Kiran Raosaheb Patil

Molecular Biology · 2005–2020

A researcher in the Science corpus: 8 papers; trusts 17 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

Whom Kiran Raosaheb Patil trusts overall

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

  1. David E. Goldberg
  2. Anthony P. Burgard
  3. Prit Pharkya
  4. E. Gertz
  5. Jochen Förster
  6. Tomer Shlomi
  7. Bernhard O. Palsson
  8. Costas D. Maranas
  9. Iman Famili
  10. Minoru Kanehisa

Who trusts Kiran Raosaheb Patil overall

  1. James C. Liao
  2. Maxime Durot
  3. Bernhard O. Palsson
  4. Rasmus Agren
  5. Intawat Nookaew
  6. Andrew R. Joyce
  7. Jens Nielsen
  8. Leif Väremo
  9. Aarash Bordbar
  10. Ali Ebrahim

Papers from Kiran Raosaheb Patil's perspective

Ordered by how strongly the researchers Kiran Raosaheb Patil trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Genome-scale microbial in silico models: the constraints-based approach. (Trends in Biotechnology)
  2. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. (Genome Research)
  3. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. (Journal of Theoretical Biology)
  4. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) (Genome Biology)
  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. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. (Science)
  8. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks (Nature Biotechnology)
  9. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. (Metabolic Engineering)
  10. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization (Biotechnology and Bioengineering)

Their papers