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DNA and Biological Computing

DNA and Biological Computing is a research topic within Molecular Biology. Science Explorer counts 17k research works in it since 1952. 13.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers explores the advancements in DNA-based computing, molecular computation, and data storage techniques using nucleic acids. It covers topics such as spiking neural P systems, membrane computing, error-correcting codes for DNA data storage, genetic algorithms, and the potential of DNA as a medium for digital data storage. The research also delves into the challenges and future prospects of utilizing DNA for information processing and storage.

  • DNA Computing
  • Molecular Computation
  • Data Storage
  • Spiking Neural P Systems
  • Membrane Computing
  • Error-Correcting Codes
  • Biological Information Storage
  • Nucleic Acid Memory
  • Genetic Algorithms
  • Digital Data Storage
Research works
17k
fractional, since 1952
In the world top 10%
2.2k
per year above
Top-10% rate
13.1%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-7%
the tick is no change

Which countries lead DNA and Biological Computing research?

By volume, China and the United States publish the most (582 and 322 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 582 works
  2. 2 United States 322 works
  3. 3 India 250 works
  4. 4 Germany 124 works
  5. 5 France 97 works
  6. 6 Japan 86 works
  7. 7 United Kingdom 69 works
  8. 8 Israel 59 works
  9. 9 Italy 59 works
  10. 10 Russia 59 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 34.1%United States: 18.9%India: 14.7%Germany: 7.3%6 others listed: 25.1%34%largest
China582 · 34.1%United States322 · 18.9%India250 · 14.7%Germany124 · 7.3%6 others listed429 · 25.1%

Shares of the rows listed above, not of the whole node.

Which institutions lead DNA and Biological Computing research?

By volume in 2022–2025, Technion – Israel Institute of Technology publishes the most DNA and Biological Computing research, followed by Technical University of Munich and Centre National de la Recherche Scientifique.

Who are the leading researchers in DNA and Biological Computing?

The most-cited researchers publishing on DNA and Biological Computing include H. Vincent Poor, Dan Boneh and Ronald L. Rivest.

  1. 1 H. Vincent Poor United States 9.5k citations
  2. 2 Dan Boneh United States 7.5k citations
  3. 3 Ronald L. Rivest United States 7.3k citations
  4. 4 Leonard M. Adleman United States 6.7k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is DNA and Biological Computing research done?

The largest centres of DNA and Biological Computing research in 2022–2025 are Beijing (China), Shanghai (China), Chengdu (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Haifa.

Largest cities, 2022–2025

  1. 1 Beijing China 91 works
  2. 2 Shanghai China 44 works
  3. 3 Chengdu China 39 works
  4. 4 Nanjing China 39 works
  5. 5 Guangzhou China 34 works
  6. 6 Chennai India 33 works
  7. 7 Paris France 31 works
  8. 8 Haifa Israel 30 works
  9. 9 Tokyo Japan 28 works
  10. 10 Seoul South Korea 27 works

Where it is the local speciality

  1. HaifaIL · 30.4 works18×
← less than its size predictsmore →

Location quotient: how much more of its research is in DNA and Biological Computing than the world average.

See DNA and Biological Computing on the map

Where is the best place to study DNA and Biological Computing?

Among universities, judged by research, Technion – Israel Institute of Technology, Xihua University and Technical University of Munich score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%mean 13.02%fractional works in this node (log) →share in the world top 10% →Technion – Israel Institute of Technology: 28, 16.0%Xihua University: 9, 20.8%Technical University of Munich: 17, 8.4%Universidad de Sevilla: 11, 19.7%Zhengzhou University of Light Industry: 14, 9.2%Shandong Normal University: 14, 5.3%Beijing University of Posts and Telecommunications: 11, 13.6%Shanghai Jiao Tong University: 14, 23.7%Justus-Liebig-Universität Gießen: 11, 5.7%Southeast University: 12, 7.8%Xihua UniversityUniversidad de SevillaTechnion – Israel In…Technical University…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Technion – Israel Institute of TechnologyIsrael 72.516.0%36.6×28 -1.2%
2 Xihua UniversityChina 64.620.8%23.3×9 +135.2%
3 Technical University of MunichGermany 61.88.4%9.0×17 +164.5%
4 Universidad de SevillaSpain 57.119.7%9.1×11 -30.0%
5 Zhengzhou University of Light IndustryChina 51.09.2%39.4×14 +52.3%
6 Shandong Normal UniversityChina 46.85.3%24.1×14 +66.5%
7 Beijing University of Posts and TelecommunicationsChina 45.413.6%7.9×11 +14.5%
8 Shanghai Jiao Tong UniversityChina 45.323.7%2.7×14 -37.2%
9 Justus-Liebig-Universität GießenGermany 42.55.7%20.9×11 +5.0%
10 Southeast UniversityChina 40.77.8%3.9×12 +195.5%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is DNA and Biological Computing research growing?

Output in 2018–2022 was 7% lower than in 2013–2017, peaking in 2025. The fastest-growing topics are DNA and Biological Computing.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.