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. 2021 May 7;14(6):2334–2342. doi: 10.1111/1751-7915.13820

Programming living sensors for environment, health and biomanufacturing

Xinyi Wan 1,2, Behide Saltepe 1, Luyang Yu 3,4, Baojun Wang 1,2,3,4,✉
PMCID: PMC8601174  PMID: 33960658

Summary

Synthetic biology offers new tools and capabilities of engineering cells with desired functions for example as new biosensing platforms leveraging engineered microbes. In the last two decades, bacterial cells have been programmed to sense and respond to various input cues for versatile purposes including environmental monitoring, disease diagnosis and adaptive biomanufacturing. Despite demonstrated proof‐of‐concept success in the laboratory, the real‐world applications of microbial sensors have been restricted due to certain technical and societal limitations. Yet, most limitations can be addressed by new technological developments in synthetic biology such as circuit design, biocontainment and machine learning. Here, we summarize the latest advances in synthetic biology and discuss how they could accelerate the development, enhance the performance and address the present limitations of microbial sensors to facilitate their use in the field. We view that programmable living sensors are promising sensing platforms to achieve sustainable, affordable and easy‐to‐use on‐site detection in diverse settings.

Synthetic biology offers new tools and capabilities of engineering microbes with defined functions as new biosensing platforms for environmental monitoring, disease diagnosis and adaptive biomanufacturing. Here, we summarize the latest advances in synthetic biology and discuss how they could accelerate the development, enhance the performance and address the present limitations of microbial sensors to facilitate their use in the field. We view that programmable living sensors are promising sensing platforms to achieve sustainable, affordable, and easy‐to‐use on‐site detection in diverse settings.

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Introduction

Microbial whole‐cell biosensors (WCBs) use the sensing functions of natural or genetically engineered microbes to achieve target detection, and are gaining increasing interests for many applications ranging from environmental monitoring to disease diagnosis in the rising era of synthetic biology. Compared to traditional biosensors, they have substantial advantages such as cost‐effective, easy‐to‐manufacture and biodegradable (van der Meer and Belkin, 2010; Saltepe et al., 2018; Hicks et al., 2020; Inda and Lu, 2020). Additionally, WCBs are renewable, providing sustainable economical solutions for food production (Rogers and Oldroyd, 2014), material synthesis (Choi and Lee, 2020), wastewater treatment and renewable energy generation (Cui et al., 2019). Particularly for biomedical applications, they have potential to achieve non‐invasive in situ diagnosis and precision treatment (Inda and Lu, 2020). Here, we summarize the latest advances in synthetic biology and discuss how they could accelerate the development, enhance the performance and address the present limitations of living microbial sensors to facilitate their wide utilization in the field.

Synthetic biology accelerates development of living sensors by providing standardized and modularized building blocks

Synthetic biology offers scientists new tools to precisely manipulate cells for achieving bespoke tasks using engineered gene circuits of varying scales and complexity. Engineered WCBs generally comprise three main modules: (i) a sensing unit, (ii) a signal processing unit and (iii) an output unit (Fig. 1) (Wang and Buck, 2012). Most sensing units currently used are adapted from the natural cellular receptors such as ligand‐responsive transcription factor (TF)‐promoter pairs (Wang et al., 2013a) or two‐component systems (TCSs) (Ravikumar et al., 2012; Wang et al., 2013a). Through optimal pathways, WCBs could be programmed to sense metal ions (Kim et al., 2016; Wan et al., 2019b), chemicals (Chong and Ching, 2016), metabolites (Liu et al., 2015a), light (Fernandez‐Rodriguez et al., 2017), electrical signals (Bhokisham et al., 2020), temperature (Inda et al., 2019) or even pressure (Fajardo‐Cavazos et al., 2012). A simple and traditional WCB consists of a genetic reporter connected downstream a sensing unit to control the expression of a detectable output. For sensors incorporating more complex signal processing circuits, the sensing unit triggers more sophisticated actions before initiating reporter expression in order to enhance sensor’s performance or allow additional functions. Such circuits include logic gates (Wang et al., 2011; Bonnet et al., 2013), transcriptional amplifiers (Wang et al., 2014; Wan et al., 2019b), synthetic DNA sponges (Wan et al., 2020), feedforward or feedback loops (Jia et al., 2019), TF‐based (i.e. toggle switch) (Elowitz and Leibler, 2000; Gardner et al., 2000) or RNA‐based switches (i.e. riboregulators) (Isaacs et al., 2004; Green et al., 2014), and memory circuits (Siuti et al., 2013; Riglar et al., 2017) (Fig. 1). Expression of any desired genes is referred as ‘reporter’ encoding detectable (Lopreside et al., 2019; Del Valle et al., 2021) or functional outputs (Hwang et al., 2014; Din et al., 2016; Chowdhury et al., 2019) (Fig. 1). A noteworthy ‘reporter’ is to use DNA barcodes to record the changes in environment instead of continuous monitoring. Barcodes not only record input changes but also their orders providing useful information especially in disease monitoring (Roquet et al., 2016; Sheth et al., 2017; Tang and Liu, 2018). Further, instead of producing sensors with a single output, sensor cell arrays could be designed to display an easy‐to‐interpret output pattern corresponding to cognate input analyte levels without using sophisticated equipment (Wan et al., 2019b; Kim et al., 2020).

Fig. 1.

Fig. 1

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Programming living sensors for environment, health and biomanufacturing.

Recent developments of synthetic biology provide numerous tools and modularized parts for programing cell‐based biosensors, including building blocks for the sensing module, the computing module and the output module. These blocks can be combined to achieve customized sensing and reporting specifications for diverse envisioned applications, such as environmental monitoring, clinical diagnosis, theranostics and biomanufacturing. R, receptor. P, promoter. gfp, gene encoding green fluorescent protein. rfp, gene encoding red fluorescent protein. luxAB, genes encoding bacterial luciferase for luminescent output. lacZ, gene coding β‐Galactosidase for colorimetric output. arg, acoustic reporter genes expressing gas vesicles that are detectable by ultrasound. luxI & lasI, genes encoding synthases for quorum sensing molecules. TF, transcriptional factor. ArsR, arsenic receptor. P arsR , ArsR’s cognate promoter. MerR, mercury receptor. P merT , MerR’s cognate promoter. TCS, two‐component system. ZraSR, zinc‐responsive histidine kinase and response regulator. P zraP , ZraR’s cognate promoter. CcaSR, green light‐responsive histidine kinase and response regulator. P cpcG2 , CcaR’s cognate promoter. DBD, DNA‐binding domain. LexA, DNA damage or DNA replication inhibition‐responsive transcriptional repressor. VHH, a single‐domain antibody. Int, integrase. P C , constitutive promoter. HrpRS, hetero regulation motif in the hrp (hypersensitive response and pathogenicity) system of Pseudomonas syringae. P hrpL , HrpRS’s cognate promoter. ECF, extracytoplasmic function sigma factor. P ecf , ECF’s cognate promoter. LOD, limit of detection.

Inspired by mature engineering disciplines, synthetic biology adopts engineering principles (e.g. standardization, modularization and modelling) to facilitate complex circuit constructions particularly using ‘Lego‐like’ standardized building blocks (Endy, 2005). Although the blocks alone do not perform spectacular actions, they can generate bespoke coordinated functions when connected. Altogether, the advances in synthetic biology now allow both fine tuning the performance of existing WCBs and creating new biosensors with unique functionalities in a more predictable and rapid manner.

Synthetic biology offers new toolkits to enhance performance of living sensors for applications in environment, health and biomanufacturing

Developments in industrialization have increased dissemination of pollutants and harmful substances which are threatening the environment and human health. Compared to traditional WCBs that use native stress response pathways to report general toxic environment (Kim et al., 2005; Saltepe et al., 2019), synthetic WCBs are able to detect specific pollutants such as heavy metals and metalloids (Wang et al., 2013a; Wan et al., 2019b), organic chemicals and pesticides (Chong and Ching, 2016), waterborne pathogens (Yong and Zhong, 2009) and explosives (Belkin et al., 2017) (Fig. 1). Although many early‐stage WCBs are insufficient to meet real‐world detection requirements in limit of detection (LOD), selectivity and output amplitude, several gene circuit‐based optimization strategies have recently been developed to improve their sensing performance (Wan et al., 2019a). In contrast to traditional optimization methods like random mutagenesis (Hakkila et al., 2011; Chong and Ching, 2016), these synthetic biology‐enabled optimization tools are based on rational design, and therefore more predictable and rapid to achieve the desired sensing specifications (Wan et al., 2019a). For example, simply integrating multiple inputs using genetic AND gates has been proven to be effective to increase WCBs’ selectivity (Wang et al., 2013a; Wang and Buck, 2014), and rationally tuning the intracellular levels of the receptor TFs can quickly lower hence improve WCBs’ LOD (Wang et al., 2015). In addition, a toggle switch (Wu et al., 2009) and a post‐translational regulation device (Wan et al., 2019b) have been designed to lower WCBs’ background expression and LOD. Further, amplification of the transduced sensor signal is another powerful strategy to further improve the sensor’s performance using strategies such as positive feedback loops (Jia et al., 2019) or transcription amplifiers (Wang et al., 2014; Wan et al., 2019b).

Programming microbes for detecting health‐related biomarkers can lead to low‐cost point‐of‐care (POC) diagnostics as well as non‐invasive in situ diagnosis or theranostics (Riglar and Silver, 2018; Inda and Lu, 2020). They could report the disease both ex vivo (e.g. in urine or blood) (Courbet et al., 2015) and in the body (e.g. in gut) (Riglar et al., 2017). To date, myriad sensors have been engineered using bacteria to detect pathogens (Hwang et al., 2014; Mao et al., 2018), micronutrients (e.g. zinc) (Watstein and Styczynski, 2018) and disease biomarkers (Anderson et al., 2006; Danino et al., 2015; Riglar et al., 2017; Isabella et al., 2018) (Fig. 1). In some studies, specific therapeutics (e.g. cytotoxic agents) have been released in situ for precision treatment (Din et al., 2016; Chowdhury et al., 2019). Additionally, changes in disease progress could be recorded via memory circuits (e.g. toggle switches (Riglar et al., 2017) or DNA recombinases (Courbet et al., 2015)). Albeit remarkable progress has been reached to date, WCBs may face challenges regarding their performance in real‐world applications such as low signal‐to‐noise ratios or non‐specific results due to complex microenvironments. Nevertheless, the aforementioned transcriptional signal amplification circuits (Courbet et al., 2015) and multiple signal integration using AND logic gates (Riglar and Silver, 2018) are viable solution to address these issues.

WCBs used in biomanufacturing have contributed to (i) real‐time monitoring of valuable compounds (e.g. nutraceuticals, pharmaceuticals and biofuels) (Liu et al., 2015a; Rogers et al., 2015), and (ii) stress monitoring in cells caused by nutrient (Brognaux et al., 2013) and oxygen deficiency (Garcia et al., 2009), or toxic intermediate production (Dahl et al., 2013) during bioprocess. WCBs offer tremendous advantages such as facilitating rapid screening and selection of high‐producing strains among large mutant libraries, real‐time monitoring of metabolic flux, and detection of labile and low metabolites (Liu et al., 2015a). Although many WCBs for metabolite monitoring are designed based on naturally occurring ligand‐responsive TFs and their cognate promoters, other approaches have recently been introduced in the field such as rational protein design to broaden the sensing capabilities of existing TFs (e.g. AraC) for metabolites with no existing receptor TFs (Tang and Cirino, 2011) and TCSs for extracellular metabolites (Ganesh et al., 2015), or RNA switches to detect metabolites at lower concentrations (Fowler et al., 2010). Moreover, dynamic sensor‐regulator circuits can be constructed in microbial cell factories to allow balancing metabolism and adaptively tuning product synthesis rate according to cell state change (Bradley and Wang, 2015; Liu et al., 2015b) (Fig. 1).

Synthetic biology provides novel strategies to overcome field‐deployable limitations of living sensors

Despite successful proof‐of‐concept demonstrations of WCBs in the laboratory, very few have made it into the market. Several barriers need to be overcome including inadequate number of sensory building blocks and insufficient knowledge of specific disease biomarkers, poor sensing performance, long‐term stability, risk of releasing genetically modified microorganisms (GMMs) and lack of practical experience in acceptance by professional stakeholders (Hicks et al., 2020; Inda and Lu, 2020). Nevertheless, synthetic biology has contributed novel strategies to address these limitations to facilitate deployment of living sensors in the field.

One of the major limitations of circuit design is the insufficient number of well‐characterized genetic parts available in the toolkit of synthetic biology. Although attempts have been made to engineer new building blocks (e.g. rational protein engineering (Wang et al., 2013b; Chang et al., 2018)), they do not fit for all cases. Thus, to expand the existing library of genetic building blocks, synthetic biology could leverage advances from other fields such as machine learning. For instance, guided by deep learning, functions of RNA switches could be predicted resulting in shortened time required for their building and testing as well as reduced cost (Angenent‐Mari et al., 2020; Valeri et al., 2020). Deep learning‐derived prediction tools have also been developed to predict the transcription initiation frequency of synthetic bacterial promoters (Van Brempt et al., 2020) and to predict TFs and their DNA‐binding domains from their protein sequences (Kim et al., 2021). Additionally, machine learning has been introduced to increase the reliability for sensitive and specific detection of small molecules (Kim et al., 2020; Saltepe et al., 2021).

Most biosensors require calibration to generate reference response curves upon testing. Therefore, a portable, durable, inexpensive and user‐friendly platform for on‐site quantification is needed. Such devices have been utilized as prototypes for environmental contamination (Buffi et al., 2011; Zhang et al., 2020a) and health monitoring (Mimee et al., 2018). Additionally, development of suitable platforms equipped with wireless connection will allow timely sensor data upload to a remote central database and easy monitoring (Liu et al., 2020). Using electrochemical output could directly trigger relevant sensor device for monitoring and wireless reporting (Webster et al., 2014); otherwise, an additional electronic device to convert the colorimetric or optical output signal into electrical signal would be required (Mimee et al., 2018). Different approaches have been applied to keep biosensor cells alive and active for field deployment including freeze‐drying of cells (Bjerketorp et al., 2006), encapsulating cells within polymers (Buffi et al., 2011; Liu et al., 2018; Wan et al., 2019b) and continuous culture (Bjerketorp et al., 2006; Wan et al., 2019b). However, some aspects of these platforms are yet to be optimized such as the self‐renewability for long‐term monitoring and in vivo biotherapy, and the choice of materials suitable for long‐term storage (Liu et al., 2017; Mimee et al., 2018). Promising solutions may include adopting harsh condition‐resistant microbial chasses (Volpetti et al., 2017; Guo et al., 2018) or repurposing existing cell strains in the native sensing environment (Nejman et al., 2020) for sensor development. Alternatively, a conventional cell chassis could be engineered or evolved to suit the target environment (Richard and Foster, 2003; Winkler et al., 2014; Crook et al., 2019).

Recent advances in synthetic biology allow harnessing the amazing sensing capabilities of microbes for versatile purposes, for example as wearable sensors for biomarker analysis in sweat to enable non‐invasive in situ real‐time health monitoring (Liu et al., 2018; Chung et al., 2019). However, biosafety concerns regarding the usage of GMMs remain an issue associated with their field applications including potential horizontal gene transfer and disruption of natural ecosystems (Dana et al., 2012). Accordingly, different genetic containment strategies have been proposed to mitigate biosafety concerns such as replacing antibiotics resistance with auxotrophy (Hirota et al., 2017) or toxin‐antitoxin systems (Wright et al., 2015), incorporating conditional kill switches (Callura et al., 2010; Chan et al., 2016) and non‐canonical amino acid or xeno‐nucleic acid substitution (Pinheiro et al., 2012; Fredens et al., 2019). However, cells are prone to evolve and may escape from the engineered genetic safeguards. Hence, entrapment of cells in a biocompatible compartment minimizes the risk of accidental release of bacteria in the environment as well as protects them from hostile environment (Volpetti et al., 2017; Liu et al., 2018; Mimee et al., 2018). Further, chromosome‐free bacterial chassis such as synthetic cells (e.g. minimal cells) constructed from bottom‐up approaches (Garamella et al., 2016) and SimCells (Fan et al., 2020) can be considered. Yet, a unique genome‐borne barcoding system for each chassis would allow handy tracing of any release and further minimizing safety concerns (de Lorenzo et al., 2020).

In the last decade, cell‐free expression systems have become increasingly popular as a new sensor platform by avoiding safety concerns associated with using living cells. Cell‐free biosensors lend faster response, higher sensitivity and more tolerance to toxic samples (Silverman et al., 2020; Zhang et al., 2020b). Various cell‐free biosensors have been demonstrated to detect antibiotics (Jung et al., 2020), pathogens (Pardee et al., 2016; Takahashi et al., 2018), toxic substances (Lopreside et al., 2019; Jung et al., 2020), etc. Moreover, cell‐free extracts comprising genetic sensors could be embedded on paper, providing a portable platform for easy‐to‐use and cost‐effective on‐site screening (Pardee et al., 2016; Takahashi et al., 2018), or in hydrogels acting as environment‐responsive biomaterials (Whitfield et al., 2020).

Outlook towards deploying living sensors in the field

Engineered living sensors have been pursued to fill the gaps left by conventional biosensing platforms by providing portable, easy‐to‐manufacture, cost‐effective and rapidly programmable platforms for on‐site detection. Despite demonstrated proof‐of‐concept success in the laboratory, few WCBs have made it into the market due to various restrictions. The latest advances in synthetic biology enable a rapid design–build–test cycle for sensor development and optimization to address the current limitations of WCBs. Yet, there are remaining challenges to be tackled both within and beyond the scope of technical developments.

Both environmental and health monitoring necessitate sensor cell exposure to complex samples and thus require complex signal processing circuits and even multiple input modules. Particularly for medical applications involving complex media compositions such as tumours, non‐specific localization of sensor cells prevents accurate diagnosis and biotherapy. To this end, engineering microbes for sensing and reporting at designated spatial locations will be crucial (Chien et al., 2019). However, microbial sensors that support multiple spatiotemporal signals detection and integration have not been seen frequently due to technical challenges. Considering a single cell has a limited capacity in resources and large complex circuits tend to burden host cells, cell consortia comprising multiple communicating sensor strains may be used instead to facilitate multiplex detection (Wang et al., 2013a; Khatun et al., 2018).

Albeit cell‐free expression systems could address many issues facing WCBs, the genetic sensing circuits cannot always be transferred with the same or similar performance expected across the two platforms due to the fundamentally different biochemical environments. In addition, cell‐free systems have their own limitations, for example batch‐to‐batch variations and incompatible for continuous usage, to be addressed. E. coli‐based cell extract is the dominating cell‐free expression system at present. To meet different application needs, further work is expected to validate the use of other non‐model organism‐based cell‐free systems as alternative cell‐free sensing platforms (Zhang et al., 2020b).

All in all, although living sensor platforms face certain restrictions, synthetic biology tools facilitate their adoption and use as promising alternative analytical devices to meet the real‐world detection requirements. To overcome remaining limitations, fundamental research is vital to identify new biomarkers and new candidate sensor elements as genetic building blocks. It will also provide necessary experimental data sets to feed and validate computational design platforms (e.g. machine learning or bioinformatics), with a goal to expand the standard and modular toolkits available for rapidly building synthetic biology‐enabled biosensors. In addition, multidisciplinary collaborations should be encouraged which will likely lead to novel practical solutions towards wide field deployment of living sensors. Considering the present biosensors for real‐world applications dominate in the healthcare sector, developing biosensors for environmental monitoring, biomanufacturing and other emerging scenarios will have significant space to grow and benefit diverse end users in the future.

This work was supported by the UK Research and Innovation Future Leaders Fellowship [MR/S018875/1], Leverhulme Trust grant [RPG‐2020‐241], US Office of Naval Research Global grant [N62909‐20‐1‐2036], Wellcome Trust Seed Awards in Science [202078/Z/16/Z] and Zhejiang University‐University of Edinburgh Joint Research Centre for Engineering Biology.

Conflict of interest

None declared.

Funding information

Microb. Biotechnol. (2021) 14(6), 2334–2342

Contributor Information

Xinyi Wan, Email: xwan@ed.ac.uk.

Behide Saltepe, Email: Behide.Saltepe@ed.ac.uk.

Luyang Yu, Email: luyangyu@zju.edu.cn.

Baojun Wang, Email: baojun.wang@ed.ac.uk.

References

  1. Anderson, J.C. , Clarke, E.J. , Arkin, A.P. , and Voigt, C.A. (2006) Environmentally controlled invasion of cancer cells by engineered bacteria. J Mol Biol 355: 619–627. [DOI] [PubMed] [Google Scholar]
  2. Angenent‐Mari, N.M. , Garruss, A.S. , Soenksen, L.R. , Church, G. , and Collins, J.J. (2020) A deep learning approach to programmable RNA switches. Nat Commun 11: 5057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Belkin, S. , Yagur‐Kroll, S. , Kabessa, Y. , Korouma, V. , Septon, T. , Anati, Y. , et al. (2017) Remote detection of buried landmines using a bacterial sensor. Nat Biotechnol 35: 308–310. [DOI] [PubMed] [Google Scholar]
  4. Bhokisham, N. , VanArsdale, E. , Stephens, K.T. , Hauk, P. , Payne, G.F. , and Bentley, W.E. (2020) A redox‐based electrogenetic CRISPR system to connect with and control biological information networks. Nat Commun 11: 2427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bjerketorp, J. , Hakansson, S. , Belkin, S. , and Jansson, J.K. (2006) Advances in preservation methods: keeping biosensor microorganisms alive and active. Curr Opin Biotechnol 17: 43–49. [DOI] [PubMed] [Google Scholar]
  6. Bonnet, J. , Yin, P. , Ortiz, M.E. , Subsoontorn, P. , and Endy, D. (2013) Amplifying genetic logic gates. Science 340: 599–603. [DOI] [PubMed] [Google Scholar]
  7. Bradley, R.W. , and Wang, B. (2015) Designer cell signal processing circuits for biotechnology. N Biotechnol 32: 635–643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brognaux, A. , Han, S.S. , Sorensen, S.J. , Lebeau, F. , Thonart, P. , and Delvigne, F. (2013) A low‐cost, multiplexable, automated flow cytometry procedure for the characterization of microbial stress dynamics in bioreactors. Microb Cell Fact 12: 100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Buffi, N. , Merulla, D. , Beutier, J. , Barbaud, F. , Beggah, S. , van Lintel, H. , et al. (2011) Development of a microfluidics biosensor for agarose‐bead immobilized Escherichia coli bioreporter cells for arsenite detection in aqueous samples. Lab Chip 11: 2369–2377. [DOI] [PubMed] [Google Scholar]
  10. Callura, J.M. , Dwyer, D.J. , Isaacs, F.J. , Cantor, C.R. , and Collins, J.J. (2010) Tracking, tuning, and terminating microbial physiology using synthetic riboregulators. Proc Natl Acad Sci USA 107: 15898–15903. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chan, C.T.Y. , Lee, J.W. , Cameron, D.E. , Bashor, C.J. , and Collins, J.J. (2016) 'Deadman' and 'Passcode' microbial kill switches for bacterial containment. Nat Chem Biol 12: 82–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chang, H.J. , Mayonove, P. , Zavala, A. , De Visch, A. , Minard, P. , Cohen‐Gonsaud, M. , and Bonnet, J. (2018) A modular receptor platform to expand the sensing repertoire of bacteria. ACS Synth Biol 7: 166–175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chien, T. , Harimoto, T. , Kepecs, B. , Gray, K. , Coker, C. , Pu, K. , et al. (2019) Multiplexed biosensors for precision bacteria tropism in vivo . bioRxiv 10.1101/851311 [DOI] [Google Scholar]
  14. Choi, Y. , and Lee, S.Y. (2020) Biosynthesis of inorganic nanomaterials using microbial cells and bacteriophages. Nat Rev Chem 4: 638–656. [DOI] [PubMed] [Google Scholar]
  15. Chong, H.Q. , and Ching, C.B. (2016) Development of colorimetric‐based whole‐cell biosensor for organophosphorus compounds by engineering transcription regulator DmpR. ACS Synth Biol 5: 1290–1298. [DOI] [PubMed] [Google Scholar]
  16. Chowdhury, S. , Castro, S. , Coker, C. , Hinchliffe, T.E. , Arpaia, N. , and Danino, T. (2019) Programmable bacteria induce durable tumor regression and systemic antitumor immunity. Nat Med 25: 1057–1063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chung, M. , Fortunato, G. , and Radacsi, N. (2019) Wearable flexible sweat sensors for healthcare monitoring: a review. J R Soc Interface 16: 20190217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Courbet, A. , Endy, D. , Renard, E. , Molina, F. , and Bonnet, J. (2015) Detection of pathological biomarkers in human clinical samples via amplifying genetic switches and logic gates. Sci Transl Med 7: 289ra283. [DOI] [PubMed] [Google Scholar]
  19. Crook, N. , Ferreiro, A. , Gasparrini, A.J. , Pesesky, M.W. , Gibson, M.K. , Wang, B. , et al. (2019) Adaptive strategies of the candidate probiotic E. coli Nissle in the mammalian gut. Cell Host Microbe 25: 499–512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Cui, Y. , Lai, B. , and Tang, X.H. (2019) Microbial fuel cell‐based biosensors. Biosensors‐Basel 9: 135612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dahl, R.H. , Zhang, F. , Alonso‐Gutierrez, J. , Baidoo, E. , Batth, T.S. , Redding‐Johanson, A.M. , et al. (2013) Engineering dynamic pathway regulation using stress‐response promoters. Nat Biotechnol 31: 1039–1046. [DOI] [PubMed] [Google Scholar]
  22. Dana, G.V. , Kuiken, T. , Rejeski, D. , and Snow, A.A. (2012) Four steps to avoid a synthetic‐biology disaster. Nature 483: 29. [DOI] [PubMed] [Google Scholar]
  23. Danino, T. , Prindle, A. , Kwong, G.A. , Skalak, M. , Li, H. , Allen, K. , et al. (2015) Programmable probiotics for detection of cancer in urine. Sci Transl Med 7: 289ra284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Del Valle, I. , Fulk, E.M. , Kalvapalle, P. , Silberg, J.J. , Masiello, C.A. , and Stadler, L.B. (2021) Translating new synthetic biology advances for biosensing into the earth and environmental sciences. Front Microbiol 11: 618373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Din, M.O. , Danino, T. , Prindle, A. , Skalak, M. , Selimkhanov, J. , Allen, K. , et al. (2016) Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536: 81–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Elowitz, M.B. , and Leibler, S. (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403: 335–338. [DOI] [PubMed] [Google Scholar]
  27. Endy, D. (2005) Foundations for engineering biology. Nature 438: 449–453. [DOI] [PubMed] [Google Scholar]
  28. Fajardo‐Cavazos, P. , Waters, S.M. , Schuerger, A.C. , George, S. , Marois, J.J. , and Nicholson, W.L. (2012) Evolution of Bacillus subtilis to enhanced growth at low pressure: up‐regulated transcription of des‐desKR, encoding the fatty acid desaturase system. Astrobiology 12: 258–270. [DOI] [PubMed] [Google Scholar]
  29. Fan, C. , Davison, P.A. , Habgood, R. , Zeng, H. , Decker, C.M. , Gesell Salazar, M. , et al. (2020) Chromosome‐free bacterial cells are safe and programmable platforms for synthetic biology. Proc Natl Acad Sci USA 117: 6752–6761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Fernandez‐Rodriguez, J. , Moser, F. , Song, M. , and Voigt, C.A. (2017) Engineering RGB color vision into Escherichia coli . Nat Chem Biol 13: 706–708. [DOI] [PubMed] [Google Scholar]
  31. Fowler, C.C. , Brown, E.D. , and Li, Y.F. (2010) Using a riboswitch sensor to examine coenzyme B‐12 metabolism and transport in E. coli . Chem Biol 17: 756–765. [DOI] [PubMed] [Google Scholar]
  32. Fredens, J. , Wang, K. , de la Torre, D. , Funke, L.F.H. , Robertson, W.E. , Christova, Y. , et al. (2019) Total synthesis of Escherichia coli with a recoded genome. Nature 569: 514–518. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ganesh, I. , Ravikumar, S. , Yoo, I.K. , and Hong, S.H. (2015) Construction of malate‐sensing Escherichia coli by introduction of a novel chimeric two‐component system. Bioprocess Biosyst Eng 38: 797–804. [DOI] [PubMed] [Google Scholar]
  34. Garamella, J. , Marshall, R. , Rustad, M. , and Noireaux, V. (2016) The all E. coli TX‐TL toolbox 2.0: a platform for cell‐free synthetic biology. ACS Synth Biol 5: 344–355. [DOI] [PubMed] [Google Scholar]
  35. Garcia, J.R. , Cha, H.J. , Rao, G. , Marten, M.R. , and Bentley, W.E. (2009) Microbial nar‐GFP cell sensors reveal oxygen limitations in highly agitated and aerated laboratory‐scale fermentors. Microb Cell Fact 8: 6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Gardner, T.S. , Cantor, C.R. , and Collins, J.J. (2000) Construction of a genetic toggle switch in Escherichia coli . Nature 403: 339–342. [DOI] [PubMed] [Google Scholar]
  37. Green, A.A. , Silver, P.A. , Collins, J.J. , and Yin, P. (2014) Toehold switches: de‐novo‐designed regulators of gene expression. Cell 159: 925–939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Guo, K.H. , Chen, P.H. , Lin, C. , Chen, C.F. , Lee, I.R. , and Yeh, Y.C. (2018) Determination of gold ions in human urine using genetically engineered microorganisms on a paper device. ACS Sens 3: 744–748. [DOI] [PubMed] [Google Scholar]
  39. Hakkila, K.M. , Nikander, P.A. , Junttila, S.M. , Lamminmaki, U.J. , and Virta, M.P. (2011) Cd‐specific mutants of mercury‐sensing regulatory protein MerR, generated by directed evolution. Appl Environ Microbiol 77: 6215–6224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Hicks, M. , Bachmann, T.T. , and Wang, B. (2020) Synthetic biology enables programmable cell‐based biosensors. ChemPhysChem 21: 132–144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hirota, R. , Abe, K. , Katsuura, Z.‐I. , Noguchi, R. , Moribe, S. , Motomura, K. , et al. (2017) A novel biocontainment strategy makes bacterial growth and survival dependent on phosphite. Sci Rep 7: 44748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Hwang, I.Y. , Tan, M.H. , Koh, E. , Ho, C.L. , Poh, C.L. , and Chang, M.W. (2014) Reprogramming microbes to be pathogen‐seeking killers. ACS Synth Biol 3: 228–237. [DOI] [PubMed] [Google Scholar]
  43. Inda, M.E. , and Lu, T.K. (2020) Microbes as biosensors. Annu Rev Microbiol 74: 337–359. [DOI] [PubMed] [Google Scholar]
  44. Inda, M.E. , Vazquez, D.B. , Fernandez, A. , and Cybulski, L.E. (2019) Reverse engineering of a thermosensing regulator switch. J Mol Biol 431: 1016–1024. [DOI] [PubMed] [Google Scholar]
  45. Isaacs, F.J. , Dwyer, D.J. , Ding, C.M. , Pervouchine, D.D. , Cantor, C.R. , and Collins, J.J. (2004) Engineered riboregulators enable post‐transcriptional control of gene expression. Nat Biotechnol 22: 841–847. [DOI] [PubMed] [Google Scholar]
  46. Isabella, V.M. , Ha, B.N. , Castillo, M.J. , Lubkowicz, D.J. , Rowe, S.E. , Millet, Y.A. , et al. (2018) Development of a synthetic live bacterial therapeutic for the human metabolic disease phenylketonuria. Nat Biotechnol 36: 857–864. [DOI] [PubMed] [Google Scholar]
  47. Jia, X.Q. , Bu, R.R. , Zhao, T.T. , and Wu, K. (2019) Sensitive and specific whole‐cell biosensor for arsenic detection. Appl Environ Microbiol 85: e00694‐00619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Jung, J.K. , Alam, K.K. , Verosloff, M.S. , Capdevila, D.A. , Desmau, M. , Clauer, P.R. , et al. (2020) Cell‐free biosensors for rapid detection of water contaminants. Nat Biotechnol 38: 1451–1459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Khatun, M.A. , Hoque, M.A. , Zhang, Y. , Lu, T. , Cui, L. , Zhou, N.Y. , and Feng, Y. (2018) Bacterial consortium‐based sensing system for detecting organophosphorus pesticides. Anal Chem 90: 10577–10584. [DOI] [PubMed] [Google Scholar]
  50. Kim, B.C. , Youn, C.H. , Ahn, J.M. , and Gu, M.B. (2005) Screening of target‐specific stress‐responsive genes for the development of cell‐based biosensors using a DNA microarray. Anal Chem 77: 8020–8026. [DOI] [PubMed] [Google Scholar]
  51. Kim, G.B , Gao, Y. , Palsson, B.O. , and Lee, S.Y. (2021) DeepTFactor: A deep learning‐based tool for the prediction of transcription factors. Proc Natl Acad Sci USA 118: e2021171118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kim, H.J. , Lim, J.W. , Jeong, H. , Lee, S.J. , Lee, D.W. , Kim, T. , and Lee, S.J. (2016) Development of a highly specific and sensitive cadmium and lead microbial biosensor using synthetic CadC‐T7 genetic circuitry. Biosens Bioelectron 79: 701–708. [DOI] [PubMed] [Google Scholar]
  53. Kim, H. , Seong, W. , Rha, E. , Lee, H. , Kim, S.K. , Kwon, K.K. , et al. (2020) Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification. Biosens Bioelectron 170: 112670. [DOI] [PubMed] [Google Scholar]
  54. Liu, D. , Evans, T. , and Zhang, F.Z. (2015a) Applications and advances of metabolite biosensors for metabolic engineering. Metab Eng 31: 35–43. [DOI] [PubMed] [Google Scholar]
  55. Liu, D. , Xiao, Y. , Evans, B.S. , and Zhang, F.Z. (2015b) Negative feedback regulation of fatty acid production based on a malonyl‐CoA sensor‐actuator. ACS Synth Biol 4: 132–140. [DOI] [PubMed] [Google Scholar]
  56. Liu, M. , Zhao, Y. , Monshat, H. , Tang, Z. , Wu, Z. , Zhang, Q. , and Lu, M. (2020) An IoT‐enabled paper sensor platform for real‐time analysis of isothermal nucleic acid amplification tests. Biosens Bioelectron 169: 112651. [DOI] [PubMed] [Google Scholar]
  57. Liu, X.Y. , Tang, T.C. , Tham, E. , Yuk, H. , Lin, S.T. , Lu, T.K. , and Zhao, X.H. (2017) Stretchable living materials and devices with hydrogel‐elastomer hybrids hosting programmed cells. Proc Natl Acad Sci USA 114: 2200–2205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Liu, X. , Yuk, H. , Lin, S. , Parada, G.A. , Tang, T.‐C. , Tham, E. , et al. (2018) 3D printing of living responsive materials and devices. Adv Mater 30: 1704821. [DOI] [PubMed] [Google Scholar]
  59. Lopreside, A. , Wan, X. , Michelini, E. , Roda, A. , and Wang, B. (2019) Comprehensive profiling of diverse genetic reporters with application to whole‐cell and cell‐free biosensors. Anal Chem 91: 15284–15292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. de Lorenzo, V. , Krasnogor, N. , and Schmidt, M. (2020) For the sake of the bioeconomy: define what a synthetic biology chassis is!. N Biotechnol 60: 44–51. [DOI] [PubMed] [Google Scholar]
  61. Mao, N. , Cubillos‐Ruiz, A. , Cameron, D.E. , and Collins, J.J. (2018) Probiotic strains detect and suppress cholera in mice. Sci Transl Med 10: eaao2586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. van der Meer, J.R. , and Belkin, S. (2010) Where microbiology meets microengineering: design and applications of reporter bacteria. Nat Rev Microbiol 8: 511–522. [DOI] [PubMed] [Google Scholar]
  63. Mimee, M. , Nadeau, P. , Hayward, A. , Carim, S. , Flanagan, S. , Jerger, L. , et al. (2018) An ingestible bacterial‐electronic system to monitor gastrointestinal health. Science 360: 915–918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Nejman, D. , Livyatan, I. , Fuks, G. , Gavert, N. , Zwang, Y. , Geller, L.T. , et al. (2020) The human tumor microbiome is composed of tumor type‐specific intracellular bacteria. Science 368: 973–980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Pardee, K. , Green, A.A. , Takahashi, M.K. , Braff, D. , Lambert, G. , Lee, J.W. , et al. (2016) Rapid, low‐cost detection of Zika virus using programmable biomolecular components. Cell 165: 1255–1266. [DOI] [PubMed] [Google Scholar]
  66. Pinheiro, V.b. , Taylor, A.i. , Cozens, C. , Abramov, M. , Renders, M. , Zhang, S. , et al. (2012) Synthetic genetic polymers capable of heredity and evolution. Science 336: 341–344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Ravikumar, S. , Ganesh, I. , Yoo, I.K. , and Hong, S.H. (2012) Construction of a bacterial biosensor for zinc and copper and its application to the development of multifunctional heavy metal adsorption bacteria. Process Biochem 47: 758–765. [Google Scholar]
  68. Richard, H.T. , and Foster, J.W. (2003) Acid resistance in Escherichia coli . Adv Appl Microbiol 52: 167–186. [DOI] [PubMed] [Google Scholar]
  69. Riglar, D.T. , Giessen, T.W. , Baym, M. , Kerns, S.J. , Niederhuber, M.J. , Bronson, R.T. , et al. (2017) Engineered bacteria can function in the mammalian gut long‐term as live diagnostics of inflammation. Nat Biotechnol 35: 653–658. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Riglar, D.T. , and Silver, P.A. (2018) Engineering bacteria for diagnostic and therapeutic applications. Nat Rev Microbiol 16: 214–225. [DOI] [PubMed] [Google Scholar]
  71. Rogers, C. , and Oldroyd, G.E.D. (2014) Synthetic biology approaches to engineering the nitrogen symbiosis in cereals. J Exp Bot 65: 1939–1946. [DOI] [PubMed] [Google Scholar]
  72. Rogers, J.K. , Guzman, C.D. , Taylor, N.D. , Raman, S. , Anderson, K. , and Church, G.M. (2015) Synthetic biosensors for precise gene control and real‐time monitoring of metabolites. Nucleic Acids Res 43: 7648–7660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Roquet, N. , Soleimany, A.P. , Ferris, A.C. , Aaronson, S. , and Lu, T.K. (2016) Synthetic recombinase‐based state machines in living cells. Science 353: aad8559. [DOI] [PubMed] [Google Scholar]
  74. Saltepe, B. , Bozkurt, E.U. , Haciosmanoglu, N. , and Seker, U.O.S. (2019) Genetic circuits to detect nanomaterial triggered toxicity through engineered heat shock response mechanism. ACS Synth Biol 8: 2404–2417. [DOI] [PubMed] [Google Scholar]
  75. Saltepe, B. , Bozkurt, E.U. , Gungen, M.A. , Cicek, A.E. , and Seker, U.O.S. (2021) Genetic circuits combined with machine learning provides fast responding living sensors. Biosens Bioelectron 178: 113028. [DOI] [PubMed] [Google Scholar]
  76. Saltepe, B. , Kehribar, E.S. , Yirmibesoglu, S.S.S. , and Seker, U.O.S. (2018) Cellular biosensors with engineered genetic circuits. ACS Sens 3: 13–26. [DOI] [PubMed] [Google Scholar]
  77. Sheth, R.U. , Yim, S.S. , Wu, F.L. , and Wang, H.H. (2017) Multiplex recording of cellular events over time on CRISPR biological tape. Science 358: 1457–1461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Silverman, A.D. , Karim, A.S. , and Jewett, M.C. (2020) Cell‐free gene expression: an expanded repertoire of applications. Nat Rev Genet 21: 151–170. [DOI] [PubMed] [Google Scholar]
  79. Siuti, P. , Yazbek, J. , and Lu, T.K. (2013) Synthetic circuits integrating logic and memory in living cells. Nat Biotechnol 31: 448–452. [DOI] [PubMed] [Google Scholar]
  80. Takahashi, M.K. , Tan, X. , Dy, A.J. , Braff, D. , Akana, R.T. , Furuta, Y. , et al. (2018) A low‐cost paper‐based synthetic biology platform for analyzing gut microbiota and host biomarkers. Nat Commun 9: 3347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Tang, S.Y. , and Cirino, P.C. (2011) Design and application of a mevalonate‐responsive regulatory protein. Angew Chem Int Ed 50: 1084–1086. [DOI] [PubMed] [Google Scholar]
  82. Tang, W.X. , and Liu, D.R. (2018) Rewritable multi‐event analog recording in bacterial and mammalian cells. Science 360: eaap8992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Valeri, J.A. , Collins, K.M. , Ramesh, P. , Alcantar, M.A. , Lepe, B.A. , Lu, T.K. , and Camacho, D.M. (2020) Sequence‐to‐function deep learning frameworks for engineered riboregulators. Nat Commun 11: 5058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Van Brempt, M. , Clauwaert, J. , Mey, F. , Stock, M. , Maertens, J. , Waegeman, W. , and De Mey, M. (2020) Predictive design of sigma factor‐specific promoters. Nat Commun 11: 5822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Volpetti, F. , Petrova, E. , and Maerkl, S.J. (2017) A microfluidic biodisplay. ACS Synth Biol 6: 1979–1987. [DOI] [PubMed] [Google Scholar]
  86. Wan, X. , Ho, T.Y.H. , and Wang, B. (2019a) Engineering prokaryote synthetic biology biosensors. In Handbook of Cell Biosensors. Thouand, G. (ed). Cham, Switzerland: Springer International Publishing, pp. 1–37. [Google Scholar]
  87. Wan, X. , Pinto, F. , Yu, L. , and Wang, B. (2020) Synthetic protein‐binding DNA sponge as a tool to tune gene expression and mitigate protein toxicity. Nat Commun 11: 5961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Wan, X. , Volpetti, F. , Petrova, E. , French, C. , Maerkl, S.J. , and Wang, B. (2019b) Cascaded amplifying circuits enable ultrasensitive cellular sensors for toxic metals. Nat Chem Biol 15: 540–548. [DOI] [PubMed] [Google Scholar]
  89. Wang, B. , Barahona, M. , and Buck, M. (2013a) A modular cell‐based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals. Biosens Bioelectron 40: 368–376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Wang, B. , Barahona, M. , Buck, M. , and Schumacher, J. (2013b) Rewiring cell signalling through chimaeric regulatory protein engineering. Biochem Soc Trans 41: 1195–1200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Wang, B. , Barahona, M. , and Buck, M. (2014) Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks. Nucleic Acids Res 42: 9484–9492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Wang, B. , Barahona, M. , and Buck, M. (2015) Amplification of small molecule‐inducible gene expression via tuning of intracellular receptor densities. Nucleic Acids Res 43: 1955–1964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Wang, B. , and Buck, M. (2012) Customizing cell signaling using engineered genetic logic circuits. Trends Microbiol 20: 376–384. [DOI] [PubMed] [Google Scholar]
  94. Wang, B. , and Buck, M. (2014) Rapid engineering of versatile molecular logic gates using heterologous genetic transcriptional modules. ChemComm 50: 11642–11644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Wang, B. , Kitney, R.I. , Joly, N. , and Buck, M. (2011) Engineering modular and orthogonal genetic logic gates for robust digital‐like synthetic biology. Nat Commun 2: 508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Watstein, D.M. , and Styczynski, M.P. (2018) Development of a pigment‐based whole‐cell zinc biosensor for human serum. ACS Synth Biol 7: 267–275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Webster, D.P. , TerAvest, M.A. , Doud, D.F.R. , Chakravorty, A. , Holmes, E.C. , Radens, C.M. , et al. (2014) An arsenic‐specific biosensor with genetically engineered Shewanella oneidensis in a bioelectrochemical system. Biosens Bioelectron 62: 320–324. [DOI] [PubMed] [Google Scholar]
  98. Whitfield, C.J. , Banks, A.M. , Dura, G. , Love, J. , Fieldsend, J.E. , Goodchild, S.A. , et al. (2020) Cell‐free protein synthesis in hydrogel materials. ChemComm 56: 7108–7111. [DOI] [PubMed] [Google Scholar]
  99. Winkler, J.D. , Garcia, C. , Olson, M. , Callaway, E. , and Kao, K.C. (2014) Evolved osmotolerant Escherichia coli mutants frequently exhibit defective N‐acetylglucosamine catabolism and point mutations in cell shape‐regulating protein MreB. Appl Environ Microbiol 80: 3729–3740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Wright, O. , Delmans, M. , Stan, G.B. , and Elis, T. (2015) GeneGuard: a modular plasmid system designed for biosafety. ACS Synth Biol 4: 307–316. [DOI] [PubMed] [Google Scholar]
  101. Wu, C.H. , Le, D. , Mulchandani, A. , and Chen, W. (2009) Optimization of a whole‐cell cadmium sensor with a toggle gene circuit. Biotechnol Prog 25: 898–903. [DOI] [PubMed] [Google Scholar]
  102. Yong, Y.C. , and Zhong, J.J. (2009) A genetically engineered whole‐cell pigment‐based bacterial biosensing system for quantification of N‐butyryl homoserine lactone quorum sensing signal. Biosens Bioelectron 25: 41–47. [DOI] [PubMed] [Google Scholar]
  103. Zhang, C.Y. , Siddiqui, S. , Navarrete, P.M. , and Yuan, J. (2020a) An integrated whole‐cell detection platform for heavy metal ions. IEEE Sens J 20: 4959–4967. [Google Scholar]
  104. Zhang, L. , Guo, W. , and Lu, Y. (2020b) Advances in cell‐free biosensors: principle, mechanism, and applications. Biotechnol J 15: e2000187. [DOI] [PubMed] [Google Scholar]

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