Abstract
Important biological mechanisms, such as signal transduction and gene expression, are mediated by numerous interacting multifunctional molecules, whose expression and activation are tightly regulated in space and time in response to stimuli. In order to describe the network of functional inter-relationships that govern such mechanisms, we use simple algorithms to interpret multiple variable measurements, identify the prominent participants, evaluate their interactions and obtain a ‘functional fingerprint’ of cell behaviour. Dynamic measurements of responses yield hierarchical information about causal relations in the underlying pathway. As a proof of principles we apply this approach to phosphorylation assays in protein gels, probing hormone and insulin signalling.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akutsu, T., S. Miyano and S. Kuhara (1999). Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput. 4, 17–28.
Alberts, B., D. Bray, J. Lewis, M. Raff, K. Roberts and J. D. Watson (1994). Molecular Biology of the Cell, 3rd edn, New York: Garland, pp. 778.
Alon, U., N. Barkai, D. A. Notterman, D. Mack, K. Gish, S. Leibler and A. J. Levine (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Am. Sci. 8, 6745–6750.
Alon, U., M. G. Surette, N. Barkai and S. Leibler (1999). Robustness in bacterial chemotaxis. Nature 397, 168–171.
Arkin, A., P. Shen and J. Ross (1997). A test case of correlation metric construction of a reaction pathway from measurements. Science 277, 1275–1279.
Beale, R. and T. Jackson (1990). Neural Computing: An Introduction, New York: Adam Hilger.
Biener, Y., R. Feinstein, M. Mayak, Y. Kaburagi, T. Kadowaki and Y. Zick (1996). Annexin II is a novel player in insulin signal transduction. Possible association between annexin II phosphorylation and insulin receptor internalization. J. Biol. Chem. 271, 29489–29496.
Bode, H. W. (1955). Network Analysis and Feedback Amplifier Design, New York: D. van Nostrand.
Bray, D. (1990). Intracellular signalling as a parallel distributed process. J. Theor. Biol. 143, 215–231.
Bray, D. (1995). Protein molecules as computational elements in living cells. Nature 376, 307–312.
Bray, D., R. B. Bourret and M. I. Simon (1993). Computer simulation of the phosphorylation cascade controlling bacterial chemotaxis. Mol. Biol. Cell 4, 469–482.
Bray, D. and S. Lay (1994). Computer simulated evolution of a network of cell signaling molecules. Biophys. J. 66, 972–977.
Brent, R. (2000). Genomic biology. Cell 100, 169–183.
Burstein, Z. (1995). Protein molecules as computational elements in living cells. J. Theor. Biol. 174, 1–11.
Celis, J. E. et al. (1996). Human 2-D PAGE databases for proteome analysis in health and disease http:biobase.dk/cgi-bin/celis. FEBS Lett. 398, 129–134.
Chock, P. B. and E. R. Stadtman (1977). Superiority of interconvertible enzyme cascades in metabolite regulation: analysis of multicyclic systems. Proc. Natl. Acad. Sci. USA 74, 2766–2770.
DeRisi, J. L., V. R. Iyer and P. O. Brown (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686.
Demuth, H. and M. Beale (1993). MATLAB Neural Network TOOLBOX, The MATH WORKS Inc., Natick, MA.
Eisen, M. B., P. T. Spellman, P. O. Brown and D. Botstein (1998). Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95, 14863–14868.
Fearon, E. R. and B. Vogelstein (1990). A genetic model for colorectal tumorigenesis. Cell 61, 759–767.
Hartwell, L. H., J. J. Hopfield, S. Leibler and A. W. Murray (1999). From molecular to modular cell biology. Nature 402(Suppl. 6761), c47–c52.
Iyer, V. R. et al. (1999). The transcriptional program in the response of human fibroblasts to serum. Science 283, 83–87.
Krauth, W. and M. Mézard (1987). J. Phys. A. (Math. Gen.) 20, L745–L752.
Kuo, F. F. (1966). Network Analysis and Synthesis, 2nd edn, New York: John Wiley Sons.
Liang, S., S. Fuhrman and R. Somogyi (1998). Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput. 3, 18–29.
Marille, E., D. Thieffry, O. Leo and M. Kaufman (1996). Toxicity and neuroendocrine regulation of the immune response: a model analysis. J. Theor. Biol. 183, 285–305.
Marnellos, G. and E. Mjolsess (1998). A gene network approach to modeling early neurogenesis in Drosophila. Pac. Symp. Biocomput. 3, 30–41.
McAdams, H. H. and A. Arkin (1999). It’s a noisy business! Genetic regulation at the nanomolar scale. Trends Gen. 15, 65–69.
Michaels, G. S., D. B. Carr, M. Askenazi, S. Fuhrman, X. Wen S. and R. Somogyi (1998). Cluster analysis and data visualization of large-scale gene expression data. Pac. Symp. Biocomput. 3, 42–53.
Østergaard, M., H. H. Rasmussen, H. V. Nielsen, H. Vorum, T. F. Ørntoft, H. Wolf and J. E. Celis (1997). Proteome profiling of bladder squamous cell carcinomas: identification of markers that define their degree of differentiation. Cancer Res. 57, 4111–4117.
Plouraboué, F., H. Atlan, G. Weisbuch and J.-P. Nadal (1992). A network model of the coupling of ion channels with secondary messenger in cell signaling. Network 3, 393–406.
Press, W. H., S. A. Teukolsky, W. T. Vetterling and B. P. Plannery (1992). Numerical Receipts, 2nd edn, Cambridge University Press, pp. 51–62.
Prigent, S. A. and W. J. Gullick (1994). Identification of c-erbB-3 binding sites for phosphatidylinositol 3′-kinase and SHC using an EGF receptor/c-erbB-3 chimera. EMBO J. 13, 2831–2841.
Remy, I. and S. W. Michnick (2001). Visualization of biochemical networks in living cells. Proc. Natl. Acad. Sci. USA 98, 7678–7683.
Romano, A., W. T. Wong, M. Santoro, P. J. Wirth, S. S. Thorgeirsson and P. P. Di Fiore (1994). The high transforming potency of erbB-2 and ret is associated with phosphorylation of paxillin and a 23 kDa protein. Oncogene 9, 2923–2933.
Skilling, H. H. (1974). Electric Networks, New York: John Wiley and Sons.
Somogyi, R. and C. A. Sniegoski (1996). Modeling the complexity of genetic networks: understanding multigenetic and pleiotropic regulation. Complexity 1, 45–63.
Spellman, P. T., G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein and B. Futcher (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297.
Thieffry, D., A. M. Huerta, E. Pérez-Rueda and J. Collado-Vides (1998). From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. BioEssays 20, 433–440.
Weaver, D. C., C. T. Workman and G. D. Stormo (1999). Modeling regulatory networks with weight matrices. Pac. Symp. Biocomput. 4, 112–123.
Wen, X., S. Fuhrman, G. S. Michaels, D. B. Car, S. Smith, J. L. Barker and R. Sologyi (1998). Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. 95, 334–339.
Whitehead, J. P., S. F. Clark, B. Urso and D. E. James (2000). Signaling through the insulin receptor. Curr. Opin. Cell Biol. 12, 222–228.
Widrow, B. and S. D. Stern (1985). Adaptive Signal Processing, New York: Prentice-Hall.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Kam, Z. Generalized analysis of experimental data for interrelated biological measurements. Bull. Math. Biol. 64, 133–145 (2002). https://doi.org/10.1006/bulm.2001.0269
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1006/bulm.2001.0269