Human Cytome Project, Cytomics & Systems Biology
Aims & Characteristics
|human cytome project initiative (PPT)||- system approach for individualized disease course predictions in medicine(= predictive medicine by cytomics), including the use of individually predictive data patterns (examples: A, B, C, D, E) for the exploration of disease inducing molecular pathways and for the detection of specific new drug targets||48, 46, 38, 37, 33, 32|
and system cytometry ( 1997, chapt: Int.Cytom.Network/ further references)
- diseases emerge from molecular perturbations in
with cells representing the elementary units of organs
- top-down information from molecular cell phenotypes is acquired by differential single cell screens in diseased versus reference individuals. Parameter selection for the acquisition of cytometric information is hypothesis driven while exhaustive bioinformatic data analysis is hypothesis-free to address new knowledge spaces inaccessible to trditional a-priori hypothesis development.
- generation of therapy related predictions of future disease course for individual patients (outcome predictions) from altered molecular cell phenotypes.
- potential for optimized therapy selection, increased therapeutic leadtime and application of preventive therapies (preventive medicine, individualized medicine, personalized medicine).
- discriminatory data patterns (molecular hotspots) from differential screens by biomedical cell systems biology leading to the detection of new molecular drug targets.
- prediction of future disease occurence in presently healthy appearing indivduals from characteristically altered molecular cell phenotypes, for example by the discrimination between inflammatory and premalignant conditions from peripheral leukocyte as circulating immune defence indicators.
|48, 44, 43, 36, 35, 34, 33, 28, 27, 26, 25, 22, 21, 20, 15, 14, 12, 10, 7, 5, 4|
|predictive medicine by systems biology||
- differential screens of perturbed experimental systems
- mathematic pathway modelling to predict and prevent disease
- high complexity when information for networks has to be generated from mixed population of cells
|predictive medicine by genomics||
- prediction of
future occurence of disease
from genetic background (predictive pharmacogenomics)
- may be inaccurate in case of preferentially exposure driven disease development because future exposure is unknown and molecular onsequences of past exposure are typically not considered.
- low public acceptance
- single cell analysis of the heterogeneity of molecular cell
phenotypes in cell systems (cytomes).
Molecular cell phenotypes result from genotype and
- exhaustive bioinformatic knowledge extraction from all analyzed cells
|17, 16, 9|
- microscopic organ tissue cytomics
- analysis of 3D-structure of biological tissues by single cell measurements
|systems biology (1998)||
- frequently bottom-up concept from genes over proteins, organelles, cells,
cell systems and organs to the behaviour of entire organisms with the aim to establish
mathematical models for different levels of organization
- systematic perturbation of cellular systems, perception and mathematical modeling of molecular differentials to understand molecular network of cellular organization.
- cellular model systems may not represent the situation in humans.
|42, 41, 29, 23, 18, 11, 8|
biomedical cell systems biology
molecular cell systems biology
cell systems biology
- top-down or bottom-up single cell oriented systems biology
- biomedical: use of molecular differentials from naturally occurring perturbations like in disease (details see: predictive medicine by cytomics)
-- respects cellular heterogeneity (cell types & functional status) of cell systems
-- immediate medical utility, facilitated investigation of molecular disease pathways and detection of new drug targets
|46, 44, 43, 42, 24|
- need for patient related studies (31,
- cytomics -> individual patients (37, 21)
- systems biology -> model systems (41)
- deductive or inductive ? (30)
- top-down or bottom-up ? (40)
- bottom-up (29)
- early efforts (3, 2, 1a,1)
|41, 40, 37, 31, 30, 29, 21, 19, 13, 3, 2, 1a, 1|
===== 2006 ======
48. G Valet, RF Murphy, JP Robinson, A Tarnok, A Kriete. Cytomics - from cell states to predictive medicine. In: Computational Systems Biology. Eds: A Kriete, R Eils. Elsevier, Amsterdam 2006, p 363-381.
===== 2005 ======
47. G Valet. Cytomics, the human cytome project and systems biology: top-down resolution of the molecular biocomplexity of organisms by single cell analysis. Cell Prolif 38: 171-174 (2005).
46. G Valet. Human cytome project: A new potential for drug discovery. In: Las Omicas genomica, proteomica, cytomica y metabolomica: modernas tecnologias para el desarrollo de farmacos. Ed: Real Academia Nacional de Farmacia, Madrid 2005 p 207-228.
45. D Lenz, B Mosch, J Bocsi, T Arendt, A Tarnok. Tissomics: detecting two and three-dimensional distribution of cells in brain tissues using laser scanning cytometry (LSC). Proceedings BIOS2005 SPIE Vol 5701-30 (2005)
44. G Valet. Human cytome project, cytomics and systems biology: the incentive for new horizons in cytometry. Cytometry 64A: 1-2 (2005).
43. G Valet. Cytomics: an entry to biomedical cell systems biology. Cytometry 63A: 67-68 (2005).
===== 2004 ======
42. Workshop Report. Computational Systems Biology (CSB) - Its future in Europe. Ed: European Commission, Research Directorate-General (2004), p20, p92.
41. L Hood, JR Heath, ME Phelps, B Lin. Systems biology and new technologies enable predictive and preventive medicine. Science (2004) 306: 640-643.
40. M Liebman. Systems biology: Top-down or bottom-up. Bio-IT World (2004).
39. A Kriete. Automated Microscopy for Tissomics. Imaging & Microscopy 3: 38-40 (2004).
38. G Valet, A Tarnok. Potential and challenges of a human cytome project. JBRHA 18: 87-91 (2004).
37. G Valet, JF Leary, A Tarnok. Cytomics - New technologies: Towards a human cytome project. Cytometry 59A: 167-171 (2004).
36. G Valet, HG Höffkes. Data pattern analysis for the individualised pretherapeutic identification of high-risk diffuse large B-cell lymphoma (DLBCL) patients by cytomics. Cytometry 59A: 232-236 (2004).
35. G Valet, A Tarnok. Cytomics and predictive medicine. In: Business Briefing: Future Drug Discovery 2005, Ed: E Cooper, World Markets Research Center Ltd, London (2004) p 1-3.
34. A Tarnok, GK Valet. Cytomics in predictive medicine. In: Advanced Biomedical and Clinical Diagnostic Systems II. Eds: GE Cohn, WS Grundfest, DA Benaron, T Vo-Dinh, Proceedings SPIE, Bellingham, WA (2004), Vol 5318, p 12-22.
33. G Valet. Predictive medicine by cytomics and the challenges of a human cytome project. In: Business Briefing: Future Drug Discovery 2004, Ed: E Cooper, World Markets Research Center Ltd, London (2004) p 46-51.
===== 2003 ======
32. P Van Osta. Human cytome project. *http://news-reader.org/article.php?group=bionet.cellbiol&post_nr=14902"* (inactive)
31. DF Horrobin. Modern biomedical research: an internally self-consistent universe with little contact with medical reality ? Nature Reviews Drug Discovery 2: 151-154 (2003).
30. DB Kell, SG Oliver. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic area. Bioessays 26: 99-105 (2003).
29. FS Collins, ED Green, AE Guttmacher, MS Guyer. A vision for the future of genomics research. Nature 422: 835-847 (2003).
28. G Valet. Past and present concepts in flow cytometry: A European perspective. JBRHA 17: 213-222 (2003).
27. G Valet, R Repp, H Link, G Ehninger, M Gramatzki and SHG-AML study group. Pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients from immunophenotype, cytogenetic and clinical parameters. Cytometry 53B: 4-10 (2003).
26. G Valet, A Tarnok. Cytomics in predictive medicine. Cytometry 53B: 1-3 (2003).
25. G Valet, J Cornelissen, G Lamers, J Gratama. Predictive medicine by cytomics: Identification of high risk patients in bone marrow stem cell transplantation. Cytometry 54B: 62-63 (2003).
===== 2002 ======
24. E Firpo, R Kong, Q Zhou, A Rudensky, J Roberts, B Franza. Antigen-specific dose-dependent system for the study of an inheritable and reversible phenotype in mouse CD4+ T cells. Immunology 107: 480-488 (2002)
23. M Liebman. Opening Pandora's Box: Clinical data and the study of complex diseases. Science's STKE (2002).
22. G Valet. Cytomics, from prognostic to predictive medicine. In: Cytomics, Ed: JP Robinson, Cytometry CD Vol.7, Purdue University, West Lafayette, ISBN 0-9717498-8-4 (2002).
21. G Valet. Predictive medicine by cytomics: potential and challenges. J.Biological Regulators Homeostatic Agents 16: 164-167(2002).
20. J Bocsi, J Hambsch, P Osmancik, P Schneider, G Valet, A Tarnok. Preoperative prediction of pediatric patients with effusions and edema following cardiopulmonary bypass surgery by serological and routine laboratory data. Critical Care 6: 226-233 (2002).
===== 2001 ======
19. DF Horrobin. Realism in drug discovery - could Cassandra be right ? Nature Biotechnology 19: 1099-1100 (2001).
18. M Chitty. Genetic manipulation & disruption: "systems biology" (2001).
17. M Chitty. -Omes and -omics glossary: "cytome, cytomics" (2001).
16. Davies E, Stankovic B, Azama K, Shibata K, Abe S. Novel components of the plant cytoskeleton: A beginning to plant 'cytomics'. Plant Sci 160: 185-196 (2001).
15. A Tarnok, J Bocsi, M Pipek, P Osmancik, G Valet, P Schneider, J Hambsch. Preoperative prediction of postoperative edema and effusion in pediatric cardiac surgery by altered antigen expression patterns on granulocytes and monocytes. Cytometry(Comm.Clin.Cytom.) 46: 247-253 (2001).
14. G Valet, H Kahle, F Otto, E Bräutigam, L Kestens. Prediction and precise diagnosis of diseases by data pattern analysis in multiparameter flow cytometry: Melanoma, juvenile asthma and human immunodeficiency virus infection. Methods in Cell Biology 64: 487-508 (2001).
===== 1966-2000 ======
13. DF Horrobin. Innovation in the pharmaceutical industry. J Royal Soc Medicine 93: 341-345 (2000).
12. G Valet. Human disease: 3.Individual patient disease course predictions by standardized multiparameter data classification (SMDC)). In: Purdue Cytometry CD-ROM, Ed: JP Robinson, Vol.5, ISBN 1-890475-05-07 (2000).
11. A Agarwal. New institute to study systems biology. Nature Biotech 17: 743-745 (1999).
10. BEM Van Driel, GK Valet, H Lyon, U Hansen, JY Song, CJF Van Noorden. Prognostic estimation of survival of colorectal cancer patients with the quantitative histochemical assay of G6PDH activity and the multiparameter classification program CLASSIF1. Cytometry(Comm.Clin.Cytom.) 38: 176-183 (1999).
9. S.Abe et al. Cytomics (1998). *http://web-mcb.agr.ehime-u.ac.jp/english/cellbiol/default.htm* (inactive)
8. L Hood. Systems biology: New opportunities arising from genomics, proteomics and beyond. Exp Hematology 26: 681-681 (1998).
7. GK Valet, G Roth, W Kellermann. Risk assessment for intensive care patients by automated classification of flow cytometric data. In: Phagocyte Function, Eds. JP Robinson, GF Babcock, Wiley-Liss Inc, New York 1998, p 289-306.
6. J Dausset. The ethics of predictive medicine. Pathologie Biologie 45: 199-204 (1997).
5. G Valet. Human disease: 2. Potential of cytometry and System cytometry, In: Purdue Cytometry CD-ROM, Ed: JP Robinson, Vol.3. ISBN 1-890473-02-2 (1997).
4. G Valet, M Valet, D Tschöpe, H Gabriel, G Rothe, W Kellermann, H Kahle. White cell and thrombocyte disorders: Standardized, self-learning flow cytometric list mode data classification with the CLASSIF1 program system. Ann NY Acad Sci 677: 233-251 (1993).
3. J Dausset. Ethical aspects and future of the predictive medicine. Pathologie Biologie 34: 812-813 (1986).
2. WL Marxer, GR Cowgill. The art of predictive medicine. Charles C Thomas, Springfield (1967) p 1-358.
1a. E Cheraskin, WM Ringsdorff, AT Setyaadmadja, RA Barrett. Biochemical profile in predictive medicine. Biomed Sci Instr 3: 3-15 (1967)
1. TB Weber J Poyer. Instrumentation methods for predictive medicine. Instrument Society of America, Pittsburg, (1966) p1-604.
|© 2023 G.Valet|