CRISPR-Cas9: Role in Processing of Modular Metabolic Engineered Bio-Based Products

*Vishnu Sharma, Tarun Kumar Kumawat, Anjali Pandit, Bhoomika Sharma, Pooja Agarwal, Bhawana Sharma, Preeti Nain and Manish Biyani*

#### **Abstract**

Biogenetic engineering is a significant technology to sensibly manage microbial metabolic product factories. Genome modification methods for efficiently controlling and modifying genes at the genome level have progressed in biogenetic engineering during the last decade. CRISPR is genome editing technology that allows for the modification of organisms' genomes. CRISPR and its related RNA-guided endonuclease are versatile advanced immune system frameworks for defending against foreign DNA and RNAs. CRISPR is efficient, accessible, and trustworthy genomic modification tool in unparalleled resolution. At present, CRISPR-Cas9 method is expanded to industrially manipulate cells. Metabolically modified organisms are quickly becoming interested in the production of different bio-based components. Here, chapter explore about the control productivity of targeted biomolecules in divergent cells based on the use of different CRISPR-related Cas9.

**Keywords:** Biogenetic engineering, CRISPR, Endonuclease, Metabolic biomolecules

#### **1. Introduction**

The manufacture of biobased metabolic products by microbial production lines offers a viable path to a continuous future. At present, numerous bacterial strains have largely been employed to producediverse variety of metabolites that are useful for diverse industries including food and pharma [1, 2]. To increase the yield of metabolic products, genome editing is widely used. Genome modification is a form of genetic manipulation in which single bases of DNA are manipulated by adding, removing, or altering the genome of bacteria [3]. Despite it, most bacterial strains still face difficulties in genetic modification that is key impediment to metabolic engineering. Conventionally the zinc finger nucleases and transcription-activator like effector nucleases have been adopted for bacterial genetic modification [4, 5]. Both genetic modifications revolve around the principle of DNA-protein recognition [6].

ZFNs owned by SangamoBioSciences is one of the oldest gene-editing technologies established in the 1990s [7]. ZFNs are the engineered proteins that bind to the desired DNA. These proteins have two domains, the first one is a manufactured

zinc-finger DNA binding domain and other is a DNA cleaving domain [8]. A basic zinc finger device has series of 4–6 binding modules. A codon is recognized by every unit [7]. Both domains are linked together via a chain of linker sequences. The DNA sequence of 24 bp is the first domain and other domain cleaves the recognized sequence in 5–7 bp spacer regions with the help of a restriction enzymeFokI [8]. FokI nucleases are type II's restriction enzymes that cause single-stranded breaks in a double-helical DNA strand. ZFN was withdrawn due to shortcomings such as the time-consuming and costly production of target enzymes, poor specificity, and elevated off-target variations, which were gradually overcome by the technological innovation [2, 7].

TALEN is another oldest gene-editing technology that was discovered as a replacement for ZFNs. It is made up of extremely repetitive DNA sequences that promote in-vivo homologous recombination. TALENs, like ZFNs, have two domains: N-terminal transcription activator-like effector (TALE) DNA-binding domain and C-terminal restriction endonuclease FokI catalytic domain [2, 8]. Both type of gene-editing are similar in having two sequence-specific DNA-binding proteins (two zinc-finger domains/TALEN domains) adjoining a target sequence, with the C-terminal of zinc-finger domain/TALEN domain being accompanied by a FokI enzyme, which cuts the target DNA in the form of a dimer [7, 9].

These methods, however, are hampered by the need to build a new nuclease pair for each genomic target. Both are also unable to target several genes at the same time. Therefore, due to complexity in designing, processing, and verifying the molecular requirements for nuclease expression and its targeting, both ZFNs and TALENs are escaped [10]. The CRISPR/Cas systems for genome editing are a novel technique that allows for the simultaneous targeting of numerous genes for the synthesis of superior strains.

#### **2. CRISPR/Cas gene structure**

The concept of CRISPR was introduced in 1987, whilst Japanese scientist Ishino and team were working on the *iap* gene in *Escherichia coli*. Entire gene encodes an alkaline phosphatase in *Escherichia coli*. They discovered repeated DNAs in bacterial genome that is not like other regular sequences [2, 11]. These re-occurring DNA sequences might be the components of recurring DNA sequences known as "Regularly clustered short palindrome repetitions" (CRISPR). Structurally, small repetitions of DNA are followed in CRISPR Systems by short spacer segments of genome, which are obtained via the standard bacterial path to a bacteriophage or plasmid. These repetitions are also related to nucleases or helicases in which particular DNA sequences are separated or unwinded [9, 12].

CRISPR is a bacterial and archaeal defense mechanism that works in hybrid with CRISPR-associated proteins. These were first discovered inside microorganisms DNA, but were subsequently extended to provide adaptive immune system for microorganisms [13]. CRISPR/Cas systems are easily adapted for genome modification because to their great practicality, comparative simplicity, and robustness [14]. CRISPR/Cas sequences are constituted of two or maybe more direct, often partially palindromatic, or frequently accurate repetitions (25–35 bp), which are separated into one or maybe more operon modules by single spacers (typically 30–40 bp) and an adjacent multiple-case cluster [15].

In genome editing, CRISPR works with a double-strand DNA cleavage at the particular target site near gRNA [16–18]. In process, CRISPR follows three separate but often interrelated stages: (i) acclimatization, (ii) pre-crRNA (pre-CRISPR RNA) expression and processing, and (iii) interference. The Cas protein complex

*CRISPR-Cas9: Role in Processing of Modular Metabolic Engineered Bio-Based Products DOI: http://dx.doi.org/10.5772/intechopen.99379*

**Figure 1.**

*Overview of CRISPR-Cas9 and recent developments in CRISPR/Cas9 genome editing.*

attaches to a intended DNA molecule during the acclimatization stage and generally inserts two double-strand (ds) breakages into the target DNA, after a clear, short (2–4 bp) pattern, known as PAM(Protospacer-adjacent motif). The released fragment is subsequently transferred into the proximal repeat units of the CRISPR assortment. It is then fixed by cellular repair machinery, resulting in proximal repeat duplication [19–21].

Later, the CRISPR array is transcribed into a single long transcript by the expressive processing stage. The transcribed transcript is recognized as pre-crRNA, which is used for producing mature crRNAs using a distinct complex of proteins from Cas, a dedicated processing nuclease (Cas6), a single large Cas protein, or an external foundation. In the end, at interference stage, cRNA is utilized to detect protospacer that stay attached to the gRNA and then cleaved or inactivated by Cas nuclease [15]. The double-stranded cleavage partakes in DNA repair by essential cellular mechanisms. Usually, it entails non-homologous end-joining module and sometimes homology-directed repair [22, 23] (**Figure 1**). In between, the Cas9 is activated via forming single guide RNA molecule and triggers double-stranded cleavage at DNA target [24].

#### **3. CRISPR classification**

CRISPR-Cas systems display extraordinary diversity, including in core genes yielded by multiple CRISPR-Cas variations, gene structure, genomically locus architecture, and the original sequences [25–27]. The current CRISPR-Cas hierarchy contains three primary kinds (I, II and III), the less prevalent, but distinct, Type IV, V & VI on the basis of diversification of Cas genes (**Figure 2**) [28, 29]. Type I has the characteristic gene Cas3 that expresses the large protein with a helicase to unwind DNA–DNA and RNA–DNA duplexes. Sometimes the domain of helicases combines with an HD domain (conserved protein region with histidine (H) and/or

#### **Figure 2.**

*The arrangement of several types of CRISPR-Cas systems.*

aspartate (D) amino acid residues) and reveals endonuclease activity to make cleavage of the target DNA [28, 30, 31].

The type I Cas system contains the Cas1, Cas2, Cas5, Cas7, and Cas6 transcripts. Cas1 and Cas2 are genes encoding the cascade complex's components (which include big and small subunits). The Cas5, Cas7, and Cas6 loci are involved in the processing of pre-crRNA transcripts. Cas system type I is categorized into six sub-genotypes: I-A through I-F, each having its own unique gene and functioning organisms. In I-F complexes, Cas3 is also linked to Cas2 gene. In contrast to other variants, I-E and I-F are deficient in the Cas4 gene [32, 33].

The CRISPR-Type II contains the Cas9 gene, which codifies and controls the cascade complexes' functions through a multidomain protein [17]. Six domains make up Cas9 protein: REC I & II, Bridge Helix, PAM Associating, HNH, and RuvC [34, 35]. Rec-I is indeed the primary subunit responsible for RNA binding. The purpose of the REC II section is unknown presently. The arginine-rich coupled helix is the area that initiates cleavage when target DNA is bound [36]. The PAM-Interacting region aids in the definition of the PAM specificity necessary for target-DNA binding. The HNH and RuvC regions are nuclease areas that catalyze single-stranded DNA cleavage (**Figure 1**) [35]. The type-II CRISPR-Cas system has three sub - types: II-A, II-B, and II-C [37, 38]. Additionally, the type II-A system has the csn2 signature gene. Although this Csn2 gene has an unknown function, it produces tetrameric rings that interact with double-stranded Genetic material through center opening [39]. Because the type II-B Cas system lacks the csn2 gene, it retains a distinct Cas4 gene. The protein produced by this distinct gene functions as a 5′ DNA exonuclease [28]. Similarly, the type II-C Cas system contains the Cas1, Cas2, and Cas9 protein-coding genes. Cas type II have been widely embraced as a powerful tool for genomic editing [40].

The CRISPR-III systems possess Cas10 as main gene and encode a palm domainlike multi-domain protein related to that employed in PolBcyclases and polymerases. Cas10 is usually fused into an HD (histidine-aspartate) family nuclease region unique from CRISPR-Cas type I HD domains [41].

When encoding Cas1 and Cas2, CRISPR-III systems utilize crRNAs supplied by the Cas array linked to either a type I or type II Cas system. This system is classified into III-A through III-D subtypes. Csm, Cas1, Cas2, and Cas6 proteins are present in III-A type. Csm is a crRNA-guided enzyme that also acts as a DNase or occasionally as cyclic oligoadenylate kinase. Only Cmr proteins are present in the III-B pathway, which is lacking of Cas1, 2, and 6 loci. According to proximity sequence of crRNA,

#### *CRISPR-Cas9: Role in Processing of Modular Metabolic Engineered Bio-Based Products DOI: http://dx.doi.org/10.5772/intechopen.99379*

Cmr identifies and degrades nucleic acids. A cyclase-inactivated Cas10 protein is discovered in the III-C type. Type III-C includes an inactivated cyclase domain Cas10 protein, while type III- includes an uncharacterized functional gene [42–44].

The CRISPR-Cas type-IV systems exist with plasmid genome of numerous bacteria. It lacks both Cas1 and Cas2, and not typically linked to CRISPR arrays andhas a high-decrease effector complex (CSF1). The CSF1 consist of csf1 (highly reduced subunit), Receptor activity-modifying protein encoding genes belonging to Cas5 (csf3) and Cas7 (csf2) family [28]. Although all CRISPR/Cas systems have certain functionality, Type II CRISPR/Cas is frequently adopted system that establishonly on Cas9 protein for the silencing of DNAi genes [45]. Cas9 protein is a large protein, involved in nucleic acid cleavage, with molecular weight of ~158 kDa. It has combine structure consisting of α-helical recognition and nuclease lobes [46].

The recognition lobe is made up of extended helix, REC1 and REC2 regions. Thenuclease region is generated of RuvC, HNH, and PAM-interacting (PI) C-terminal domain (CTD) [35, 47]. RuvC is named after the RuvC segment of *E. coli*, which decides formation of Holliday junctions [23]. In structure, protein motifs associates with spacer precursors or protospacers from the DNA of an attacking bacteriophage. These proto-spacer adjacent motifs are widely known as PAMs [9]. The crRNA and tracrRNA can be combined into guide-RNA, which enables the engagement of Cas9, which is necessary for double-stranded DNA cleavage [6].

The V-CRISPR-Cas12 system was designed for external genome editing applications such as gene expression suppression or activation, epigenome editing, in-situ genomic imaging, and large-scale genome screening [38, 47, 48]. CRISPR-Cas13 type VI is a tool for various RNA handling in the context of RNA interference (RNAi), in-vivo RNA visualization, and nucleic acid detection [49, 50].

#### **4. Modern achievements in CRISPR-Cas9 mediated system**

CRISPR/Cas9 technology has enabled a qualitative change in the range of gene functions for transcriptional control, gene targeting, epigenetic correction, gene therapy, and drug delivery of host genomes [51]. CRISPR/Cas technology possesses multiloci genome editing without the integration of a gene marker on the selection genome and saves time and exertion in metabolic engineering. Although several genetic modifications are available; the CRISPR/Cas9 technology significantly enhanced the efficiency of genetic engineering and is adopted as an extraordinary "gift." The CRISPR/Cas9 technology improved industrial micro-organisms' performance in strengthening of microbial factories that are valuable in processing of new value-added molecules from the low-cost feedstock.

There are abundant examples of bacteria, yeasts, and filamentous fungi which are reviewed in several studies of solicitations of the CRISPR/Cas9 system [51–57]. For example, *E. coli*, *S. cerevisiae*, *Bacillus sp.*, *Clostridium sp.*, *Corynebacterium sp.*, *Lactobacillus sp.*, *Mycobacterium sp.*, *Pseudomonas sp.*, *Streptomyces sp.* etc. [52, 58–69] are employed in the CRISPR/Cas system to improve yield of various metabolic products in field of industrial biotechnology. As a proof of concept, Zheng et al. employed Type I-F system to engineer *Zymomonas mobilis* as a synthetic chassis for sustainable economic biofuel and biochemical productions [70].

A study was designed to distinguish the orthogonal CRISPR method using *E. coli* for chromosomal addition of the Spd-Cas9based CRISPR module. Here found that out of SaCas9, St1Cas9, and FnCas12a, the St1Cas9 and SaCas9 are highly efficient to cause double stranded DNA break without associating with the sgRNA. This characteristic renders St1Cas9 into the *E. coli* chromosome as a hopeful Cas9 ortholog to combine whole or inadequate modules for succinate productionwith



*CRISPR-Cas9: Role in Processing of Modular Metabolic Engineered Bio-Based Products DOI: http://dx.doi.org/10.5772/intechopen.99379*

#### **Table 1.**

*CRISPR biotechnology applications in production of variable metabolic product.*

178% improvement. It also efficiently hinders production of byproducts including lactate, formate, and ethanol [71].

Another research sought to increase CRISPR/Cas9 expression in methylotrophic fungus *Pichia pastoris*. Numerous genomic areas, including the Cas9 DNA sequence, gRNA regions, RNA synthetase II & III promoters, have been thoroughly examined and shown to have near-perfect targeting efficiency. Additionally, the altered strain was shown to be able to fulfill future requirements in synthetic biology, biotechnology, and metabolic pathway engineering. Zhang et al. focused on the soya bean plant's competing metabolic pathways for isoflavone production. Through the use of CRISPR/Cas9-mediated multiplex gene editing, the GmF3H1, GmF3H2, and GmFNSII-1 genes were deleted from the genistein competing route in this research [72].

Yang et al. utilized the RNP-based CRISPR–Cas9 technology to modify the genome of *Aspergillus niger* to increase succinic acid synthesis in CRISPR modified metabolic products. The desired strain was changed in this research by interrupting genes responsible to synthesize gluconic acid and oxalic acid. Indeed the C4-dicarboxylate transporter and the NADH-dependent fumarate reductase were overexpressed in this manner. The resultant strain generated 17 g/L succinic acid, while the wild-type strain grown on a synthetic substrate produced none [73].

Generally, genome modification in Schizosaccharomyces pombe is more complex than in *S. cerevisiae* owing to the reduced effectiveness of foreign DNA adjunction by homologous recombination [74]. As a result, Ozaki et al. modified the *S. pombe* strain using the CRISPR-Cas9 system and synthesized D-lactic acid from both glucose and cellobiose. The active genes for pyruvate decarboxylases, dehydrogenase, and glycerol-3-phosphate dehydrogenase were deleted in this research, and the D-lactate dehydrogenase gene from *Lactobacillus plantarum* was incorporated into the *S. pombegenome* [75]. The applications of CRISPR biotechnology to specified host species are outlined below in order to generate varied metabolic products. (**Table 1**).

#### **5. Challenges in CRISPR/Cas9 applications**

CRISPR/Cas9 provides tremendous genome-control capabilities, but there are still numerous obstacles to be overcome. The lack of a reliable DNA repair is the most significant of the difficulties associated with CRISPR/Cas technology, according to the researchers. As a consequence, numerous researches are increasing the CRISPR mechanisms, with the gene-editing technique likely to continual evolution for the foreseeable future. Similarly, lack of related techniques for creating single guide RNA is a distinct impediment. Limited methods for combining CRISPR/Cas9 with other genome-editing technologies, Cas9 endonuclease toxicity, off-target effects, the incidence of undesired mutations, and ethical issues are among the remaining issues. To counter these limitations, researchers have attempted to create and access various base editing approaches [91]. Besides, human genome has only one-sixteenth PAM sites, restrict the number of gene targetable sequences. So, novel Cas9 varieties are required to search and increase PAM interaction in the new experiments.

#### **6. Future perspective**

The future of new genetic mutations engineering should be to enhance the effectiveness of imminent models by joining innovative characteristics. In comparison to conventional genome editing systems, the CRISPR/Cas9 approach has provided rapid multiple genome sites editing of industrial strains at a time. Future models of CRISPR-Cas9 not only enable us to predict the success of editing but also the outcome. In this respect, the integration of droplet-based micro fluidics with

*CRISPR-Cas9: Role in Processing of Modular Metabolic Engineered Bio-Based Products DOI: http://dx.doi.org/10.5772/intechopen.99379*

CRISPR/Cas9 could begin breakthroughs in modern biology. However, researchers can extract particular DNA segments but through micro homology can delete specific DNA segments and control CRISPR-Cas9 results. This approach enables to take advantage of the micro homology-mediated repair mechanism. These features will combine into both on- and off-target activity predictions for an optimal projected pipeline of CRISPR, where a Cas9 fusion protein will modify one target sequence into another without cleavage.

#### **7. Conclusions**

The CRISPR/Cas9 executes genome engineering technology feasible for utilization in many fields. The multiple genes targeting in a genome by CRISPR technology allows the learning of synergistic outcomes via the suppression of essential genes. Additionally, this approach sheds new light on design of many metaboliteproducing microorganisms/bioreactors used in industrial biotechnology. However, certain drawbacks endure the potential uses of CRISPR-Cas systems. Conversely, the development of CRISPR-edited products and services faces sociopolitical obstacles, public acceptability, and government regulations. We must be stay update on the challenges by adding new features to improve CRISPR/Cas9 accuracy. We can anticipate that a lot of researchers from many fields concentrating their efforts towards this system will resolve the integrated limitations so that CRISPR will work its way into the emerging culture.

#### **Author details**

Vishnu Sharma1 \*, Tarun Kumar Kumawat1 , Anjali Pandit1 , Bhoomika Sharma1 , Pooja Agarwal1 , Bhawana Sharma1 , Preeti Nain1 and Manish Biyani1,2

1 Department of Biotechnology, Biyani Girls College, Jaipur, Rajasthan, India

2 Department of Bioscience and Biotechnology, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

\*Address all correspondence to: vishnusharma666@yahoo.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 5**

## Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research

*Kevin S. Farquhar, Michael Tyler Guinn, Gábor Balázsi and Daniel A. Charlebois*

#### **Abstract**

Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers. Mathematical modeling guides the rational design of synthetic gene circuits. These systems are then assembled into unified constructs from existing and/or modified genetic components from a range of organisms. In this chapter, we describe modeling tools for the design and characterization of chemical- and light-inducible synthetic gene circuits in different organisms and highlight how synthetic gene circuits are advancing biomedical research. Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.

**Keywords:** antimicrobial resistance, synthetic gene circuits, mathematical models, optogenetics, cancer

#### **1. Introduction**

A primary goal of synthetic biology is to rationally design and engineer synthetic gene circuits as tools to advance basic research [1, 2], optimize the production of chemicals or biofuels [3, 4], build biocomputational systems [5], and enhance clinical therapeutics [6]. Control of synthetic gene circuits at the transcriptional level (transcription is the process of transcribing mRNA from a DNA template) has been demonstrated through chemical- and light-based stimuli [7, 8]. The transcriptional network architecture (how genes are connected to and regulate each other through transcription factor proteins) affects the properties of gene expression, in terms of average expression levels as well as the degree of expression variability inside a single cell or across a cell population [9]. Throughout this chapter, we will use the term "synthetic gene circuits" to describe synthetic systems and the term "gene networks" to described natural systems.

Fluctuations in the biochemical processes of transcription and translation (translation is the process of translating amino acid-based proteins from a nucleotide-based mRNA template) are referred to as gene expression noise [10]. Gene expression noise leads to heterogeneity among genetically identical cells in the same environment and can affect the survival of microorganisms [11]. For instance, gene expression noise has been shown to promote drug resistance in microbes [12]. Similarly, gene expression noise is thought to play an important role in tumorigenesis and the development of resistance during cancer chemotherapy [13]. Mathematical models and synthetic gene circuits have established that the architecture of the gene network modulates gene expression noise [14].

Electronic circuits inspired the development of synthetic gene circuits, with mathematical representations of natural and synthetic networks successfully predicting their effects on gene expression [15]. A milestone study in *E. coli* demonstrated that negative feedback stabilizes the gene circuit's response to expression fluctuations [16]. The first synthetic toggle switch circuit in *E. coli* mimicked the electronic version and served as a simplified version of the naturally occurring bacteriophage lambda switch [17, 18]. The construction of a synthetic biological clock in *E. coli* permitted oscillations in gene expression to be tuned to a particular frequency [19]. By mimicking natural gene networks, synthetic gene circuits generate insights on how complex biological systems work by breaking down natural networks into their components, which is highly beneficial in basic biomedical research [20].

Optogenetics is the control of cellular components using electromagnetic radiation. Like other synthetic systems, optogenetic components can be engineered into gene circuits to precisely control cellular processes such as gene expression or protein activity in prokaryotic and eukaryotic cells; the performance of optogenetic gene circuits can be optimized in an iterative model-experiment cycle. However, unlike previous gene circuits, optogenetics offers the ability to control gene expression at a single-cell resolution. The fast temporal and single-cell spatial resolutions that light provides as a stimulus for gene circuits is unmatched; chemical stimulus regulates transcription on longer timescales and at a cell-population level. Like their gene circuit predecessors, optogenetic gene circuits can be used to control functional proteins. Optogenetic tools are especially suited to investigate gene function at the single-cell level. For instance, researchers can take a gene of interest, such as KRAS which is often found mutated in cancers [21], and integrate it into an optogenetic gene circuit to explore the transcriptional and translational effects on cellular phenotypes by stimulating individual cells with visible light. It is worth noting that although in this chapter we focus on optogenetic applications involving visible light, some optogenetic tools have been developed using other regions of the electromagnetic spectrum, including near-infrared [22] and UV [23] radiation.

This chapter describes the construction and characterization of synthetic gene circuits in yeast and mammalian cells (Section 2) and optogenetic gene circuits in mammalian cells (Section 3) with various transcriptional network architectures, along with their applications in biomedical research. The mathematical approaches to model synthetic and optogenetic gene circuits are also discussed.

#### **2. Synthetic gene circuits**

#### **2.1 Positive feedback gene circuits in yeast**

A positive feedback synthetic gene circuit was first constructed in yeast to convert a continuous gradient of a constitutively expressed transcriptional activator into a cell phenotype switch, resembling analog to digital signal conversion [24]. Subsequently, a positive feedback (PF) gene circuit was genomically integrated into *Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

#### **Figure 1.**

*(A) Schematic of the positive feedback (PF) synthetic gene circuit in yeast (top left). The regulator rtTA is toxic when active, but the gene circuit prevents Zeocin toxicity by activating the ZeoR gene (top right). The role of cellular memory in optimizing fitness is shown in the bottom panel. (B-E) dose responses. (B) Mean expression levels of the PF gene circuit and of the cell sorted low- and high-expression states. (C) Gene expression noise as determined from the coefficient of variation (CV). (D) Subpopulation ratio of low-expressing cells to highexpressing cells. (E) Steady-state gene expression distributions at a single-cell resolution. From Figure 2 "characterization of gene expression in cells bearing PF circuit" by Nevozhay, D. and Adams, R. et al. in [25] located at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002480 under a CC BY 4.0 license with panel label font modified.*

the budding yeast *Saccharomyces cerevisiae* to investigate how cell population fitness (growth rate) and subpopulations emerge from the molecular-level kinetics of gene networks and single-cell division rates [25]. Synthetic gene circuits with positive feedback and cooperativity can display bistability, where cells switch between two gene expression states with a cellular "memory" that corresponds to the temporal maintenance of each state [26, 27].

In the PF gene circuit, the regulator reverse tetracycline-controlled trans-activator (rtTA) binds to its own promoter in the presence of tetracyclines (**Figure 1A**, top left) [28]. The genetic engineering approaches mirrored the assembly of a negative feedback (NF) circuit in yeast [29]. Unlike the NF circuit, toxicity exists after activating the regulator gene rtTA, which sequesters general transcription factors from vital cellular processes [30]. Additionally, the construct controlled the drug resistance gene ZeoR, which confers resistance to the antibiotic Zeocin (**Figure 1A**, top right).

The PF gene circuit exhibits a sigmoidal gene expression dose response at the population level (**Figure 1B**). The expression dose–responses for low- and highexpressing subpopulations were determined using a bimodality detection algorithm [28]. The gene expression noise level peaked at an intermediate inducer (anhydrotetracycline or ATc) concentration (**Figure 1C**). The shift in the gene expression distribution peaks over increasing inducer levels is reflected by the subpopulation ratio changes (**Figure 1D**). The gene expression distributions for the ATc dose response display bimodality (**Figure 1E**).

Testing multiple levels of inducer and drug determined a "fitness landscape" that quantitatively mapped population growth rates to unique combinations of ATc and Zeocin concentrations. The yeast PF system demonstrated the need to incorporate the cellular memory associated with gene expression states (**Figure 1A**, bottom) to computationally predict the fitness landscape [31]; the growth rate under Zeocin treatment was the highest at the minimal level of induction that lead to bimodal expression [28]. These computational predictions guided laboratory experiments to identify environmental conditions that defined a "sweet spot" of drug resistance, which balanced the costs of expressing rtTA with benefits of expressing ZeoR.

The yeast PF synthetic gene circuit was subsequently used to study evolutionary dynamics under various levels of induction and drug treatment [32]. This microbial evolution experiment included conditions at one edge or both edges (saturating molecular levels or none) of the fitness landscape, as well as at intermediate levels of induction with or without Zeocin. The evolved populations were frozen at specific time points and subsequently reestablished to test in various conditions compared to ancestral cells. Additionally, the evolution experiments were combined with simulations to predict the types of mutations that could arise under induction and drug treatment [32].

Full induction without drug treatment led to breakdown of the regulator rtTA over time through full and partial knock-out mutations, which improved fitness without Zeocin [32]. Yet, during follow-up evolution experiments in high induction with Zeocin partial rtTA knockout mutants regained function [31]. Full Zeocin treatment without induction eventually established populations with higher expression, potentially through mutations in the drug resistance gene and promoter linked with extra-circuit mutations. High induction with Zeocin led to accumulated mutations in rtTA, possibly lowering the effectiveness of the regulator as shown by decreased inducer sensitivity [32]. After intermediate induction with Zeocin, experiments on evolved populations during reintroduction of drug under intermediate induction uncovered the two expression distribution peaks shifting towards each other leading to a single fitness peak; this highlighted the role of noise in driving evolution through the trade-off between rtTA toxicity and drug resistance.

*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

#### **Figure 2.**

*(a) Natural pleiotropic drug resistance (PDR) network in yeast. (b) Synthetic PDR gene circuit. (c) Core PDR network architecture. From Figure 1 "network schematics" by Brendan Camellato et al. in [27] located at https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/enb.2019.0009 under a CC BY-NC-ND 3.0 license.*

#### **2.2 Experiments and computational models of positive feedback and feedforward circuits in yeast**

Along with positive feedback and negative feedback circuit architectures, feedforward loop (FFL) architectures (a three-gene network composed of two input transcription factors, one of which regulates the other, both jointly regulating a target gene) may have evolved in natural gene regulatory networks to enhance fitness [26, 33]. In *S. cerevisiae*, the pleiotropic drug resistance (PDR) network contains a positive feedback loop embedded in a feedforward loop (FFL + PF) (**Figure 2a**) [34]. The PDR network provides multidrug resistance through an ABC transporter pump protein encoded by the PDR5 gene. A similar FFL + PF network may enhance drug resistance in human cancer cells [35].

Mathematical models of gene regulatory networks can predict biological responses, which is essential to optimally design synthetic gene circuits and to guide experiments. A minimal model of the PDR network found that the positive feedback and feedforward loop architectures sustain transcription and can stabilize expression of the network when the drug is transient or fluctuating [33]. The minimal model also predicted increased gene expression noise (in terms of increased noise magnitude and longer cellular memory timescales) in the FFL and FFL + PF networks. Overall, the FFL and FFL + PF network architectures were found to enhance drug resistance *in silico*.

The minimal model of the PDR network was described by the following system of coupled ordinary differential equations (ODEs) [33]:

$$\frac{dPDR3}{dt} = a\_{PDR3} a\_1 f\_{PDR3} (PDR1, PDR3) - PDR3 \tag{1}$$

$$\frac{dPDR5}{dt} = a\_{PDR5} f\_{PDR5} (PDR1, PDR3) - PDR5$$

where PDR1 was treated as an adjustable parameter. *αPDR*<sup>3</sup> and *αPDR*<sup>5</sup> are the maximum levels of activated protein production for the variables PDR3 and PDR5, respectively. The Boolean parameter *ω*<sup>1</sup> describes the activation of PDR3 by PDR1. Here the dilution and degradation rates of PDR3 and PDR5 were set to unity. The functions that describe how PDR3 and PDR5 are regulated are given by:

$$f\_{PDR3}(PDR1, PDR3) = \frac{(PDR1 + \alpha\_2 PDR3)^n}{K^n + (PDR1 + \alpha\_2 PDR3)^n} \tag{2}$$

$$f\_{PDR5}(PDR1, PDR3) = \frac{(PDR1 + PDR3)^n}{K^n + (PDR1 + PDR3)^n}$$

where, *n* and *K* are the Hill coefficient and half-maximal activation parameter, respectively. The Boolean parameter *ω*<sup>2</sup> describes the presence or absence of positive feedback regulation on PDR3. The minimal model and a more comprehensive model (presented below) were translated into biochemical reactions that were simulated using the Gillespie stochastic simulation algorithm [36, 37].

A more comprehensive model, known as the PDR5 transcriptional network model, incorporated the dynamics of the PDR5 efflux protein pump and the negative feedback produced when PDR5 eliminates the drug from the cell [33]. The PDR5 transcriptional network model can be described by the following system of coupled ODEs:

$$\frac{dPDR1}{dt} = a\_0 + a\_{PDR1} \frac{D\_{int}}{K\_{PDR1} + D\_{int}} - \delta\_{PDR1} PDR1$$

$$\frac{dPDR3}{dt} = a\_{PDR3} \frac{(PDR1 + PDR3)^{\text{wpR3}}}{K\_{PDR3}^{\text{wpR3}} + (PDR1 + PDR3)^{\text{wpR3}}} - \delta\_{PDR3} PDR3 \tag{3}$$

$$\frac{dPDR5}{dt} = a\_{PDR5} \frac{(PDR1 + PDR3)^{\text{wpR5}}}{K\_{PDR5}^{\text{wpR5}} + (PDR1 + PDR3)^{\text{wpR5}}} - \delta\_{PDR5} PDR5$$

$$\frac{dD\_{int}}{dt} = k\_{dif} (D\_{ext} - D\_{int}) - k\_{pump} PDR5 \left(\frac{D\_{int}}{K\_{int} + D\_{int}}\right)$$

where PDR1 and the intracellular drug concentration (*Dint*) were incorporated as variables. *α*<sup>0</sup> is the basal rate of PDR1 transcription, *Dext* the extracellular drug concentration, *kdiff* the rate of passive diffusion of the drug across the cellular membrane, *kint* half-maximum saturation coefficient for the PDR5 efflux pump, and *kpump* the efflux rate of the drug via PDR5 efflux pump. It was assumed that drug entry and exit from the cells occurred through a combination of passive and active transport, and that the activation of PDR1 by the drug can be described by Michaelis–Menten kinetics. The PDR5 transcriptional network model predicted that PDR5 expression level would increase after application of the drug and that cell population fitness would oscillate before stabilizing during drug treatment.

To confirm the predictions from the PDR5 transcriptional network model, a synthetic gene circuit (**Figure 2b**) was constructed with molecular cloning techniques [38] and integrated into a yeast strain without a native PDR5 gene [27]. The construction technique ran multiple overlap PCR steps, where two fragments with overlapping regions on their ends were amplified together initially without primers, using the overlapping sequence as a *de facto* primer. Homologous recombination facilitated the integration of the synthetic gene circuit into the yeast genome. This synthetic PDR gene circuit has rtTA activating its own expression through a tetracycline-inducible promoter (**Figure 2b**), mimicking the positive feedback

*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

activation of PDR3 (**Figure 2a**). The Doxycycline inducible promoter regulating rtTA expression is also controlled by a *β*-estradiol inducible GEV regulator, representing PDR1, which also activates the PDR5 gene in this synthetic gene circuit [27], completing the core PDR network architecture (**Figure 2c**) [34].

The PDR synthetic gene circuit was experimentally compared to simplified PDR circuit components, like direct activation (DA: PDR1 directly activates PDR5), cascade (CAS: PDR1 activates PDR5 through PDR3), a cascade with positive feedback loop (CAS + PF: PDR1 activates PDR5 through PDR3, with PDR3 activating its own expression), and a feedforward loop (FFL: PDR1 activates PDR5 directly as well as indirectly through PDR3) [27]. The FFL + PF circuit represented the PDR network. This separation of components tested the effect of specific network motifs in drug resistance and gene expression compared to the full PDR synthetic circuit. Direct activation in the FFL circuit was found experimentally to increase the speed of expression changes compared to indirect activation of PDR5. Direct activation was required for PDR5-mediated drug resistance. Indirect activation enhanced drug resistance, which provided evidence that the delayed reduction in PDR5 expression protected cells from the toxic effects of drug (Cycloheximide) exposure (**Figure 3a**). **Figure 3b** shows that strains carrying a gene circuit with a positive feedback loop grew faster in the presence of drug compared to strains in which this circuit architecture was missing. This provided evidence that the positive feedback regulation of PDR5 expression protects cells from drug exposure. Finally, reducing the strength of positive feedback regulation through mutation decreased drug resistance (**Figure 3c**).

Overall, the PDR network was recapitulated in mathematical and synthetic gene circuit models that demonstrated that the network architecture is optimized for drug resistance, with gene expression noise making important contributions to fitness during drug treatment.

#### **2.3 Experiments and computational models of mammalian negative and positive feedback gene circuits**

The yeast NF gene circuit was transferred to mammalian cells to test an organism transfer workflow using design and optimization cycles aided by computational modeling [40]. This workflow led to updates to the yeast NF gene circuit, including the optimization of the tet promoter operator site locations, the introduction of an intron upstream of the regulator, codon optimization for mammalian translation,

#### **Figure 3.**

*(a) Genetically engineered yeast strains with direct activation (DA) and indirect activation networks (FFL and CAS). (b) Yeast strains with indirect activation alone (CAS) or combined with positive feedback (CAS + PFL). (c) Yeast strains with feedforward activation alone (FFL) or combined with positive feedback (FFL +PFL) or mutated positive feedback (FFL+PFLm). Plates contain 5 μg/ml doxycycline (inducer), 0.025 (a) or 0.05 μg/ml (b, c) Cycloheximide (drug) and no β-estradiol (inducer). Gray triangles represent decreasing density of the spotted cell culture, 1:10 serial dilutions from OD600 0.1 to 1 x 10–3. From Figure 6 "indirect activation and positive feedback enhance drug resistance in spot assay experiment" by Brendan Camellato et al. in [27] located at https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/enb.2019.0009 under a CC BY-NC-ND 3.0 license.*

and the addition of a Kozak sequence near the start codon, which was stably but randomly transfected into MCF-7 breast cancer cells. This NF circuit in mammalian cells exhibited a linear dose response with low gene expression noise, similar to the NF circuit in yeast cells [29]. Though the adaptability of the yeast NF circuit to mammalian cells did not require any additional design features, optimization of parts responsible for gene expression and protein location was required to replicate the features of the yeast NF circuit. These results support the "abstraction principle" in the field of synthetic biology, namely that different parts of a biological network can be optimized for improved functionality in new settings, while leaving the original network design intact [40].

In a subsequent study, the yeast PF circuit was transferred to mammalian cells, which was coupled with a Flp-recombinase site-specific integration system (**Figure 4a**) [39]. This mammalian positive feedback (mPF) circuit displayed a sigmoidal mean gene expression dose response (**Figure 4b**). Gene expression noise increased at intermediate inducer (Doxycycline) levels (**Figure 4c**), with broad unimodal gene expression distributions (**Figure 4d**). A lack of bimodality is unexpected for an induced bistable circuit and may have been attributed to similar cellular growth and cellular memory time scales.

The previously reported mammalian negative feedback circuit was also connected to the Flp-In integration system in Chinese Hamster Ovary (CHO) cells (**Figure 5a**) [40]. This negative feedback circuit was separately integrated into the same genomic site as the mPF circuit and subsequently called the mammalian negative feedback (mNF) circuit. The mNF circuit displayed a linear dose response in mean gene expression (**Figure 5b**). Gene expression noise was low across all inducer (Doxycycline) levels (**Figure 5c**) with narrow gene expression distributions (**Figure 5d**).

#### **Figure 4.**

*(a) Schematic of the mammalian positive feedback (mPF) synthetic gene network. (b-d) dose responses. (b) Mean gene expression. (c) Gene expression noise determined from the coefficient of variation (CV). (d) Single-cell gene expression distributions. From Figure 2 "dose–response of the mPF-PuroR gene circuit" by Farquhar, K.S. et al. in [39] located at https://www.nature.com/articles/s41467-019-10330-w under a CC BY 4.0 license.*

*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

**Figure 5.**

*(a) Schematic of the mammalian negative feedback (mNF) synthetic gene network. (b-d) dose responses. (b) Mean gene expression. (c) Gene expression noise determined from the coefficient of variation (CV). (d) Singlecell gene expression distributions. From Figure 3 "dose–response of the mNF-PuroR gene circuit" by Farquhar, K.S. et al. in [39] located at https://www.nature.com/articles/s41467-019-10330-w under a CC BY 4.0 license.*

The mPF and mNF gene circuits controlled the EGFP fluorescent protein and the PuroR drug resistance gene each separated by self-cleaving 2A motifs [39, 40]. The, the integration into the same genomic site and the introduction of self-cleaving 2A motifs and the PuroR drug resistance gene did not affect the function of these circuits.

In an evolution experiment with multiple drug (Puromycin) concentrations, the mNF and mPF circuits were tuned to the same mean expression level to decouple gene expression noise from the mean gene expression prior to drug treatment [39]. After adaptation, the drug was removed while induction was either maintained or removed. Finally, the adapted populations were retreated with the previous level of drug to uncover potential adaptation mechanisms. The evolution experiment demonstrated that low gene expression noise from the mNF circuit was beneficial in adaptation compared to mPF under low levels of drug [39]. In contrast, the high noise from the mPF circuit was beneficial compared to the mNF circuit under high levels of drug.

Mutations were found in the TetR regulator gene from the mNF circuit that knocked out repression, which explained why the mNF populations maintained high expression with or without inducer after the temporary removal of the drug. The mPF circuits did not mutate, which was consistent with the drug retreatment period where uninduced mPF populations struggled to adapt while the induced populations adapted faster [39]. Overall, the decoupling of gene expression noise from the mean demonstrated the power of using synthetic gene circuits to uncover novel insights into mammalian drug resistance.

A stochastic population dynamics model was developed to predict the emergence and switching dynamics of persister (*P*), nongenetically drug-resistant (*N*), and genetically drug-resistant (*G*) subpopulations (**Figure 6a**), which was described mathematically by the following set of coupled ODEs [39]:

$$\frac{dP}{dt} = r\_{P,N}N - r\_{N,P}P - r\_{G,P}P$$

$$\frac{dN}{dt} = -r\_{P,N}N + r\_{N,P}P - r\_{G,N}N + k\_NN - \mathbf{g}\_NN \tag{4}$$

$$\frac{dG}{dt} = r\_{G,P}P + r\_{G,N}N + k\_GG - \mathbf{g}\_GG$$

#### **Figure 6.**

*(a) Schematic depicting the effects of drug (Puromycin) concentration on Chinese hamster ovary (CHO) cell population composition and survival. Nongenetically drug-resistant cells (green cells – Brighter cells have higher PuroR expression level and are therefore more resistant) and nongrowing persister cells (gray cells) can switch phenotypes (dashed bidirectional arrow). Persister cells and growing nongenetically resistant cells can also become stably drug-resistant cells (black cells). When no drug is present, a genetically identical (clonal) cell population with heterogeneous gene expression exists (center). Under low drug treatment conditions (left arrow), cells with low PuroR expression perish and a small fraction of the surviving clonal cells become persister cells. For high drug treatment conditions (right arrow), only cells with high PuroR expression levels can survive drug treatment while the rest die (dark blue cells), and a higher fraction of the surviving cells become persisters. As persister and nongenetically resistant cells can become stably drug resistant, the population on the right panel becomes increasingly heterogeneous over the course of treatment. (b-f) Representative growth curves for simulated mPF-PuroR and mNF-PuroR CHO cell populations under (b) 0, (c) 10, (d) 22.5, (e) 35, and (f) 50 μg/mL of Puromycin. Growth curves shown in panels in (b-f) correspond to: (left) mPF subpopulations, (center) mNF subpopulations, and (right) mPF and mNF populations. (g) Adaptation times corresponding to the mPF-PuroR and mNF-PuroR populations shown in panels (b-f). From Figure 6 "Modeling the adaptation of mPF-PuroR and mNF-PuroR cells in various concentrations of Puromycin" by Farquhar, K.S. et al. in [39] located at https://www.nature.com/articles/s41467-019-10330-w under a CC BY 4.0 license.*

*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

where *ri,j* is transition rate from genotype or phenotype *j* to *i*, *ki* is the growth rate of *i*, and *gi* is the death rate of *i*. Noise was incorporated into this model by drawing the parameters describing the initial number of cells that survived Puromycin treatment and the carrying capacity of the cell culture environment from a normal distribution. Numerical simulations of Eq. (4) are shown in **Figure 6b**–**f**. These simulation results agreed with the data from the evolution experiments [39]. The modeling indicated that nongenetic phenotypic variability could facilitate the adaptation of the mPF and mNF strains to lower drug concentrations (**Figure 6c**), but that population dynamics in terms of the *P* to *G* conversion was required to capture the long experimental adaptation times at higher drug concentrations (**Figure 6d**–**g**).

#### **3. Optogenetic gene circuits**

A major focus of synthetic biology has been to engineer gene circuits to control cellular processes. This has mainly been achieved through small molecules that activate or inactivate various components of synthetic gene circuits [17, 19, 41–46]. Chemical stimuli has many advantages, including easy titration for inducing gene expression over large dynamic ranges, characterized affinity for existing proteins, and minimal off-target effects [9]. However, controlling gene circuits with chemicals is often not instantaneous and makes it difficult to control individual cells in a population.

Light stimulus can achieve many of the same advantages as chemicals without the above limitations. Like the cellular proteins that respond to chemical stimuli, proteins have been found in nature that respond to light [47, 48]. The discovery of light-activated proteins provided the elements necessary to build optogenetic gene circuits. By engineering light-responsive elements with existing components in gene circuits, synthetic biologists were able to adapt endogenous proteins from natural organisms to experimental model organisms, including yeast [7] and mammalian cell lines [49].

The use of light-inducible systems in eukaryotic organisms has expanded to cover nearly as many applications as chemical systems, including the control of gene expression, protein alterations, metabolic reactions, epigenetic states, and animal behavior [50–55]. A common theme among light-activated and chemicalinduced circuits is the genetic architecture of the system. For chemically regulated gene circuits, classic engineering architectures [26] have been produced including negative regulation, positive regulation, positive feedback, negative feedback, and many others [39, 40, 42, 56–59]. Optogenetic systems have begun to incorporate these circuit architectures [49, 60]. Optogenetic tools respond to a variety of wavelengths of light [61, 62] and can be used transiently [63, 64] or as stable systems [60].

Negative feedback is an important gene circuit architecture that has been implemented in optogenetic circuits. Negative feedback is a desirable architecture in synthetic biology because it provides two advantages: 1) negative feedback reduces gene expression noise and 2) negative feedback allows tunability of system output to a "transfer function", which describes the relationship between an input and an output function [65, 66]. In synthetic biology, many systems are designed with desired inputs and outputs in mind and therefore knowing the relationship or transfer function between these variables is crucial [67]. Additionally, such features also occur in natural systems [68, 69], which synthetic systems are often designed to mimic [14]. The negative feedback circuit architecture has been engineered into synthetic gene circuits in bacterial, fungal, and mammalian systems, all controllable by small chemical molecules [29, 40, 70, 71]. This circuit architecture was recently engineered in an optogenetic system and found to offer many of the same advantages as the chemical-induced negative feedback gene circuits; namely, low gene circuit noise, wide system tunability, and a characterized transfer function between input and output (i.e., light and a fluorescence reporter) [60].

The optogenetic NF system (**Figure 7A**) was inspired from previous chemical gene circuits [40] and from computational modeling [57]. A well-known tetracycline-responsive system [72, 73] provided the foundation to engineer a lightresponsive system, by fusing the TetR protein with a LOV2 domain [52, 74, 75] and either a degradation tag [74] or a small peptide [76] that inhibits TetR (**Figure 7B**). When light is absent, the degradation tag or the inhibitory peptide remains hidden. When blue light is present, the LOV2 protein undergoes a confirmational change and reveals one of the two domains. By employing this engineered light protein, an optogenetic gene circuit can be constructed with operator sites upstream of the gene for this protein to allow down regulation of its own expression (as well as another functional gene). The light stimulus can then be used to control gene expression output with the benefits of low noise and titratable expression levels.

Computational modeling was used to investigate how system performance could be enhanced in the optogenetic NF system [60]. This methodology of build, model, improve is crucial when developing synthetic gene circuits. To achieve this, the design and construction of the optogenetic NF system focused on changes that could decrease gene circuit noise, lower basal expression of the circuits, increase fold-change of the circuit, and enhance the range of circuit response to stimuli. A

#### **Figure 7.**

*(A) Schematic illustrating an optogenetic gene circuit with a negative feedback architecture. Specifically, this architecture produces a transcription factor (blue) that inhibits its own production. This transcription factor is also engineered to have a light-responsive domain (pink) and inhibitory peptide (orange). When light is added to the system, a confirmational change occurs and the hidden inhibitory peptide is exposed to inhibit the DNAbinding function of the transcription factor. When this occurs, increased transcription and translation occurs for the reporter gene. (B) Schematic illustration of the transcription factor shown in (A). The transcription factor is the TetR protein fused with a linker peptide which is fused with the light-responsive LOV2 domain, which contains a Jα-helix that is fused with a functional domain such as a degradation tag or an inhibitory peptide (TIP). When light is added, the Jα-helix opens exposing the functional domain. Figure used with permission from Guinn [55].*

*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

#### **Figure 8.**

*The "pipeline" used to model the NF optogenetic gene circuit. The network schematic of the system of interest includes transcription factors, promoters that can be bound by transcription factors, reporters, RNA, etc. (A). The network schematic is converted into a set of chemical reactions (B). These chemical reactions are then converted into a set of ordinary differential equations (ODEs) (C). Finally, the ODEs are simulated numerically, providing quantitative predictions that can be used to improve the optogenetic gene circuit (D). Figure used with permission from Guinn [55].*

quantitative gene expression model identified parameters that optimized the performance of the optogenetic NF system. The "pipeline" for modeling the NF optogenetic gene circuit is shown in **Figure 8**. The NF optogenetic circuit was represented by a network schematic of the main optogenetic gene circuit's components (**Figure 8A**). These components were formalized as a set of chemical reactions (**Figure 8B**). The chemical reactions were then described by a system of ordinary differential equations (**Figure 8C**), which was solved numerically [57]. Lastly, the equations describing each reaction were explored to investigate whether a given optogenetic gene circuit component should be changed experimentally (**Figure 8D**). The design of the optogenetic NF system was improved by changing the transcription and translation rates of the optogenetic inhibitory protein.

In addition to validation and improving optogenetic gene circuits, various architectures can be utilized for expressing functional proteins at precise levels inside of single cells. Controlling gene expression in single cells can allow for exploration of phenotypic landscapes as a function of protein levels and time. For example, the optogenetic NF system was used to control the mutated oncogene KRAS (G12V), which showed expression and function could be controlled in a dose-responsive manner with low optogenetic gene circuit noise [60]. This system can be modified to contain any functional gene allowing single-cell gene expression studies using microscopy equipment such as digital mirror device (DMD) [77]. DMD technology can allow system feedback for controlling optogenetic gene circuits *in silico* at the computer-microscopy interface [78] and *in vitro* using gene architecture designs responsive to light. Coupling technology like the DMD with optogenetic gene circuits like the optogenetic NF system will allow researchers to better understand cellular processes and single-cell biology.

Overall, optogenetic gene circuits allow researchers to perturb single cells to distinguish between individual and population-level behavior. Optogenetic tools are anticipated to be important for elucidating mechanisms in drug resistance and cancer metastasis, where single-cell behavior and spatial–temporal factors may dictate biological fate.

#### **4. Conclusions**

The ability of synthetic gene circuits to fulfill engineered design principles and facilitate scientific discoveries is expected to grow over time. However, evolutionary forces can undermine the integrity of synthetic gene circuits [25, 32]. It will be crucial to design gene circuits in the future to mitigate the effects of evolution to maintain their functional integrity. One approach is to use DNA sponges to change the response of gene circuits while lowering protein toxicity [79]. Another approach is to use evolution itself to repair broken synthetic gene circuit components, resulting in more robust gene circuits [31]. Overall, as the library of biological parts increases, the discovery of new "BioBricks" (standardized and interchangeable gene circuits components) will aid in resolving the integrity issues presently associated with synthetic gene circuits. Genomic mining is a promising approach for discovering BioBricks, including identifying novel TetR-family regulators from prokaryotic genomes [80] and CRISPR-Cas systems in microbes [81].

Clinical applications of synthetic gene circuits will continue to expand and could lead to successful treatments for various diseases, including autoimmune disorders [82] and cancers [83]. CAR-T technologies to fight cancer increasingly include synthetic gene circuits and synthetic intercellular pathways to avoid adverse inflammatory reactions that damage healthy cells and to improve the targeting of cancer cells [83, 84]. Additionally, investigating drug resistance in microbial pathogens will require gene circuits that can be introduced into pathogens, which have native gene networks relevant to drug resistance that are complex and incompletely characterized. Relatedly, increasing complexity in gene circuits remains a challenge and will require multiple orthogonal components [80, 85] as well as more advanced computational methods to predict the dynamics of large-scale, nonlinear networks [86]. Ultimately, improvements in our ability to model, design, and construct synthetic gene circuits will benefit biomedical applications as well as increase our understanding of natural gene networks.

Optogenetic gene circuits allow researchers to utilize the strengths that have been developed through two decades of synthetic biology research, as well as to achieve more precise control of living cells. The use of light as a stimulus enables the single-cell control of gene circuit response, which can complement existing systems to study cell populations. The generation of single-cell data will allow researchers to address questions on how individual cells give rise to population level phenomenon and how neighboring cells affect adjacent or distal cells. Answering such questions will be important for extracting information on biological processes such as tissue development [87], epithelial-to-mesenchymal transition [88], and the effects of the microenvironment on cancer progression [89, 90].

While using optogenetic tools will be important for answering a broad range biological questions and for biomedical applications, challenges remain in terms of the scalability and precision of cellular control. There have been applications of optogenetic technology that address these challenges individually. For instance, the light plate apparatus (LPA) [91] is a simple to construct and inexpensive system that can be adapted and used for scaling light-induced conditions *in vitro*. Additionally technology like digital micromirror devices (DMDs) [92] have been used to control single cells in real time. The LPA technology offers scalability but is currently limited in the precision of single-cell control. The DMD on the other hand offers precise single-cell control but is limited by scalability of conditions that can be controlled in a single experiment. Coupling these two types of tools, or their subsequent technological successors, may allow researchers to maximize scalability and optogenetic gene circuit control.

#### **Acknowledgements**

DC was supported by funding from the Government of Canada's New Frontiers in Research Fund – Exploration grant program (NFRFE-2019-01208) and the

*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

University of Alberta. GB was supported by the National Institutes of Health, NIGMS MIRA Program (R35 GM122561) and by the Laufer Center for Physical and Quantitative Biology. MTG was supported by the National Defense Science and Engineering Graduate Fellowship Program.

### **Author details**

Kevin S. Farquhar1†, Michael Tyler Guinn2,3,4†, Gábor Balázsi2,3 and Daniel A. Charlebois5,6\*

1 Independent Researcher, Houston, USA

2 The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, USA

3 Department of Biomedical Engineering, Stony Brook University, Stony Brook, USA

4 Renaissance School of Medicine, Stony Brook University, Stony Brook, USA

5 Department of Physics, University of Alberta, Edmonton, Canada

6 Department of Biological Sciences, University of Alberta, Edmonton, Canada

\*Address all correspondence to: dcharleb@ualberta.ca

† These authors contributed equally to this work.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research DOI: http://dx.doi.org/10.5772/intechopen.99329*

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*Edited by Miguel Fernández-Niño and Luis H. Reyes*

The current advances in sequencing, data mining, DNA synthesis, cloning, in silico modeling, and genome editing have opened a new field of research known as Synthetic Genomics. The main goal of this emerging area is to engineer entire synthetic genomes from scratch using pre-designed building blocks obtained by chemical synthesis and rational design. This has opened the possibility to further improve our understanding of genome fundamentals by considering the effect of the whole biological system on biological function. Moreover, the construction of non-natural biological systems has allowed us to explore novel biological functions so far not discovered in nature. This book summarizes the current state of Synthetic Genomics, providing relevant examples in this emerging field.

Published in London, UK © 2022 IntechOpen © ktsimage / iStock

Synthetic Genomics - From BioBricks to Synthetic Genomes

Synthetic Genomics

From BioBricks to Synthetic Genomes

*Edited by Miguel Fernández-Niño* 

*and Luis H. Reyes*