[{"day":"01","language":[{"iso":"eng"}],"publication":"PNAS","publication_status":"published","year":"2015","doi":"10.1073/pnas.1510282112","volume":112,"issue":"48","date_published":"2015-12-01T00:00:00Z","date_created":"2018-12-11T11:52:47Z","page":"14906 - 14911","oa_version":"None","acknowledgement":"This work is part of the research program of the Foundation for Fundamental Research on Matter, which is part of the Netherlands Organization for Scientific Research (NWO). M.G.J.d.V. was (partially) funded by NWO Earth and Life Sciences (ALW), project 863.14.015. We thank D. M. Weinreich, J. A. G. M. de Visser, T. Paixão, J. Polechová, T. Friedlander, and A. E. Mayo for reading and commenting on earlier versions of the manuscript and B. Houchmandzadeh, O. Rivoire, and M. Hemery for discussions and suggestions on the Markov computation. Furthermore, we thank F. J. Poelwijk for sharing plasmid pCascade5 and pRD007 and Y. Yokobayashi for sharing plasmid pINV-110. We also thank the anonymous reviewers for remarks on the initial version of the manuscript.","abstract":[{"lang":"eng","text":"Epistatic interactions can frustrate and shape evolutionary change. Indeed, phenotypes may fail to evolve when essential mutations are only accessible through positive selection if they are fixed simultaneously. How environmental variability affects such constraints is poorly understood. Here, we studied genetic constraints in fixed and fluctuating environments using the Escherichia coli lac operon as a model system for genotype-environment interactions. We found that, in different fixed environments, all trajectories that were reconstructed by applying point mutations within the transcription factor-operator interface became trapped at suboptima, where no additional improvements were possible. Paradoxically, repeated switching between these same environments allows unconstrained adaptation by continuous improvements. This evolutionary mode is explained by pervasive cross-environmental tradeoffs that reposition the peaks in such a way that trapped genotypes can repeatedly climb ascending slopes and hence, escape adaptive stasis. Using a Markov approach, we developed a mathematical framework to quantify the landscape-crossing rates and show that this ratchet-like adaptive mechanism is robust in a wide spectrum of fluctuating environments. Overall, this study shows that genetic constraints can be overcome by environmental change and that crossenvironmental tradeoffs do not necessarily impede but also, can facilitate adaptive evolution. Because tradeoffs and environmental variability are ubiquitous in nature, we speculate this evolutionary mode to be of general relevance."}],"month":"12","intvolume":" 112","quality_controlled":"1","publisher":"National Academy of Sciences","scopus_import":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"de Vos, M., Dawid, A., Šunderlíková, V., & Tans, S. (2015). Breaking evolutionary constraint with a tradeoff ratchet. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1510282112","ama":"de Vos M, Dawid A, Šunderlíková V, Tans S. Breaking evolutionary constraint with a tradeoff ratchet. PNAS. 2015;112(48):14906-14911. doi:10.1073/pnas.1510282112","ieee":"M. de Vos, A. Dawid, V. Šunderlíková, and S. Tans, “Breaking evolutionary constraint with a tradeoff ratchet,” PNAS, vol. 112, no. 48. National Academy of Sciences, pp. 14906–14911, 2015.","short":"M. de Vos, A. Dawid, V. Šunderlíková, S. Tans, PNAS 112 (2015) 14906–14911.","mla":"de Vos, Marjon, et al. “Breaking Evolutionary Constraint with a Tradeoff Ratchet.” PNAS, vol. 112, no. 48, National Academy of Sciences, 2015, pp. 14906–11, doi:10.1073/pnas.1510282112.","ista":"de Vos M, Dawid A, Šunderlíková V, Tans S. 2015. Breaking evolutionary constraint with a tradeoff ratchet. PNAS. 112(48), 14906–14911.","chicago":"Vos, Marjon de, Alexandre Dawid, Vanda Šunderlíková, and Sander Tans. “Breaking Evolutionary Constraint with a Tradeoff Ratchet.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1510282112."},"date_updated":"2021-01-12T06:51:40Z","title":"Breaking evolutionary constraint with a tradeoff ratchet","department":[{"_id":"ToBo"}],"author":[{"id":"3111FFAC-F248-11E8-B48F-1D18A9856A87","first_name":"Marjon","last_name":"De Vos","full_name":"De Vos, Marjon"},{"last_name":"Dawid","full_name":"Dawid, Alexandre","first_name":"Alexandre"},{"last_name":"Šunderlíková","full_name":"Šunderlíková, Vanda","first_name":"Vanda"},{"full_name":"Tans, Sander","last_name":"Tans","first_name":"Sander"}],"publist_id":"5600","_id":"1571","status":"public","type":"journal_article"},{"date_created":"2018-12-11T11:52:50Z","doi":"10.1016/j.cell.2015.04.009","date_published":"2015-04-23T00:00:00Z","issue":"3","volume":161,"page":"431 - 432","publication":"Cell","language":[{"iso":"eng"}],"day":"23","publication_status":"published","year":"2015","intvolume":" 161","month":"04","publisher":"Cell Press","scopus_import":"1","quality_controlled":"1","oa_version":"None","abstract":[{"text":"In animal embryos, morphogen gradients determine tissue patterning and morphogenesis. Shyer et al. provide evidence that, during vertebrate gut formation, tissue folding generates graded activity of signals required for subsequent steps of gut growth and differentiation, thereby revealing an intriguing link between tissue morphogenesis and morphogen gradient formation.","lang":"eng"}],"title":"Gradients are shaping up","department":[{"_id":"ToBo"},{"_id":"CaHe"}],"article_processing_charge":"No","author":[{"first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach"},{"orcid":"0000-0002-0912-4566","full_name":"Heisenberg, Carl-Philipp J","last_name":"Heisenberg","first_name":"Carl-Philipp J","id":"39427864-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5590","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_updated":"2022-08-25T13:56:10Z","citation":{"ista":"Bollenbach MT, Heisenberg C-PJ. 2015. Gradients are shaping up. Cell. 161(3), 431–432.","chicago":"Bollenbach, Mark Tobias, and Carl-Philipp J Heisenberg. “Gradients Are Shaping Up.” Cell. Cell Press, 2015. https://doi.org/10.1016/j.cell.2015.04.009.","short":"M.T. Bollenbach, C.-P.J. Heisenberg, Cell 161 (2015) 431–432.","ieee":"M. T. Bollenbach and C.-P. J. Heisenberg, “Gradients are shaping up,” Cell, vol. 161, no. 3. Cell Press, pp. 431–432, 2015.","ama":"Bollenbach MT, Heisenberg C-PJ. Gradients are shaping up. Cell. 2015;161(3):431-432. doi:10.1016/j.cell.2015.04.009","apa":"Bollenbach, M. T., & Heisenberg, C.-P. J. (2015). Gradients are shaping up. Cell. Cell Press. https://doi.org/10.1016/j.cell.2015.04.009","mla":"Bollenbach, Mark Tobias, and Carl-Philipp J. Heisenberg. “Gradients Are Shaping Up.” Cell, vol. 161, no. 3, Cell Press, 2015, pp. 431–32, doi:10.1016/j.cell.2015.04.009."},"status":"public","type":"journal_article","_id":"1581"},{"day":"01","publication":"Trends in Biotechnology","language":[{"iso":"eng"}],"year":"2015","publication_status":"published","doi":"10.1016/j.tibtech.2015.03.009","issue":"6","volume":33,"date_published":"2015-06-01T00:00:00Z","date_created":"2018-12-11T11:52:52Z","page":"352 - 361","oa_version":"None","abstract":[{"text":"Through metabolic engineering cyanobacteria can be employed in biotechnology. Combining the capacity for oxygenic photosynthesis and carbon fixation with an engineered metabolic pathway allows carbon-based product formation from CO2, light, and water directly. Such cyanobacterial 'cell factories' are constructed to produce biofuels, bioplastics, and commodity chemicals. Efforts of metabolic engineers and synthetic biologists allow the modification of the intermediary metabolism at various branching points, expanding the product range. The new biosynthesis routes 'tap' the metabolism ever more efficiently, particularly through the engineering of driving forces and utilization of cofactors generated during the light reactions of photosynthesis, resulting in higher product titers. High rates of carbon rechanneling ultimately allow an almost-complete allocation of fixed carbon to product above biomass.","lang":"eng"}],"month":"06","intvolume":" 33","publisher":"Elsevier","scopus_import":1,"quality_controlled":"1","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Angermayr A, Gorchs A, Hellingwerf K. 2015. Metabolic engineering of cyanobacteria for the synthesis of commodity products. Trends in Biotechnology. 33(6), 352–361.","chicago":"Angermayr, Andreas, Aleix Gorchs, and Klaas Hellingwerf. “Metabolic Engineering of Cyanobacteria for the Synthesis of Commodity Products.” Trends in Biotechnology. Elsevier, 2015. https://doi.org/10.1016/j.tibtech.2015.03.009.","ama":"Angermayr A, Gorchs A, Hellingwerf K. Metabolic engineering of cyanobacteria for the synthesis of commodity products. Trends in Biotechnology. 2015;33(6):352-361. doi:10.1016/j.tibtech.2015.03.009","apa":"Angermayr, A., Gorchs, A., & Hellingwerf, K. (2015). Metabolic engineering of cyanobacteria for the synthesis of commodity products. Trends in Biotechnology. Elsevier. https://doi.org/10.1016/j.tibtech.2015.03.009","ieee":"A. Angermayr, A. Gorchs, and K. Hellingwerf, “Metabolic engineering of cyanobacteria for the synthesis of commodity products,” Trends in Biotechnology, vol. 33, no. 6. Elsevier, pp. 352–361, 2015.","short":"A. Angermayr, A. Gorchs, K. Hellingwerf, Trends in Biotechnology 33 (2015) 352–361.","mla":"Angermayr, Andreas, et al. “Metabolic Engineering of Cyanobacteria for the Synthesis of Commodity Products.” Trends in Biotechnology, vol. 33, no. 6, Elsevier, 2015, pp. 352–61, doi:10.1016/j.tibtech.2015.03.009."},"date_updated":"2021-01-12T06:51:46Z","department":[{"_id":"ToBo"}],"title":"Metabolic engineering of cyanobacteria for the synthesis of commodity products","author":[{"id":"4677C796-F248-11E8-B48F-1D18A9856A87","first_name":"Andreas","orcid":"0000-0001-8619-2223","full_name":"Angermayr, Andreas","last_name":"Angermayr"},{"last_name":"Gorchs","full_name":"Gorchs, Aleix","first_name":"Aleix"},{"first_name":"Klaas","last_name":"Hellingwerf","full_name":"Hellingwerf, Klaas"}],"publist_id":"5585","_id":"1586","status":"public","type":"journal_article"},{"publication":"Biotechnology for Biofuels","day":"25","year":"2015","has_accepted_license":"1","date_created":"2018-12-11T11:53:05Z","date_published":"2015-11-25T00:00:00Z","doi":"10.1186/s13068-015-0380-2","oa":1,"publisher":"BioMed Central","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Hammar, Petter, Andreas Angermayr, Staffan Sjostrom, Josefin Van Der Meer, Klaas Hellingwerf, Elton Hudson, and Hakaan Joensson. “Single-Cell Screening of Photosynthetic Growth and Lactate Production by Cyanobacteria.” Biotechnology for Biofuels. BioMed Central, 2015. https://doi.org/10.1186/s13068-015-0380-2.","ista":"Hammar P, Angermayr A, Sjostrom S, Van Der Meer J, Hellingwerf K, Hudson E, Joensson H. 2015. Single-cell screening of photosynthetic growth and lactate production by cyanobacteria. Biotechnology for Biofuels. 8(1), 193.","mla":"Hammar, Petter, et al. “Single-Cell Screening of Photosynthetic Growth and Lactate Production by Cyanobacteria.” Biotechnology for Biofuels, vol. 8, no. 1, 193, BioMed Central, 2015, doi:10.1186/s13068-015-0380-2.","ama":"Hammar P, Angermayr A, Sjostrom S, et al. Single-cell screening of photosynthetic growth and lactate production by cyanobacteria. Biotechnology for Biofuels. 2015;8(1). doi:10.1186/s13068-015-0380-2","apa":"Hammar, P., Angermayr, A., Sjostrom, S., Van Der Meer, J., Hellingwerf, K., Hudson, E., & Joensson, H. (2015). Single-cell screening of photosynthetic growth and lactate production by cyanobacteria. Biotechnology for Biofuels. BioMed Central. https://doi.org/10.1186/s13068-015-0380-2","short":"P. Hammar, A. Angermayr, S. Sjostrom, J. Van Der Meer, K. Hellingwerf, E. Hudson, H. Joensson, Biotechnology for Biofuels 8 (2015).","ieee":"P. Hammar et al., “Single-cell screening of photosynthetic growth and lactate production by cyanobacteria,” Biotechnology for Biofuels, vol. 8, no. 1. BioMed Central, 2015."},"title":"Single-cell screening of photosynthetic growth and lactate production by cyanobacteria","publist_id":"5537","author":[{"full_name":"Hammar, Petter","last_name":"Hammar","first_name":"Petter"},{"id":"4677C796-F248-11E8-B48F-1D18A9856A87","first_name":"Andreas","last_name":"Angermayr","full_name":"Angermayr, Andreas","orcid":"0000-0001-8619-2223"},{"first_name":"Staffan","last_name":"Sjostrom","full_name":"Sjostrom, Staffan"},{"first_name":"Josefin","last_name":"Van Der Meer","full_name":"Van Der Meer, Josefin"},{"full_name":"Hellingwerf, Klaas","last_name":"Hellingwerf","first_name":"Klaas"},{"last_name":"Hudson","full_name":"Hudson, Elton","first_name":"Elton"},{"full_name":"Joensson, Hakaan","last_name":"Joensson","first_name":"Hakaan"}],"article_number":"193","language":[{"iso":"eng"}],"file":[{"creator":"system","date_updated":"2020-07-14T12:45:07Z","file_size":2914089,"date_created":"2018-12-12T10:10:11Z","file_name":"IST-2016-467-v1+1_s13068-015-0380-2.pdf","access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_id":"4796","checksum":"172b0b6f4eb2e5c22b7cec1d57dc0107"}],"publication_status":"published","issue":"1","volume":8,"oa_version":"Published Version","abstract":[{"text":"Background\r\nPhotosynthetic cyanobacteria are attractive for a range of biotechnological applications including biofuel production. However, due to slow growth, screening of mutant libraries using microtiter plates is not feasible.\r\nResults\r\nWe present a method for high-throughput, single-cell analysis and sorting of genetically engineered l-lactate-producing strains of Synechocystis sp. PCC6803. A microfluidic device is used to encapsulate single cells in picoliter droplets, assay the droplets for l-lactate production, and sort strains with high productivity. We demonstrate the separation of low- and high-producing reference strains, as well as enrichment of a more productive l-lactate-synthesizing population after UV-induced mutagenesis. The droplet platform also revealed population heterogeneity in photosynthetic growth and lactate production, as well as the presence of metabolically stalled cells.\r\nConclusions\r\nThe workflow will facilitate metabolic engineering and directed evolution studies and will be useful in studies of cyanobacteria biochemistry and physiology.\r\n","lang":"eng"}],"intvolume":" 8","month":"11","scopus_import":1,"ddc":["570"],"date_updated":"2021-01-12T06:52:04Z","department":[{"_id":"ToBo"}],"file_date_updated":"2020-07-14T12:45:07Z","_id":"1623","pubrep_id":"467","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article"},{"intvolume":" 27","month":"06","scopus_import":1,"oa_version":"Published Version","abstract":[{"text":"Combining antibiotics is a promising strategy for increasing treatment efficacy and for controlling resistance evolution. When drugs are combined, their effects on cells may be amplified or weakened, that is the drugs may show synergistic or antagonistic interactions. Recent work revealed the underlying mechanisms of such drug interactions by elucidating the drugs'; joint effects on cell physiology. Moreover, new treatment strategies that use drug combinations to exploit evolutionary tradeoffs were shown to affect the rate of resistance evolution in predictable ways. High throughput studies have further identified drug candidates based on their interactions with established antibiotics and general principles that enable the prediction of drug interactions were suggested. Overall, the conceptual and technical foundation for the rational design of potent drug combinations is rapidly developing.","lang":"eng"}],"ec_funded":1,"volume":27,"language":[{"iso":"eng"}],"file":[{"content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"1683bb0f42ef892a5b3b71a050d65d25","file_id":"5277","date_updated":"2020-07-14T12:45:17Z","file_size":1047255,"creator":"system","date_created":"2018-12-12T10:17:23Z","file_name":"IST-2016-493-v1+1_1-s2.0-S1369527415000594-main.pdf"}],"publication_status":"published","pubrep_id":"493","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"1810","file_date_updated":"2020-07-14T12:45:17Z","department":[{"_id":"ToBo"}],"ddc":["570"],"date_updated":"2021-01-12T06:53:21Z","oa":1,"quality_controlled":"1","publisher":"Elsevier","date_created":"2018-12-11T11:54:08Z","date_published":"2015-06-01T00:00:00Z","doi":"10.1016/j.mib.2015.05.008","page":"1 - 9","publication":"Current Opinion in Microbiology","day":"01","year":"2015","has_accepted_license":"1","project":[{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","grant_number":"303507","name":"Optimality principles in responses to antibiotics"},{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013"}],"title":"Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution","author":[{"first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach"}],"publist_id":"5298","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"M.T. Bollenbach, Current Opinion in Microbiology 27 (2015) 1–9.","ieee":"M. T. Bollenbach, “Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution,” Current Opinion in Microbiology, vol. 27. Elsevier, pp. 1–9, 2015.","ama":"Bollenbach MT. Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. 2015;27:1-9. doi:10.1016/j.mib.2015.05.008","apa":"Bollenbach, M. T. (2015). Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. Elsevier. https://doi.org/10.1016/j.mib.2015.05.008","mla":"Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications for Drug Discovery and Resistance Evolution.” Current Opinion in Microbiology, vol. 27, Elsevier, 2015, pp. 1–9, doi:10.1016/j.mib.2015.05.008.","ista":"Bollenbach MT. 2015. Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. 27, 1–9.","chicago":"Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications for Drug Discovery and Resistance Evolution.” Current Opinion in Microbiology. Elsevier, 2015. https://doi.org/10.1016/j.mib.2015.05.008."}},{"file":[{"file_name":"IST-2015-395-v1+1_807.full.pdf","date_created":"2018-12-12T10:14:34Z","file_size":1273573,"date_updated":"2020-07-14T12:45:17Z","creator":"system","file_id":"5087","checksum":"4289b518fbe2166682fb1a1ef9b405f3","content_type":"application/pdf","relation":"main_file","access_level":"open_access"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":11,"issue":"4","ec_funded":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways."}],"month":"04","intvolume":" 11","scopus_import":1,"ddc":["570"],"date_updated":"2021-01-12T06:53:26Z","file_date_updated":"2020-07-14T12:45:17Z","department":[{"_id":"ToBo"}],"_id":"1823","status":"public","pubrep_id":"395","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"day":"01","publication":"Molecular Systems Biology","has_accepted_license":"1","year":"2015","date_published":"2015-04-01T00:00:00Z","doi":"10.15252/msb.20156098","date_created":"2018-12-11T11:54:12Z","publisher":"Nature Publishing Group","quality_controlled":"1","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology, vol. 11, no. 4, 807, Nature Publishing Group, 2015, doi:10.15252/msb.20156098.","ieee":"G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction mechanisms,” Molecular Systems Biology, vol. 11, no. 4. Nature Publishing Group, 2015.","short":"G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015).","ama":"Chevereau G, Bollenbach MT. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 2015;11(4). doi:10.15252/msb.20156098","apa":"Chevereau, G., & Bollenbach, M. T. (2015). Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. Nature Publishing Group. https://doi.org/10.15252/msb.20156098","chicago":"Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology. Nature Publishing Group, 2015. https://doi.org/10.15252/msb.20156098.","ista":"Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 11(4), 807."},"title":"Systematic discovery of drug interaction mechanisms","publist_id":"5283","author":[{"first_name":"Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","full_name":"Chevereau, Guillaume","last_name":"Chevereau"},{"full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias"}],"article_number":"807","project":[{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions"},{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013"},{"_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"303507","name":"Optimality principles in responses to antibiotics"}]},{"related_material":{"record":[{"status":"public","id":"1619","relation":"used_in_publication"}]},"date_published":"2015-11-18T00:00:00Z","doi":"10.1371/journal.pbio.1002299.s001","date_created":"2021-07-23T11:53:50Z","year":"2015","day":"18","publisher":"Public Library of Science","month":"11","oa_version":"Published Version","author":[{"id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume","full_name":"Chevereau, Guillaume","last_name":"Chevereau"},{"first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","last_name":"Lukacisinova","orcid":"0000-0002-2519-8004","full_name":"Lukacisinova, Marta"},{"full_name":"Batur, Tugce","last_name":"Batur","first_name":"Tugce"},{"first_name":"Aysegul","full_name":"Guvenek, Aysegul","last_name":"Guvenek"},{"last_name":"Ayhan","full_name":"Ayhan, Dilay Hazal","first_name":"Dilay Hazal"},{"last_name":"Toprak","full_name":"Toprak, Erdal","first_name":"Erdal"},{"first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach"}],"article_processing_charge":"No","department":[{"_id":"ToBo"}],"title":"Excel file containing the raw data for all figures","citation":{"chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Excel File Containing the Raw Data for All Figures.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s001.","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Excel file containing the raw data for all figures, Public Library of Science, 10.1371/journal.pbio.1002299.s001.","mla":"Chevereau, Guillaume, et al. Excel File Containing the Raw Data for All Figures. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s001.","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015).","ieee":"G. Chevereau et al., “Excel file containing the raw data for all figures.” Public Library of Science, 2015.","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., & Bollenbach, M. T. (2015). Excel file containing the raw data for all figures. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s001","ama":"Chevereau G, Lukacisinova M, Batur T, et al. Excel file containing the raw data for all figures. 2015. doi:10.1371/journal.pbio.1002299.s001"},"date_updated":"2023-02-23T10:07:02Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","type":"research_data_reference","status":"public","_id":"9711"},{"status":"public","type":"research_data_reference","_id":"9765","title":"Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs","department":[{"_id":"ToBo"}],"author":[{"first_name":"Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","full_name":"Chevereau, Guillaume","last_name":"Chevereau"},{"first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004","last_name":"Lukacisinova"},{"last_name":"Batur","full_name":"Batur, Tugce","first_name":"Tugce"},{"first_name":"Aysegul","full_name":"Guvenek, Aysegul","last_name":"Guvenek"},{"first_name":"Dilay Hazal","full_name":"Ayhan, Dilay Hazal","last_name":"Ayhan"},{"first_name":"Erdal","full_name":"Toprak, Erdal","last_name":"Toprak"},{"last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias"}],"article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-23T10:07:02Z","citation":{"ama":"Chevereau G, Lukacisinova M, Batur T, et al. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. 2015. doi:10.1371/journal.pbio.1002299.s008","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., & Bollenbach, M. T. (2015). Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s008","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015).","ieee":"G. Chevereau et al., “Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs.” Public Library of Science, 2015.","mla":"Chevereau, Guillaume, et al. Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s008.","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs, Public Library of Science, 10.1371/journal.pbio.1002299.s008.","chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s008."},"month":"11","publisher":"Public Library of Science","oa_version":"Published Version","doi":"10.1371/journal.pbio.1002299.s008","date_published":"2015-11-18T00:00:00Z","related_material":{"record":[{"status":"public","id":"1619","relation":"used_in_publication"}]},"date_created":"2021-08-03T07:05:16Z","day":"18","year":"2015"},{"department":[{"_id":"JiFr"},{"_id":"ToBo"}],"file_date_updated":"2020-07-14T12:44:59Z","date_updated":"2023-10-10T14:10:24Z","ddc":["570"],"type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"status":"public","pubrep_id":"497","_id":"1509","volume":4,"ec_funded":1,"publication_status":"published","file":[{"file_name":"IST-2016-497-v1+1_10.12688_f1000research.7143.1_20151102.pdf","date_created":"2018-12-12T10:16:12Z","file_size":4414248,"date_updated":"2020-07-14T12:44:59Z","creator":"system","file_id":"5198","checksum":"8beae5cbe988e1060265ae7de2ee8306","content_type":"application/pdf","relation":"main_file","access_level":"open_access"}],"language":[{"iso":"eng"}],"scopus_import":"1","month":"10","intvolume":" 4","abstract":[{"lang":"eng","text":"The Auxin Binding Protein1 (ABP1) has been identified based on its ability to bind auxin with high affinity and studied for a long time as a prime candidate for the extracellular auxin receptor responsible for mediating in particular the fast non-transcriptional auxin responses. However, the contradiction between the embryo-lethal phenotypes of the originally described Arabidopsis T-DNA insertional knock-out alleles (abp1-1 and abp1-1s) and the wild type-like phenotypes of other recently described loss-of-function alleles (abp1-c1 and abp1-TD1) questions the biological importance of ABP1 and relevance of the previous genetic studies. Here we show that there is no hidden copy of the ABP1 gene in the Arabidopsis genome but the embryo-lethal phenotypes of abp1-1 and abp1-1s alleles are very similar to the knock-out phenotypes of the neighboring gene, BELAYA SMERT (BSM). Furthermore, the allelic complementation test between bsm and abp1 alleles shows that the embryo-lethality in the abp1-1 and abp1-1s alleles is caused by the off-target disruption of the BSM locus by the T-DNA insertions. This clarifies the controversy of different phenotypes among published abp1 knock-out alleles and asks for reflections on the developmental role of ABP1."}],"oa_version":"Published Version","author":[{"first_name":"Jaroslav","id":"483727CA-F248-11E8-B48F-1D18A9856A87","full_name":"Michalko, Jaroslav","last_name":"Michalko"},{"full_name":"Dravecka, Marta","orcid":"0000-0002-2519-8004","last_name":"Dravecka","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta"},{"last_name":"Bollenbach","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Tobias"},{"first_name":"Jirí","id":"4159519E-F248-11E8-B48F-1D18A9856A87","full_name":"Friml, Jirí","orcid":"0000-0002-8302-7596","last_name":"Friml"}],"publist_id":"5668","article_processing_charge":"No","title":"Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene","citation":{"mla":"Michalko, Jaroslav, et al. “Embryo-Lethal Phenotypes in Early Abp1 Mutants Are Due to Disruption of the Neighboring BSM Gene.” F1000 Research , vol. 4, F1000 Research, 2015, doi:10.12688/f1000research.7143.1.","apa":"Michalko, J., Lukacisinova, M., Bollenbach, M. T., & Friml, J. (2015). Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene. F1000 Research . F1000 Research. https://doi.org/10.12688/f1000research.7143.1","ama":"Michalko J, Lukacisinova M, Bollenbach MT, Friml J. Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene. F1000 Research . 2015;4. doi:10.12688/f1000research.7143.1","ieee":"J. Michalko, M. Lukacisinova, M. T. Bollenbach, and J. Friml, “Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene,” F1000 Research , vol. 4. F1000 Research, 2015.","short":"J. Michalko, M. Lukacisinova, M.T. Bollenbach, J. Friml, F1000 Research 4 (2015).","chicago":"Michalko, Jaroslav, Marta Lukacisinova, Mark Tobias Bollenbach, and Jiří Friml. “Embryo-Lethal Phenotypes in Early Abp1 Mutants Are Due to Disruption of the Neighboring BSM Gene.” F1000 Research . F1000 Research, 2015. https://doi.org/10.12688/f1000research.7143.1.","ista":"Michalko J, Lukacisinova M, Bollenbach MT, Friml J. 2015. Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene. F1000 Research . 4."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"call_identifier":"FP7","_id":"25716A02-B435-11E9-9278-68D0E5697425","name":"Polarity and subcellular dynamics in plants","grant_number":"282300"}],"doi":"10.12688/f1000research.7143.1","date_published":"2015-10-01T00:00:00Z","date_created":"2018-12-11T11:52:26Z","has_accepted_license":"1","year":"2015","day":"01","publication":"F1000 Research ","publisher":"F1000 Research","quality_controlled":"1","oa":1,"acknowledgement":"This work was supported by ERC Independent Research grant (ERC-2011-StG-20101109-PSDP to JF). JM internship was supported by the grant “Action Austria – Slovakia”.\r\nData associated with the article are available under the terms of the Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication). \r\n\r\nData availability: \r\nF1000Research: Dataset 1. Dataset 1, 10.5256/f1000research.7143.d104552\r\n\r\nF1000Research: Dataset 2. Dataset 2, 10.5256/f1000research.7143.d104553\r\n\r\nF1000Research: Dataset 3. Dataset 3, 10.5256/f1000research.7143.d104554"},{"article_number":"e1002299","project":[{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013"},{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"},{"_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"303507","name":"Optimality principles in responses to antibiotics"}],"citation":{"mla":"Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology, vol. 13, no. 11, e1002299, Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak, E., & Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299","ama":"Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 2015;13(11). doi:10.1371/journal.pbio.1002299","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak, M.T. Bollenbach, PLoS Biology 13 (2015).","ieee":"G. Chevereau et al., “Quantifying the determinants of evolutionary dynamics leading to drug resistance,” PLoS Biology, vol. 13, no. 11. Public Library of Science, 2015.","chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 13(11), e1002299."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5547","author":[{"first_name":"Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","last_name":"Chevereau","full_name":"Chevereau, Guillaume"},{"last_name":"Dravecka","orcid":"0000-0002-2519-8004","full_name":"Dravecka, Marta","first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Batur, Tugce","last_name":"Batur","first_name":"Tugce"},{"first_name":"Aysegul","full_name":"Guvenek, Aysegul","last_name":"Guvenek"},{"first_name":"Dilay","last_name":"Ayhan","full_name":"Ayhan, Dilay"},{"first_name":"Erdal","full_name":"Toprak, Erdal","last_name":"Toprak"},{"full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"title":"Quantifying the determinants of evolutionary dynamics leading to drug resistance","oa":1,"publisher":"Public Library of Science","quality_controlled":"1","year":"2015","has_accepted_license":"1","publication":"PLoS Biology","day":"18","date_created":"2018-12-11T11:53:04Z","doi":"10.1371/journal.pbio.1002299","date_published":"2015-11-18T00:00:00Z","_id":"1619","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","pubrep_id":"468","status":"public","date_updated":"2024-03-27T23:30:28Z","ddc":["570"],"department":[{"_id":"ToBo"}],"file_date_updated":"2020-07-14T12:45:07Z","abstract":[{"text":"The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.","lang":"eng"}],"oa_version":"Published Version","scopus_import":1,"intvolume":" 13","month":"11","publication_status":"published","language":[{"iso":"eng"}],"file":[{"file_name":"IST-2016-468-v1+1_journal.pbio.1002299.pdf","date_created":"2018-12-12T10:09:00Z","file_size":1387760,"date_updated":"2020-07-14T12:45:07Z","creator":"system","file_id":"4723","checksum":"0e82e3279f50b15c6c170c042627802b","content_type":"application/pdf","relation":"main_file","access_level":"open_access"}],"ec_funded":1,"related_material":{"record":[{"id":"9711","status":"public","relation":"research_data"},{"status":"public","id":"9765","relation":"research_data"},{"id":"6263","status":"public","relation":"dissertation_contains"}]},"volume":13,"issue":"11"},{"day":"26","language":[{"iso":"eng"}],"publication":"Science","year":"2014","publication_status":"published","doi":"10.1126/science.1254927","date_published":"2014-09-26T00:00:00Z","issue":"6204","volume":345,"date_created":"2018-12-11T11:55:22Z","oa_version":"Submitted Version","abstract":[{"lang":"eng","text":"Development requires tissue growth as well as cell diversification. To address how these processes are coordinated, we analyzed the development of molecularly distinct domains of neural progenitors in the mouse and chick neural tube. We show that during development, these domains undergo changes in size that do not scale with changes in overall tissue size. Our data show that domain proportions are first established by opposing morphogen gradients and subsequently controlled by domain-specific regulation of differentiation rate but not differences in proliferation rate. Regulation of differentiation rate is key to maintaining domain proportions while accommodating both intra- and interspecies variations in size. Thus, the sequential control of progenitor specification and differentiation elaborates pattern without requiring that signaling gradients grow as tissues expand. "}],"month":"09","intvolume":" 345","quality_controlled":"1","scopus_import":1,"publisher":"American Association for the Advancement of Science","main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228193/"}],"oa":1,"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:54:55Z","citation":{"ista":"Kicheva A, Bollenbach MT, Ribeiro A, Pérez Valle H, Lovell Badge R, Episkopou V, Briscoe J. 2014. Coordination of progenitor specification and growth in mouse and chick spinal cord. Science. 345(6204), 1254927.","chicago":"Kicheva, Anna, Mark Tobias Bollenbach, Ana Ribeiro, Helena Pérez Valle, Robin Lovell Badge, Vasso Episkopou, and James Briscoe. “Coordination of Progenitor Specification and Growth in Mouse and Chick Spinal Cord.” Science. American Association for the Advancement of Science, 2014. https://doi.org/10.1126/science.1254927.","ama":"Kicheva A, Bollenbach MT, Ribeiro A, et al. Coordination of progenitor specification and growth in mouse and chick spinal cord. Science. 2014;345(6204). doi:10.1126/science.1254927","apa":"Kicheva, A., Bollenbach, M. T., Ribeiro, A., Pérez Valle, H., Lovell Badge, R., Episkopou, V., & Briscoe, J. (2014). Coordination of progenitor specification and growth in mouse and chick spinal cord. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.1254927","short":"A. Kicheva, M.T. Bollenbach, A. Ribeiro, H. Pérez Valle, R. Lovell Badge, V. Episkopou, J. Briscoe, Science 345 (2014).","ieee":"A. Kicheva et al., “Coordination of progenitor specification and growth in mouse and chick spinal cord,” Science, vol. 345, no. 6204. American Association for the Advancement of Science, 2014.","mla":"Kicheva, Anna, et al. “Coordination of Progenitor Specification and Growth in Mouse and Chick Spinal Cord.” Science, vol. 345, no. 6204, 1254927, American Association for the Advancement of Science, 2014, doi:10.1126/science.1254927."},"title":"Coordination of progenitor specification and growth in mouse and chick spinal cord","department":[{"_id":"ToBo"}],"publist_id":"5011","author":[{"last_name":"Kicheva","full_name":"Kicheva, Anna","first_name":"Anna"},{"full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Ana","full_name":"Ribeiro, Ana","last_name":"Ribeiro"},{"first_name":"Helena","full_name":"Pérez Valle, Helena","last_name":"Pérez Valle"},{"first_name":"Robin","last_name":"Lovell Badge","full_name":"Lovell Badge, Robin"},{"last_name":"Episkopou","full_name":"Episkopou, Vasso","first_name":"Vasso"},{"first_name":"James","full_name":"Briscoe, James","last_name":"Briscoe"}],"article_number":"1254927","_id":"2040","status":"public","type":"journal_article"},{"oa":1,"publisher":"Cell Press","quality_controlled":"1","page":"439 - 440","date_created":"2018-12-11T11:56:24Z","doi":"10.1016/j.chembiol.2014.04.004","date_published":"2014-04-24T00:00:00Z","year":"2014","publication":"Chemistry and Biology","day":"24","external_id":{"pmid":["24766845"]},"publist_id":"4747","author":[{"full_name":"De Vos, Marjon","last_name":"De Vos","first_name":"Marjon","id":"3111FFAC-F248-11E8-B48F-1D18A9856A87"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias"}],"title":"Suppressive drug interactions between antifungals","citation":{"ista":"de Vos M, Bollenbach MT. 2014. Suppressive drug interactions between antifungals. Chemistry and Biology. 21(4), 439–440.","chicago":"Vos, Marjon de, and Mark Tobias Bollenbach. “Suppressive Drug Interactions between Antifungals.” Chemistry and Biology. Cell Press, 2014. https://doi.org/10.1016/j.chembiol.2014.04.004.","ama":"de Vos M, Bollenbach MT. Suppressive drug interactions between antifungals. Chemistry and Biology. 2014;21(4):439-440. doi:10.1016/j.chembiol.2014.04.004","apa":"de Vos, M., & Bollenbach, M. T. (2014). Suppressive drug interactions between antifungals. Chemistry and Biology. Cell Press. https://doi.org/10.1016/j.chembiol.2014.04.004","short":"M. de Vos, M.T. Bollenbach, Chemistry and Biology 21 (2014) 439–440.","ieee":"M. de Vos and M. T. Bollenbach, “Suppressive drug interactions between antifungals,” Chemistry and Biology, vol. 21, no. 4. Cell Press, pp. 439–440, 2014.","mla":"de Vos, Marjon, and Mark Tobias Bollenbach. “Suppressive Drug Interactions between Antifungals.” Chemistry and Biology, vol. 21, no. 4, Cell Press, 2014, pp. 439–40, doi:10.1016/j.chembiol.2014.04.004."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pubmed/24766845"}],"scopus_import":1,"intvolume":" 21","month":"04","abstract":[{"text":"In this issue of Chemistry & Biology, Cokol and colleagues report a systematic study of drug interactions between antifungal compounds. Suppressive drug interactions occur more frequently than previously realized and come in different flavors with interesting implications.","lang":"eng"}],"oa_version":"Published Version","pmid":1,"volume":21,"issue":"4","publication_status":"published","publication_identifier":{"issn":["10745521"]},"language":[{"iso":"eng"}],"type":"journal_article","status":"public","_id":"2220","department":[{"_id":"ToBo"}],"date_updated":"2021-01-12T06:56:06Z"},{"title":"Bacterial responses to antibiotics and their combinations","publist_id":"5076","author":[{"last_name":"Mitosch","full_name":"Mitosch, Karin","id":"39B66846-F248-11E8-B48F-1D18A9856A87","first_name":"Karin"},{"first_name":"Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Tobias","last_name":"Bollenbach"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"K. Mitosch and M. T. Bollenbach, “Bacterial responses to antibiotics and their combinations,” Environmental Microbiology Reports, vol. 6, no. 6. Wiley, pp. 545–557, 2014.","short":"K. Mitosch, M.T. Bollenbach, Environmental Microbiology Reports 6 (2014) 545–557.","apa":"Mitosch, K., & Bollenbach, M. T. (2014). Bacterial responses to antibiotics and their combinations. Environmental Microbiology Reports. Wiley. https://doi.org/10.1111/1758-2229.12190","ama":"Mitosch K, Bollenbach MT. Bacterial responses to antibiotics and their combinations. Environmental Microbiology Reports. 2014;6(6):545-557. doi:10.1111/1758-2229.12190","mla":"Mitosch, Karin, and Mark Tobias Bollenbach. “Bacterial Responses to Antibiotics and Their Combinations.” Environmental Microbiology Reports, vol. 6, no. 6, Wiley, 2014, pp. 545–57, doi:10.1111/1758-2229.12190.","ista":"Mitosch K, Bollenbach MT. 2014. Bacterial responses to antibiotics and their combinations. Environmental Microbiology Reports. 6(6), 545–557.","chicago":"Mitosch, Karin, and Mark Tobias Bollenbach. “Bacterial Responses to Antibiotics and Their Combinations.” Environmental Microbiology Reports. Wiley, 2014. https://doi.org/10.1111/1758-2229.12190."},"project":[{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013"},{"name":"Optimality principles in responses to antibiotics","grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"date_created":"2018-12-11T11:55:08Z","doi":"10.1111/1758-2229.12190","date_published":"2014-06-22T00:00:00Z","page":"545 - 557","publication":"Environmental Microbiology Reports","day":"22","year":"2014","quality_controlled":"1","publisher":"Wiley","department":[{"_id":"ToBo"}],"date_updated":"2023-09-07T12:00:25Z","status":"public","type":"journal_article","_id":"2001","ec_funded":1,"related_material":{"record":[{"status":"public","id":"818","relation":"dissertation_contains"}]},"issue":"6","volume":6,"language":[{"iso":"eng"}],"publication_status":"published","intvolume":" 6","month":"06","scopus_import":1,"oa_version":"None","abstract":[{"text":"Antibiotics affect bacterial cell physiology at many levels. Rather than just compensating for the direct cellular defects caused by the drug, bacteria respond to antibiotics by changing their morphology, macromolecular composition, metabolism, gene expression and possibly even their mutation rate. Inevitably, these processes affect each other, resulting in a complex response with changes in the expression of numerous genes. Genome‐wide approaches can thus help in gaining a comprehensive understanding of bacterial responses to antibiotics. In addition, a combination of experimental and theoretical approaches is needed for identifying general principles that underlie these responses. Here, we review recent progress in our understanding of bacterial responses to antibiotics and their combinations, focusing on effects at the levels of growth rate and gene expression. We concentrate on studies performed in controlled laboratory conditions, which combine promising experimental techniques with quantitative data analysis and mathematical modeling. While these basic research approaches are not immediately applicable in the clinic, uncovering the principles and mechanisms underlying bacterial responses to antibiotics may, in the long term, contribute to the development of new treatment strategies to cope with and prevent the rise of resistant pathogenic bacteria.","lang":"eng"}]},{"date_created":"2018-12-11T11:59:43Z","date_published":"2013-06-27T00:00:00Z","doi":"10.1371/journal.pgen.1003580","publication":"PLoS Genetics","day":"27","year":"2013","has_accepted_license":"1","oa":1,"publisher":"Public Library of Science","quality_controlled":"1","title":"Environmental dependence of genetic constraint","publist_id":"4075","author":[{"last_name":"De Vos","full_name":"De Vos, Marjon","first_name":"Marjon","id":"3111FFAC-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Poelwijk","full_name":"Poelwijk, Frank","first_name":"Frank"},{"full_name":"Battich, Nico","last_name":"Battich","first_name":"Nico"},{"first_name":"Joseph","full_name":"Ndika, Joseph","last_name":"Ndika"},{"last_name":"Tans","full_name":"Tans, Sander","first_name":"Sander"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"de Vos, Marjon, et al. “Environmental Dependence of Genetic Constraint.” PLoS Genetics, vol. 9, no. 6, e1003580, Public Library of Science, 2013, doi:10.1371/journal.pgen.1003580.","short":"M. de Vos, F. Poelwijk, N. Battich, J. Ndika, S. Tans, PLoS Genetics 9 (2013).","ieee":"M. de Vos, F. Poelwijk, N. Battich, J. Ndika, and S. Tans, “Environmental dependence of genetic constraint,” PLoS Genetics, vol. 9, no. 6. Public Library of Science, 2013.","apa":"de Vos, M., Poelwijk, F., Battich, N., Ndika, J., & Tans, S. (2013). Environmental dependence of genetic constraint. PLoS Genetics. Public Library of Science. https://doi.org/10.1371/journal.pgen.1003580","ama":"de Vos M, Poelwijk F, Battich N, Ndika J, Tans S. Environmental dependence of genetic constraint. PLoS Genetics. 2013;9(6). doi:10.1371/journal.pgen.1003580","chicago":"Vos, Marjon de, Frank Poelwijk, Nico Battich, Joseph Ndika, and Sander Tans. “Environmental Dependence of Genetic Constraint.” PLoS Genetics. Public Library of Science, 2013. https://doi.org/10.1371/journal.pgen.1003580.","ista":"de Vos M, Poelwijk F, Battich N, Ndika J, Tans S. 2013. Environmental dependence of genetic constraint. PLoS Genetics. 9(6), e1003580."},"article_number":"e1003580","volume":9,"issue":"6","language":[{"iso":"eng"}],"file":[{"content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_id":"4713","checksum":"7a4736dd80496d29ff6908b6f2329b4e","file_size":474655,"date_updated":"2020-07-14T12:45:48Z","creator":"system","file_name":"IST-2016-412-v1+1_journal.pgen.1003580.pdf","date_created":"2018-12-12T10:08:51Z"}],"publication_status":"published","intvolume":" 9","month":"06","scopus_import":1,"oa_version":"Published Version","abstract":[{"text":"The epistatic interactions that underlie evolutionary constraint have mainly been studied for constant external conditions. However, environmental changes may modulate epistasis and hence affect genetic constraints. Here we investigate genetic constraints in the adaptive evolution of a novel regulatory function in variable environments, using the lac repressor, LacI, as a model system. We have systematically reconstructed mutational trajectories from wild type LacI to three different variants that each exhibit an inverse response to the inducing ligand IPTG, and analyzed the higher-order interactions between genetic and environmental changes. We find epistasis to depend strongly on the environment. As a result, mutational steps essential to inversion but inaccessible by positive selection in one environment, become accessible in another. We present a graphical method to analyze the observed complex higher-order interactions between multiple mutations and environmental change, and show how the interactions can be explained by a combination of mutational effects on allostery and thermodynamic stability. This dependency of genetic constraint on the environment should fundamentally affect evolutionary dynamics and affects the interpretation of phylogenetic data.","lang":"eng"}],"file_date_updated":"2020-07-14T12:45:48Z","department":[{"_id":"ToBo"}],"ddc":["570"],"date_updated":"2021-01-12T06:59:52Z","pubrep_id":"412","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"2810"},{"page":"527 - 532","issue":"6","date_published":"2012-12-01T00:00:00Z","volume":22,"doi":"10.1016/j.gde.2012.08.004","date_created":"2018-12-11T12:00:37Z","year":"2012","publication_status":"published","day":"01","publication":"Current Opinion in Genetics & Development","language":[{"iso":"eng"}],"quality_controlled":"1","publisher":"Elsevier","scopus_import":1,"month":"12","intvolume":" 22","abstract":[{"lang":"eng","text":"Morphogen gradients regulate the patterning and growth of many tissues, hence a key question is how they are established and maintained during development. Theoretical descriptions have helped to explain how gradient shape is controlled by the rates of morphogen production, spreading and degradation. These effective rates have been measured using fluorescence recovery after photobleaching (FRAP) and photoactivation. To unravel which molecular events determine the effective rates, such tissue-level assays have been combined with genetic analysis, high-resolution assays, and models that take into account interactions with receptors, extracellular components and trafficking. Nevertheless, because of the natural and experimental data variability, and the underlying assumptions of transport models, it remains challenging to conclusively distinguish between cellular mechanisms."}],"oa_version":"None","acknowledgement":"AK is currently supported by an MRC CDF. MGG and OW were supported by the Swiss National Science Foundation, grants from the Swiss SystemsX.ch initiative, LipidX-2008/011, an ERC advanced investigator grant and the Polish-Swiss research program.","author":[{"first_name":"Anna","id":"3959A2A0-F248-11E8-B48F-1D18A9856A87","last_name":"Kicheva","orcid":"0000-0003-4509-4998","full_name":"Kicheva, Anna"},{"last_name":"Bollenbach","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias"},{"first_name":"Ortrud","last_name":"Wartlick","full_name":"Wartlick, Ortrud"},{"first_name":"Frank","full_name":"Julicher, Frank","last_name":"Julicher"},{"first_name":"Marcos","full_name":"Gonzalez Gaitan, Marcos","last_name":"Gonzalez Gaitan"}],"publist_id":"3739","title":"Investigating the principles of morphogen gradient formation: from tissues to cells","department":[{"_id":"ToBo"}],"date_updated":"2021-01-12T07:40:09Z","citation":{"chicago":"Kicheva, Anna, Mark Tobias Bollenbach, Ortrud Wartlick, Frank Julicher, and Marcos Gonzalez Gaitan. “Investigating the Principles of Morphogen Gradient Formation: From Tissues to Cells.” Current Opinion in Genetics & Development. Elsevier, 2012. https://doi.org/10.1016/j.gde.2012.08.004.","ista":"Kicheva A, Bollenbach MT, Wartlick O, Julicher F, Gonzalez Gaitan M. 2012. Investigating the principles of morphogen gradient formation: from tissues to cells. Current Opinion in Genetics & Development. 22(6), 527–532.","mla":"Kicheva, Anna, et al. “Investigating the Principles of Morphogen Gradient Formation: From Tissues to Cells.” Current Opinion in Genetics & Development, vol. 22, no. 6, Elsevier, 2012, pp. 527–32, doi:10.1016/j.gde.2012.08.004.","ama":"Kicheva A, Bollenbach MT, Wartlick O, Julicher F, Gonzalez Gaitan M. Investigating the principles of morphogen gradient formation: from tissues to cells. Current Opinion in Genetics & Development. 2012;22(6):527-532. doi:10.1016/j.gde.2012.08.004","apa":"Kicheva, A., Bollenbach, M. T., Wartlick, O., Julicher, F., & Gonzalez Gaitan, M. (2012). Investigating the principles of morphogen gradient formation: from tissues to cells. Current Opinion in Genetics & Development. Elsevier. https://doi.org/10.1016/j.gde.2012.08.004","short":"A. Kicheva, M.T. Bollenbach, O. Wartlick, F. Julicher, M. Gonzalez Gaitan, Current Opinion in Genetics & Development 22 (2012) 527–532.","ieee":"A. Kicheva, M. T. Bollenbach, O. Wartlick, F. Julicher, and M. Gonzalez Gaitan, “Investigating the principles of morphogen gradient formation: from tissues to cells,” Current Opinion in Genetics & Development, vol. 22, no. 6. Elsevier, pp. 527–532, 2012."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","type":"journal_article","status":"public","_id":"2970"},{"abstract":[{"lang":"eng","text":"There is a long-running controversy about how early cell fate decisions are made in the developing mammalian embryo. 1,2 In particular, it is controversial when the first events that can predict the establishment of the pluripotent and extra-embryonic lineages in the blastocyst of the pre-implantation embryo occur. It has long been proposed that the position and polarity of cells at the 16- to 32-cell stage embryo influence their decision to either give rise to the pluripotent cell lineage that eventually contributes to the inner cell mass (ICM), comprising the primitive endoderm (PE) and the epiblast (EPI), or the extra-embryonic trophectoderm (TE) surrounding the blastocoel. The positioning of cells in the embryo at this developmental stage could largely be the result of random events, making this a stochastic model of cell lineage allocation. Contrary to such a stochastic model, some studies have detected putative differences in the lineage potential of individual blastomeres before compaction, indicating that the first cell fate decisions may occur as early as at the 4-cell stage. Using a non-invasive, quantitative in vivo imaging assay to study the kinetic behavior of Oct4 (also known as POU5F1), a key transcription factor (TF) controlling pre-implantation development in the mouse embryo, 3-5 a recent study identifies Oct4 kinetics as a predictive measure of cell lineage patterning in the early mouse embryo. 6 Here, we discuss the implications of such molecular heterogeneities in early development and offer potential avenues toward a mechanistic understanding of these observations, contributing to the resolution of the controversy of developmental cell lineage allocation."}],"oa_version":"None","publisher":"Taylor and Francis","quality_controlled":"1","scopus_import":1,"month":"06","intvolume":" 11","publication_status":"published","year":"2012","day":"01","language":[{"iso":"eng"}],"publication":"Cell Cycle","page":"2055 - 2058","date_published":"2012-06-01T00:00:00Z","doi":"10.4161/cc.20118","volume":11,"issue":"11","date_created":"2018-12-11T12:01:44Z","_id":"3160","type":"journal_article","status":"public","citation":{"apa":"Pantazis, P., & Bollenbach, M. T. (2012). Transcription factor kinetics and the emerging asymmetry in the early mammalian embryo. Cell Cycle. Taylor and Francis. https://doi.org/10.4161/cc.20118","ama":"Pantazis P, Bollenbach MT. Transcription factor kinetics and the emerging asymmetry in the early mammalian embryo. Cell Cycle. 2012;11(11):2055-2058. doi:10.4161/cc.20118","short":"P. Pantazis, M.T. Bollenbach, Cell Cycle 11 (2012) 2055–2058.","ieee":"P. Pantazis and M. T. Bollenbach, “Transcription factor kinetics and the emerging asymmetry in the early mammalian embryo,” Cell Cycle, vol. 11, no. 11. Taylor and Francis, pp. 2055–2058, 2012.","mla":"Pantazis, Periklis, and Mark Tobias Bollenbach. “Transcription Factor Kinetics and the Emerging Asymmetry in the Early Mammalian Embryo.” Cell Cycle, vol. 11, no. 11, Taylor and Francis, 2012, pp. 2055–58, doi:10.4161/cc.20118.","ista":"Pantazis P, Bollenbach MT. 2012. Transcription factor kinetics and the emerging asymmetry in the early mammalian embryo. Cell Cycle. 11(11), 2055–2058.","chicago":"Pantazis, Periklis, and Mark Tobias Bollenbach. “Transcription Factor Kinetics and the Emerging Asymmetry in the Early Mammalian Embryo.” Cell Cycle. Taylor and Francis, 2012. https://doi.org/10.4161/cc.20118."},"date_updated":"2021-01-12T07:41:28Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publist_id":"3531","author":[{"first_name":"Periklis","last_name":"Pantazis","full_name":"Pantazis, Periklis"},{"full_name":"Bollenbach, Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","first_name":"Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"title":"Transcription factor kinetics and the emerging asymmetry in the early mammalian embryo","department":[{"_id":"ToBo"}]},{"_id":"3429","status":"public","type":"journal_article","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T07:43:24Z","citation":{"ama":"Plachta N, Bollenbach MT, Pease S, Fraser S, Pantazis P. Oct4 kinetics predict cell lineage patterning in the early mammalian embryo. Nature Cell Biology. 2011;13(2):117-123. doi:10.1038/ncb2154","apa":"Plachta, N., Bollenbach, M. T., Pease, S., Fraser, S., & Pantazis, P. (2011). Oct4 kinetics predict cell lineage patterning in the early mammalian embryo. Nature Cell Biology. Nature Publishing Group. https://doi.org/10.1038/ncb2154","short":"N. Plachta, M.T. Bollenbach, S. Pease, S. Fraser, P. Pantazis, Nature Cell Biology 13 (2011) 117–123.","ieee":"N. Plachta, M. T. Bollenbach, S. Pease, S. Fraser, and P. Pantazis, “Oct4 kinetics predict cell lineage patterning in the early mammalian embryo,” Nature Cell Biology, vol. 13, no. 2. Nature Publishing Group, pp. 117–123, 2011.","mla":"Plachta, Nicolas, et al. “Oct4 Kinetics Predict Cell Lineage Patterning in the Early Mammalian Embryo.” Nature Cell Biology, vol. 13, no. 2, Nature Publishing Group, 2011, pp. 117–23, doi:10.1038/ncb2154.","ista":"Plachta N, Bollenbach MT, Pease S, Fraser S, Pantazis P. 2011. Oct4 kinetics predict cell lineage patterning in the early mammalian embryo. Nature Cell Biology. 13(2), 117–123.","chicago":"Plachta, Nicolas, Mark Tobias Bollenbach, Shirley Pease, Scott Fraser, and Periklis Pantazis. “Oct4 Kinetics Predict Cell Lineage Patterning in the Early Mammalian Embryo.” Nature Cell Biology. Nature Publishing Group, 2011. https://doi.org/10.1038/ncb2154."},"title":"Oct4 kinetics predict cell lineage patterning in the early mammalian embryo","department":[{"_id":"ToBo"}],"publist_id":"2971","author":[{"first_name":"Nicolas","last_name":"Plachta","full_name":"Plachta, Nicolas"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias"},{"last_name":"Pease","full_name":"Pease, Shirley","first_name":"Shirley"},{"first_name":"Scott","full_name":"Fraser, Scott","last_name":"Fraser"},{"first_name":"Periklis","last_name":"Pantazis","full_name":"Pantazis, Periklis"}],"acknowledgement":"This work was supported by the Beckman Institute and Biological Imaging Center at the California Institute of Technology and by the NHGRI Center of Excellence in Genomic Science grant P50HG004071.","oa_version":"None","abstract":[{"lang":"eng","text":"Transcription factors are central to sustaining pluripotency, yet little is known about transcription factor dynamics in defining pluripotency in the early mammalian embryo. Here, we establish a fluorescence decay after photoactivation (FDAP) assay to quantitatively study the kinetic behaviour of Oct4, a key transcription factor controlling pre-implantation development in the mouse embryo. FDAP measurements reveal that each cell in a developing embryo shows one of two distinct Oct4 kinetics, before there are any morphologically distinguishable differences or outward signs of lineage patterning. The differences revealed by FDAP are due to differences in the accessibility of Oct4 to its DNA binding sites in the nucleus. Lineage tracing of the cells in the two distinct sub-populations demonstrates that the Oct4 kinetics predict lineages of the early embryo. Cells with slower Oct4 kinetics are more likely to give rise to the pluripotent cell lineage that contributes to the inner cell mass. Those with faster Oct4 kinetics contribute mostly to the extra-embryonic lineage. Our findings identify Oct4 kinetics, rather than differences in total transcription factor expression levels, as a predictive measure of developmental cell lineage patterning in the early mouse embryo."}],"intvolume":" 13","month":"01","publisher":"Nature Publishing Group","scopus_import":1,"publication":"Nature Cell Biology","language":[{"iso":"eng"}],"day":"23","year":"2011","publication_status":"published","date_created":"2018-12-11T12:03:17Z","volume":13,"doi":"10.1038/ncb2154","date_published":"2011-01-23T00:00:00Z","issue":"2","page":"117 - 123"},{"publist_id":"3231","author":[{"full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Kishony","full_name":"Kishony, Roy","first_name":"Roy"}],"title":"Resolution of gene regulatory conflicts caused by combinations of antibiotics","citation":{"ista":"Bollenbach MT, Kishony R. 2011. Resolution of gene regulatory conflicts caused by combinations of antibiotics. Molecular Cell. 42(4), 413–425.","chicago":"Bollenbach, Mark Tobias, and Roy Kishony. “Resolution of Gene Regulatory Conflicts Caused by Combinations of Antibiotics.” Molecular Cell. Cell Press, 2011. https://doi.org/10.1016/j.molcel.2011.04.016.","ama":"Bollenbach MT, Kishony R. Resolution of gene regulatory conflicts caused by combinations of antibiotics. Molecular Cell. 2011;42(4):413-425. doi:10.1016/j.molcel.2011.04.016","apa":"Bollenbach, M. T., & Kishony, R. (2011). Resolution of gene regulatory conflicts caused by combinations of antibiotics. Molecular Cell. Cell Press. https://doi.org/10.1016/j.molcel.2011.04.016","short":"M.T. Bollenbach, R. Kishony, Molecular Cell 42 (2011) 413–425.","ieee":"M. T. Bollenbach and R. Kishony, “Resolution of gene regulatory conflicts caused by combinations of antibiotics,” Molecular Cell, vol. 42, no. 4. Cell Press, pp. 413–425, 2011.","mla":"Bollenbach, Mark Tobias, and Roy Kishony. “Resolution of Gene Regulatory Conflicts Caused by Combinations of Antibiotics.” Molecular Cell, vol. 42, no. 4, Cell Press, 2011, pp. 413–25, doi:10.1016/j.molcel.2011.04.016."},"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","oa":1,"quality_controlled":"1","publisher":"Cell Press","acknowledgement":"This work was supported by a Feodor Lynen Fellowship of the Alexander von Humboldt Foundation (to T.B.).","page":"413 - 425","date_created":"2018-12-11T12:02:59Z","doi":"10.1016/j.molcel.2011.04.016","date_published":"2011-05-20T00:00:00Z","year":"2011","publication":"Molecular Cell","day":"20","type":"journal_article","status":"public","_id":"3376","department":[{"_id":"ToBo"}],"date_updated":"2021-01-12T07:43:03Z","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143497/","open_access":"1"}],"scopus_import":1,"intvolume":" 42","month":"05","abstract":[{"text":"Regulatory conflicts occur when two signals that individually trigger opposite cellular responses are present simultaneously. Here, we investigate regulatory conflicts in the bacterial response to antibiotic combinations. We use an Escherichia coli promoter-GFP library to study the transcriptional response of many promoters to either additive or antagonistic drug pairs at fine two-dimensional (2D) resolution of drug concentration. Surprisingly, we find that this data set can be characterized as a linear sum of only two principal components. Component one, accounting for over 70% of the response, represents the response to growth inhibition by the drugs. Component two describes how regulatory conflicts are resolved. For the additive drug pair, conflicts are resolved by linearly interpolating the single drug responses, while for the antagonistic drug pair, the growth-limiting drug dominates the response. Importantly, for a given drug pair, the same conflict resolution strategy applies to almost all genes. These results provide a recipe for predicting gene expression responses to antibiotic combinations.","lang":"eng"}],"oa_version":"Submitted Version","volume":42,"issue":"4","publication_status":"published","language":[{"iso":"eng"}]}]