1 / 30

Other models to understand the cancer cell metabolic flux

Other models to understand the cancer cell metabolic flux . Zoltan N. Oltvai Department of Pathology University of Pittsburgh School of Medicine. Metabolomics in diseases, such as cancer. Disease: reorganization of physiological states on the cell, organ, and organism level.

mya
Télécharger la présentation

Other models to understand the cancer cell metabolic flux

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Other models to understand the cancer cell metabolic flux Zoltan N. Oltvai Department of Pathology University of Pittsburgh School of Medicine

  2. Metabolomics in diseases, such as cancer Disease: reorganization of physiological states on the cell, organ, and organism level • Normal state: ACB active • Disease state: ADB active • Consequence: • Reorganized cell metabolism • Appearance of signal ‘D’

  3. The promise of cancer metabolomics Omics platform to understand pathophysiology Biomarkers: normal – precancerous – malignant Drug intervention points

  4. Prerequisites of these goals • Reconstruction of human metabolic networks human genome and other ‘omics’ projects • Understanding their system-level organization systems biology of model organisms

  5. Physicochemical constraints in system-level metabolic organization: equilibrium of mass (Flux balance analysis) Schilling & Palsson, P.N.A.S., 1998

  6. Restriction of solution space by defining the desired output substrates and by optimizing for maximal growth

  7. Global flux organization in the Esherichia coli metabolic network Almaas et al, Nature, 2004

  8. The dominant in- and outgoing reaction fluxes define a high flux backbone (HFB) of E. coli metabolism Glutamate-rich growth media Succinate-rich growth media

  9. Type I (ON/ OFF) and Type II (variably ON) flux changes upon environmental changes predominantly affects the HFB

  10. The number of always active metabolic reactions under optimal growth conditions Almaas et al, PLOS Comp. Bio., in press

  11. The metabolic core of E. coli Enzymes: Red: essential green: dispensable

  12. Correlation of flux rates within and outside of the E. coli metabolic core Red: essential enzymes green: core reactions

  13. Individual reactions with high flux display mono- or multi-modal flux distribution under different growth conditions

  14. The E. coli transcriptional regulatory network

  15. The specificity of signal recognition and the hierarchy of information flow define TR subnetworks (origons) Layer 0 Layer 1 Layer 2 Layer 3 Layer 4 Balazsi et al., P.N.A.S., 2005

  16. input intermediate output Generic topology of origons Regulators, e.g. metabolites, oxygen 0 1 2 3

  17. Three-node subgraphs in origons of E. coli

  18. Types of origons and their topological relationship in the E. coli TR network Black circle: simple tree, Red circle: tree/FFL origons; green links: % shared nodes Convergence (CNV) subgraph – NEVER within origon!

  19. Testing the origon concept with microarray data • E. coli microarray data were downloaded from: https://asap.ahabs.wisc.edu/annotation/php/ASAP1.htmhttp://www.ou.edu/microarray/ • The following data sets are available: • 12+48=50 cDNA experiments • 5+36=41 Affymetrix experiments • 3+38=41 aerobic-anaerobic shift experiments • 104 experiments in various conditions from Oklahoma University

  20. Definition of node-signal covariance

  21. Distribution of covNS within origons

  22. Definition of node-node correlation

  23. Binary prediction of node-node correlations

  24. The quality of corNN predictions <Pred x Meas> Predicted Measured Pred x Meas 0.3049 fnr origon, fnr‑specific stimulus 0.0454 fnr origon, non‑ specific stimulus 0.0693 crp origon, fnr‑specific stimulus 0.0377 crp origon, non‑specific stimulus

  25. Topological relationship of origons in the TR network of S. cerevisiae Node size: ~ log # of genes in the origon Large differences between origon sizes Strong overlaps Data: Harbison et al, Nature, 2004

  26. P*X GY G*Y P*X PY Biochemical steps of signal processing by a single-regulatory interaction (SRI) Pr PX

  27. Mass-action kinetic modeling of the signal filtering properties of the SRI, FFL and CAS subgraphs Balazsi et al., P.N.A.S., 2005

  28. Recognition and processing of complex environmental signals in TR networks Decomposition to elementary signals by signal-spec. recognition Parallel processing Integration of response (through CNV subgraphs) Balazsi & Oltvai, Science STKE, 2005

  29. Sx Sx X X Sy Sy Y Y Z Z CAS FFL aggregated FFL CAS and FFL integrates related signals within origons (e.g., glucose OFF, maltose ON), but FFL eliminates the filtering effect of CAS, allowing rapid response to signal change

  30. Acknowledgements Laszlo Barabasi (U. Notre Dame) Metabolic network utilization Eivind Almaas (U. Notre Dame) Transcriptional regulatory network utilization Gabor Balazsi (Northwestern U.) U.S. Department of Energy, National Institute of Health (NIGMS)

More Related