David Heckmann

1.6k total citations
17 papers, 979 citations indexed

About

David Heckmann is a scholar working on Molecular Biology, Genetics and Renewable Energy, Sustainability and the Environment. According to data from OpenAlex, David Heckmann has authored 17 papers receiving a total of 979 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 4 papers in Genetics and 3 papers in Renewable Energy, Sustainability and the Environment. Recurrent topics in David Heckmann's work include Microbial Metabolic Engineering and Bioproduction (8 papers), Photosynthetic Processes and Mechanisms (7 papers) and Evolution and Genetic Dynamics (3 papers). David Heckmann is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (8 papers), Photosynthetic Processes and Mechanisms (7 papers) and Evolution and Genetic Dynamics (3 papers). David Heckmann collaborates with scholars based in Germany, United States and Denmark. David Heckmann's co-authors include Martin J. Lercher, Andreas P.M. Weber, Bernhard Ø. Palsson, Peter Westhoff, Udo Gowik, Urte Schlüter, Nathan Mih, Erol Kavvas, Colton J. Lloyd and Jonathan M. Monk and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

David Heckmann

17 papers receiving 965 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Heckmann Germany 13 776 238 132 110 79 17 979
Jingjing Jin China 16 508 0.7× 168 0.7× 45 0.3× 56 0.5× 93 1.2× 28 745
Arvind K. Chavali United States 14 721 0.9× 130 0.5× 162 1.2× 47 0.4× 29 0.4× 17 1.0k
Leah Soriaga United States 6 659 0.8× 229 1.0× 118 0.9× 268 2.4× 46 0.6× 7 1.0k
Stefan P. Albaum Germany 20 777 1.0× 298 1.3× 139 1.1× 38 0.3× 172 2.2× 41 1.2k
Christophe P. Tissier United States 9 1.5k 1.9× 859 3.6× 101 0.8× 45 0.4× 92 1.2× 9 1.8k
Liviu Popescu Romania 7 867 1.1× 115 0.5× 181 1.4× 30 0.3× 130 1.6× 29 1.1k
Gino Baart Netherlands 14 628 0.8× 79 0.3× 95 0.7× 224 2.0× 79 1.0× 17 945
Shinsuke Ohnuki Japan 17 553 0.7× 130 0.5× 163 1.2× 79 0.7× 25 0.3× 44 795
Nabih A. Baeshen Saudi Arabia 18 731 0.9× 286 1.2× 75 0.6× 13 0.1× 115 1.5× 45 1.1k
Dorjee G. Tamang United States 10 539 0.7× 191 0.8× 70 0.5× 19 0.2× 134 1.7× 11 907

Countries citing papers authored by David Heckmann

Since Specialization
Citations

This map shows the geographic impact of David Heckmann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Heckmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Heckmann more than expected).

Fields of papers citing papers by David Heckmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Heckmann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Heckmann. The network helps show where David Heckmann may publish in the future.

Co-authorship network of co-authors of David Heckmann

This figure shows the co-authorship network connecting the top 25 collaborators of David Heckmann. A scholar is included among the top collaborators of David Heckmann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Heckmann. David Heckmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Sastry, Anand V., et al.. (2021). Independent component analysis recovers consistent regulatory signals from disparate datasets. PLoS Computational Biology. 17(2). e1008647–e1008647. 27 indexed citations
2.
Kroll, Alexander, Martin K. M. Engqvist, David Heckmann, & Martin J. Lercher. (2021). Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biology. 19(10). e3001402–e3001402. 74 indexed citations
3.
Lercher, Martin J., et al.. (2021). Modeling photosynthetic resource allocation connects physiology with evolutionary environments. Scientific Reports. 11(1). 15979–15979. 3 indexed citations
4.
Heckmann, David, Anaamika Campeau, Colton J. Lloyd, et al.. (2020). Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers. Proceedings of the National Academy of Sciences. 117(37). 23182–23190. 59 indexed citations
5.
Phaneuf, Patrick V., James T. Yurkovich, David Heckmann, et al.. (2020). Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity. BMC Genomics. 21(1). 514–514. 24 indexed citations
6.
Mih, Nathan, Jonathan M. Monk, Xin Fang, et al.. (2020). Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes. BMC Bioinformatics. 21(1). 162–162. 8 indexed citations
7.
Kavvas, Erol, Laurence Yang, Jonathan M. Monk, David Heckmann, & Bernhard Ø. Palsson. (2020). A biochemically-interpretable machine learning classifier for microbial GWAS. Nature Communications. 11(1). 2580–2580. 57 indexed citations
8.
Kavvas, Erol, Edward Catoiu, Nathan Mih, et al.. (2018). Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nature Communications. 9(1). 4306–4306. 129 indexed citations
9.
Heckmann, David, Daniel C. Zielinski, & Bernhard Ø. Palsson. (2018). Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates. Nature Communications. 9(1). 5270–5270. 12 indexed citations
10.
Heckmann, David, Colton J. Lloyd, Nathan Mih, et al.. (2018). Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nature Communications. 9(1). 5252–5252. 155 indexed citations
11.
Heckmann, David, Urte Schlüter, & Andreas P.M. Weber. (2017). Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra. Molecular Plant. 10(6). 878–890. 90 indexed citations
12.
Heckmann, David, et al.. (2017). BLISTER Regulates Polycomb-Target Genes, Represses Stress-Regulated Genes and Promotes Stress Responses in Arabidopsis thaliana. Frontiers in Plant Science. 8. 1530–1530. 18 indexed citations
13.
Li, Yuanyuan, David Heckmann, Martin J. Lercher, & Verónica G. Maurino. (2016). Combining genetic and evolutionary engineering to establish C4metabolism in C3plants. Journal of Experimental Botany. 68(2). 117–125. 11 indexed citations
14.
Heckmann, David. (2016). C4 photosynthesis evolution: the conditional Mt. Fuji. Current Opinion in Plant Biology. 31. 149–154. 18 indexed citations
15.
Heckmann, David. (2015). Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis. Biochemical Society Transactions. 43(6). 1172–1176. 3 indexed citations
16.
Heckmann, David, Andrea Bräutigam, Martin J. Lercher, et al.. (2014). The role of photorespiration during the evolution of C4 photosynthesis in the genus Flaveria. eLife. 3. e02478–e02478. 152 indexed citations
17.
Heckmann, David, Stefanie Schulze, Alisandra K. Denton, et al.. (2013). Predicting C4 Photosynthesis Evolution: Modular, Individually Adaptive Steps on a Mount Fuji Fitness Landscape. Cell. 153(7). 1579–1588. 139 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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