World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
39
Citations
8852
World Ranking
7546
National Ranking
468

Neil Swainston publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Neil Swainston sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Neil Swainston D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Neil Swainston sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 39 D-Index — 24th percentile

24% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Neil Swainston is affiliated with Epoch BioDesign in the United Kingdom. Their research primarily falls within the broad field of Biochemistry, Genetics and Molecular Biology, with a significant focus on Molecular Biology. Other subfields of study include Computational Theory and Mathematics, Spectroscopy, Materials Chemistry, and Biomedical Engineering.

The scientist's work spans several main topics, with notable concentrations in Microbial Metabolic Engineering and Bioproduction, Gene Regulatory Network Analysis, and Computational Drug Discovery Methods. Additional research interests cover Metabolomics and Mass Spectrometry Studies, Bioinformatics and Genomic Networks, as well as Machine Learning applications within Materials Science and Mass Spectrometry Techniques and Applications.

Neil Swainston has contributed to the publication of numerous papers in various reputable scientific journals. Recent notable publications include:

  • SBML Level 3: an extensible format for the exchange and reuse of biological models (2020) in Molecular Systems Biology
  • The RESOLUTE consortium: unlocking SLC transporters for drug discovery (2020) in Nature Reviews Drug Discovery
  • Engineering Escherichia coli towards de novo production of gatekeeper (2S)-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol (2020) in Synthetic Biology
  • DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach (2020) in Journal of Cheminformatics
  • Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers (2020) in Metabolic Engineering

Many of Swainston's papers have appeared frequently in a select group of publication venues. These include bioRxiv (Cold Spring Harbor Laboratory), Synthetic Biology, Molecular Systems Biology, Nature Reviews Drug Discovery, and the Journal of Cheminformatics.

The scientist has collaborated extensively with several frequent co-authors, including Douglas B. Kell, Soumitra Samanta, Pablo Carbonell, Valentin Zulkower, and Marina Wright Muelas, reflecting active engagement in collaborative research networks.

Best Publications

  • A community-driven global reconstruction of human metabolism

    Ines Thiele;Neil Swainston;Ronan M T Fleming;Andreas Hoppe

  • ChEBI in 2016: Improved services and an expanding collection of metabolites.

    Janna Hastings;Gareth Owen;Adriano Dekker;Marcus Ennis

  • A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    Markus Herrgard;Neil Swainston;Paul Dobson;Warwick B. Dunn

  • Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

    Andrew Currin;Neil Swainston;Philip J. Day;Douglas B. Kell

  • Recon 2.2: from reconstruction to model of human metabolism

    Neil Swainston;Kieran Smallbone;Hooman Hefzi;Paul D. Dobson

  • Growth control of the eukaryote cell: a systems biology study in yeast

    Juan I Castrillo;Leo A Zeef;David C Hoyle;Nianshu Zhang

  • Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics

    Marie Brown;Warwick B. Dunn;P. Dobson;Y. Patel

  • SBML Level 3: an extensible format for the exchange and reuse of biological models

    Sarah M. Keating;Sarah M. Keating;Dagmar Waltemath;Matthias König;Fengkai Zhang

  • An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

    Pablo Carbonell;Adrian J. Jervis;Christopher J. Robinson;Cunyu Yan

  • Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis

    Douglas B. Kell;Neil Swainston;Pınar Pir;Stephen G. Oliver

  • Towards a genome-scale kinetic model of cellular metabolism

    Kieran Smallbone;Evangelos Simeonidis;Neil Swainston;Pedro Mendes;Pedro Mendes

  • Improving metabolic flux predictions using absolute gene expression data

    Dave Lee;Kieran Smallbone;Warwick B Dunn;Ettore Murabito

  • Large-scale generation of computational models from biochemical pathway maps

    Finja Büchel;Nicolas Rodriguez;Neil Swainston;Clemens Wrzodek

  • A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2

    Ines Thiele;Daniel R Hyduke;Benjamin Steeb;Guy Fankam

  • Path2Models: large-scale generation of computational models from biochemical pathway maps

    Finja Buchel;Finja Buchel;Nicolas Rodriguez;Nicolas Rodriguez;Neil Swainston;Clemens Wrzodek

  • Identifiers for the 21st century : How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

    Julie A. McMurry;Nick Juty;Niklas Blomberg;Tony Burdett

  • Further developments towards a genome-scale metabolic model of yeast

    Paul D. Dobson;Kieran Smallbone;Daniel Jameson;Evangelos Simeonidis

  • Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

    Karin Radrich;Karin Radrich;Yoshimasa Tsuruoka;Yoshimasa Tsuruoka;Paul D. Dobson;Albert Gevorgyan;Albert Gevorgyan

  • Selenzyme: enzyme selection tool for pathway design

    Pablo Carbonell;Jerry Wong;Neil Swainston;Eriko Takano;Eriko Takano

  • A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

    Steve O′Hagan;Neil Swainston;Julia Handl;Douglas B. Kell

  • Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli

    Adrian J. Jervis;Pablo Carbonell;Maria Vinaixa;Mark S. Dunstan

  • The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks.

    Neil Swainston;Kieran Smallbone;Pedro Mendes;Douglas B. Kell

  • SBOL Visual: A Graphical Language for Genetic Designs

    Jacqueline Y. Quinn;Robert Sidney Cox;Aaron Adler;Jacob Beal

  • Bioinformatics for the synthetic biology of natural products: integrating across the Design–Build–Test cycle

    Pablo Carbonell;Andrew Currin;Adrian J. Jervis;Nicholas J. W. Rattray

  • SpeedyGenes: An improved gene synthesis method for the efficient production of error-corrected, synthetic protein libraries for directed evolution

    Andrew Currin;Neil Swainston;Philip J. Day;Douglas B. Kell

Frequent Co-Authors

Douglas B. Kell
Douglas B. Kell University of Liverpool
Pedro Mendes
Pedro Mendes University of Connecticut
Nigel S. Scrutton
Nigel S. Scrutton University of Manchester
Jean-Loup Faulon
Jean-Loup Faulon University of Paris-Saclay
Eriko Takano
Eriko Takano University of Manchester
Rainer Breitling
Rainer Breitling University of Manchester
Warwick B. Dunn
Warwick B. Dunn University of Liverpool
Nicolas Le Novère
Nicolas Le Novère Babraham Institute
Nicholas J. Turner
Nicholas J. Turner University of Manchester
Hans V. Westerhoff
Hans V. Westerhoff Vrije Universiteit Amsterdam

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