Zhang, J., Campbell, R. E., Ting, A. Y. & Tsien, R. Y. Creating new fluorescent probes for cell biology. Nat. Rev. Mol. Cell Biol. 3, 906–918 (2002).
Yagi, K. Applications of whole-cell bacterial sensors in biotechnology and environmental science. Appl. Microbiol. Biotechnol. 73, 1251–1258 (2007).
Rodrigo-Navarro, A., Sankaran, S., Dalby, M. J., del Campo, A. & Salmeron-Sanchez, M. Engineered living biomaterials. Nat. Rev. Mater. 6, 1175–1190 (2021).
Kang, J. H. & Chung, J.-K. Molecular-genetic imaging based on reporter gene expression. J. Nucl. Med. 49, 164S–179S (2008).
Ghim, C. M., Lee, S. K., Takayama, S. & Mitchell, R. J. The art of reporter proteins in science: past, present and future applications. BMB Rep. 43, 451–460 (2010).
Hall, C. V., Jacob, P. E., Ringold, G. M. & Lee, F. Expression and regulation of Escherichia coli lacZ gene fusions in mammalian cells. J. Mol. Appl. Genet. 2, 101–109 (1983).
Nielsen, D. A., Chou, J., MacKrell, A. J., Casadaban, M. J. & Steiner, D. F. Expression of a preproinsulin-β-galactosidase gene fusion in mammalian cells. Proc. Natl Acad. Sci. USA 80, 5198–5202 (1983).
Chalfie, M., Tu, Y., Euskirchen, G., Ward, W. W. & Prasher, D. C. Green fluorescent protein as a marker for gene expression. Science 263, 802–805 (1994).
Shepherd, E. S., DeLoache, W. C., Pruss, K. M., Whitaker, W. R. & Sonnenburg, J. L. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557, 434–438 (2018).
Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
Geddes, B. A. et al. Engineering transkingdom signalling in plants to control gene expression in rhizosphere bacteria. Nat. Commun. 10, 3430 (2019).
Heidecker, G. & Müller-Hill, B. Synthetic multifunctional proteins. Mol. Gen. Genet. 155, 301–307 (1977).
Sharma, S. K., Poudel Sharma, S. & Leblanc, R. M. Methods of detection of β-galactosidase enzyme in living cells. Enzyme Microb. Technol. 150, 109885 (2021).
Miller, J. H. Experiments in Molecular Genetics (Cold Spring Habour Laboratory, 1972).
Hui, C.-y et al. Genetic control of violacein biosynthesis to enable a pigment-based whole-cell lead biosensor. RSC Adv. 10, 28106–28113 (2020).
Hui, C.-y, Guo, Y., Li, H., Gao, C.-x & Yi, J. Detection of environmental pollutant cadmium in water using a visual bacterial biosensor. Sci. Rep. 12, 6898 (2022).
Hui, C.-y et al. Indigoidine biosynthesis triggered by the heavy metal-responsive transcription regulator: a visual whole-cell biosensor. Appl. Microbiol. Biotechnol. 105, 6087–6102 (2021).
Yoshida, K. et al. Novel carotenoid-based biosensor for simple visual detection of arsenite: characterization and preliminary evaluation for environmental application. Appl. Environ. Microbiol. 74, 6730–6738 (2008).
Hui, C.-y et al. Metabolic engineering of the carotenoid biosynthetic pathway toward a specific and sensitive inorganic mercury biosensor. RSC Adv. 12, 36142–36148 (2022).
He, Y., Zhang, T., Sun, H., Zhan, H. & Zhao, Y. A reporter for noninvasively monitoring gene expression and plant transformation. Hortic. Res. 7, 152 (2020).
Zalatan, J. G. et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 160, 339–350 (2015).
Sandell, J. L. & Zhu, T. C. A review of in-vivo optical properties of human tissues and its impact on PDT. J. Biophotonics 4, 773–787 (2011).
McKinnon, K. M. Flow cytometry: an overview. Curr. Protoc. Immunol. 120, 5.1.1–5.1.11 (2018).
Dietrich, J. A., McKee, A. E. & Keasling, J. D. High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–590 (2010).
Prabowo, C. P. S. et al. Production of natural colorants by metabolically engineered microorganisms. Trends Chem. 4, 608–626 (2022).
Ando, R., Hama, H., Yamamoto-Hino, M., Mizuno, H. & Miyawaki, A. An optical marker based on the UV-induced green-to-red photoconversion of a fluorescent protein. Proc. Natl Acad. Sci. USA 99, 12651–12656 (2002).
Shaner, N. C. et al. Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat. Biotechnol. 22, 1567–1572 (2004).
Shcherbakova, D. M. & Verkhusha, V. V. Near-infrared fluorescent proteins for multicolor in vivo imaging. Nat. Methods 10, 751–754 (2013).
Drepper, T. et al. Reporter proteins for in vivo fluorescence without oxygen. Nat. Biotechnol. 25, 443–445 (2007).
Belkin, S. et al. Remote detection of buried landmines using a bacterial sensor. Nat. Biotechnol. 35, 308–310 (2017).
Rigoulot, S. B. et al. Imaging of multiple fluorescent proteins in canopies enables synthetic biology in plants. Plant Biotechnol. J. 19, 830–843 (2021).
Shcherbakova, D. M., Stepanenko, O. V., Turoverov, K. K. & Verkhusha, V. V. Near-infrared fluorescent proteins: multiplexing and optogenetics across scales. Trends Biotechnol. 36, 1230–1243 (2018).
Liu, P., Mu, X., Zhang, X.-D. & Ming, D. The near-infrared-II fluorophores and advanced microscopy technologies development and application in bioimaging. Bioconjug. Chem. 31, 260–275 (2019).
Shu, X. et al. Mammalian expression of infrared fluorescent proteins engineered from a bacterial phytochrome. Science 324, 804–807 (2009).
Piatkevich, K. D. et al. Near-infrared fluorescent proteins engineered from bacterial phytochromes in neuroimaging. Biophys. J. 113, 2299–2309 (2017).
Rodriguez, E. A. et al. A far-red fluorescent protein evolved from a cyanobacterial phycobiliprotein. Nat. Methods 13, 763–769 (2016).
Ow, D. W. et al. Transient and stable expression of the firefly luciferase gene in plant cells and transgenic plants. Science 234, 856–859 (1986).
Millar, A. J., Short, S. R., Chua, N. H. & Kay, S. A. A novel circadian phenotype based on firefly luciferase expression in transgenic plants. Plant Cell 4, 1075–1087 (1992).
Liu, Y., Golden, S. S., Kondo, T., Ishiura, M. & Johnson, C. H. Bacterial luciferase as a reporter of circadian gene expression in cyanobacteria. J. Bacteriol. 177, 2080–2086 (1995).
McElroy, W. D. The energy source for bioluminescence in an isolated system. Proc. Natl Acad. Sci. USA 33, 342–345 (1947).
Love, A. C. & Prescher, J. A. Seeing (and using) the light: recent developments in bioluminescence technology. Cell Chem. Biol. 27, 904–920 (2020).
Cheng, H.-Y., Masiello, C. A., Bennett, G. N. & Silberg, J. J. Volatile gas production by methyl halide transferase: an in situ reporter of microbial gene expression in soil. Environ. Sci. Technol. 50, 8750–8759 (2016).
Cheng, H.-Y. et al. Ratiometric gas reporting: a nondisruptive approach to monitor gene expression in soils. ACS Synth. Biol. 7, 903–911 (2018).
Shu, X. et al. A genetically encoded tag for correlated light and electron microscopy of intact cells, tissues, and organisms. PLoS Biol. 9, e1001041 (2011).
Shapiro, M. G. et al. Biogenic gas nanostructures as ultrasonic molecular reporters. Nat. Nanotechnol. 9, 311–316 (2014).
Farhadi, A., Ho, G. H., Sawyer, D. P., Bourdeau, R. W. & Shapiro, M. G. Ultrasound imaging of gene expression in mammalian cells. Science 365, 1469–1475 (2019).
Wang, L. V. & Yao, J. A practical guide to photoacoustic tomography in the life sciences. Nat. Methods 13, 627–638 (2016).
Genove, G., DeMarco, U., Xu, H., Goins, W. F. & Ahrens, E. T. A new transgene reporter for in vivo magnetic resonance imaging. Nat. Med. 11, 450–454 (2005).
Luker, G. D. et al. Noninvasive imaging of protein–protein interactions in living animals. Proc. Natl Acad. Sci. USA 99, 6961–6966 (2002).
Daeffler, K. N. M. et al. Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Mol. Syst. Biol. 13, 923 (2017).
Del Valle, I. et al. Translating new synthetic biology advances for biosensing into the earth and environmental sciences. Front. Microbiol. 11, 618373 (2021).
McNerney, M. P., Doiron, K. E., Ng, T. L., Chang, T. Z. & Silver, P. A. Theranostic cells: emerging clinical applications of synthetic biology. Nat. Rev. Genet. 22, 730–746 (2021).
Voigt, C. A. Genetic parts to program bacteria. Curr. Opin. Biotechnol. 17, 548–557 (2006).
Lazar, J. T. & Tabor, J. J. Bacterial two-component systems as sensors for synthetic biology applications. Curr. Opin. Syst. Biol. 28, 100398 (2021).
Nielsen, A. A. et al. Genetic circuit design automation. Science 352, aac7341 (2016).
Meyer, A. J., Segall-Shapiro, T. H., Glassey, E., Zhang, J. & Voigt, C. A. Escherichia coli ‘Marionette’ strains with 12 highly optimized small-molecule sensors. Nat. Chem. Biol. 15, 196–204 (2019).
Basu, S., Mehreja, R., Thiberge, S., Chen, M.-T. & Weiss, R. Spatiotemporal control of gene expression with pulse-generating networks. Proc. Natl Acad. Sci. USA 101, 6355–6360 (2004).
Wilke, C. Remote sensing for crops spots pests and pathogens. ACS Cent. Sci. 9, 339–342 (2023).
do Prado Ribeiro, L. et al. Hyperspectral imaging to characterize plant–plant communication in response to insect herbivory. Plant Methods 14, 54 (2018).
Stuart, M. B., McGonigle, A. J. & Willmott, J. R. Hyperspectral imaging in environmental monitoring: a review of recent developments and technological advances in compact field deployable systems. Sensors 19, 3071 (2019).
Silva, C. S. et al. Near infrared hyperspectral imaging for forensic analysis of document forgery. Analyst 139, 5176–5184 (2014).
Chen, H.-W., McGurr, M. & Brickhouse, M. in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI (eds Velez-Reyes, M. & Kruse, F. A.) 947202 (SPIE, 2015).
Mahlein, A.-K., Kuska, M. T., Behmann, J., Polder, G. & Walter, A. Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annu. Rev. Phytopathol. 56, 535–558 (2018).
Leblanc, G., Kalacska, M. & Soffer, R. Detection of single graves by airborne hyperspectral imaging. Forensic Sci. Int. 245, 17–23 (2014).
Briechle, S., Molitor, N., Krzystek, P. & Vosselman, G. Detection of radioactive waste sites in the Chornobyl exclusion zone using UAV-based lidar data and multispectral imagery. ISPRS J. Photogramm. Remote Sens. 167, 345–362 (2020).
Lang, M., Stelzer, M. & Schomburg, D. BKM-react, an integrated biochemical reaction database. BMC Biochem. 12, 42 (2011).
Bansal, P. et al. Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res. 50, D693–D700 (2021).
Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief. Bioinform. 20, 1085–1093 (2019).
Taniguchi, M. & Lindsey, J. S. Database of absorption and fluorescence spectra of >300 common compounds for use in PhotoChemCAD. Photochem. Photobiol. 94, 290–327 (2018).
Noelle, A. et al. UV/Vis+ photochemistry database: structure, content and applications. J. Quant. Spectrosc. Radiat. Transf. 253, 107056 (2020).
Vokáčová, Z. & Burda, J. V. Computational study on spectral properties of the selected pigments from various photosystems: structure–transition energy relationship. J. Phys. Chem. A 111, 5864–5878 (2007).
Phan, K., De Meester, S., Raes, K., De Clerck, K. & Van Speybroeck, V. A comparative study on the photophysical properties of anthocyanins and pyranoanthocyanins. Chemistry 27, 5956–5971 (2021).
Jacquemin, D., Perpète, E. A., Ciofini, I. & Adamo, C. Accurate simulation of optical properties in dyes. Acc. Chem. Res. 42, 326–334 (2009).
Charaf-Eddin, A., Planchat, A., Mennucci, B., Adamo, C. & Jacquemin, D. Choosing a functional for computing absorption and fluorescence band shapes with TD-DFT. J. Chem. Theory Comput. 9, 2749–2760 (2013).
Conradie, J., Wamser, C. C. & Ghosh, A. Understanding hyperporphyrin spectra: TDDFT calculations on diprotonated tetrakis(p-aminophenyl)porphyrin. J. Phys. Chem. A 125, 9953–9961 (2021).
Jacquemin, D., Wathelet, V., Perpète, E. A. & Adamo, C. Extensive TD-DFT benchmark: singlet-excited states of organic molecules. J. Chem. Theory Comput. 5, 2420–2435 (2009).
Gritsenko, O. & Baerends, E. J. Asymptotic correction of the exchange–correlation kernel of time-dependent density functional theory for long-range charge-transfer excitations. J. Chem. Phys. 121, 655–660 (2004).
Laurent, A. D. & Jacquemin, D. TD-DFT benchmarks: a review. Int. J. Quant. Chem. 113, 2019–2039 (2013).
Duan, C., Nandy, A., Meyer, R., Arunachalam, N. & Kulik, H. J. A transferable recommender approach for selecting the best density functional approximations in chemical discovery. Nat. Comput. Sci. 3, 38–47 (2023).
Shao, Y., Mei, Y., Sundholm, D. & Kaila, V. R. I. Benchmarking the performance of time-dependent density functional theory methods on biochromophores. J. Chem. Theory Comput. 16, 587–600 (2020).
Weimer, A., Kohlstedt, M., Volke, D. C., Nikel, P. I. & Wittmann, C. Industrial biotechnology of Pseudomonas putida: advances and prospects. Appl. Microbiol. Biotechnol. 104, 7745–7766 (2020).
Steunou, A.-S., Astier, C. & Ouchane, S. Regulation of photosynthesis genes in Rubrivivax gelatinosus: transcription factor PpsR is involved in both negative and positive control. J. Bacteriol. 186, 3133–3142 (2004).
Lachaud, F. et al. Ground and excited state properties of new porphyrin based dyads: a combined theoretical and experimental study. J. Phys. Chem. A 116, 10736–10744 (2012).
Kantorovich, L. V. Mathematical methods of organizing and planning production. Manag. Sci. 6, 366–422 (1960).
Ramdas, A., García Trillos, N. & Cuturi, M. On Wasserstein two-sample testing and related families of nonparametric tests. Entropy 19, 47 (2017).
Kotlobay, A. A. et al. Genetically encodable bioluminescent system from fungi. Proc. Natl Acad. Sci. USA 115, 12728–12732 (2018).
Gallo, G., Longo, G., Pallottino, S. & Nguyen, S. Directed hypergraphs and applications. Discret. Appl. Math. 42, 177–201 (1993).
Chen, G. E. et al. Complete enzyme set for chlorophyll biosynthesis in Escherichia coli. Sci. Adv. 4, eaaq1407 (2018).
Bioucas-Dias, J. M. et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 354–379 (2012).
Xu, L., Li, J., Wong, A. & Peng, J. K-P-Means: a clustering algorithm of K ‘purified’ means for hyperspectral endmember estimation. IEEE Geosci. Remote Sens. Lett. 11, 1787–1791 (2014).
Prades, J., Safont, G., Salazar, A. & Vergara, L. Estimation of the number of endmembers in hyperspectral images using agglomerative clustering. Remote Sensing 12, 3585 (2020).
Wegele, R., Tasler, R., Zeng, Y., Rivera, M. & Frankenberg-Dinkel, N. The heme oxygenase (s)-phytochrome system of Pseudomonas aeruginosa. J. Biol. Chem. 279, 45791–45802 (2004).
Maiti, A. et al. Structural and photophysical characterization of the small ultra-red fluorescent protein. Nat. Commun. 14, 4155 (2023).
Boo, A. et al. Synthetic microbe-to-plant communication channels. Nat. Commun. 15, 1817 (2024).
Chen, G. E. & Hunter, C. N. Engineering chlorophyll, bacteriochlorophyll, and carotenoid biosynthetic pathways in Escherichia coli. ACS Synth. Biol. 12, 2236–2244 (2023).
Saga, Y. et al. Selective oxidation of B800 bacteriochlorophyll a in photosynthetic light-harvesting protein LH2. Sci. Rep. 9, 3636 (2019).
Becker, M., Nagarajan, V. & Parson, W. W. Properties of the excited singlet states of bacteriochlorophyll a and bacteriopheophytin a in polar solvents. J. Am. Chem. Soc. 113, 6840–6848 (1991).
Yokobayashi, Y., Weiss, R. & Arnold, F. H. Directed evolution of a genetic circuit. Proc. Natl Acad. Sci. USA 99, 16587–16591 (2002).
Tabor, J. J., Levskaya, A. & Voigt, C. A. Multichromatic control of gene expression in Escherichia coli. J. Mol. Biol. 405, 315–324 (2011).
Fernandez-Rodriguez, J., Moser, F., Song, M. & Voigt, C. A. Engineering RGB color vision into Escherichia coli. Nat. Chem. Biol. 13, 706–708 (2017).
Bradley, A. P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30, 1145–1159 (1997).
Gouterman, M. Study of the effects of substitution on the absorption spectra of porphin. J. Chem. Phys. 30, 1139–1161 (1959).
Taniguchi, M., Bocian, D. F., Holten, D. & Lindsey, J. S. Beyond green with synthetic chlorophylls—connecting structural features with spectral properties. J. Photoch. Photobio. C 52, 100513 (2022).
Gouterman, M. Spectra of porphyrins. J. Mol. Spectrosc. 6, 138–163 (1961).
Taniguchi, M. & Lindsey, J. S. Absorption and fluorescence spectral database of chlorophylls and analogues. Photochem. Photobiol. 97, 136–165 (2021).
Lakomy, I. et al. C45– and C50-carotenoids. Part 8. Synthesis of (all-E,2S,2′S)-bacterioruberin, (all-E,2S,2′S)-monoanhydrobacterioruberin, (all-E,2S,2′S)-bisanhydrobacterioruberin, (all-E,2R,2′R)-3,4,3′,4′-tetrahydrobisanhydrobacterioruberin, and (all-E,S)-2-isopentenyl-3,4-dehydrorhodopin. Helvetica 80, 472–486 (2010).
Levin, I., Liu, M., Voigt, C. A. & Coley, C. W. Merging enzymatic and synthetic chemistry with computational synthesis planning. Nat. Commun. 13, 7747 (2022).
Sankaranarayanan, K. et al. Similarity based enzymatic retrosynthesis. Chem. Sci. 13, 6039–6053 (2022).
Probst, D. et al. Biocatalysed synthesis planning using data-driven learning. Nat. Commun. 13, 964 (2022).
Koch, M., Duigou, T. & Faulon, J.-L. Reinforcement learning for bioretrosynthesis. ACS Synth. Biol. 9, 157–168 (2019).
Lin, G.-M., Warden-Rothman, R. & Voigt, C. A. Retrosynthetic design of metabolic pathways to chemicals not found in nature. Curr. Opin. Syst. Biol. 14, 82–107 (2019).
Koscher, B. A. et al. Autonomous, multiproperty-driven molecular discovery: from predictions to measurements and back. Science 382, eadi1407 (2023).
Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).
Chemla, Y., Sweeney, C. J., Wozniak, C. A. & Voigt, C. A. Design and regulation of engineered bacteria for environmental release. Nat. Microbiol. 10, 281–300 (2025).
Bloch, S. E. et al. Biological nitrogen fixation in maize: optimizing nitrogenase expression in a root-associated diazotroph. J. Exp. Bot. 71, 4591–4603 (2020).
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
Riniker, S. & Landrum, G. A. Better informed distance geometry: using what we know to improve conformation generation. J. Chem. Inf. Model. 55, 2562–2574 (2015).
Halgren, T. A. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 17, 490–519 (1996).
Bannwarth, C., Ehlert, S. & Grimme, S. GFN2-xTB—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15, 1652–1671 (2019).
Cossi, M. & Barone, V. Time-dependent density functional theory for molecules in liquid solutions. J. Chem. Phys. 115, 4708–4717 (2001).
Chemla, Y. et al. Parallel engineering of environmental bacteria and performance over years under jungle-simulated conditions. PLoS ONE 17, e0278471 (2022).
Johnson, S. C. Hierarchical clustering schemes. Psychometrika 32, 241–254 (1967).
Jiang, J., Liu, D., Gu, J. & Süsstrunk, S. What is the space of spectral sensitivity functions for digital color cameras? In Proc. 2013 IEEE Workshop on Applications of Computer Vision (WACV) 168–179 (IEEE, 2013).
Chemla, Y. Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria. Zenodo https://doi.org/10.5281/zenodo.14756888 (2025).
Levin, I. et al. VoigtLab/bioHSI: v.1.0.0. Zenodo https://doi.org/10.5281/zenodo.14827800 (2025).
Levin, I. et al. VoigtLab/npspec-webapp: v1.0.0. Zenodo https://zenodo.org/records/14827805 (2025).