about
Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugstargetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysisConstruction and analysis of protein-protein interaction networksThe evolvability of programmable hardwareMycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance.Revisiting robustness and evolvability: evolution in weighted genotype spaces.Critical assessment of genome-scale metabolic networks: the need for a unified standard.A General Mechanism for the Propagation of Mutational Effects in Proteins.Identification of putative and potential cross-reactive chickpea (Cicer arietinum) allergens through an in silico approach.Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks.The organisational structure of protein networks: revisiting the centrality-lethality hypothesis.Predicting Novel Metabolic Pathways through Subgraph Mining.Flux balance analysis of biological systems: applications and challenges.Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL.Elucidating the biosynthetic pathways of volatile organic compounds in Mycobacterium tuberculosis through a computational approach.Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis.Hallmarks of mycolic acid biosynthesis: a comparative genomics study.Enumerating all possible biosynthetic pathways in metabolic networks.A systems perspective of host–pathogen interactions: predicting disease outcome in tuberculosisNetwork-based features enable prediction of essential genes across diverse organismsEvolutionary design principles in metabolismEvolvability and robustness in a complex signalling circuitDeciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic modelsMycobacterium tuberculosis (Mtb) lipid-mediated lysosomal rewiring in infected macrophages modulates intracellular Mtb trafficking and survivalMachine Learning Applications for Mass Spectrometry-Based Metabolomics
P50
Q21145695-52E2682F-27CA-41AB-9EEC-76D3F612A4E9Q21202793-D17E3E26-F28A-4E2B-94B5-9F4F5D7DA5B7Q24632014-6150E812-B783-4656-9EF8-7001C7422058Q30390157-35E9001F-EFBB-42AD-AD4C-227D6CB357C8Q33395486-CD504EA7-9038-438A-A921-C7916BA95CAEQ35414018-319C663D-2A1C-4026-BD37-F27C49EA97F4Q38415568-B47E46A6-E27A-4765-8E42-5017342249EDQ39105721-2CBB0765-7AE0-4093-8F03-A9AFED926DB0Q39338316-5D134A0F-F803-49FA-80D4-6CC5C81D1BEAQ40825327-99D6BD25-A94E-493A-B8FC-1A496E8EBDC8Q41859361-95A8E3C1-E417-4400-A0F0-75746EDA551EQ42696816-7FDF9774-41D9-4FBC-B9D4-976565BF29BEQ43740877-A0365C22-FDB7-458E-B2EC-9D46483A8771Q46081774-6CC5A57C-379B-42FC-93CE-B5E134CEE4FEQ46414085-40A55280-E88C-43F4-9148-53EDF9018F87Q52594611-1695C61B-F3E2-4A13-83FA-C1280FF88A04Q54094052-9520CD81-688F-4262-BF66-80E81678D763Q55517656-9D904B4F-6AFF-472E-A088-FD7EBAF84A92Q57038569-51B79509-14BA-43DF-AD7D-EEE92F3F764DQ60302601-8EDA139B-213B-41D9-919D-B24C128F0AF9Q62489667-9E7EFDEC-E814-468F-958B-9664A5B7A1EDQ83171946-C75A2FD7-0BF6-4A90-9A05-DE89F5347920Q91657188-34C99EF4-1669-4F13-BCDC-CE37F3547E8BQ95323802-6199982F-03B2-4F0D-AC3F-BE9EF4A6C097Q96429273-1EB5CD05-3262-4277-B99D-7BA662DF8E1A
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Karthik Raman
@ast
Karthik Raman
@en
Karthik Raman
@es
Karthik Raman
@fr
Karthik Raman
@nl
Karthik Raman
@sl
type
label
Karthik Raman
@ast
Karthik Raman
@en
Karthik Raman
@es
Karthik Raman
@fr
Karthik Raman
@nl
Karthik Raman
@sl
prefLabel
Karthik Raman
@ast
Karthik Raman
@en
Karthik Raman
@es
Karthik Raman
@fr
Karthik Raman
@nl
Karthik Raman
@sl
P1053
A-6459-2011
P106
P1153
21743559700
P21
P2456
P31
P496
0000-0002-9311-7093
P569
2000-01-01T00:00:00Z