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Quantum Interactomics and Cancer Molecular Mechanisms

Baianu, Dr. I.C. (1987) Quantum Interactomics and Cancer Molecular Mechanisms. [Departmental Technical Report] (In Press)

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Abstract

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments. *Communicated to: The Institute of Genomic Biology (currently under construction at UIUC, at 905 S. Goodwin Avenue, Urbana,IL.61801,USA). KEYWORDS: Cancer cell interactomics; Somatic cell genomics and Proteomics; current limitations of modular models of carcinogenesis; Complex quantum dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments.

Item Type:Departmental Technical Report
Additional Information:Complex Systems Biology, Łukasiewicz-Topos and Higher-Dimensional Algebraic Models of Cell Interactomics; cell interactomics, dynamics of coupled genetic-proteomic networks and signaling pathways, development, regeneration, and control mechanisms of cell dynamic programming in cells, neoplastic transformations and oncogenesis; complex system modeling and biomolecular network representations in categories of Łukasiewicz Logic Algebras and Łukasiewicz-Topos Relational and Molecular Biology, Cell Genomics and Proteomics, and Cancer Cell Interactomics are represented in Supercategories defined currently as n-categories (or higher dimensional algebra), Axiomatic definitions of Categories and Supercategories of Relational, Complex Biological Systems, Dynamic Computations with Algebraic Varieties, Cell Transformations to Malignant Cancer. Early, Reliable and Sensitive Detection of Cancers by Ultra-sensitive, in vivo, Non-Invasive detection methods.
Keywords:Quantum Computation,Quantum Automata, Carcinogenesis; Malignant Tumors; Cancer Cell Interactomics; dynamics of genetic-proteomic networks and signalling pathways, development, regeneration, the control mechanisms of cell dynamic programming in cells; Neoplastic Transformations and Oncogenesis; Categories and Functors; Homology Theory applications to Qualitative Dynamics; Quantum Genetics; Relational Oscillations; Organismic Supercategories; Qualitative Dynamics of Systems in Organismic Supercategories; Algebraic Geometry in Biology, Cell Division Control and Dynamic Programnming;Cancer Cell Cycling; Categorical Dynamic Systems; Observables Generating Diagram, Relational Biology;Single Molecule Dynamics; Quantum, Electron Tunneling mechanisms in Enzyme Catalized reactions; Cell Transformations to Malignant Cancer;Telomerase and Reverse Transcriptase roles; c-Myc , TP53 and Ras tumor suppressor genes; p27 and p21 inhibition and uncontrolled cell cycling leading to neoplastic transformation/ malignant cell re-differentiation; rational, individualized therapy of cancers; rational clinical trials; molecular medicine, high-throughput genomics and proteomics technologies, tumor cell lines separation and complete genomic analysis; cancer cell biomarker pattern identification; Early, Reliable and Sensitive Detection of Cancers by Ultra-sensitive, in vivo, Non-Invasive Detection methods.
Subjects:Computer Science > Dynamical Systems
Computer Science > Complexity Theory
Computer Science > Artificial Intelligence
Biology > Theoretical Biology
ID Code:3810
Deposited By: Baianu, Professor I. C.
Deposited On:07 Sep 2004
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Alle KM, Henshall SM, Field AS, and Sutherland RL. 1998. Cyclin D1 protein is overexpressed in

hyperplasia and intraductal carcinoma of the breast. Clin. Cancer Res., 4:847–854.

Andersen G, Busso D, Poterszman A, Hwang JR, Wurtz J, Ripp R, Thierry J, Egly J and Moras D.

1997. The structure of cyclin H: common mode of kinase activation and specific features. EMBO J,

16(5):958–967.

1. Arbib, M. 1966. Categories of (M,R)-Systems. Bull. Math. Biophys., 28: 511-517.

2. Baianu,I C; Korban, S S; Costescu, D; You, T; Lozano, P; Hofmann, N E. 2004a. Fourier Transform Near Infrared Microspectroscopy, Infrared Chemical Imaging, High-Resolution Nuclear Magnetic Resonance and Fluorescence Microspectroscopy Detection of Single Cancer Cells and Single Viral Particles . CERN Preprint- EXT-2004-069: Single Cancer Cells from Human tumors are being detected and imaged by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR)Hyperspectral Imaging and Fluorescence Correlation Microspectroscopy. [...]

3. Baianu, I.C. 2004b. Quantum Genetics in terms of Quantum Reversible Automata and Computation of Genetic Codes and Reverse Transcription.,Cogprints, UK, Accepted July 06, 2004.

4. Baianu, I.C. 2004c. Molecular Representations in Relational Biology and the Realization Conjecture. Cogprints and CERN Preprints.

5. Baianu, I.C. and Marinescu, M. 1968. Organismic Supercategories:I. Proposals for a General Unitary Theory of Systems., Bull. Math. Biophys., 30: 625-635.

6. Baianu, I. 1970. Organismic Supercategories: III. On Multistable Systems. Bull. Math. Biophys., 32: 539-561.

7. Baianu, I. 1971. Organismic Supercategories and Qualitative Dynamics of Systems. Bull. Math. Biophys., 33: 339-354.

8. Baianu, I. 1971. Categories, Functors and Automata Theory. The 4th Intl. Congress LMPS, August-Sept. 1971.

9. Baianu, I. and Scripcariu, D. 1973. On Adjoint Dynamical Systems. Bull. Math. Biology., 35: 475-486.

10. Baianu, I. 1973. "Some Algebraic Properties of (M, R)-Systems." Bull. Math. Biol., 35: 213-217.

11. Baianu, I. 1973. Some Algebraic Properties of (M,R)-Systems in Categories. Bull. Math. Biophys, 35: 213-218.

12. Baianu, I. and Marinescu, M. 1974. A Functorial Construction of (M,R)-Systems. Rev. Roum. Math. Pures et Appl., 19: 389-392.

13. Baianu, I.C. 1977. A Logical Model of Genetic Activities in Lukasiewicz Algebras: The Non-Linear Theory., Bull. Math. Biol.,39:249-258.

14. Baianu, I.C. 1980. Natural Transformations of Organismic Structures. Bull.Math. Biology, 42:431-446.

15. Baianu, I.C.1983. Natural Transformations Models in Molecular Biology. SIAM Natl. Meeting, Denver, CO, USA.

16. Baianu, I.C. 1984. A Molecular-Set-Variable Model of Structural and Regulatory Activities in Metabolic and Genetic Systems., Fed. Proc. Amer. Soc. Experim. Biol. 43:917.

17. Baianu, I.C. 1987. Computer Models and Automata Theory in Biology and Medicine. In: "Mathematical Models in Medicine.",vol.7., M. Witten, Ed., Pergamon Press: New York, pp.1513-1577.

18. Carnap. R. 1938. "'The Logical Syntax of Language" New York: Harcourt, Brace and Co.

19. Cazanescu, D. 1967. On the Category of Abstract Sequential Machines. Ann. Univ. Buch., Maths & Mech. series, 16 (1):31-37.

20. Georgescu, G. and C. Vraciu 1970. "On the Characterization of Lukasiewicz Algebras." J Algebra, 16 4, 486-495.

21. Hilbert, D. and W. Ackerman. 1927. Grunduge.der Theoretischen Logik, Berlin: Springer.

22. McCulloch, W and W. Pitts. 1943. “A logical Calculus of Ideas Immanent in Nervous Activity” Ibid., 5, 115-133.

23. Pitts, W. 1943. “The Linear Theory of Neuron Networks” Bull. Math. Biophys., 5, 23-31.

24. Rosen, R.1958.a. ”A Relational Theory of Biological Systems” Bull. Math. Biophys., 20, 245-260.

1. Rosen, R. 1958a. The Representation of Biological Systems from the Standpoint of the Theory of Categories." Bull. Math. Biophys. 20: 317-341.

2. Rosen, Robert. 1964. Abstract Biological Systems as Sequential Machines, Bull. Math. Biophys., 26: 103-111; 239-246; 27:11-14;28:141-148.

25. Rosen, Robert. 1968. On Analogous Systems. Bull. Math. Biophys., 30: 481-492.

26. Rosen, Robert. 1973. On the Dynamical realization of (M,R)-Systems. Bull. Math. Biology., 35:1-10.

27. Russel, Bertrand and A.N. Whitehead, 1925. Principia Mathematica, Cambridge: Cambridge Univ. Press.

28. Warner, M. 1982. Representations of (M,R)-Systems by Categories of Automata., Bull. Math. Biol., 44:661-668.

Bagui TK, Jackson RJ, Agrawal D,and Pledger WJ. 2000. Analysis of cyclin D3-cdk4 complexes

in fibroblasts expressing and lacking p27kip1 and p21cip1. Mol. Cell. Biol., 20:8748– 8757.

Baianu,I.C. 1969. Chs. 3 to 5 in: “ Theoretical and Experimental Models of Carcinogenesis. M.S.

Thesis, Physics and Medical Biophysics Depts.”, BU.

Baianu, I.C.1983. Model of Structural and Regulatory Activities in Metabolic and Genetic Systems., Fed. Proc. Amer. Soc. Experim. Biol. 43:917.

Baianu, I.C. 2004d. Cell Genome and Interactome Nonlinear Dynamic Models in Łukasiewicz Logic Algebras. Neoplastic Transformation Models in a Łukasiewicz Topos. Preprint, arcXiv q-bio/GN#0406051, June 25th, 2004.

Bryja V, Pachernik J, Faldikova L, Krejci P, Pogue R, Nevriva I I, Dvorak P, Hampl A. 2004 May.

The role of p27(Kip1) in maintaining the levels of D-type cyclins in vivo. Biochim Biophys Acta,

3;1691(2-3):105-116.

Cheng M, Olivier P, Diehl JA, Fero M, Roussel MF, Roberts JM, and Sherr CJ. 1999. The

p21Cip1 and p27kip1 ‘inhibitors’ are essential activators of cyclin D-dependent kinases in murine

fibroblasts. EMBO J, 18:1571– 1583.

Dobashi, Y., Goto, A., Fukayama, M., Abe, A., and Ooi, A. 2004. Overexpression of Cdk4/Cyclin

D1, a possible mediator of apoptosis and an indicator of prognosis in human primary lung

carcinoma. Int.J.Cancer:110,532 41.

Dobashi Y, Jiang SX, Shoji M, et al., 2003. Diversity in expression and prognostic significance of

G1/S cyclins in human primary lung carcinomas. J Pathol, 199:208-20.

Fukuse T, Hirata T, Naiki H, Hitomi S, and Wada H. 2000. Prognostic significance of cyclin E

overexpression in resected non-small cell lung cancer. Cancer Res., 60:242-4.

Gillett C, Fantl V, Smith R, Fisher C, et al. (1994). Amplification and overexpression of cyclin D1

in breast cancer detected by immunohistochemical staining. Cancer Res., 54:1812–1817.

Handa K, Yamakawa M, Takeda H, Kimura S, and Takahashi T. 1999. Expression of cell cycle

markers in colorectal carcinoma:superiority of cyclin A as an indicator of poor prognosis. Int J

Cancer, 84:225-33.

Hershko A and Ciechanover A. 1998. The ubiquitin system. Annu. Rev. Biochem, 67:425–479.

Klint P, and Claesson-Welsh L. 1999. Signal transduction by fibroblast

growth factor receptors. Front. Biosci., 4,D165–D177.

Koziczak M, Holbro T., and Hynes NE. 2004. Blocking of FGFR signaling inhibits breast cancer

cell proliferation through downregulation of D-type cyclins. Oncogene, 23:3501–3508.

Loden M, Sighall M, Nielsen NH, Roos G, Emdin SO, et al. 2002. The cyclin D1 high and cyclin

E high subgroups of breast cancer: Separate pathways in tumorigenesis based on pattern of genetic

aberrations and inactivation of the pRb node. Oncogene, 21:4680–4690.

Malumbres M, and Barbacid M. 2001. To cycle or not to cycle: A critical decision in cancer. Nat.

Rev. Cancer, 1:222–231.

Morgan, DO. 1995. Principles of CDK regulation. Nature, 374:131-4.

Muraoka RS, Lenferink AEG, Simpson J, Brantley DM, Roebuck LR, Yakes FM, and Arteaga CL.

2001. Cyclin-dependent kinase inhibitor p27kip1 is required for mouse mammary gland

morphogenesis and function, J. Cell Biol, 153:917–931.

Noguchi T, Dobashi Y, Minehara H, Itoman M, and Kameya T. 2000. Involvement of cyclins in

cell proliferation and their clinical implications in soft tissue smooth muscle tumors. Am J Pathol,

156:2135–47.

Ohta T, and Fukuda M. 2004. Ubiquitin and breast cancer. Oncogene, 23(11):2079-88.

Ormandy CJ, Musgrove EA, Hui R, Daly RJ, and Sutherland RL. 2003. Cyclin D1, EMS1 and

11q13 amplification in human breast cancers. Breast Cancer Res. Treat., 78:323–335.

Sutherland RL, Musgrove EA. 2004 Jan. Cyclins and breast cancer. J Mammary Gland Biol

Neoplasia., 9(1):95-104.

van Diest PJ, Michalides RJ, Jannink L,van der Valk P, Peterse HL, de Jong JS, Meijer CJ, and

Baak JP. 1995. Cyclin D1 expression in invasive breast cancer: Correlation and prognostic value.

Am J Pathol 1995; 150:705-11.

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