about
Using Genome-scale Models to Predict Biological CapabilitiesWdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional networkSystems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.What Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus LeptospiraCharacterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow.Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicityGenome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments.Constraint-based models predict metabolic and associated cellular functions.Machine learning in computational biology to accelerate high-throughput protein expression.Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpCSystems biology-guided identification of synthetic lethal gene pairs and its potential use to discover antibiotic combinations.The aldehyde dehydrogenase, AldA, is essential for L-1,2-propanediol utilization in laboratory-evolved Escherichia coli.Integration of Comparative Genomics with Genome-Scale Metabolic Modeling to Investigate Strain-Specific Phenotypical Differences.iML1515, a knowledgebase that computes Escherichia coli traits.ssbio: A Python Framework for Structural Systems Biology.Escherichia coli B2 strains prevalent in inflammatory bowel disease patients have distinct metabolic capabilities that enable colonization of intestinal mucosa.Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choiceMachine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistanceMetagenomics-Based, Strain-Level Analysis of Escherichia coli From a Time-Series of Microbiome Samples From a Crohn's Disease PatientGenome-scale metabolic reconstructions of multiple Salmonella strains reveal serovar-specific metabolic traitsA computational knowledge-base elucidates the response of Staphylococcus aureus to different media typesMachine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogensGapless, Unambiguous Genome Sequence for Escherichia coli C, a Workhorse of Industrial BiologySystems-level analysis of NalD mutation, a recurrent driver of rapid drug resistance in acute Pseudomonas aeruginosa infectionPredicting Antimicrobial Resistance and Associated Genomic Features from Whole-Genome SequencingA biochemically-interpretable machine learning classifier for microbial GWAS
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description
researcher ORCID ID = 0000-0002-3895-8949
@en
name
Jonathan M Monk
@ast
Jonathan M Monk
@en
Jonathan M Monk
@es
Jonathan M Monk
@nl
type
label
Jonathan M Monk
@ast
Jonathan M Monk
@en
Jonathan M Monk
@es
Jonathan M Monk
@nl
prefLabel
Jonathan M Monk
@ast
Jonathan M Monk
@en
Jonathan M Monk
@es
Jonathan M Monk
@nl
P21
P31
P496
0000-0002-3895-8949