title: Reconstructing signal transduction pathways: challenges and opportunities.
abstract: In this chapter, we will review how signal transduction pathways have been assembled in the past, bringing us to our present understanding of this area of research. The methods employed have relied heavily upon the genetics of yeast, worms, flies, mice, and humans. The use of second site suppressors and epistasis has permitted the detection of interacting elements and the sequence of genetic activities. Biochemistry has been employed to elucidate metabolic pathways, demonstrate protein complexes, and identify functions of gene products. The tools of molecular biology-knocking concentration of protein products down or up-have been helpful to trace the function of pathways in vivo. The study of disease states has led to the identification of a set of altered genes and helped define a network that is altered and gives rise to the disease. We will also discuss some serious limitations in these approaches. After reviewing how signal transduction pathways are constructed and investigated, we will turn our attention to an example that demonstrates the inter-relationships between pathways and the regulation of a specific set of pathways. We will examine how the p53 pathway in responding to stress shuts down the AKT-1 and mTOR pathways so as to limit the error frequency of cell growth and division during a stressful time where homeostatic mechanisms are required to respond and increase the fidelity of these processes.
authors.affiliation: School of Natural Sciences, Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540-0631, USA. alevine@ias.edu
authors.forename: Arnold J
authors.initials: AJ
authors.lastname: Levine
authors.forename: Wenwei
authors.initials: W
authors.lastname: Hu
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Feng
authors.forename: German
authors.initials: G
authors.lastname: Gil
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome