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Evolvability and hierarchy in rewired bacterial gene networks
Open accessAbstract
Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily moulded the contents of a given genome. Though the effect of knocking out or overexpressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or sigma-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that approximately 95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage.
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“Microbial cells have an intrinsic capacity to balance self preservation (stress protection) and nutritional competence (SPANC).”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [158]
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“This SPANC balance was intensively studied in E. coli and is governed by strict control of processes such as stress and stringent response [158,159].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [158]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
The complete genome sequence of Escherichia coli K-12.
Blattner, F., Plunkett, G., Bloch, C., Perna, N., Burland, V., Riley, M., Collado-Vides, J., Glasner, J. D., Rode, C., Mayhew, G., et al. (1997) · Science · doi:10.1126/science.277.5331.1453 · Cited by 7,996
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554
The Evolutionary Fate and Consequences of Duplicate Genes
Lynch, M., & Conery, J. S. (2000) · Science · doi:10.1126/science.290.5494.1151 · Cited by 4,988
A synthetic oscillatory network of transcriptional regulators
Elowitz, M., & Leibler, S. (2000) · Nature · doi:10.1038/35002125 · Cited by 4,812
Construction of a genetic toggle switch in Escherichia coli
Gardner, T. S., Cantor, C. R., & Collins, J. J. (2000) · Nature · doi:10.1038/35002131 · Cited by 4,310
Flagged first, then by citations
Research works printed but not linked: 2
- Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)
- Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995) · DOI · Paper page
Cited by
Sorted by citations
Items 201–300 of 306
Inoue, M., & Kaneko, K. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033183 · Cited by 9
“[50] Isalan M, Lemerle C, Michalodimitrakis K, Horn C, Beltrao P, Raineri E, et al.”
BackgroundShibai, A., Kotani, H., Sakata, N., Furusawa, C., & Tsuru, S. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac235 · Cited by 7
“In addition, it has been pointed out that ORFs can some-how determine their own expression levels (Isalan et al. 2008).”
Synthetic Biology & Bioinformatics Prospects in the Cancer Arena
Rodrigues, L. R., & Leon, D. (2011) · InTech eBooks · doi:10.5772/21505 · Cited by 8
“The majority of the synthetic biology advances has been achieved purely in vitro (Isalan et al., 2008), or in microorganisms involving the design of small gene circuits without a direct practical application, although scientifically very exciting.”
BackgroundTargeting and tinkering with interaction networks
Aloy, P. (2008) · FEBS Letters · doi:10.1016/j.febslet.2008.02.069 · Cited by 8
Ohm’s Law for increasing fitness gene expression with selection pressurePreprint
Ciechonska, M., Sturrock, M., Grob, A., Larrouy-Maumus, G. J., Shahrezaei, V., & Isalan, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/693234 · Cited by 8
Self-citationEmergent expression of fitness-conferring genes by phenotypic selection
Ciechonska, M., Sturrock, M., Grob, A., Larrouy-Maumus, G. J., Shahrezaei, V., & Isalan, M. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac069 · Cited by 7
“Transformants were grown up and recombinase expression was induced as described previously (37).”
MethodSelf-citationStomel, J. M., Wilson, J. W., León, M. A., Stafford, P., & Chaput, J. C. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007385 · Cited by 7
Weighted self-similar networks under preferential attachment
Chrysafis, O., & Cannings, C. (2009) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2009.03.030 · Cited by 7
Synthetic Biology: opportunities for Chilean bioindustry and education
Federici, F., Rudge, T. J., Pollak, B., Haseloff, J. P., & Gutiérrez, R. A. (2013) · Biological Research · doi:10.4067/s0716-97602013000400010 · Cited by 10
“…and performance of gene regulation (Damle and Davidson 2012; Becskei and Serrano, 2000; Cantone et al., 2009; Isaacs et al., 2003); animal physiology (Chow and Boyden, 2011); signaling mechanisms in immune cells (Schamel and Reth, 2012); and genome-wide network evolvability (Isalan et al., 2008).”
BackgroundIntroduction to Philosophy of Systems Biology
Green, S. (2016) · History, philosophy and theory of the life sciences · doi:10.1007/978-3-319-47000-9_1 · Cited by 7
Yu, H., Mitra, R., Yang, J., Li, Y., & Zhao, Z. (2014) · Science China Life Sciences · doi:10.1007/s11427-014-4762-7 · Cited by 7
Engineered sensor bacteria evolve master-level gameplay through accelerated adaptationPreprint
Prakash, S., Racovita, A., Varela, C., Walsh, M. D., Galizi, R., Isalan, M., & Jaramillo, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.22.489191 · Cited by 7
Self-citationFeature ranking in transcriptional networks: Packet receipt as a dynamical metric
Kamapantula, B. K., Mayo, M. L., Perkins, E. J., Abdelzaher, A. F., & Ghosh, P. (2015) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.4108/icst.bict.2014.257930 · Cited by 6
Dynamical impacts from structural redundancy of transcriptional motifs in gene-regulatory networks
Kamapantula, B. K., Mayo, M. L., Perkins, E. J., & Ghosh, P. (2015) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.4108/icst.bict.2014.257928 · Cited by 6
Learning to Love Animal (Models) (or) How (Not) to Study Genes as a Social Scientist
Conley, D. (2010) · Handbooks of sociology and social research · doi:10.1007/978-1-4419-7261-3_27 · Cited by 6
Synthetic Biology: Life as App Store
Newman, S. A. (2012) · Capitalism Nature Socialism · doi:10.1080/10455752.2011.648832 · Cited by 6
Addressing Evolutionary Questions with Synthetic Biology
Baier, F., & Schaerli, Y. (2021) · Springer eBooks · doi:10.1007/978-3-030-71737-7_7 · Cited by 6
Systems Biology: Principles and Applications in Plant Research
Coruzzi, G. M., Burga, A. R., Katari, M. S., & Gutiérrez, R. A. (2017) · Wiley eBooks · doi:10.1002/9781119312994.apr0372 · Cited by 6
Diversity of Transcriptional Regulatory Adaptation in E. coli
Dalldorf, C., Hefner, Y., Szubin, R., Johnsen, J., Mohamed, E., Li, G., Krishnan, J., Feist, A. M., Palsson, B. Ø., & Zielinski, D. C. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae240 · Cited by 5
“…number of genes many of which have only it as a regulator and connects many TFs to each other in the TRN. Similar to how some of our removed TRN connections enabled new growth capabilities, another study showed how creating new regulatory links can also confer a growth benefit (Isalan et al. 2008).”
Engineering Tunable, Low Latency Spatial Computation with Dual Input Quorum Sensing Promoters
Tica, J., Chen, H., Luo, S., Chen, M., & Isalan, M. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00068 · Cited by 7
Self-citationSelection Shapes the Robustness of Ligand-Binding Amino Acids
Bilgin, T., Kurnaz, I. A., & Wagner, A. (2013) · Journal of Molecular Evolution · doi:10.1007/s00239-013-9564-1 · Cited by 5
From noise to synthetic nucleoli: can synthetic biology achieve new insights?
Ciechonska, M., Grob, A., & Isalan, M. (2016) · Integrative Biology · doi:10.1039/c5ib00271k · Cited by 6
“and indeed take advantage of, extensive rewiring regimes (41).”
BackgroundSelf-citationLi, J., Guo, Y., Li, J., Zhang, H., Wang, L., & Liang, C. (2024) · Molecular Catalysis · doi:10.1016/j.mcat.2024.113904 · Cited by 5
Risch, T., Kolling, D., Mostert, D., Seedorf, T., Heimann, D., Kohnhäuser, D., Deschner, F., Fries, F., Solga, D., Hilgers, J.-S., et al. (2024) · npj Antimicrobials and Resistance · doi:10.1038/s44259-024-00050-7 · Cited by 5
“Furthermore, it incorporates a DNA binding domain and shows homology to GyrI-like proteins 15 .”
Bacterial Molecular Networks: Bridging the Gap Between Functional Genomics and Dynamical Modelling
van Helden, J., Toussaint, A., & Thieffry, D. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-361-5_1 · Cited by 5
Payne, J. L., & Moore, J. H. (2011) · European Conference on Artificial Life · doi:10.7551/978-0-262-29714-1-ch097 · Cited by 5
The Role of Mutations in Whole Genome Duplication
Pan, Q., Darabos, C., & Moore, J. H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29066-4_11 · Cited by 5
Entangled adaptive landscapes facilitate the evolution of gene regulation by exaptationPreprint
Westmann, C. A., Goldbach, L., & Wagner, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.10.620926 · Cited by 8
“Regulatory rewiring is widespread across bacterial lineages 88,118–121 .”
Robust set stability of large-scale Boolean networks with disturbance via network aggregation
Li, W. (2024) · International Journal of Control · doi:10.1080/00207179.2024.2342948 · Cited by 4
“Due to the existence of gene mutations (Gu et al., 2003) and modelling errors (Schlitt & Brazma, 2007) in genetic regulatory networks (GRNs), the robustness of GRNs has become a meaningful topic systems in the last few decades (Isalan et al., 2008; Ren & Cao,2008 ).”
Abdelzaher, A. F., Mayo, M. L., Perkins, E. J., & Ghosh, P. (2015) · Nano Communication Networks · doi:10.1016/j.nancom.2015.04.002 · Cited by 4
“coli have been conducted where transcription factors were rewired and the tolerance of the bacteria was analyzed [27].”
BackgroundCarrera, J., & Elena, S. F. (2012) · Scientific Reports · doi:10.1038/srep01006 · Cited by 4
“Recently, many groups have proposed and implemented different approaches for genome-wide re-design, by knocking out and over-expressing genes, of prokaryotes and eukaryotes to control global gene expression 29-32 .”
BackgroundMutations in Transcriptional Regulators Allow Selective Engineering of Signal Integration Logic
Semsey, S. (2014) · mBio · doi:10.1128/mbio.01171-14 · Cited by 4
Reducing metabolic burden in the PACEmid evolver system by remastering high‐copy phagemid vectors
Davenport, B., Tica, J., & Isalan, M. (2022) · Engineering Biology · doi:10.1049/enb2.12021 · Cited by 3
Self-citationFlgM as a Secretion Moiety for the Development of an Inducible Type III Secretion System
Heel, T., Vogel, G. F., Lammirato, A., Schneider, R., & Auer, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059034 · Cited by 4
“Further investigations will be necessary to determine if the observed growth retardation of the generated mutant strains is indeed a result of the metabolic burden due to the strong induction of the master operon or a direct connection between flagellar morphogenesis and cell proliferation, and therefore remains contr…”
The Survival of the Fittest and the Reign of the Most Robust: In Biology and Elsewhere
Schuster, A., & Yamaguchi, Y. (2009) · Minds and Machines · doi:10.1007/s11023-009-9156-4 · Cited by 4
“Although there are other contributions in the field, in terms of rewiring, this section has a focus on a study undertaken by Isalan et al. (2008) .”
BackgroundMonteiro, L. M. O., Sanches-Medeiros, A., Westmann, C. A., & Silva‐Rocha, R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/614396 · Cited by 4
Siegele, D. A., Bain, S. V., & Mao, W. (2010) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2010.02021.x · Cited by 3
ON MINIMALLY COUPLED BOOLEAN NETWORKS
Alonso‐Sanz, R., & Bull, L. (2009) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127409023743 · Cited by 3
Phenotype selection due to mutational robustness
Kikuchi, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0311058 · Cited by 3
“However, the development of robustness through evolution is difficult to investigate experimentally.”
The Phenotype Paradox: Lessons From Natural Transcriptome Evolution on How to Engineer Plants
Law, J., Ng, K., & Windram, O. P. F. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00075 · Cited by 3
Feedback topology and XOR-dynamics in Boolean networks with varying input structure
Ciandrini, L., Maffi, C., Motta, A., Bassetti, B., & Lagomarsino, M. C. (2009) · Physical Review E · doi:10.1103/physreve.80.026122 · Cited by 3
Towards a Methodology for Systems Biology
Groß, F. (2016) · History, philosophy and theory of the life sciences · doi:10.1007/978-3-319-47000-9_10 · Cited by 3
Inferring Gene Regulatory Networks from Multiple Datasets
Penfold, C. A., Gherman, I., Sybirna, A., & Wild, D. L. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8882-2_11 · Cited by 3
Structure and Evolution of Transcriptional Regulatory Networks
Chalancon, G., & Babu, M. M. (2014) · ASM Press eBooks · doi:10.1128/9781555816841.ch1 · Cited by 3
“These observations are also supported by a study by Isalan et al (Isalan et al. 2008) which has shown that artificial incorporation of new regulatory interactions into E.”
InfluentialShepherd, M. J., Pierce, A. P., & Taylor, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499626 · Cited by 3
Boutonnet, C., Giniès, C., Alpha‐Bazin, B., Armengaud, J., Château, A., & Duport, C. (2023) · Journal of Proteomics · doi:10.1016/j.jprot.2023.105007 · Cited by 2
Hierarchical dynamics of a transcription factors network in E. coli
Martínez‐Antonio, A., Velázquez-Ramírez, D. A., Sánchez-Mondragón, J. J., & Santillán, M. (2012) · Molecular BioSystems · doi:10.1039/c2mb25236h · Cited by 4
A Heuristic Gene Regulatory Networks Model for Cardiac Function and Pathology
Zarnegar, A., Vamplew, P. W., Stranieri, A., & Jelinek, H. F. (2016) · Computing in cardiology · doi:10.22489/cinc.2016.101-135 · Cited by 2
“The final step was to use hubs to construct the backbone on which the GRN can be built [12].”
MethodGenotype Networks and Evolutionary Innovations in Biological Systems
Wagner, A. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00013-7 · Cited by 2
Mixed Degree-Degree Correlations in Directed Social Networks
Mayo, M. L., Abdelzaher, A. F., & Ghosh, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12691-3_42 · Cited by 2
“Complex networks exhibit interesting topological properties, such as power-law degree distributions [4,5], short path-lengths [47], robustness to random failures [2], and resilience to rewiring [16] or adding new links [18].”
BackgroundNo tradeoff between versatility and robustness in gene circuit motifs
Payne, J. L. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.12.065 · Cited by 2
The Structural Role of Feed-Forward Loop Motif in Transcriptional Regulatory Networks
Kamapantula, B. K., Mayo, M. L., Perkins, E. J., & Ghosh, P. (2016) · Mobile Networks and Applications · doi:10.1007/s11036-016-0708-6 · Cited by 2
“For example, some bacteria have been shown to survive despite extensive ‘rewiring’ of their transcriptional network topologies [9].”
BackgroundExperimental evolution of Escherichia coli harboring an ancient translation proteinPreprint
Kaçar, B., Ge, X., Sanyal, S., & Gaucher, E. A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/040626 · Cited by 3
Ma, X., Ma, L., & Huo, Y. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821753-5.00005-8 · Cited by 1
Taylor, T., & Rice, A. M. (2026) · Advanced Science · doi:10.1002/advs.202520406 · Cited by 1
“It suggests that, under the experimental conditions used to select for motility recovery, evolutionary routes to rewiring are uneven, likely driven by context-dependent mutational opportunity, evolutionary history, and/or regulatory affinity [ 25, 26 ].”
Species loss alters the mesoscale structure of mutualistic networks
Miguel, M. F., Jordano, P., Burkle, L. A., Emer, C., & Vázquez, D. P. (2026) · Journal of Animal Ecology · doi:10.1111/1365-2656.70280 · Cited by 1
Automated design of bacterial genome sequences
Carrera, J., & Jaramillo, A. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-108 · Cited by 1
“(2008) to exhaustively explore the effect of multiple genome rewirings on growth rate [10].”
BackgroundEVOLVABILITY OF COMPUTATIONAL GENETIC REGULATORY NETWORKS
Knabe, J. F. (2009) · Cited by 1
Abdelzaher, A. F., Mayo, M. L., Ghosh, P., & Perkins, E. J. (2015) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.4108/icst.bict.2014.257916 · Cited by 1
Integrating Biological Heuristics and Gene Expression Data for Gene Regulatory Network Inference
Zarnegar, A., Jelinek, H. F., Vamplew, P. W., & Stranieri, A. (2019) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/3290688.3290741 · Cited by 1
“Hubs are connected to other hubs to form a primary structure, a network of hubs, which acts as a backbone to the GRN [46].”
BackgroundImproving microbial chemical production strains through transcriptional regulatory network rewiring
Rodrigues, R. (2015) · doi:10.25560/30834 · Cited by 1
Dynamics and Correlated Noise in Gene Regulation
Dunlop, M. J. (2008) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/ac8v-6s05 · Cited by 1
Patra, B. N., Kon, Y., Yadav, G., Sevold, A., Frumkin, J. P., Vallabhajosyula, R. R., Hintze, A., Østman, B., Schossau, J., Bhan, A., et al. (2013) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/000265 · Cited by 2
Forging New Ties between E. coli Genes
Ideker, T. E. (2008) · Cell · doi:10.1016/j.cell.2008.06.003 · Cited by 1
“Nature 424, 147– 151.”
BackgroundInfluentialHebbar, A., Moger, A., Hari, K., & Jolly, M. K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.08.463488 · Cited by 1
“Coli, wherein new regulatory interactions were added to GRNs, [44] 95 % of these modified gene interaction networks were tolerated by the bacteria and some conferred selective advantages in particular environments.”
BackgroundUnderstanding the regulation of acid resistance in E. coli using whole genome techniques
Johnson, M. D. (2011) · University of Birmingham Institutional Research Archive (University of Birmingham)
Jenkins, D. J. (2009) · University of Birmingham Institutional Research Archive (University of Birmingham)
Integrative bioinformatics of metabolic networks.
Notebaart, R. A. (2009) · Radboud Repository (Radboud University)
Händel, N. (2015) · UvA-DARE (University of Amsterdam)
AUTOMATIC COMPUTATIONAL DESIGN OF SYNTHETIC GENOMES
Carrera, J. (2012) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/35380
“timated from basic physical principles and a simple phenomenological model, and remarkably they are in tune with previous experimental work [1].”
BackgroundResultInfluentialDynamics and evolution of random Boolean networks
Mihaljev, T. (2008) · Technischen Universität Darmstadt · doi:10.26083/tuprints-00001188
Doerr, A. J. (2008) · Nature Methods · doi:10.1038/nmeth0608-464b
Modular Organization in a Cell: Concepts and Applications
Yang, R., & Su, B. (2009) · Current Bioinformatics · doi:10.2174/157489309789071075
Pechenick, D. A., Payne, J. L., & Moore, J. H. (2013) · European Conference on Artificial Life · doi:10.7551/978-0-262-31709-2-ch053 · Cited by 1
“, 2001) or the rewiring of regulatory interactions (Isalan et al., 2008).”
Background2008: Signaling Breakthroughs of the Year
Adler, E. M. (2009) · Science Signaling · doi:10.1126/scisignal.252eg1
“In a fascinating demonstration of the nature of biological regulatory networks and of how the complex and robust machinery that we observe in signaling networks might evolve (9, 10), Isalan et al.”
BackgroundEvolution of Boolean Networks: Contingency Versus Necessity in Biology
Gilmore, S. J. (2012) · The Open Evolution Journal · doi:10.2174/1874404401206010001
“tive idea that selection effects a co i [33], indicate that this network is highly evolvable.”
BackgroundResultKamapantula, B. K. (2015) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/xzks-0p19
“For example, some bacteria have been shown to survive despite extensive ‘rewiring’ of their transcriptional network topologies [23].”
BackgroundExpanded functionality of the bacterial global regulator Lrp
Hart, B. R. (2010) · OhioLink ETD Center (Ohio Library and Information Network)
EDGE PERTURBATION IN THE TRANSCRIPTION NETWORK OF YEAST
Ricerca, D. D. (2010)
Mathematical Modeling of the PI3K-AKT-TOR Pathway
PhD, G. W. (2013) · CRC Press eBooks · doi:10.1201/b16259-18
Federici, F., Rudge, T. J., Pollak, B., & Gutiérrez, R. A. (2013)
Transcriptional Regulation in Synthetic Gene Networks
Nagaraj, S. (2010) · TSpace (University of Toronto)
Process-driven inference of biological network structure: feasibility, minimality, and multiplicity
Zeng, C. (2012) · Bulletin of the American Physical Society
Philosophy of Synthetic Biology
Nabipour, I. (2018) · Iranian South Medical Journal · doi:10.29252/ismj.21.1.1
Brödel, A. K., Rodrigues, R., Jaramillo, A., & Isalan, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/725739
Self-citationEvolutionary regain of lost gene circuit functionPreprint
Gouda, M. K., Manhart, M., & Balázsi, G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/804187
Shimeld, S. M. (2009) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.1168004.630110
MAPK transgenic circuit to improve plant stress-tolerance?Preprint
Moustafa, K. (2018) · doi:10.31221/osf.io/kud5v
Siegal, M. L. (2014) · Oxford University Press eBooks · doi:10.1093/obo/9780199941728-0010
Improving plant stress tolerance: potential applications of engineered MAPK cascadesPreprint
Moustafa, K. (2018) · doi:10.31221/osf.io/vsw3d
Fu, Y., Tsai, T.-H., Mao, C., Mun, S. K., Ressom, H. W., Wang, M., Zhang, Z., & Wang, Y. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816034-3.00003-1
Prochiantz, A. (2010) · L’annuaire du Collège de France · doi:10.4000/annuaire-cdf.309
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813635-5.16001-x
A. Optimization of Insulin Action
PhD, G. W. (2013) · CRC Press eBooks · doi:10.1201/b16259-23
MAPK transgenic circuit to improve plant tolerancePreprint
Moustafa, K. (2018) · doi:10.31226/osf.io/wrfq3
Regulatory network structure and environmental signals constrain transcription factor innovationPreprint
Shepherd, M. J., Reynolds, M., Pierce, A. P., Rice, A. M., & Taylor, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.04.539253
Lynch, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780192847287.003.0021
Metadata
- Authors
- Isalan, M., Lemerle, C., Michalodimitrakis, K., Horn, C., Beltrao, P., Raineri, E., Garriga-Canut, M., & Serrano, L.
- Affiliations
- European Molecular Biology Laboratory (ES)
- European Molecular Biology Laboratory (DE)
- Centre for Genomic Regulation (ES)
- Corresponding author
- Isalan, Mark
- Venue
- Nature
- Published
- 2008-04
- Volume
- 452
- Issue
- 7189
- Pages
- 840-845
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC2666274
- OpenAlex
- W2159993062
- Cited by
- 307
- References
- 46
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 9 · 2023: 8 · 2022: 11 · 2021: 7 · 2020: 8 · 2019: 12 · 2018: 13 · 2017: 9 · 2016: 13 · 2015: 19 · 2014: 18 · 2013: 29 · 2012: 27
- FWCI
- 16.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Wellcome Trust (066543, WT066543)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- gene regulatory network rewiring, Escherichia coli, transcription factors, sigma factor, network hierarchy, synthetic gene networks, genome rearrangement, bacterial fitness
- MeSH terms
- Escherichia coli, Genes, Bacterial, Genetic Engineering, Promoter Regions, Genetic, Selection, Genetic, Serial Passage, Sigma Factor, Transcription Factors, Gene Expression Regulation, Bacterial, Open Reading Frames, Heat-Shock Response, Evolution, Molecular, Oligonucleotide Array Sequence Analysis, Escherichia coli Proteins, Gene Regulatory Networks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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