Micromolecules: definitions & links | Cellular domains: Genome, preoteome & metabolome |
Metabolic diversity: Scale, dynamics, & phyletics | Phenotyping strategies: proteomics vs metabolomics |
CMP Platforms: Platforms and workflow overview | CMP Probes: The probe library |
CMP Substrates: Molecular trapping & detection | CMP Datasets: Data arrays for multichannel imaging |
CMP Analysis: pattern recognition theory and tools | CMP Exploration: N-space visualization tools |
CMP Annotation: browsing & annotating data |
Micromolecules | Links and Tools |
Unique cell classes can be distinguished by their small molecule or micromolecular signatures. Micromolecules include elements and their molecular forms (e.g. O<font-size=8pt>2), ions, inorganic compounds, organic species formed by group transfer and oligomers formed by ligation reactions (e.g. glutathione). Signatures are partial representations of formal metabolic phenotypes, track physiologic and pathologic metabolic transformations, and can identify novel cell classes. Computational signature visualization in complex heterocellular tissues of all eukaryotic Kingdoms (Plantae, Fungae, and Animalia) reveals unexpectedly diverse metabolic phenotypes across cell classes. Metabolic phenotypes are challenging to interpret, as they arise from genetic and epigenetic demands: maintaining group transfer potentials for molecular synthesis, vectorial molecular transport processes, osmoregulation, redox and reactive oxygen species control, energetics regulation, intercellular signaling and coupling, cell and tissue growth, and protein synthesis. Though no coherent theory of metabolic differentiation predicts these phenotypes, once visualized, micromolecular phenotypes inform models and constrain modes of pharmacologic intervention. Visualizing signatures, tracking phenotype dynamics, and screening molecular interventions all require quantitative micromolecular profiling across cell classes. These measures can be uniquely acquired via Computational Molecular Phenotyping (CMP) with anti-hapten IgG libraries. | Metabolism KEGG PUMA2 Roche Appl Biosci Pathways via ExPASy European Bioinformatics Inst (EBI) BRENDA HMDAMolecules KLOTHO LIGAND Ligands-EBI U West Indies MonaTools RasMol Chime iMol for OS X Gepasi: metabolic simulation |
CMP Targets for Metabolomics Technology Development |
Aliphatics |
|
Aromatics |
|
ROS Molecules |
|
Nucleics |
|
Carboxylates |
|
Metabolite analogues |
|
Sugars |
|
Cofactors |
|
Steroids / Precursors |
|
Bilins, Bile Acids |
|
Eicosanoids |
|
Polyketides / peptides |
|