipartite graph network representation) (Figure 3B). Earlier pathway finding algorithms,

i.e., that use simple edges to represent reactions from one compound to another, are limited in

identifying linear pathways and are not capable of predicting branched pathways with more

complex topologies, such as, condensation or synthetic reaction steps. Retropath2.0 is already

an advance in this sense as it uses hypergraph network representation of the pathway

network, where the details of a reaction can be stored perfectly by a directed hyper-edge

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connecting multiple substrates and products (Klamt et al., 2009). However, the limitation is

that it allows each promiscuous enzymatic reaction to take only one non-native substrate.

There are also other algorithms that try to identify branched pathways through merging linear

pathways, such as LPAT (Heath et al., 2011). However, to our knowledge, there is no current

computational method that uses hyper-edge reaction rules which would allow multiple non-

native substrates in the reaction. Though using hyper-edge rules will heavily increase the

computational burden, hyper-edge reaction rules will allow identification of literally all

potentially reachable compounds and broaden the chemical repertoire in the putative network.

2.2 Pathway Ranking and Selection

Pathway ranking, as shown in Figure 3E, is another important step in the biosynthetic

pathway design workflow. Resulting pathways from the identification step are usually very

numerous and cannot be analysed manually. These pathways are, therefore, ranked based on

pathway length, number of non-native reaction steps, and thermodynamic feasibility, in

which a more negative delta G is more favorable (Li et al., 2004). GSMM (Genome-scale

metabolic models) and FBA (flux balance analysis), which will be discussed in section 4.1,

are also performed in some methods to estimate the maximum product yield.

Some recently developed pathway design methods combine different pathway restraints and

factors and sum them into an overall score for pathway prioritization. For example, the

method proposed by Calhoun et al. (2018) includes molecular docking, cheminformatics,

similarity ensemble approach (SEA) and chemical transformation feasibility in the scoring

function they have developed for identifying pathways. Other possible approaches for

pathway ranking include assessing the toxicity of intermediate compounds and binding site

covalence (i.e., Cho et al., 2010).

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Finally, artificial intelligence may bring new solutions to pathway discovery and ranking. A

recent computer-aided tool for chemical synthesis used a deep neural network trained on

millions of chemical reactions to rank transformation patterns and determine the feasible

synthesis method (Segler et al., 2018). Such tool uses Monte Carlo tree search and symbolic

artificial intelligence to learn from the database and rank the most promising retrosynthetic

steps, simulating the decision-making process of an expert. Although it was used in the

context of designing retrosynthesis steps for organic chemistry, it could also be applied for de

novo biochemical pathway design.

3. Enzyme discovery and optimization

A subsequent step following in-silico pathway design is to find suitable enzymes that will

make the consecutive chemical transformations predicted by the computational tools.

However, even considering that most of the prediction tools apply methods based on

experimentally identified enzymatic reaction rules, it is very likely that most of the pathways

will contain at least one unknown enzyme, which is especially the case concerning pathways

for non-natural chemicals (Campodonico et al., 2014).

In some scenarios, researchers will have an idea of which enzyme, or set of enzymes, is the

best candidate for making the target chemical (Lee et al., 2012). However, besides this

rational design, recent advances in enzyme engineering expanded the possibilities of

implementation of the in-silico pathway designs in microbial hosts. For example, directed

evolution is used to further optimize natural enzymes for increased activity and substrate

specificity (Arnold, 2017). Additionally, even if no enzyme is found for the proposed

reaction, related enzymes can be modified via rational design (Tiwari et al., 2012), or could,

potentially, be created de novo by using protein simulation algorithms (Zanghellini, 2014).

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Nevertheless, it is worth to mention that while directed evolution is at an advanced stage of

development, there are not many successful cases of enzymes being created de novo despite

the recent progress in the field.

In this section, we will discuss genome mining for enzyme selection, de novo protein

engineering and directed evolution, focusing on the cutting-edge techniques that are allowing

the production of non-natural chemicals in microorganisms.

3.1 Genome mining for enzyme selection and optimization

A starting point for the construction of the pathway is to evaluate the subclass of the enzyme

predicted by the prediction algorithms and search in the available databases for enzymes that

would perform the proposed reaction. If a suitable enzyme for a determined substrate is not

found, there is still the possibility for the discovery of enzymes that may react,

promiscuously, with the desired substrate, since many enzymes act on multiple substrates

(Prather and Martin, 2008). Searching for enzymes in genome databases using only genetic

information and without a structure at hand is often called “genome mining” (Ziemert et al.,

2016) and precedes the process of expression and screening for performance. The best

candidates are then selected for combinatorial expression in a heterologous host.

One example of this concept of enzyme promiscuity screening being applied to pathways for

producing non-natural chemicals is the production of adipic acid via the muconic acid

pathway. Until recently, the enzyme needed to reduce both double bonds of muconic acid to

make adipic acid was unknown. To this end, Joo et al. (2017) cloned and screened about 25

genes from putative old yellow enzymes and 6 genes from putative enoate reductases (ER)

from GenBank database for activity against muconic acid. They found that at least 2 of such

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ERs, from Bacillus coagulans and Clostidium acetobutylicum had excellent activity reducing

muconic acid, discovering the missing link of that pathway.

Another example is the 1,4 BDO production in E. coli using the synthetic pathway predicted

in-silico by SimPheny (Yim et al., 2011), already mentioned in section 2.1. The 7 step

pathway required 5 heterologous enzymes, that had to be screened from different

microorganisms, including cat2, sucD and 4hbd, from Porphyromonas gingivalis, sucA from

Mycobacterium bovis, and adhE2 from Clostridium acetobutylicum (Yim et al., 2011).

In-silico pathway prediction models can aid in this “screen and select” approach by

identifying and ranking candidate enzymes. Carbonell and Faulon (2010) created a method

for predicting catalytic and substrate promiscuity using gene sequence similarities and

supervised machine learning algorithms. In the same manner, the genome-wide method

PROPER (enzyme PROmiscuity PrEdictoR) predicts promiscuous activities of metabolic

genes (Oberhardt et al., 2016). For that, it uses a permissive PSI-BLAST approach to search

for distant gene sequence similarities and analyze phylogenetic trees to assign secondary

activities to enzymes. This method was used in combination with genome-scale metabolic

modeling (GEM-PROPER) to predict the effect of exchanging enzymes with promiscuous

analogues from alternative metabolic pathways. A next step to further advance those models

would be to validate the promiscuity score based on gene sequence correspondence by using

similarities in binding site topography.

3.2 Directed evolution and rational design of enzymes

If a suitable promiscuous enzyme cannot be found to complete the metabolic pathway, it is

necessary to design novel enzymes for such reactions that have not been identified to occur in

nature, for example, using directed evolution and rational design on enzymes (Figure 4A).

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Directed evolution is an experimental technique used for protein engineering that imitates the

natural process of evolution, but in a considerably faster manner. For that, a large library

made from a starting enzyme mutant undergoes several rounds of iterative mutagenesis under

a selection pressure to elect the ones capable of best performing a desired function (Cobb et

al., 2013).

Directed evolution is a powerful tool in metabolic engineering as it allows researchers to

select for the enzyme with the right features, such as, improved activity in non-native

substrates, changing enantioselectivity, or even to create a new non-natural enzyme activity,

without having to devote resources into elucidating why those features make the enzyme

behave as desired (Packer and Liu, 2015; Arnold, 2017).

The first stage of directed evolution is obtaining the library of mutants. Several mutagenesis

methods have been developed for this purpose, including, random mutagenesis, site-directed

mutagenesis and site-saturated mutagenesis (Marcheschi et al., 2013). DNA shuffling, or

recombination, can also be used to make random combinatorial libraries. It involves

randomly fragmenting and rejoining DNA sequences and is often used in place of or in

addition to error-prone PCR to generate a diverse combinatorial library (Cobb et al., 2013).

Other random mutagenesis methods include transcription factor engineering, multiplex

genome engineering (MAGE), and are covered in the review by Marcheschi et al. (2013).

Directed evolution was used to adapt a series of enzymes to perform totally novel non-natural

reactions, including the enantioselective cyclopropanation of styrenes (Coelho et al., 2013), a

metal-oxo–mediated anti-Markovnikov oxidation of styrenes (Hammer et al., 2017) and an

asymmetric cyclopropanation using evolved myoglobin (Bajaj et al., 2016). The use of

directed evolution has also been used for the production of non-natural chemicals, such as the

non-natural carotenoid C50 astaxanthin (Furubayashi et al., 2015).

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The primary obstacle to any directed evolution work is the development of an adequately

sensitive and robust screen for the enzyme activity that is being selected for (Cobb et al.,

2013). The current gold standards for such measurements, gas and liquid chromatography

methods (GC and LC) and mass spectrometry (MS), are not scalable for high-throughput

operations due to factors such as reagent cost and signal to noise ratio (Dietrich et. al, 2010).

As the libraries generated for directed evolution are large (102 – 10

6 variants) (Dietrich et. al,

2010), these methods of measurement become the bottleneck of the process. While this

limitation can be overcome through the reduction of libraries by using rational design prior to

directed evolution (Cobb et al, 2013), such libraries may still be considered large for LC-MS

based methods.

Rational protein design, instead of creating random mutants straightaway, proposes a

preliminary in-silico screening, in which many combinations of amino acid exchanges would

be tested beforehand by computational simulation to identify the changes that are most likely

to succeed, thus creating a much smaller library of mutants to screen compared to random

mutagenesis. This method allows solutions with the mutation of dozens of amino acids at a

time. Several Molecular Dynamics (MD) simulations tools are available to analyse the

tridimensional enzyme structures and guide rational designs.

Recently, Walther et al. (2017) demonstrated the usefulness of rational design of enzymes in

the context of non-natural chemicals. They used molecular modeling and structural analysis

for engineering the enzymes needed for the production of the non-natural chemical 2,4

dehydroxybutyric acid (2,4 DHB): malate kinase, malate semialdehyde dehydrogenase and

malate semialdehyde reductase. Similarly, Tai et al. (2016) and Wang et al. (2017b) also used

structural analysis to redesign native 2-ketoacid decarboxylase and diol dehydratase,

respectively, for heterologous production of 1,4 BDO.

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3.3 High-throughput library screening with biosensors

As the systems biology-based techniques rely on the acquisition of large amounts of data, a

significant bottleneck is the screening and analysis. While the generation of mutant libraries

can be very high throughput, their characterization is limited by the use of lower throughput

techniques such as chromatography (e.g. HPLC, GC) and mass spectrometry (Rogers et al.,

2016; Skjoedt et al., 2016). An alternative method to overcome this limitation in screening is

the development of biosensors to enable the screening or selection of massive libraries, for

example, in directed evolution (Rogers and Church, 2016; Rogers et al., 2016).

Biosensors are based on the use of biological mechanism of molecular recognition for a target

chemical. In principle, a signal molecule, that can be detected by color, fluorescence, or

which ensures cell fitness, is coupled with the presence of the target chemical (Rogers et al.,

2016).

Thus, biosensors can be used to screen libraries of enzymes when searching for novel

biocatalysts for the purposes of host development and optimization (Figure 4B). For example,

biosensors expressed in vivo can help identify the highest producers of a certain non-natural

product in a set of clones obtained from metabolic engineering strategies, such as, the

introduction of a heterologous production pathway (Snoek et al., 2018). Additionally,

biosensors can be applied to the identification of the best-performing enzymes to react on a

given substrate (Ho et al., 2018).

Most biosensors are based on either a transcription factor or a sensor-actuator system. For

example, when applied to the production of non-natural chemicals, the chemical produced

serves as the input signal, which upon transcription factor recognition will result in the

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expression of an actuator component such as a reporter gene (e.g. GFP) or an antibiotic

resistance gene that will enable survival (Dietrich et al., 2013; Li et al., 2015; Leavitt et al.,

2017; Snoek et al., 2018).

Ligand-recognition orthogonality is a key feature of any biosensor. When a biosensor is

designed to respond to a non-natural chemical that is being produced, the system has an

inherent element of orthogonality since, in theory, no other chemical should be detected.

However, cross-reactivity with highly similar molecules is possible and needs to be addressed

through methods such as negative selection. Dixon et al. (2010) strategically mutated a

purine-binding riboswitch native to E. coli by targeting the most important base pairs

involved in ligand binding. They were able to construct two mutant switches with high

selectivity for ammeline and azacytosine (two non-natural ligands in said organism) and

virtually none towards the original purine ligands (Dixon et al., 2010).

Another biosensor feature that is promising for future high-throughput screens is the addition

of levels of control by changing media condition at a given time point. Snoek et al. (2018)

developed a biosensor-selector system which responds to cis, cis-muconic acid concentration

by inducing a G418 resistance-conferring gene, with the false positive rate being altered by

changing the media conditions. Analogously, Chen et al. (2018) showed in their study that

response time of their putrescine-detecting biosensor was dependent on the nutrients

available in the media, particularly with regards to nitrogen source. De Paepe et al. (2017)

and Zhang et al. (2015) provide an in-depth review of the different biosensor strategies and

engineering techniques that can be used to alter biosensor binding specificity as well as the

operational and dynamic ranges.

Higher biosensor efficiency and response times are also possible by using cis-acting RNA

biosensors, also known as riboswitches. These systems work on a faster time scale, since only

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transcription of the encoding RNA is necessary and there are no regulatory proteins to be

translated. Essentially, riboswitches consist of three main components: an aptamer (sensor), a

reporter or selector gene (actuator), and a bridge that links the two and promotes structural

stability (Qian and Cirino, 2016). Following a binding event, a conformational change occurs

in the three-dimensional structure of the aptamer component, leading to an exposed

ribosomal binding site (RBS) upstream of the actuator locus (Qian and Cirino, 2016). Gene

expression is hence activated by the production of the target chemical, which could be

cofactors, metal ions or amino-acids.

Recently, efforts from Jang et al. (2017) resulted in the development of a bacterial riboswitch

which activates the expression of a GFP reporter upon detection of naringenin, a flavonoid

molecule of plant origin. This riboswitch features a tetracycline resistance marker upstream

of the reporter, thus adding another layer of control (Jang et al., 2017). By adding tetracycline

to the media, the system is thus able to report which strains are the highest producers while

enriching the resultant library of clones for those which contain the encoding construct (Jang

et al., 2017).

Encoding the biosensor in a separate construct can be exploited to achieve higher biosensor

modularity, making the strain optimization faster. Building on the naringenin riboswitch, Xiu

et al. (2017) developed a co-culture version of the biosensor with the biosynthetic pathway

and biosensor components divided into two separate modules. Thus, each of the two bacterial

strains in the community carries one of the two components, minimizing the negative impact

on one of the components when the other is altered or optimized.

3.4 De novo protein engineering

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Considering that the average size of a protein is 200 amino acid residues, and that there are

20 different natural amino acids, the number of diverse amino acid sequences that could

possibly occur is in the order of 20200

. However, the number of distinct proteins that are

produced by existing organisms is only about 1012

. From this, one can deduce that evolution

has explored and selected just a small fraction of the array of all possible protein structures

(Huang et al., 2015).

In this context, de novo protein engineering is useful to generate and explore proteins that do

not exist in nature, including entirely novel enzymes to catalyse non-natural reactions. This

promising approach is an ultimate tool to fill the biochemical gaps in the in-silico pathway

designs that could not be completed by standard enzyme searching or re-engineering existing

enzymes.

De novo protein engineering attempts to create artificial proteins using computational tools

that predict chemical reactions, interactions and tridimensional structures using the principles

of molecular dynamics, quantum mechanics and protein biophysics. Several reviews take a

deeper look at this topic, including Kiss et al. (2013), Zanghellini (2014), Wolfson et al.

(2015) and Huang et al. (2015).

One of the most relevant approach for de novo enzyme design is the “inside out” method, as

revised by Kiss et al. (2013) and Zanghellini (2014). This method consists of four steps,

illustrated in Figure 4C. The first step is to elect an appropriate catalytic mechanism, establish

the transition state of the reaction, and then use quantum mechanics calculations to choose

the tridimensional arrangements of amino acids residues around the transition state that

maximizes its stability. This arrangement is, thus, an idealized active site, also named

“theozyme”, or theoretical enzyme (Kiss et al., 2013). Next, RosettaMatch algorithm is used

to match the putative active site with different protein backbone scaffolds, stored in its

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database, which can support the geometry of the theoretical structural pocket. Subsequently,

RosettaDesign (Liu and Kuhlman, 2006) algorithm is used to optimize the identity and

position of the amino acid residues surrounding the transition state to both stabilize its

conformation and to maximize the affinities of the residues with the transition state. Finally,

as several possible designs are created for each transition state structure, the designs are

ranked based on geometrical compatibility with substrates and products, as well as on

transition-state binding energy.

The pioneering studies of Jiang et al., (2008) and Röthlisberger et al. (2008) used this

approach to successfully expand the range of possible enzymatic reactions by creating totally

novel enzymes. Röthlisberger et al. (2008) computationally designed a protein catalyst for

Kemp elimination. Their designed enzyme performs the Kemp elimination of 5-

nitrobenzisoxazole, where a proton (H) transfer from carbon, simultaneously with a cut of the

nitrogen–oxygen (N-O) bond, resulted in the product cyanophenol. Jiang et al., (2008)

designed retro-aldolases that use four different catalytic motifs to catalyze the breaking of a

carbon-carbon bond in a non-natural substrate, in this case, 4-hydroxy-4-(6-methoxy-2-

naphthyl)-2-butanone. Since then, the same “inside out” approach was used to create a vast

range of enzymes for non-natural reactions, including the Diels-Alder reaction (Siegel et al.,

2010) and Morita−Baylis−Hillman reactions (Bjelic et al., 2013). Zastrow and Pecoraro

(2014) further extended the range of novel catalytic proteins to the more complex

metalloenzymes, by creating an enzyme for p-nitrophenyl acetate hydrolysis that uses two

different metal ions: a Zn(II) ion, which is important for catalytic activity, and a Hg(II) ion,

which provides structural stability.

Two of the latest challenges for de novo protein design, the creation of novel β-barrel proteins

and the introduction of binding sites for small molecules, were also recently addressed. Dou

et al (2018) remarkably created for the first time a novel and functional β-barrel protein, as

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well as the first full de novo design of a small-molecule-binding protein. Their protein

construct binds a chromophore DFHBI (3,5-difluoro-4-hydroxy-benzylidene imidazolinone)

and was successfully expressed in E. coli, yeast and mammalian cells. Besides the proven

usefulness of the designed protein for fluorescence-based imaging and sensing applications,

their work was a proof of concept that the field of de novo protein design is accelerating

towards increasingly advanced ligand-binding proteins, sensors and catalysts.

In conclusion, while rational design and de novo protein engineering are still the most

difficult ways to obtain a new enzyme due to the requirement of sophisticated computational

methods, recent examples of successfully applied rational and de novo protein designs are

bringing new prospects for the field. We also expect that further advances in the automated

workflows based on biosensors and robotics in recently established biofoundries will aid the

analysis required for the discovery and engineering of novel catalysts by these different

protein engineering techniques.

4. Host optimization

Host optimization is critical for transferring the production concept from laboratory scale to

commercialization. Even after prediction, ranking and selection of a suitable synthetic

pathway, with the most favorable thermodynamic and minimal reaction steps, and, despite

properly expressing the pathway enzymes and optimizing enzyme activity, frequently,

synthetic pathways for non-natural chemicals are not economically feasible. Recurrent

problems that challenge those pathways are related to energy and cofactors imbalance,

carbons diverted to by-products, cell burden caused by overexpression of the pathway,

toxicity of products and intermediates and so forth. A more comprehensive view of the cell’s

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interactions and behaviours is thus warranted and hence, there is need to leverage genomic

scale metabolic models (Mahadevan et al., 2005).

Ideally, the host strain would divert as much flux as possible to the desired production

pathway. However, the excess demand for carbon, cofactors and energy for the pathway may

impose a burden for the cell, decreasing growth and cell fitness (Chubukov et al., 2016).

Thus, cofactor usage and generation, substrate consumption, strain oxygenation conditions

and energy balances are parameters that must be considered.

Aside from computational methods such as metabolic modeling, there are various concepts

within systems biology which can be experimentally implemented. Several strategies can be

used in the framework of GSMMs, including finding growth-coupled production (section

4.1.2; Klamt and Mahadevan, 2015) or orthogonality strategies (section 4.4; Pandit et al.,

2017). On the other hand, to address the toxicity of products and intermediates, a good

strategy is to engineer the regulation machinery of the cell by applying the concept of

dynamic control (section 4.3; Chen and Liu, 2018), while also balancing the pathway

expression.

One example of the importance of host optimization is demonstrated by the work of Yim et

al. (2011). They were the first to demonstrate the production of the non-natural chemical 1,4

BDO in vivo, with the initial strain producing 1.2 g/L in 50 h. However, their research group

raised the productivity to about 80-90 g/L by applying genome-scale metabolic models

(GSMMs) and flux balance analysis (FBA) to identify targets for gene deletion, increasing

the flux to products (Burgard et al., 2016; Andreozzi et al., 2016). In this section, we review

how these systems biology concepts can be applied for host optimization.

4.1 Genome-scale models and analysis

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With the onset of whole-genome sequencing arose the development of genome-scale

metabolic models (GSMMs), which are powerful tools that facilitate the in-silico study of

metabolism. The models are mathematically represented by the stoichiometry of all the

reactions in the metabolic network of an organism (O’Brien et al., 2015; Lee and Kim, 2015).

Developments in next-generation sequencing have allowed faster sequencing than ever

before, and combined with other technologies, has greatly increased the availability and

speed at which GSMMs are constructed (Monk et al., 2014; Henry et al., 2010). Due to the

relative simplicity of GSMMs, exogenous metabolites, reactions and genes may be added

relatively easily using model manipulation platforms such as COBRA Toolbox and may

include gene-protein-reactions (GPRs) (Reed et al., 2003). In addition, large libraries of

GSMMs, such as BiGG Models (King et al., 2015), and open source platforms such as KBase

(Arkin et al., 2018), have made accessing and manipulating metabolic models a simple task.

4.1.1 Phenotype Simulation Methods

An important step in the pipeline for synthetic biology systems is phenotype simulation.

Here, GSMMs are used along with constraint-based modeling (CBM) methods to predict

phenotypic traits such as growth rate and reaction fluxes under various conditions. At this

point, all CBM methods rely on the assumption of an intracellular steady state in terms of

metabolite concentrations. The most commonly used CBM method for phenotype simulations

is Flux Balance Analysis (FBA) (Orth et al. 2010; O’Brien et al., 2015; Lewis et al., 2012).

This method is often used under the assumption that organisms will naturally operate with the

objective of maximizing growth. Other methods that assume different objective functions,

including minimization of metabolic adjustment (MOMA) (Segre et al., 2002), regulatory

on/off minimization (ROOM) (Shlomi et al., 2005), relative change (RELATCH) (Kim et al.,

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2011), and maximum entropy (MAXENT) (Martino and Martino, 2018) have also been

shown to be effective. There are additionally several variants of FBA that help to achieve

different objectives, including minimization of network flux (Lewis et al., 2010),

consideration of dynamic systems (Mahadevan et al., 2002), including both dynamics and

enzyme usage (Jabarivelisdeh and Waldherr, 2018), and incorporation of regulatory

information (Covert et al., 2001; Shlomi et al., 2007). These methods may be accessed

through open-source platforms such as the Constrain-Based Reconstruction and Analysis

(COBRA) Toolbox (Schellenberger et al., 2011), COBRApy (Ebrahim et al., 2013), Cameo

(Cardoso et al., 2018) and the Portable System for the Analysis of Metabolic Models

(PSAMM) (Steffensen et al., 2016).

4.1.2 Computational Strain Optimization Methods

To achieve a certain network functionality, such as, the increase of metabolic flux through a

non-natural pathway, Computational Strain Optimization Methods (CSOMs) can be used to

identify genetic perturbations such as genetic knockouts, over/under expression of genes,

heterologous insertions and cofactor specificity swapping. Such methods attempt to achieve a

desired network functionality by taking an optimization approach to solutions, as opposed to

complete enumeration of all possible results (Maia et al., 2015). Each of these methods

generally requires the use of a metabolic model as well as the implementation of a phenotype

simulation method. The most important CSOMs are presented in Figure 5.

Several CSOMs have been published, however, this review will focus primarily on

constraint-based methods because they can be built directly from stoichiometric data, which

is, in turn, easily obtained from genome sequencing (Maia et al., 2015). As opposed to the

traditional building of manually curated GSMMs, the open-source platform CarveMe

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presents a top-down automated methodology of genomic reconstruction that uses a manually

curated universal model which is then combined with annotated genomes to create species-

specific or community models in a fast and scalable manner (Machado et al., 2018).

Growth-coupled pathway design

Synthetic chemical production pathways often impose a metabolic burden on the organism.

Consequently, within a population, the strain that loses the functionality of the synthetic

pathway may outcompete the producing strains. One strain optimization approach that is

frequently used to circumvent this disadvantage is to couple growth with the synthetic

pathway.

This strategy will ensure that product will be consistently produced over time, meaning that

growth, or even the survival of the cell, can only happen if there is flux though the synthetic

pathway. In addition, it is possible to use adaptive laboratory evolution to select for

increasing chemical production capacity while screening for maximum growth (Conrad et al.,

2011).

The principle of this strain optimization approach is to use CSOMs, such as OptKnock, to

find deletion strategies that either force flux through the synthetic pathway to balance energy

and redox cofactors, or, to ensure the synthesis of biomass precursors, for example, by

reducing the cell’s ability to synthetize building block metabolites. As a result, the growth of

the organism itself becomes the driving force of chemical production (Klamt and Mahadevan,

2015).

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The coupling can be either weak, or strong. Weak coupling happens when satisfactory

product yield is only obtained if the cell grows with maximal or close-to-maximal biomass

yield. Strong coupling, otherwise, means that chemical production must happen, even without

growth, to ensure cell’s survival, (Klamt and Mahadevan, 2015). A depiction of the

production envelopes for strong and weak growth coupling is shown in Figure 5A. It has been

shown by Klamt and Mahadevan (2015) that, under certain conditions, any metabolite of the

central metabolism can be strongly coupled in E. coli. Von Kamp and Klamt (2017) further

validated this theory in S. cerevisae, Corynebacterium glutamicum, Aspergillus niger and the

cyanobacterium Synechocystis sp..

Although there are not many examples of this strategy being applied to producing non-natural

compounds, its suitability has been recently demonstrated for coupling the production of a

series of natural compounds in several different microorganisms, both aerobically and

anaerobically.

FBA-based methods

The first CSOM that predicts growth-coupled designs to be developed was OptKnock, which

suggests a set of reaction knockouts that maximize production of a target metabolite (Burgard

et al., 2003). While the solutions proposed by OptKnock are growth-coupled, this coupling is

often weak. Some methods that produce strong growth-coupled strategies were developed to

address this and are discussed in this review. As abovementioned, a representation of

OptKnock, as well as the difference between strong and weak coupling, are shown in Figure

5A.

Methods such as OptKnock produce solutions which are considered to be “optimistic”, as the

optimal solution is used as the operating point in terms of the engineering objective. This

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issue was addressed through RobustKnock, which considers alternate solutions that could

divert flux from the target compound, as well as by the pair of methods P-OptKnock and P-

ROOM, which assume a “worst-case scenario” for the engineering objective (Tepper and

Shlomi, 2009), (Apaydin et al., 2017). The first method to introduce gene over and under

expression alongside genetic knockouts was OptReg (Pharkya and Maranas, 2006). Later, an

algorithm termed Enhancing Metabolism with Iterative Linear Optimization (EMILiO) was

formulated by Yang et al. (2011), which provided results similar to OptReg but using a

computationally scalable approach. Methods that suggest heterologous reactions as genetic

perturbations include OptStrain and SimOptStrain, which calculate heterologous additions

and knockouts separately and simultaneously, respectively (Pharkya, 2004; Kim et al., 2011).

A final method worth mentioning is OptSwap, which is one of the few CSOMs that predicts

modulations in cofactor specificity alongside genetic knockouts (King and Feist, 2013).

EMA-Based Methods

CSOMs relying on Elementary Mode Analysis (EMA) are powerful methods that compute

sets of elementary flux modes (EMs), which are non-decomposable sets of reactions in a

network (Ruckerbauer et al., 2015). A minimal cut set (MCS) is defined as an irreducible set

of reactions based on elementary flux modes of which when inactivated, results in metabolic

network failure for a particular function (Klamt and Gilles, 2004). In application, these

undesired modes can include any network arrangement in which a minimum yield for some

target product is not achieved. In effect, elimination of the undesired modes leads to strong

growth coupling. One can also include additional constraints, such as to biomass yield, to

select for desired modes in the form of constrained minimal cut sets (cMCSs) (Hädicke and

Klamt, 2011). A depiction of the solution spaces for both MCS and cMCS is shown in Figure

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5B and C. The results of these methods are given as a list of genetic knockout sets that

produce growth-coupled strategies. Recently, these methods have been adapted to incorporate

two-stage fermentations strategies, termed the Metabolic Valve Enumerator (MoVE)

(Venayak et al, 2018a). MoVE calculates a set of metabolic valves, which are reactions

expressed during a stage of growth then turned off during a stage of production, as well as a

set of reaction knockouts which remain inactive throughout both stages. The result is a strain

that achieves a minimal biomass yield during the growth stage, and a strong growth-coupled

production of a target compound during the production stage, as is shown in Figure 5D.

Methods based on these principles may be used in conjunction with the CellNetAnalyzer

framework (von Kamp and Klamt, 2017).

The scalability of MCS and other EM-based methods on GSMMs is poor due to the

computational nature of mixed integer linear programming (MILP) problems. The

MCSEnumerator was developed to produce MCSs from the smallest number of perturbations

to the largest. This is done by enumerating all elementary modes, mapping the MCSs to the

modes and then determining which EMs are the smallest (von Kamp and Klamt, 2014).

Regulatory information was incorporated to eliminate results containing non-enzymatic or

non-annotated reactions, denoted as regulatory constrained MCS (rcMCS) (Jungreuthmayer

and Zanghellini, 2012), while constrained regulatory MCS (cRegMCS) was developed to

consider both genetic knockouts and over/underexpression of genes, resulting in more

solutions requiring fewer genetic perturbations (Mahadevan et al., 2015). Whereas most

MCS-based methods are approached at the reaction-level, many reactions in GSMM are poor

targets for experimental knockouts, which was an issue addressed by genetic MCS (gMCS), a

method which incorporates gene expression data (Apaolaza et al., 2017). Attempts to sort

through cMCSs by pure strain performance such as genetic algorithm MCS (GA-MCS) have

been published, but their efficacy on GSMMs has not yet been shown (Nair et al., 2015). An

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EMA-based method that does not focus on MCSs is the modular cell (MODCELL)

framework, which aims to design a strain that is auxotrophic for some exchangeable

production mode that would be coupled to target compound production and growth (Trinh et

al., 2015). In addressing the computational burden of EMA-based methods, it was shown

that alternative integer linear programming (AILP) could be used to significantly reduce the

computational time required in calculating MCSs (Song et al., 2017).

Metaheuristic methods

Metaheuristic CSOMs are advantageous in that they are much less computationally expensive

than FBA- and EMA-based methods. This is due to the decoupling of the biological and

engineering objective functions, allowing for independent calculations for each. This is

beneficial, as computational power is made readily available for more complex phenotype

simulation methods. Despite the benefits of metaheuristic methods, due to their nature these

methods cannot guarantee an optimal solution. A schematic of the typical workflow for

metaheuristic methods is shown in Figure 5E. The first metaheuristic method that was

published was OptGene, which uses a genetic algorithm modeled to evolutionary theory to

predict sets of genetic knockouts (Patil et al., 2005). Other methods incorporate the presence

of noise (Costanza et al., 2012), build iteratively upon previous solutions (Lun et al., 2009;

Rockwell et al., 2013), or consider shadow price analysis (Ohno et al., 2014). Metaheuristic

methods hold promise in the design of organisms that produce non-natural chemicals due to

the facilitated scalability with GSMMs.

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4.2 Kinetic Modeling for Strain Improvement

As previously mentioned, GSMMs are uncomplicated since they incorporate only

stoichiometric data. While this has some advantages, it also makes them limited in terms of

bioengineering applications. While stoichiometric models assume a quasi-steady state and set

the change in metabolite concentrations with time to zero, kinetic models do not make this

assumption. This leads to the requirement to incorporate enzymatic expression and kinetic

data as well as other dynamics that are not captured by stoichiometric models. Kinetic models

may therefore be advantageous in the analysis of transient behaviours of strains that are often

difficult to estimate.

As an alternative to traditional constraint-based models, kinetic models further include

physiologically relevant data, such as time-dependent metabolite concentrations and

enzymatic regulation. This enables the dynamic analysis of biological systems for enhanced

in-silico hypothesis generation. Kinetic models, however, suffer from the burden of

estimating several parameters which are usually not readily available, so the number of

reactions that they explicitly describe is often mostly limited to core metabolism (Srinivasan

et al, 2015). However, it is expected that as metabolomic, proteomic, and transcriptomic data

becomes more accessible under various conditions, kinetic models will become more useful.

Machine learning algorithms have been suggested as a promising solution to the issue of

parameterization. The ORACLE (Optimization and Risk Analysis of Complex Living

Entities) framework effectively integrates kinetics with thermodynamic, omics data (such as

fluxomics and metabolomics), along with network stoichiometry is depicted in Figure 5E

(Miskovic and Hatzimanikatis, 2010). ORACLE was successfully implemented by Andreozzi

et al. (2016) to predict a modular pathway for the biosynthesis of 1,4-BDO in E. coli. Other

machine learning algorithms were also applied to build kinetic models for the production of

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limonene and isopentenol in E. coli, giving qualitatively accurate results (Costello and

Martin, 2018).

4.3 Dynamic control and pathway balancing

A frequent difficulty in the metabolic engineering of microorganisms is achieving the finest

control possible over the production of the target chemical (natural or non-natural). Ideally, at

any given time, an efficient cell should modulate its level of chemical production in

accordance with its environmental conditions and intracellular metabolic state. Thus, the idea

behind pathway balancing is to optimize the inadequacies that can arise from the introduction

of heterologous pathways.

Traditional methods of pathway balancing often resort to static strategies like pathway

overexpression, gene deletions to deviate fluxes, or promoter engineering for altering

expression levels (Xu et al., 2014). The most widely recognized balancing strategy is

preventing the accumulation of problematic metabolites by increasing its downstream fluxes

while decreasing upstream ones (Brockman and Prather, 2015; Jones et al., 2015). However,

such modifications only scratch the surface, as production relies on cell fitness, which

depends on the organism’s ability to sense and respond to recurrent environmental

perturbations (Venayak et al., 2015; Dahl et al., 2013; Ewald et al., 2017; Leonard et al.,

2010). Overexpression can also have negative implications, such as growth inhibition (Liu et

al., 2015; Xu, 2018). Jones et al. (2015) provide a more detailed review on the existent

approaches for pathway balancing, including ribosomal binding site engineering, post-

translational modifications, and others mentioned previously.

Accordingly, there is an existing consensus that cells with dynamic and automated self-

regulation are better candidates for industrial use and scale-up (Ewald et al., 2017). The most

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prominent form of dynamic control uses genetic circuits based on inducible and modular

gene expression, usually featuring activation and repression of activity of a particular

transcription factor (Liu et al., 2015; Rantasalo et al., 2018; Xu et al., 2014). An advantage of

this system is the high specificity of transcription factors to recognize an input signal; for

example, the presence of a metabolite or a change in temperature or other condition (Xu,

2018). Gene circuits that modulate pathway overexpression according to the levels of

available product lead to enhanced survival and improved titers (Liu et al., 2015). Other

studies use metabolic switches for the separate modulation of expression of upstream and

downstream reactions to a metabolite according to its abundance (Xu et al., 2014). This better

manages cellular resources but can also respond to the need for optimized fermentations

which typically require multiple steps with different substrates or oxygen uptake rates, and in

which either growth or production are prioritized at different times (Figure 6C). Two-state

(bistable) dynamic control provided by tools such as genetic toggle switches facilitates such

fermentation protocols by quickly turning off and restoring wild type growth phenotypes

when production is not prioritized, thereby minimizing the cellular effects of growth burden

(Anesiadis et al., 2013; Bothfeld et al., 2017; Venayak et al., 2018b). The central idea

underlying the concept of dynamic control is the temporal segregation of cellular growth and

bioproduction pathways, so that both are optimized in such a way that the trade-off that

usually exists between them is minimized (Figure 6C). This approach allows the same strain

to maximize yield from a given substrate and minimize production of undesired by-products,

while still growing unaffected by the detrimental effects a bioproduction pathway would

typically impose (Bothfeld et al., 2017).

Dynamic control has been implemented for the production of some high-value non-natural

chemicals over the years, although its use is still infrequent compared to natural chemical

production. Liu et al. (2015) introduced a 1,4-BDO production pathway into E. coli and

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demonstrated that a two-part biosynthetic strategy with separate carbon fluxes for production

and dynamic regulation achieved autonomous production. Their system not only increases the

efficiency of 1,4-BDO synthesis but is also inherently trivial to optimize given the low degree

of commonalities between the production and regulatory pathways (Liu et al., 2015). This last

feature is known as orthogonality and is discussed in the next section in more detail, as well

as in the context of the Liu et al. (2015). Self-regulation has also been explored to prevent

toxic intermediate accumulation in the mevalonate pathway, which can be used to make a

variety of terpene-derived synthetic drugs (Dahl et al., 2013). Other technologies such as

CRISPR-based repressible promoter regulation are also used in the design of genetic circuits

for minimized crosstalk and high responsiveness, which is directly applicable to non-natural

chemical production (Didovyk et al., 2016).

Dynamic regulation for the production of non-natural chemicals is still not widespread.

However, given the ineffectiveness of static methods alone and the great advantages

automated control presents for host optimization, it could become useful to achieve more

efficient and competitive bioproduction (Zhang et al., 2015; Wang et al., 2017c; Wang et al.,

2018). Recent innovations in dynamic control include the use of gene circuit toolboxes and

the integration of multiple gene circuits for more robust and even more sophisticated

expression. Despite being promising for scale-up, only a few recent efforts have attempted

such a task (Rantasalo et al., 2018). Solutions to the issue of using chemical inducers have

also been explored, since toxic effects and low responsiveness are relevant concerns. For

example, the use of electromagnetic radiation has been considered for dynamic gene

expression, as it can provide a proven, quick, and highly reversible signal (Salinas et al.,

2017).

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4.4 Orthogonality principles applied to pathway design

Another strategy to decouple production from cellular growth is the use of orthogonality

principles. Metabolic pathways are orthogonal when they operate with minimal interference

between the synthetic pathway and the biomass-producing native pathways. In principle,

those pathways do not share any enzymes and may be connected by a single metabolite

(Pandit et al., 2017). This metabolite can be used as a precursor for either biomass or

chemical production, and it is possible to design strategies that introduce metabolic “valves”

to isolate the growth and the chemical production pathways in a parallel fashion (Pandit et al.,

2017). The more enzymes these pathways share, the more opportunity for cross-talk between

them will exist (Figure 6A). On the other hand, by minimizing the common reactions

between the two network branches, it is possible to create a more orthogonal system (Figure

6B). Orthogonal networks can bypass large portions of native pathways transforming sugars

directly to chemicals, or yet, include junctions or valves through which precursors can be

used for growth, and, when convenient, can be diverged for chemical production. Such

pathways are considered to be growth-independent, meaning that orthogonality is contrary to

growth-coupling.

Accounting for substrate orthogonality is also important. Metabolites like glucose or fatty

acids are highly involved in core metabolism, making more orthogonal substrates such as

ethylene glycol and xylose more desirable (Pandit et al., 2017). Xylose-based orthogonal

pathways could be beneficial for future non-natural chemical production, since it is

extensively found in agricultural residues as part of hemicellulose and does not compete with

the food supply. One example of this concept being applied in the production of non-natural

chemicals is presented by Liu and Lu (2015). In this work, they produced 1,4 BDO from

xylose in E. coli. As this microbe is naturally able to catabolise xylose, they deleted three

native genes encoding common enzymes to make the synthetic pathway orthogonal to central

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metabolism. Their strategy was to use xylose for chemical production and other carbon

sources for cellular growth. A quorum-sensing mechanism was also exploited for production

control, and the resulting strain was able to produce of 1,4 BDO autonomously, although in

quite low amounts (Liu and Lu, 2015).

A problem that would predictably arise from the construction of highly integrated systems,

such as gene circuits, is the need for greater orthogonality in their design. A lack of

orthogonality can lead to unexpected imbalances in expression modulation due to

interference of certain DNA-binding factors with the successful association of the correct

factor-promoter complexes, thereby defeating the purpose of dynamic circuits. This problem

was handled by Rantasalo et al. (2018) by the creation of a transcription factor-promoter

toolbox library. In that work, they characterized different promoter regions and engineered

transcription regulators for optimized pathway expression in S. cerevisiae, and then used it to

develop a memory-enabled genetic switch able to induce and repress production according to

need. The key feature of their transcription factor-promoter toolbox library is the high

orthogonality between the proteins and their respective binding sites.

Another promising strategy for the implementation of orthogonality in metabolic engineering

is the use of non-natural cofactors to create orthogonal gene circuits for energy transfer.

Redox cofactor balancing is well-studied, and, in most cases, involves changing the

specificity of proteins to use a different electron carrier (e.g. NAD instead of NADP) or

overproducing the required cofactor (Lee et al., 2012). However, natural cofactors, such as

NAD, link multiple energy transfer modules and metabolic circuits, and thus, concerns have

been raised about the use of natural cofactors for production pathways, given their low

orthogonality (Figure 7A). The use of synthetic cofactors for highly selective energy transfer

has hence been proposed as an orthogonal alternative that further segregates production and

central metabolism at the pathway level (Figure 7B). Such synthetic cofactor-linked circuits

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contain two parts; one which strips the electrons from the energy carrier and another which

replaces them. The system proposed by Wang et al. (2017c) uses phosphite dehydrogenase

and malate dehydrogenase to recycle the synthetic cofactor nicotinamide cytosine

dinucleotide or NCD (Figure 7C). This way, competition for natural cofactors (i.e. NAD)

does not affect non-natural chemical production pathways, facilitating strain optimization.

Orthogonal pathway construction also has a place in the optimization of production of non-

natural metabolites, such as synthetic amino acids, for in vivo reactions mediated by

recombinant proteins. Ma et al. (2018) constructed a heterologous and orthogonal pathway

for azidohomoalanine production in E. coli, an unconventional amino acid with

biotechnological applications. Such strategies could be repurposed to produce completely

synthetic amino acids unheard of in nature, thus helping to reduce bioproduction costs and

meet demands for new non-natural chemical and biochemical products (Völler and Budisa,

2017).

5. Conclusion

In this review, we highlighted the emerging case studies for the direct production of non-

natural chemicals in microorganisms. In fact, this shift to non-natural chemical production

has intensified in the past 5 years, with more than 40 different compounds now demonstrated

in laboratory scale. In addition, at least one of them, 1,4 BDO, is being produced using a

commercial scale bioprocess.

In fact, a variety of new computational and experimental systems biology tools are available

for metabolic engineering and can significantly influence the development of hosts for non-

natural chemical production and feasibility of its commercialization. Such tools include

biosensors, high throughput robotic screening platforms, de novo protein design, directed

evolution and dynamic control methods based on orthogonality. However, not all tools are as

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impactful on microbial production. For instance, computational strain optimization methods

have the potential to multiply chemical production several times. On the other hand, pathway

balancing sometimes results only in a small increase in the overall production and it is only

practical once the appropriate enzymes have been selected and implemented successfully.

Finally, chemical production is absolutely absent without the precise enzymes to catalyze the

reactions involved in the designed pathway. Hence, finding the required enzymes is critical

for implementing synthetic pathways.

In fact, while in-silico pathway design is sufficiently advanced to be used to find pathways

for virtually any organic chemical, one of the main hindrances for developing new pathways

for non-natural chemicals is the discovery and engineering of novel enzymes encoding the

non-natural activity. Usually there is high prospect for finding promiscuous enzymes or for

adapting a natural enzyme for the desired reaction. However, if no enzyme could be selected

then, having to resort for de novo enzyme design results in an enormous challenge. Due to

this difficulty of engineering non-natural enzymes, the combination of synthetic pathways

with chemical transformations still might be required for some cases (Lee et al., 2019).

However, it is clear that many of the advanced methods for host optimization have not yet

been used in most studies of non-natural chemical bioproduction. Hence, there is an

opportunity to integrate such approaches for host optimization with the emerging automated

robotic workflows to accelerate the development of new microbial cell factories.

Acknowledgements:

This work was supported by the Natural Sciences and Engineering Research Council

(NSERC), the NSERC Industrial Biocatalysis Network (IBN), Biochemicals from Cellulosic

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Biomass (BioCeB) grant from the Ontario Research Fund (Research Excellence) and a grant

from the Genome Canada Genomics Applied Partnership Program (GAPP).

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Tables and Figure Captions

Table 1. Non-natural chemicals currently being chemically produced from biomass

Chemical Uses Raw material Company

FDCA, PEF Bioplastic glucose (YXY

technology)

Avantium, The

Netherlands

Lactide, PLA Bioplastic bio-lactic acid NatureWorks, MN,

U.S.A.

Corbion, The

Netherlands

Butadiene Biorubber bio 1,3 BDO Eni-Versalis, Italy

Synthetic Paraffinic

Kerosene (ATJ-SPK)

Jetfuel bioethanol LanzaTech, IL, U.S.A.

Methyl Acrylate,

Isosorbide Diacrylate

bio-based acrylate resins glucose PTT MCC Biochem,

Thailand

Adipic acid Nylon fibers glucose Verdezyne*, CA, U.S.A.

Rennovia*, CA, U.S.A.

Isobutylene Fuel additives glucose Global Bioenergies,

France

hydroxymethylamine Nylon fibers glucose Rennovia*, CA, U.S.A.

1,6-Hexanediol polyurethanes glucose Rennovia*, CA, U.S.A.

Levoglucosenone,

Hydroxymethyl

Butyrolactone

Drugs, herbicides,

flavourings and fragrances

cellulose Circa, Australia

Abbreviations: FDCA – 2,5 furan dicarboxylic acid; PEF – polyethylene furanoate; PLA –

polylactic acid; BDO – butanediol; * Currently in finatial recuperation.

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Table 2. Laboratory scale bioprocess for non-natural chemicals production

Compound Titer (g/L) Microrganism Substrate Systems Biology

Strategy

Reference

1,4 BDO 18.0 Escherichia coli glucose SimPheny

Biopathway

Predictor;

Promiscuous

enzyme screen;

OptKnock

deletions

Yim et al.,

2011

1,4 BDO 0.5 Escherichia coli D-xylose Promiscuous

enzyme screen;

dynamic control;

orthogonality

Liu and Lu,

2015

1,4 BDO 16.5 Escherichia coli D-xylose Promiscuous

enzyme screen;

enzyme rational

design

Tai et al., 2016

1,4 BDO 0.2 Escherichia coli D-xylose Enzyme rational

design

Wang et al.,

2017b

1,3 BDO 2.4 Escherichia coli glucose Promiscuous

enzyme screen

Nemr et al.,

2018

1,3 PDO 0.416 Escherichia coli glucose,

propionate

- Kataoka et al.,

2017

1,2‐Phenylethanediol 1.23 Escherichia coli glucose

Promiscuous

enzyme screen

Mckeena et al.,

2013

4-Methyl-1,3-

pentanediol

0.072 Escherichia coli glucose,

isobutyrate

- Kataoka et al.,

2017

1,2,4-Butanetriol 0.003 Escherichia coli glucose,

glycolate

- Kataoka et al.,

2017

Isobutanol 22.0 Escherichia coli glucose - Atsumi and

Liao, 2008

Isobutanol 1.62 Saccharomyces

cerevisiae

glucose - Matsuda et al.,

2013

Isobutanol 25.4 Corynebacterium

glutamicum

glucose - Yamamoto et

al., 2013

Isobutanol 5.4 Clostridium

thermocellum

cellulose - Lin et al., 2015

Isobutanol 2.22 Pichia pastoris glucose Promiscuous

enzyme screen

Siripong et al.,

2018

2-Methyl-1-

butanol 1.25 Escherichia coli glucose

Promiscuous

enzyme screen

Cann and Liao,

2008

2-Methyl-1-

butanol 0.37

Corynebacterium

glutamicum glucose

- Vogt et al.,

2016

Isopentanol 9.5 Escherichia coli glucose

- Connor et al.,

2010

Isopentanol 0.086 Escherichia coli glucose

- Guo et al.,

2014

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1-Pentanol 1.137 Escherichia coli

trans-2-

pentanoate

and formate

Promiscuous

enzyme screen Tseng and

Prather, 2012

1-Pentanol 4.3 Escherichia coli glucose

Enzyme rational

design

Chen et al.,

2017

1-Pentanol 0.137 Escherichia coli

1,2

pentanediol

Enzyme rational

design Dai et al., 2017

1-Pentanol 0.118 Escherichia coli glucose

Enzyme rational

design; genome

mining

Mak et al.,

2015

1-Hexanol 0.341 Escherichia coli glucose Enzyme rational

design; genome

mining

Mak et al.,

2015

1-Heptanol 0.269 Escherichia coli glucose Enzyme rational

design; genome

mining

Mak et al.,

2015

Nonane 0.327 Escherichia coli glucose

- Choi and Lee,

2013

Dodecane 0.136 Escherichia coli glucose

- Choi and Lee,

2013

Tridecane 0.064 Escherichia coli glucose

- Choi and Lee,

2013

2-Methyl-

dodecane 0.042 Escherichia coli glucose

- Choi and Lee,

2013

Tetradecane 0.009 Escherichia coli glucose

- Choi and Lee,

2013

Isobutyl acetate 0.051 Pichia pastoris glucose

Promiscuous

enzyme screen

Siripong et al.,

2018

2,4-

dihydroxybutyric

acid 1.8 Escherichia coli glucose

Thermodynamic

and stoichiometric

analysis;

enzyme

rational design;

pathway balancing

Walther et al.,

2017

Muconic acid 36.6 Escherichia coli glucose

- Niu et al.,

2008

Muconic acid 59 Escherichia coli cathecol

- Kaneko et al.,

2011

Muconic acid 0.141

Saccharomyces

cerevisiae glucose

FBA; Pathway

balancing

Curran et al.,

2013

Muconic acid 2.1

Saccharomyces

cerevisiae glucose

Biosensor Leavitt et al.,

2017

Muconic acid 64.2

Pseudomonas

putida cathecol

Pathway balancing Kohlstedt et

al., 2018

Muconic acid 3.1 Escherichia coli glucose

Thermodynamic;

Elementary Flux

Mode Analysis

Thompson et

al., 2017

Muconic acid 1.8

Corynebacterium

glutamicum lignin

- Becker et al.,

2018

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Muconic acid 85

Corynebacterium

glutamicum cathecol

- Becker et al.,

2018

Muconic acid 5.1

Saccharomyces

cerevisiae glucose

- Pyne et al.,

2018

Muconic acid 52.3

Pseudomonas

putida

benzoate,

coumarate,

corn stover

-

Salvachua et

al., 2018

Adipic acid <0.001 Escherichia coli glucose - Yu et al., 2014

Adipic acid 2.5 Escherichia coli glycerol Cheong et al.,

2016

Adipic acid 0.003 Saccharomyces

cerevisiae

glucose -

Raj et al., 2018

Adipic acid 68 Escherichia coli glucose Pathway balancing Zhao et al.,

2018

Acrylic acid 0.12 Escherichia coli glucose

Promiscuous

enzyme screen

Chu et al.,

2015

4-

Hydroxybenzoic

Acid 1.82 Escherichia coli glucose

-

Noda et al.,

2016

4-

Hydroxybenzoic

Acid 36.6

Corynebacterium

glutamicum glucose

Promiscuous

enzyme screen Kitade et al.,

2018

2-

hydroxyisovaleric

acid 7.8 Escherichia coli glucose

- Cheong et al.,

2018

2-Methylsuccinic

acid 3.61 Escherichia coli glucose

Promiscuous

enzyme screen

Wang et al.,

2018

Polylactic acid 3%CDW Escherichia coli glucose

MOMA; FBA Jung et al.,

2010

Butyrolactam 54.14 Escherichia coli glucose

In-silico flux

response analysis;

thermodynamic

analysis;

promiscuous

enzyme screen

Chae et al.,

2017

Valerolactam 1.18 Escherichia coli glucose

In-silico flux

response analysis;

thermodynamic

analysis;

promiscuous

enzyme screen

Chae et al.,

2017

Caprolactam <0.001 Escherichia coli glucose

In-silico flux

response analysis;

thermodynamic

analysis;

promiscuous

enzyme screen

Chae et al.,

2017

Caprolactam 0.160 Escherichia coli glucose Promiscuous Turk et al.,

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enzyme screen 2016

6-Aminocaproic

acid 0.048 Escherichia coli glucose

Pathway balancing Zhou et al.,

2015

5-Aminovaleric

acid 33.1

Corynebacterium

glutamicum glucose

- Shin et al.,

2016

D-Phenyllactic

acid 1.62 Escherichia coli glucose

Promiscuous

enzyme screen

Yang et al.,

2018

Poly (3HB-co-D-

phenyllactate) 13.9 Escherichia coli glucose

Promiscuous

enzyme screen

Yang et al.,

2018

Shinorine 0.019

Corynebacterium

glutamicum

sodium

gluconate

- Tsuge et al.,

2018

Shinorine 0.154

Streptomyces

avermitilis glucose

Genome mining Miyamoto et

al., 2014

Styrene 0.3 Escherichia coli glucose

Promiscuous

enzyme screen

Mckenna and

Nielsen, 2011

Styrene oxide 1.32 Escherichia coli glucose

Promiscuous

enzyme screen

Mckeena et al.,

2013

Styrene 0.029

Saccharomyces

cerevisiae glucose

- Mckenna et al.,

2014

Styrene 0.35 Escherichia coli glucose

Promiscuous

enzyme screen;

pathway balancing Liu et al., 2018

3,4-

Dihydroxystyrene 0.063 Escherichia coli glucose

- Kang et al.,

2015

4-Hydroxy-3-

methoxy styrene 0.064 Escherichia coli glucose

- Kang et al.,

2015

4-Hydroxy

styrene 0.355 Escherichia coli glucose

- Kang et al.,

2015

FDCA 30.1

Pseudomonas

putida 5-HMF

- Koopman et

al., 2010

FDCA 13.9

Raoultella

ornithinolytica 5-HMF

- Hussain et al.,

2016

FDCA 34.5

Raoultella

ornithinolytica 5-HMF

Pathway balancing Yuan et al.,

2018

Phenol 1.69 Escherichia coli glucose

- Kim et al.,

2013

Phenol 0.472 Escherichia coli glucose

- Ren et al.,

2015

Phenol 0.377 Escherichia coli glucose

Thermodynamic

analysis;

promiscuous

enzyme screen;

elementar flux

modes

Thompson et

al., 2018

C50 astaxanthin

770 μg

gDCW−1 Escherichia coli

glucose

Directed evolution Furubayashi et

al., 2015

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Abbreviations: FBA – Flux Balance Analysis; MOMA – Minimization of Metabolic

Adjustment; FDCA - 2,5-Furandicarboxylic acid; 5-HMF – Hydroxymethylfurfural.

Figure 1. Synthetic pathways for non-natural chemicals. 1,4 BDO can be produced from

xylose by manipulating native xylose catabolism in two different ways or from the central

metabolism, using ketoglutarate and succinyl-CoA as intermediates. 1,3 BDO can be

produced from acetyl-CoA either using acetaldehyde or butyryl-CoA as intermediates.

Acrylic acid can be produced by extrapolating from the propanoate metabolism, using

acryloyl-CoA as a precursor. Adipic acid can be produced either from benzoate or from

central metabolism by using a reverse benzoate degradation pathway, or from shikimate

pathway, using dehydroshikimate (DHS) as intermediate and muconic acid, a non-natural

chemical itself, as precursor. Shikimate pathway can also be extended for producing 4-

hydroxybenzoic acid or phenol. In addition, several amino acids are key intermediates for

non-natural chemical production. Isoleucine and valine biosynthesis pathways can be

manipulated for producing 2-methyl-1-butanol, isobutanol and isobutyl acetate. Styrene and

styrene derivatives can be produced from tyrosine, by extrapolating from phenylpropanoid

biosynthesis. Finally, lysine and glutamate degradation pathways can be manipulated for

producing 5-aminovalerate, valerolactam, 6-aminocaproate, caprolactam and butyrolactam.

Figure 2. Typical workflow for implementation of systems biology based tools in

microorganisms. (A) Genomic, transcriptomic and proteomic data are pulled from databases

and analyzed. (B) Pathway prediction tools are used to compute intermediates from the

target product to the starting compound. (C) Natural enzymes that catalyze pathway

reactions are pulled from databases; if activities are low or not available, rational engineering,

directed evolution or de novo enzyme design may be applied. (D) Strain design algorithms

are applied to genome-scale metabolic models to determine a set of genetic perturbations for

achieving an engineering objective. (E) Engineered enzymes and genetic perturbations are

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applied to the host strain in vivo. (F) Several strains are screened for optimal activity using

high-throughput methods. (G) Scale-up engineering at the bioreactor level is completed for

high-performing strains.

Figure 3. Key steps of in-silico non-native pathway design. (A) Databases for known

biochemical reactions and molecules and databases for hypothetical extended reaction

network. (B) Network representation used in different pathway computation tools. In a simple

graph network, only one major substrate and one product are kept for each reaction. In a

bipartite graph, the two types of nodes represent reaction operators and intermediate

compounds, respectively. In a (directed) hypergraph, each hyper arc represents a different

reaction connecting multiple reactants and products. (C) Three types of molecular and

reaction representation incorporated in different tools, 1. BEM or bond-electron-matrix

representation of reactants and products uses a matrix to include all the bond information

between each pair of non-hydrogen atoms, 2. SMILES representation of molecular structure

and SMARTS/SMIRKS way of writing reaction rule, 3. Atom mapping tracks each atom in

the reactants. (D) Different search algorithms include breadth-first search, depth first search

(from source compounds), retrosynthesis (search starting from target compound) and double-

direction search. (E) Various pathway ranking/prioritization methods include estimating

chemical structural similarity, pathway length, enzyme docking, thermodynamic feasibility

and number of non-native reaction steps.

Figure 4. Enzyme engineering approaches. (A) Directed evolution process starts with mining

genomic metadata for suitable starting enzymes or identifying key residues for catalytic

activity in an existing starting enzyme. The next step involves creating a combinatorial

library by random mutagenesis of the gene of interest (error-prone PCR, DNA-shuffling) or

targeted mutagenesis of the residues of interest (site-directed mutagenesis, site-saturated

mutagenesis). The variants are then expressed in vivo and those with adequately robust

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growth (assayed by fluorescence-based methods) (ie. FACS), culturing in liquid medium or

on LB Agar plates, or phage display are selected for production screens using a variety of

methods of measurement, including gas and liquid chromatography, biosensors and mass

spectrometry. For combinatorial libraries generated by random mutagenesis, the variants that

pass the screen for production are iteratively mutated, selected for, and screened until those

that remain exhibit a satisfactory level of performance. For libraries obtained by targeted

mutagenesis, the enzyme structure is updated to include the most successful mutation and

may undergo targeted mutagenesis for another key residues one-by-one or simultaneously

(MAGE). (B) Biosensors take the production of the target compound as an input signal, and

output either an increased expression of a reporter gene such as GFP or an increased

expression of an antibiotic resistance gene. (C) In the “inside out” enzyme design, the

catalytic mechanism is first chosen by defining the reactants and products, after which the

transition state of the reaction is established. Then, complex calculations are performed

computationally to obtain the 3-D structure of the amino acids around the transition state to

maximize stability (the ‘theozyme’). Finally, the RosettaDesign algorithm is used to predict

the 3-D structure of amino acids around the theozyme to stabilize it by matching a protein

scaffold.

Figure 5. (A) Mixed-Integer Linear Programming methods such as OptKnock can suggest

knockout strategies that result in strong or weak growth coupling. (B) Minimal cut sets

(MCS) eliminate undesired phenotypes by incorporating a minimum product yield as a

constraint. (C) Constrained minimal cut sets (cMCS) build on minimal cut sets by

incorporating an additional minimum biomass yield as a constraint. (D) MoVE suggests

metabolic valves to switch between a growth state and a production state. (E) Metaheuristic

methods have decoupled cellular and engineering objectives. (F) Kinetic models are

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burdened by parameterization requirements which may be addressed using computational

alternatives.

Figure 6. Orthogonal production and dynamic control as alternatives to growth-coupled

production. (A) Pathways featuring a greater number of reactions shared between the biomass

or growth (native metabolism) and production branches will suffer from interference more

characteristic of growth-coupled systems. (B) Minimizing the number of common enzymatic

steps leads to a reduction in the cross-talk and an increase in orthogonality can be attained at

the organism level. Such orthogonal networks are characterized by minimal cross-talk

between growth and biosynthetic pathways. (C) Dynamic control tools such as genetic toggle

switches can reversibly divert flux through the biomass and production branches at any given

time. This is achieved by an external signal such as the presence of chemical inducer, absence

of substrate, or a change in temperature. The bistable switch allows for the minimization of

the trade-off between growth and production that usually acts as a barrier for both to be

optimal. In state 1, the chemical inducer is represented as pink molecules and it leads to the

induction of expression of the pink repressor protein as well as the pink gene. The pink

repressor blocks expression of the yellow gene and the yellow repressor protein. In state 2,

the chemical inducer is represented as yellow molecules and it leads to the induction of

expression of the yellow repressor protein and the yellow gene instead. Consequently, the

yellow repressor blocks the expression of the pink gene and the pink repressor protein.

Hence, this bistable switch alternates between states during which one of the genes is

expressed, and the other is repressed. CP refers to a common precursor shared between the

biomass and production branches of the network.

Figure 7. Non-natural redox cofactors may be used to increase the orthogonality of

biosynthetic pathways. (A) When the redox states in a biosynthetic pathway of interest are

mediated by natural redox cofactors, cross-talk will exist with the cell’s natural metabolism.

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(B) The use of non-natural redox cofactors for a biosynthetic pathway while the cellular

metabolism continues recognizing natural cofactors can reduce interference and increase the

orthogonality at the pathway level. The most popular non-natural redox cofactors used for

this purpose include nicotinamide cytosine dinucleotide (NCD) and nicotinamide flucytosine

dinucleotide (NFCD), both based on the natural backbone of nicotinamide adenine

dinucleotide (NAD), represented in (C) as nicotinamide nucleobase dinucleotide (NND).

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Highlights

Pathways to more than 60 Non-natural chemicals described

Computational tools for pathway prediction and host optimization for non-natural chemical

summarized

Enzyme discovery and engineering methods using directed evolution, biosensors and de novo

design presented

Emerging concepts in orthogonal pathway design and dynamic control discussed

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