Evan Appleton

1.1k total citations
19 papers, 572 citations indexed

About

Evan Appleton is a scholar working on Molecular Biology, Genetics and Biomedical Engineering. According to data from OpenAlex, Evan Appleton has authored 19 papers receiving a total of 572 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 5 papers in Genetics and 5 papers in Biomedical Engineering. Recurrent topics in Evan Appleton's work include CRISPR and Genetic Engineering (8 papers), Gene Regulatory Network Analysis (6 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Evan Appleton is often cited by papers focused on CRISPR and Genetic Engineering (8 papers), Gene Regulatory Network Analysis (6 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). Evan Appleton collaborates with scholars based in United States, United Kingdom and Netherlands. Evan Appleton's co-authors include Douglas Densmore, George M. Church, Kenneth A. Oye, Todd Kuiken, Andrea L. Smidler, James P. Collins, Kevin M. Esvelt, Flaminia Catteruccia, Nicholas Roehner and Curtis Madsen and has published in prestigious journals such as Science, Cell and Nature Communications.

In The Last Decade

Evan Appleton

18 papers receiving 551 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Evan Appleton United States 12 460 102 81 63 63 19 572
Hui Kwon Kim South Korea 11 1.3k 2.8× 237 2.3× 59 0.7× 43 0.7× 29 0.5× 15 1.5k
Ardalan Naseri United States 9 968 2.1× 171 1.7× 35 0.4× 27 0.4× 25 0.4× 23 1.1k
Jocelynn R. Pearl United States 7 466 1.0× 123 1.2× 18 0.2× 156 2.5× 41 0.7× 10 659
Heidi E. Klumpe United States 4 473 1.0× 79 0.8× 57 0.7× 9 0.1× 35 0.6× 6 557
Joy Y. Wang United States 7 687 1.5× 136 1.3× 50 0.6× 33 0.5× 46 0.7× 8 807
Younggwang Kim South Korea 6 593 1.3× 116 1.1× 20 0.2× 19 0.3× 12 0.2× 10 632
Jared Carlson-Stevermer United States 10 478 1.0× 124 1.2× 12 0.1× 52 0.8× 33 0.5× 18 751
Josh Tycko United States 12 839 1.8× 255 2.5× 32 0.4× 34 0.5× 31 0.5× 20 965
Sungtae Lee South Korea 7 660 1.4× 142 1.4× 22 0.3× 30 0.5× 9 0.1× 11 690
Xiaoyi Cao United States 13 917 2.0× 140 1.4× 28 0.3× 62 1.0× 11 0.2× 24 1.0k

Countries citing papers authored by Evan Appleton

Since Specialization
Citations

This map shows the geographic impact of Evan Appleton'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 Evan Appleton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evan Appleton more than expected).

Fields of papers citing papers by Evan Appleton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Evan Appleton. 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 Evan Appleton. The network helps show where Evan Appleton may publish in the future.

Co-authorship network of co-authors of Evan Appleton

This figure shows the co-authorship network connecting the top 25 collaborators of Evan Appleton. A scholar is included among the top collaborators of Evan Appleton 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 Evan Appleton. Evan Appleton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Appleton, Evan, Jenhan Tao, Songlei Liu, et al.. (2025). Machine-guided cell-fate engineering. Cell Reports. 44(6). 115726–115726. 1 indexed citations
2.
Appleton, Evan, Noushin Mehdipour, Iman Haghighi, et al.. (2024). Algorithms for Autonomous Formation of Multicellular Shapes from Single Cells. ACS Synthetic Biology. 13(9). 2753–2763.
3.
Tian, Songhai, et al.. (2022). Targeted intracellular delivery of Cas13 and Cas9 nucleases using bacterial toxin-based platforms. Cell Reports. 38(10). 110476–110476. 20 indexed citations
4.
Chao, George, et al.. (2022). helixCAM: A platform for programmable cellular assembly in bacteria and human cells. Cell. 185(19). 3551–3567.e39. 24 indexed citations
5.
Jung, Sascha, Evan Appleton, Muhammad Ali, George M. Church, & Antonio del Sol. (2021). A computer-guided design tool to increase the efficiency of cellular conversions. Nature Communications. 12(1). 1659–1659. 23 indexed citations
6.
Zaki, Mark M., Emal Lesha, Khaled Saïd, et al.. (2021). Cell therapy strategies for COVID-19: Current approaches and potential applications. Science Advances. 7(33). 17 indexed citations
7.
Vaidyanathan, Prashant, et al.. (2021). Algorithms for the selection of fluorescent reporters. Communications Biology. 4(1). 118–118. 3 indexed citations
8.
Kunjapur, Aditya M., Michael G. Napolitano, Eriona Hysolli, et al.. (2021). Synthetic auxotrophy remains stable after continuous evolution and in coculture with mammalian cells. Science Advances. 7(27). 25 indexed citations
9.
Trump, Benjamin D., Evan Appleton, Marie‐Valentine Florin, et al.. (2020). Building biosecurity for synthetic biology. Molecular Systems Biology. 16(7). e9723–e9723. 22 indexed citations
10.
Appleton, Evan, Curtis Madsen, Nicholas Roehner, & Douglas Densmore. (2017). Design Automation in Synthetic Biology. Cold Spring Harbor Perspectives in Biology. 9(4). a023978–a023978. 56 indexed citations
11.
Appleton, Evan, Douglas Densmore, Curtis Madsen, & Nicholas Roehner. (2017). Needs and opportunities in bio-design automation: four areas for focus. Current Opinion in Chemical Biology. 40. 111–118. 18 indexed citations
12.
Haddock, Traci L., Douglas Densmore, Evan Appleton, et al.. (2015). BBF RFC 94: Type IIS Assembly for Bacterial Transcriptional Units: A Standardized Assembly Method for Building Bacterial Transcriptional Units Using the Type IIS Restriction Enzymes BsaI and BbsI. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations
13.
Appleton, Evan, et al.. (2014). Owl: Electronic Datasheet Generator. ACS Synthetic Biology. 3(12). 966–968. 3 indexed citations
14.
Appleton, Evan, et al.. (2014). Designing Safety Policies to Meet Evolving Needs: iGEM as a Testbed for Proactive and Adaptive Risk Management. ACS Synthetic Biology. 3(12). 983–985. 6 indexed citations
15.
Appleton, Evan, Jenhan Tao, Traci L. Haddock, & Douglas Densmore. (2014). Interactive assembly algorithms for molecular cloning. Nature Methods. 11(6). 657–662. 40 indexed citations
16.
Oye, Kenneth A., Kevin M. Esvelt, Evan Appleton, et al.. (2014). Regulating gene drives. Science. 345(6197). 626–628. 223 indexed citations
17.
Yordanov, Boyan, Evan Appleton, Ebru Aydın Göl, et al.. (2012). Experimentally driven verification of synthetic biological circuits. OpenMETU (Middle East Technical University). 236–241. 3 indexed citations
18.
Beal, Jacob, Ron Weiss, Douglas Densmore, et al.. (2012). An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications. ACS Synthetic Biology. 1(8). 317–331. 71 indexed citations
19.
Crooker, N. U., Evan Appleton, N. A. Schwadron, & M. J. Owens. (2010). Suprathermal electron flux peaks at stream interfaces: Signature of solar wind dynamics or tracer for open magnetic flux transport on the Sun?. Journal of Geophysical Research Atmospheres. 115(A11). 16 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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