[{"article_processing_charge":"No","volume":18,"oa":1,"date_updated":"2022-04-04T10:21:53Z","oa_version":"Published Version","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"We thank Virgile Andreani for useful discussions about the model and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"_id":"10939","citation":{"ista":"Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. 18(3), e1009950.","short":"A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022).","ama":"Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. <i>PLoS Computational Biology</i>. 2022;18(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">10.1371/journal.pcbi.1009950</a>","mla":"Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” <i>PLoS Computational Biology</i>, vol. 18, no. 3, e1009950, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">10.1371/journal.pcbi.1009950</a>.","chicago":"Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” <i>PLoS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">https://doi.org/10.1371/journal.pcbi.1009950</a>.","ieee":"A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level,” <i>PLoS Computational Biology</i>, vol. 18, no. 3. Public Library of Science, 2022.","apa":"Davidović, A., Chait, R. P., Batt, G., &#38; Ruess, J. (2022). Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">https://doi.org/10.1371/journal.pcbi.1009950</a>"},"publication_status":"published","author":[{"full_name":"Davidović, Anđela","last_name":"Davidović","first_name":"Anđela"},{"first_name":"Remy P","last_name":"Chait","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Gregory","last_name":"Batt","full_name":"Batt, Gregory"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","last_name":"Ruess","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"}],"abstract":[{"text":"Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system.","lang":"eng"}],"article_number":"e1009950","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ddc":["570","000"],"related_material":{"link":[{"url":"https://gitlab.pasteur.fr/adavidov/inferencelnakf","relation":"software"}]},"doi":"10.1371/journal.pcbi.1009950","year":"2022","title":"Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level","file_date_updated":"2022-04-04T10:14:39Z","publication":"PLoS Computational Biology","issue":"3","type":"journal_article","day":"18","status":"public","intvolume":"        18","department":[{"_id":"CaGu"}],"has_accepted_license":"1","file":[{"date_created":"2022-04-04T10:14:39Z","checksum":"458ef542761fb714ced214f240daf6b2","file_name":"2022_PLoSCompBio_Davidovic.pdf","file_size":2958642,"date_updated":"2022-04-04T10:14:39Z","access_level":"open_access","success":1,"creator":"dernst","file_id":"10947","content_type":"application/pdf","relation":"main_file"}],"date_created":"2022-04-03T22:01:42Z","date_published":"2022-03-18T00:00:00Z","article_type":"original","month":"03","language":[{"iso":"eng"}],"scopus_import":"1","publisher":"Public Library of Science"},{"file":[{"success":1,"content_type":"application/pdf","relation":"main_file","creator":"asandaue","file_id":"9833","file_name":"2021_ACSAppliedMaterialsAndInterfaces_Zisis.pdf","file_size":7123293,"date_created":"2021-08-09T09:44:03Z","checksum":"b043a91d9f9200e467b970b692687ed3","date_updated":"2021-08-09T09:44:03Z","access_level":"open_access"}],"date_created":"2021-08-08T22:01:28Z","department":[{"_id":"MiSi"},{"_id":"GaTk"},{"_id":"Bio"},{"_id":"CaGu"}],"has_accepted_license":"1","language":[{"iso":"eng"}],"publisher":"American Chemical Society","scopus_import":"1","article_type":"original","date_published":"2021-08-04T00:00:00Z","month":"08","page":"35545–35560","file_date_updated":"2021-08-09T09:44:03Z","issue":"30","publication":"ACS Applied Materials and Interfaces","status":"public","intvolume":"        13","type":"journal_article","day":"04","ddc":["620","570"],"isi":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)"},"external_id":{"pmid":["34283577"],"isi":["000683741400026"]},"title":"Sequential and switchable patterning for studying cellular processes under spatiotemporal control","ec_funded":1,"year":"2021","doi":"10.1021/acsami.1c09850","acknowledgement":"We would like to thank Charlott Leu for the production of our chromium wafers, Louise Ritter for her contribution of the IF stainings in Figure 4, Shokoufeh Teymouri for her help with the Bioinert coated slides, and finally Prof. Dr. Joachim Rädler for his valuable scientific guidance.","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","quality_controlled":"1","project":[{"grant_number":"724373","call_identifier":"H2020","_id":"25FE9508-B435-11E9-9278-68D0E5697425","name":"Cellular navigation along spatial gradients"}],"oa_version":"Published Version","_id":"9822","pmid":1,"publication_identifier":{"issn":["19448244"],"eissn":["19448252"]},"oa":1,"volume":13,"date_updated":"2023-08-10T14:22:48Z","article_processing_charge":"Yes (in subscription journal)","author":[{"first_name":"Themistoklis","full_name":"Zisis, Themistoklis","last_name":"Zisis"},{"id":"346C1EC6-F248-11E8-B48F-1D18A9856A87","last_name":"Schwarz","full_name":"Schwarz, Jan","first_name":"Jan"},{"first_name":"Miriam","last_name":"Balles","full_name":"Balles, Miriam"},{"first_name":"Maibritt","full_name":"Kretschmer, Maibritt","last_name":"Kretschmer"},{"full_name":"Nemethova, Maria","last_name":"Nemethova","first_name":"Maria","id":"34E27F1C-F248-11E8-B48F-1D18A9856A87"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P","full_name":"Chait, Remy P","last_name":"Chait","orcid":"0000-0003-0876-3187"},{"id":"4E01D6B4-F248-11E8-B48F-1D18A9856A87","first_name":"Robert","full_name":"Hauschild, Robert","last_name":"Hauschild","orcid":"0000-0001-9843-3522"},{"full_name":"Lange, Janina","last_name":"Lange","first_name":"Janina"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet","full_name":"Guet, Calin C"},{"last_name":"Sixt","full_name":"Sixt, Michael K","orcid":"0000-0002-4561-241X","first_name":"Michael K","id":"41E9FBEA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Zahler, Stefan","last_name":"Zahler","first_name":"Stefan"}],"abstract":[{"text":"Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science.","lang":"eng"}],"publication_status":"published","citation":{"apa":"Zisis, T., Schwarz, J., Balles, M., Kretschmer, M., Nemethova, M., Chait, R. P., … Zahler, S. (2021). Sequential and switchable patterning for studying cellular processes under spatiotemporal control. <i>ACS Applied Materials and Interfaces</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acsami.1c09850\">https://doi.org/10.1021/acsami.1c09850</a>","ieee":"T. Zisis <i>et al.</i>, “Sequential and switchable patterning for studying cellular processes under spatiotemporal control,” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 30. American Chemical Society, pp. 35545–35560, 2021.","chicago":"Zisis, Themistoklis, Jan Schwarz, Miriam Balles, Maibritt Kretschmer, Maria Nemethova, Remy P Chait, Robert Hauschild, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” <i>ACS Applied Materials and Interfaces</i>. American Chemical Society, 2021. <a href=\"https://doi.org/10.1021/acsami.1c09850\">https://doi.org/10.1021/acsami.1c09850</a>.","mla":"Zisis, Themistoklis, et al. “Sequential and Switchable Patterning for Studying Cellular Processes under Spatiotemporal Control.” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 30, American Chemical Society, 2021, pp. 35545–35560, doi:<a href=\"https://doi.org/10.1021/acsami.1c09850\">10.1021/acsami.1c09850</a>.","ama":"Zisis T, Schwarz J, Balles M, et al. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. <i>ACS Applied Materials and Interfaces</i>. 2021;13(30):35545–35560. doi:<a href=\"https://doi.org/10.1021/acsami.1c09850\">10.1021/acsami.1c09850</a>","ista":"Zisis T, Schwarz J, Balles M, Kretschmer M, Nemethova M, Chait RP, Hauschild R, Lange J, Guet CC, Sixt MK, Zahler S. 2021. Sequential and switchable patterning for studying cellular processes under spatiotemporal control. ACS Applied Materials and Interfaces. 13(30), 35545–35560.","short":"T. Zisis, J. Schwarz, M. Balles, M. Kretschmer, M. Nemethova, R.P. Chait, R. Hauschild, J. Lange, C.C. Guet, M.K. Sixt, S. Zahler, ACS Applied Materials and Interfaces 13 (2021) 35545–35560."}},{"main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pubmed/30169679","open_access":"1"}],"isi":1,"external_id":{"isi":["000452567200006"],"pmid":["30169679"]},"title":"Nonoptimal gene expression creates latent potential for antibiotic resistance","year":"2018","doi":"10.1093/molbev/msy163","publication_identifier":{"issn":["0737-4038"]},"pmid":1,"_id":"19","quality_controlled":"1","oa_version":"Submitted Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","date_updated":"2023-10-17T11:51:06Z","volume":35,"oa":1,"publist_id":"8036","abstract":[{"lang":"eng","text":"Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses."}],"author":[{"full_name":"Palmer, Adam","last_name":"Palmer","first_name":"Adam"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","first_name":"Remy P"},{"first_name":"Roy","full_name":"Kishony, Roy","last_name":"Kishony"}],"citation":{"ama":"Palmer A, Chait RP, Kishony R. Nonoptimal gene expression creates latent potential for antibiotic resistance. <i>Molecular Biology and Evolution</i>. 2018;35(11):2669-2684. doi:<a href=\"https://doi.org/10.1093/molbev/msy163\">10.1093/molbev/msy163</a>","mla":"Palmer, Adam, et al. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” <i>Molecular Biology and Evolution</i>, vol. 35, no. 11, Oxford University Press, 2018, pp. 2669–84, doi:<a href=\"https://doi.org/10.1093/molbev/msy163\">10.1093/molbev/msy163</a>.","short":"A. Palmer, R.P. Chait, R. Kishony, Molecular Biology and Evolution 35 (2018) 2669–2684.","ista":"Palmer A, Chait RP, Kishony R. 2018. Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. 35(11), 2669–2684.","apa":"Palmer, A., Chait, R. P., &#38; Kishony, R. (2018). Nonoptimal gene expression creates latent potential for antibiotic resistance. <i>Molecular Biology and Evolution</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/molbev/msy163\">https://doi.org/10.1093/molbev/msy163</a>","ieee":"A. Palmer, R. P. Chait, and R. Kishony, “Nonoptimal gene expression creates latent potential for antibiotic resistance,” <i>Molecular Biology and Evolution</i>, vol. 35, no. 11. Oxford University Press, pp. 2669–2684, 2018.","chicago":"Palmer, Adam, Remy P Chait, and Roy Kishony. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” <i>Molecular Biology and Evolution</i>. Oxford University Press, 2018. <a href=\"https://doi.org/10.1093/molbev/msy163\">https://doi.org/10.1093/molbev/msy163</a>."},"publication_status":"published","date_created":"2018-12-11T11:44:11Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"scopus_import":"1","publisher":"Oxford University Press","language":[{"iso":"eng"}],"month":"08","article_type":"original","date_published":"2018-08-28T00:00:00Z","publication":"Molecular Biology and Evolution","issue":"11","page":"2669 - 2684","intvolume":"        35","status":"public","day":"28","type":"journal_article"},{"status":"public","intvolume":"         8","type":"journal_article","day":"01","file_date_updated":"2020-07-14T12:47:20Z","issue":"1","publication":"Nature Communications","language":[{"iso":"eng"}],"publisher":"Nature Publishing Group","scopus_import":1,"date_published":"2017-12-01T00:00:00Z","month":"12","file":[{"access_level":"open_access","date_updated":"2020-07-14T12:47:20Z","file_size":1951699,"file_name":"IST-2017-911-v1+1_s41467-017-01683-1.pdf","checksum":"44bb5d0229926c23a9955d9fe0f9723f","date_created":"2018-12-12T10:16:05Z","relation":"main_file","content_type":"application/pdf","creator":"system","file_id":"5190"}],"date_created":"2018-12-11T11:47:30Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"has_accepted_license":"1","author":[{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P","orcid":"0000-0003-0876-3187","last_name":"Chait","full_name":"Chait, Remy P"},{"first_name":"Jakob","last_name":"Ruess","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"},{"id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5396-4346","full_name":"Bergmiller, Tobias","last_name":"Bergmiller","first_name":"Tobias"},{"last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","last_name":"Guet","full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","first_name":"Calin C"}],"abstract":[{"text":"Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.","lang":"eng"}],"publication_status":"published","citation":{"chicago":"Chait, Remy P, Jakob Ruess, Tobias Bergmiller, Gašper Tkačik, and Calin C Guet. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” <i>Nature Communications</i>. Nature Publishing Group, 2017. <a href=\"https://doi.org/10.1038/s41467-017-01683-1\">https://doi.org/10.1038/s41467-017-01683-1</a>.","apa":"Chait, R. P., Ruess, J., Bergmiller, T., Tkačik, G., &#38; Guet, C. C. (2017). Shaping bacterial population behavior through computer interfaced control of individual cells. <i>Nature Communications</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/s41467-017-01683-1\">https://doi.org/10.1038/s41467-017-01683-1</a>","ieee":"R. P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, and C. C. Guet, “Shaping bacterial population behavior through computer interfaced control of individual cells,” <i>Nature Communications</i>, vol. 8, no. 1. Nature Publishing Group, 2017.","ista":"Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. 2017. Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. 8(1), 1535.","short":"R.P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, C.C. Guet, Nature Communications 8 (2017).","mla":"Chait, Remy P., et al. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” <i>Nature Communications</i>, vol. 8, no. 1, 1535, Nature Publishing Group, 2017, doi:<a href=\"https://doi.org/10.1038/s41467-017-01683-1\">10.1038/s41467-017-01683-1</a>.","ama":"Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. Shaping bacterial population behavior through computer interfaced control of individual cells. <i>Nature Communications</i>. 2017;8(1). doi:<a href=\"https://doi.org/10.1038/s41467-017-01683-1\">10.1038/s41467-017-01683-1</a>"},"acknowledgement":"We are grateful to M. Lang, H. Janovjak, M. Khammash, A. Milias-Argeitis, M. Rullan, G. Batt, A. Bosma-Moody, Aryan, S. Leibler, and members of the Guet and Tkačik groups for helpful discussion, comments, and suggestions. We thank A. Moglich, T. Mathes, J. Tabor, and S. Schmidl for kind gifts of strains, and R. Hauschild, B. Knep, M. Lang, T. Asenov, E. Papusheva, T. Menner, T. Adletzberger, and J. Merrin for technical assistance. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. [291734]. (to R.C. and J.R.), Austrian Science Fund grant FWF P28844 (to G.T.), and internal IST Austria Interdisciplinary Project Support. J.R. acknowledges support from the Agence Nationale de la Recherche (ANR) under Grant Nos. ANR-16-CE33-0018 (MEMIP), ANR-16-CE12-0025 (COGEX) and ANR-10-BINF-06-01 (ICEBERG).","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","project":[{"grant_number":"291734","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF"}],"quality_controlled":"1","_id":"613","publication_identifier":{"issn":["20411723"]},"oa":1,"publist_id":"7191","date_updated":"2021-01-12T08:06:15Z","volume":8,"article_processing_charge":"Yes (in subscription journal)","pubrep_id":"911","title":"Shaping bacterial population behavior through computer interfaced control of individual cells","ec_funded":1,"year":"2017","doi":"10.1038/s41467-017-01683-1","ddc":["576","579"],"article_number":"1535","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"}},{"pubrep_id":"662","title":"Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments","year":"2016","doi":"10.1038/ncomms10333","ddc":["570","579"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_number":"10333","abstract":[{"text":"Antibiotic-sensitive and -resistant bacteria coexist in natural environments with low, if detectable, antibiotic concentrations. Except possibly around localized antibiotic sources, where resistance can provide a strong advantage, bacterial fitness is dominated by stresses unaffected by resistance to the antibiotic. How do such mixed and heterogeneous conditions influence the selective advantage or disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels of tetracyclines potentiate selection for or against tetracycline resistance around localized sources of almost any toxin or stress. Furthermore, certain stresses generate alternating rings of selection for and against resistance around a localized source of the antibiotic. In these conditions, localized antibiotic sources, even at high strengths, can actually produce a net selection against resistance to the antibiotic. Our results show that interactions between the effects of an antibiotic and other stresses in inhomogeneous environments can generate pervasive, complex patterns of selection both for and against antibiotic resistance.","lang":"eng"}],"author":[{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P","last_name":"Chait","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187"},{"full_name":"Palmer, Adam","last_name":"Palmer","first_name":"Adam"},{"first_name":"Idan","full_name":"Yelin, Idan","last_name":"Yelin"},{"last_name":"Kishony","full_name":"Kishony, Roy","first_name":"Roy"}],"publication_status":"published","citation":{"short":"R.P. Chait, A. Palmer, I. Yelin, R. Kishony, Nature Communications 7 (2016).","ista":"Chait RP, Palmer A, Yelin I, Kishony R. 2016. Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. 7, 10333.","ama":"Chait RP, Palmer A, Yelin I, Kishony R. Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. <i>Nature Communications</i>. 2016;7. doi:<a href=\"https://doi.org/10.1038/ncomms10333\">10.1038/ncomms10333</a>","mla":"Chait, Remy P., et al. “Pervasive Selection for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.” <i>Nature Communications</i>, vol. 7, 10333, Nature Publishing Group, 2016, doi:<a href=\"https://doi.org/10.1038/ncomms10333\">10.1038/ncomms10333</a>.","chicago":"Chait, Remy P, Adam Palmer, Idan Yelin, and Roy Kishony. “Pervasive Selection for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.” <i>Nature Communications</i>. Nature Publishing Group, 2016. <a href=\"https://doi.org/10.1038/ncomms10333\">https://doi.org/10.1038/ncomms10333</a>.","ieee":"R. P. Chait, A. Palmer, I. Yelin, and R. Kishony, “Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments,” <i>Nature Communications</i>, vol. 7. Nature Publishing Group, 2016.","apa":"Chait, R. P., Palmer, A., Yelin, I., &#38; Kishony, R. (2016). Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. <i>Nature Communications</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/ncomms10333\">https://doi.org/10.1038/ncomms10333</a>"},"_id":"1332","acknowledgement":"This work was partially supported by US National Institutes of Health grant R01-GM081617, Israeli Centers of Research Excellence I-CORE Program ISF Grant No. 152/11, and the European Research Council FP7 ERC Grant 281891.","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","quality_controlled":"1","volume":7,"publist_id":"5936","date_updated":"2021-01-12T06:49:57Z","oa":1,"publisher":"Nature Publishing Group","scopus_import":1,"language":[{"iso":"eng"}],"month":"01","date_published":"2016-01-20T00:00:00Z","file":[{"creator":"system","file_id":"5039","content_type":"application/pdf","relation":"main_file","date_created":"2018-12-12T10:13:52Z","checksum":"ef147bcbb8bd37e9079cf3ce06f5815d","file_name":"IST-2016-662-v1+1_ncomms10333.pdf","file_size":1844107,"date_updated":"2020-07-14T12:44:44Z","access_level":"open_access"}],"date_created":"2018-12-11T11:51:25Z","has_accepted_license":"1","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"intvolume":"         7","status":"public","day":"20","type":"journal_article","publication":"Nature Communications","file_date_updated":"2020-07-14T12:44:44Z"},{"status":"public","author":[{"last_name":"Baym","full_name":"Baym, Michael","first_name":"Michael"},{"last_name":"Lieberman","full_name":"Lieberman, Tami","first_name":"Tami"},{"first_name":"Eric","full_name":"Kelsic, Eric","last_name":"Kelsic"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P","last_name":"Chait"},{"first_name":"Rotem","full_name":"Gross, Rotem","last_name":"Gross"},{"last_name":"Yelin","full_name":"Yelin, Idan","first_name":"Idan"},{"full_name":"Kishony, Roy","last_name":"Kishony","first_name":"Roy"}],"abstract":[{"text":"A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front.While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front,we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate provides a versatile platformfor studying microbial adaption and directly visualizing evolutionary dynamics.","lang":"eng"}],"intvolume":"       353","type":"journal_article","publication_status":"published","day":"09","citation":{"mla":"Baym, Michael, et al. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.” <i>Science</i>, vol. 353, no. 6304, American Association for the Advancement of Science, 2016, pp. 1147–51, doi:<a href=\"https://doi.org/10.1126/science.aag0822\">10.1126/science.aag0822</a>.","ama":"Baym M, Lieberman T, Kelsic E, et al. Spatiotemporal microbial evolution on antibiotic landscapes. <i>Science</i>. 2016;353(6304):1147-1151. doi:<a href=\"https://doi.org/10.1126/science.aag0822\">10.1126/science.aag0822</a>","ista":"Baym M, Lieberman T, Kelsic E, Chait RP, Gross R, Yelin I, Kishony R. 2016. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 353(6304), 1147–1151.","short":"M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony, Science 353 (2016) 1147–1151.","apa":"Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., &#38; Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes. <i>Science</i>. American Association for the Advancement of Science. <a href=\"https://doi.org/10.1126/science.aag0822\">https://doi.org/10.1126/science.aag0822</a>","ieee":"M. Baym <i>et al.</i>, “Spatiotemporal microbial evolution on antibiotic landscapes,” <i>Science</i>, vol. 353, no. 6304. American Association for the Advancement of Science, pp. 1147–1151, 2016.","chicago":"Baym, Michael, Tami Lieberman, Eric Kelsic, Remy P Chait, Rotem Gross, Idan Yelin, and Roy Kishony. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.” <i>Science</i>. American Association for the Advancement of Science, 2016. <a href=\"https://doi.org/10.1126/science.aag0822\">https://doi.org/10.1126/science.aag0822</a>."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","quality_controlled":"1","_id":"1342","date_updated":"2021-01-12T06:50:01Z","volume":353,"oa":1,"publist_id":"5911","page":"1147 - 1151","issue":"6304","publication":"Science","language":[{"iso":"eng"}],"publisher":"American Association for the Advancement of Science","title":"Spatiotemporal microbial evolution on antibiotic landscapes","scopus_import":1,"date_published":"2016-09-09T00:00:00Z","year":"2016","month":"09","doi":"10.1126/science.aag0822","date_created":"2018-12-11T11:51:29Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/","open_access":"1"}]},{"title":"Compounds that select against the tetracycline-resistance efflux pump","doi":"10.1038/nchembio.2176","year":"2016","main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069154/"}],"author":[{"first_name":"Laura","full_name":"Stone, Laura","last_name":"Stone"},{"last_name":"Baym","full_name":"Baym, Michael","first_name":"Michael"},{"first_name":"Tami","full_name":"Lieberman, Tami","last_name":"Lieberman"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P","last_name":"Chait","first_name":"Remy P"},{"first_name":"Jon","last_name":"Clardy","full_name":"Clardy, Jon"},{"first_name":"Roy","last_name":"Kishony","full_name":"Kishony, Roy"}],"abstract":[{"text":"We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline-resistance efflux pump in Escherichia coli and identified two hits, β-thujaplicin and disulfiram. Treating a tetracycline-resistant population with β-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline.","lang":"eng"}],"citation":{"ama":"Stone L, Baym M, Lieberman T, Chait RP, Clardy J, Kishony R. Compounds that select against the tetracycline-resistance efflux pump. <i>Nature Chemical Biology</i>. 2016;12(11):902-904. doi:<a href=\"https://doi.org/10.1038/nchembio.2176\">10.1038/nchembio.2176</a>","mla":"Stone, Laura, et al. “Compounds That Select against the Tetracycline-Resistance Efflux Pump.” <i>Nature Chemical Biology</i>, vol. 12, no. 11, Nature Publishing Group, 2016, pp. 902–04, doi:<a href=\"https://doi.org/10.1038/nchembio.2176\">10.1038/nchembio.2176</a>.","short":"L. Stone, M. Baym, T. Lieberman, R.P. Chait, J. Clardy, R. Kishony, Nature Chemical Biology 12 (2016) 902–904.","ista":"Stone L, Baym M, Lieberman T, Chait RP, Clardy J, Kishony R. 2016. Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. 12(11), 902–904.","ieee":"L. Stone, M. Baym, T. Lieberman, R. P. Chait, J. Clardy, and R. Kishony, “Compounds that select against the tetracycline-resistance efflux pump,” <i>Nature Chemical Biology</i>, vol. 12, no. 11. Nature Publishing Group, pp. 902–904, 2016.","apa":"Stone, L., Baym, M., Lieberman, T., Chait, R. P., Clardy, J., &#38; Kishony, R. (2016). Compounds that select against the tetracycline-resistance efflux pump. <i>Nature Chemical Biology</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/nchembio.2176\">https://doi.org/10.1038/nchembio.2176</a>","chicago":"Stone, Laura, Michael Baym, Tami Lieberman, Remy P Chait, Jon Clardy, and Roy Kishony. “Compounds That Select against the Tetracycline-Resistance Efflux Pump.” <i>Nature Chemical Biology</i>. Nature Publishing Group, 2016. <a href=\"https://doi.org/10.1038/nchembio.2176\">https://doi.org/10.1038/nchembio.2176</a>."},"publication_status":"published","quality_controlled":"1","oa_version":"Preprint","acknowledgement":"This work was supported in part by National Institute of Allergy and Infectious Diseases grant U54 AI057159, US National Institutes of Health grants R01 GM081617 (to R.K.) and GM086258 (to J.C.), European Research Council FP7 ERC grant 281891 (to R.K.) and a National Science Foundation Graduate Fellowship (to L.K.S.).\r\n","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1290","publist_id":"6026","volume":12,"oa":1,"date_updated":"2021-01-12T06:49:39Z","language":[{"iso":"eng"}],"scopus_import":1,"publisher":"Nature Publishing Group","date_published":"2016-11-01T00:00:00Z","month":"11","date_created":"2018-12-11T11:51:10Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"status":"public","intvolume":"        12","type":"journal_article","day":"01","page":"902 - 904","publication":"Nature Chemical Biology","issue":"11"},{"year":"2013","doi":"10.1126/science.1229858","month":"01","date_published":"2013-01-04T00:00:00Z","publisher":"American Association for the Advancement of Science","scopus_import":1,"title":"Dynamic persistence of antibiotic-stressed mycobacteria","language":[{"iso":"eng"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:46:48Z","publication_status":"published","citation":{"mla":"Wakamoto, Yurichi, et al. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.” <i>Science</i>, vol. 339, no. 6115, American Association for the Advancement of Science, 2013, pp. 91–95, doi:<a href=\"https://doi.org/10.1126/science.1229858\">10.1126/science.1229858</a>.","ama":"Wakamoto Y, Dhar N, Chait RP, et al. Dynamic persistence of antibiotic-stressed mycobacteria. <i>Science</i>. 2013;339(6115):91-95. doi:<a href=\"https://doi.org/10.1126/science.1229858\">10.1126/science.1229858</a>","ista":"Wakamoto Y, Dhar N, Chait RP, Schneider K, Signorino Gelo F, Leibler S, Mckinney J. 2013. Dynamic persistence of antibiotic-stressed mycobacteria. Science. 339(6115), 91–95.","short":"Y. Wakamoto, N. Dhar, R.P. Chait, K. Schneider, F. Signorino Gelo, S. Leibler, J. Mckinney, Science 339 (2013) 91–95.","ieee":"Y. Wakamoto <i>et al.</i>, “Dynamic persistence of antibiotic-stressed mycobacteria,” <i>Science</i>, vol. 339, no. 6115. American Association for the Advancement of Science, pp. 91–95, 2013.","apa":"Wakamoto, Y., Dhar, N., Chait, R. P., Schneider, K., Signorino Gelo, F., Leibler, S., &#38; Mckinney, J. (2013). Dynamic persistence of antibiotic-stressed mycobacteria. <i>Science</i>. American Association for the Advancement of Science. <a href=\"https://doi.org/10.1126/science.1229858\">https://doi.org/10.1126/science.1229858</a>","chicago":"Wakamoto, Yurichi, Neraaj Dhar, Remy P Chait, Katrin Schneider, François Signorino Gelo, Stanislas Leibler, and John Mckinney. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.” <i>Science</i>. American Association for the Advancement of Science, 2013. <a href=\"https://doi.org/10.1126/science.1229858\">https://doi.org/10.1126/science.1229858</a>."},"day":"04","type":"journal_article","abstract":[{"lang":"eng","text":"Exposure of an isogenic bacterial population to a cidal antibiotic typically fails to eliminate a small fraction of refractory cells. Historically, fractional killing has been attributed to infrequently dividing or nondividing &quot;persisters.&quot; Using microfluidic cultures and time-lapse microscopy, we found that Mycobacterium smegmatis persists by dividing in the presence of the drug isoniazid (INH). Although persistence in these studies was characterized by stable numbers of cells, this apparent stability was actually a dynamic state of balanced division and death. Single cells expressed catalase-peroxidase (KatG), which activates INH, in stochastic pulses that were negatively correlated with cell survival. These behaviors may reflect epigenetic effects, because KatG pulsing and death were correlated between sibling cells. Selection of lineages characterized by infrequent KatG pulsing could allow nonresponsive adaptation during prolonged drug exposure."}],"intvolume":"       339","author":[{"last_name":"Wakamoto","full_name":"Wakamoto, Yurichi","first_name":"Yurichi"},{"first_name":"Neraaj","full_name":"Dhar, Neraaj","last_name":"Dhar"},{"last_name":"Chait","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Schneider, Katrin","last_name":"Schneider","first_name":"Katrin"},{"full_name":"Signorino Gelo, François","last_name":"Signorino Gelo","first_name":"François"},{"first_name":"Stanislas","full_name":"Leibler, Stanislas","last_name":"Leibler"},{"first_name":"John","last_name":"Mckinney","full_name":"Mckinney, John"}],"status":"public","issue":"6115","publication":"Science","publist_id":"7321","date_updated":"2021-01-12T08:01:06Z","volume":339,"page":"91 - 95","_id":"499","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","oa_version":"None"},{"date_created":"2018-12-11T12:07:43Z","date_published":"2009-01-01T00:00:00Z","month":"01","doi":"10.1016/j.cell.2009.10.025","year":"2009","publisher":"Cell Press","title":"Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions","volume":139,"publist_id":"1890","date_updated":"2021-01-12T07:55:27Z","page":"707 - 718","issue":"4","publication":"Cell","quality_controlled":0,"_id":"4228","extern":1,"type":"journal_article","publication_status":"published","day":"01","citation":{"short":"T. Bollenbach, S. Quan, R.P. Chait, R. Kishony, Cell 139 (2009) 707–718.","ista":"Bollenbach T, Quan S, Chait RP, Kishony R. 2009. Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions. Cell. 139(4), 707–718.","mla":"Bollenbach, Tobias, et al. “Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions.” <i>Cell</i>, vol. 139, no. 4, Cell Press, 2009, pp. 707–18, doi:<a href=\"https://doi.org/10.1016/j.cell.2009.10.025\">10.1016/j.cell.2009.10.025</a>.","ama":"Bollenbach T, Quan S, Chait RP, Kishony R. Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions. <i>Cell</i>. 2009;139(4):707-718. doi:<a href=\"https://doi.org/10.1016/j.cell.2009.10.025\">10.1016/j.cell.2009.10.025</a>","chicago":"Bollenbach, Tobias, Selwyn Quan, Remy P Chait, and Roy Kishony. “Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions.” <i>Cell</i>. Cell Press, 2009. <a href=\"https://doi.org/10.1016/j.cell.2009.10.025\">https://doi.org/10.1016/j.cell.2009.10.025</a>.","apa":"Bollenbach, T., Quan, S., Chait, R. P., &#38; Kishony, R. (2009). Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions. <i>Cell</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.cell.2009.10.025\">https://doi.org/10.1016/j.cell.2009.10.025</a>","ieee":"T. Bollenbach, S. Quan, R. P. Chait, and R. Kishony, “Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions,” <i>Cell</i>, vol. 139, no. 4. Cell Press, pp. 707–718, 2009."},"author":[{"last_name":"Bollenbach","full_name":"Bollenbach, Tobias","first_name":"Tobias"},{"first_name":"Selwyn","full_name":"Quan, Selwyn","last_name":"Quan"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0876-3187","full_name":"Remy Chait","last_name":"Chait","first_name":"Remy P"},{"first_name":"Roy","full_name":"Kishony, Roy","last_name":"Kishony"}],"status":"public","abstract":[{"lang":"eng","text":"Suppressive drug interactions, in which one antibiotic can actually help bacterial cells to grow faster in the presence of another, occur between protein and DNA synthesis inhibitors. Here, we show that this suppression results from nonoptimal regulation of ribosomal genes in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. To test whether ribosomal gene expression under DNA stress is nonoptimal for growth rate, we sequentially deleted up to six of the seven ribosomal RNA operons. These synthetic manipulations of ribosomal gene expression correct the protein-DNA imbalance, lead to improved survival and growth, and completely remove the suppressive drug interaction. A simple mathematical model explains the nonoptimal regulation in different nutrient environments. These results reveal the genetic mechanism underlying an important class of suppressive drug interactions."}],"intvolume":"       139"}]