Adam M. Feist

Molecular Biology · 2007–2020

A researcher in the Science corpus: 11 papers; trusts 30 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

Whom Adam M. Feist trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Bernhard O. Palsson
  2. Jennie L. Reed
  3. Nathan D. Price
  4. Jeremy S. Edwards
  5. C. Schilling
  6. T. Baba
  7. Rafael U. Ibarra
  8. Radhakrishnan Mahadevan
  9. Christopher S. Henry
  10. Ines Thiele

Who trusts Adam M. Feist overall

  1. Bernhard O. Palsson
  2. Ines Thiele
  3. D. McCloskey
  4. Daniel Machado
  5. Karthik Raman
  6. Jeffrey D. Orth
  7. Jan Schellenberger
  8. Matthew A. Oberhardt
  9. Rasmus Agren
  10. Maxime Durot

Papers from Adam M. Feist's perspective

Ordered by how strongly the researchers Adam M. Feist trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Genome-scale models of microbial cells: evaluating the consequences of constraints (Nature Reviews Microbiology)
  2. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) (Genome Biology)
  3. Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli (Journal of Bacteriology)
  4. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. (Proceedings of the National Academy of Sciences of the United States of America)
  5. KEGG: Kyoto Encyclopedia of Genes and Genomes (Nucleic Acids Res.)
  6. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth (Nature)
  7. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection (Molecular Systems Biology)
  8. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. (Proceedings of the National Academy of Sciences of the United States of America)
  9. Towards multidimensional genome annotation (Nature reviews genetics)
  10. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks (Bioinform.)

Their papers