Reverse engineering intracellular biochemical networks.

Although much is known about the molecular components of cellular signaling pathways, very little is known about how these multicomponent biochemical machineries process complex extracellular signals to generate a consolidated cellular response. A newly developed theoretical approach for reverse engineering network structure—analyzing how perturbations propagate in a network—can be combined with chemical perturbations and quantitative detection approaches to reveal the causal connections within protein networks in cells. This information indicates the dynamic capabilities of a network and thereby its potential function.

[1]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[2]  Peter G. Schultz,et al.  A chemical switch for inhibitor-sensitive alleles of any protein kinase , 2000, Nature.

[3]  R Y Tsien,et al.  Genetically encoded reporters of protein kinase A activity reveal impact of substrate tethering , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[4]  F. Wouters,et al.  Imaging biochemistry inside cells. , 2001, Trends in cell biology.

[5]  R Y Tsien,et al.  Genetically encoded fluorescent reporters of protein tyrosine kinase activities in living cells , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Eduardo Sontag,et al.  Untangling the wires: A strategy to trace functional interactions in signaling and gene networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[7]  P. Swain,et al.  Stochastic Gene Expression in a Single Cell , 2002, Science.

[8]  Katherine C. Chen,et al.  Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. , 2003, Current opinion in cell biology.

[9]  Eduardo D. Sontag,et al.  Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data , 2004, Bioinform..

[10]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[11]  M. Mann,et al.  Mass spectrometry–based proteomics turns quantitative , 2005, Nature chemical biology.

[12]  Tobias Meyer,et al.  An inducible translocation strategy to rapidly activate and inhibit small GTPase signaling pathways , 2005, Nature Methods.

[13]  L. Banaszynski,et al.  Conditional control of protein function , 2006, Chemistry & biology.

[14]  A. Levitzki,et al.  Tyrphostins and other tyrosine kinase inhibitors. , 2006, Annual review of biochemistry.

[15]  B. Kholodenko Cell-signalling dynamics in time and space , 2006, Nature Reviews Molecular Cell Biology.

[16]  T. Tuschl,et al.  On the art of identifying effective and specific siRNAs , 2006, Nature Methods.

[17]  M. Mann,et al.  Global, In Vivo, and Site-Specific Phosphorylation Dynamics in Signaling Networks , 2006, Cell.

[18]  P. Schwille,et al.  Fluorescence cross-correlation spectroscopy in living cells , 2006, Nature Methods.

[19]  Michael Knop,et al.  Spatial regulation of Fus3 MAP kinase activity through a reaction-diffusion mechanism in yeast pheromone signalling , 2007, Nature Cell Biology.

[20]  Carsten Schultz,et al.  Live-Cell Imaging of Enzyme-Substrate Interaction Reveals Spatial Regulation of PTP1B , 2007, Science.

[21]  Chao Zhang,et al.  Construction of conditional analog-sensitive kinase alleles in the fission yeast Schizosaccharomyces pombe , 2007, Nature Protocols.

[22]  P. Bastiaens,et al.  Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate , 2007, Nature Cell Biology.

[23]  K. Shokat,et al.  Chemical Genetics: Where Genetics and Pharmacology Meet , 2007, Cell.

[24]  Jessica Melin,et al.  Microfluidic large-scale integration: the evolution of design rules for biological automation. , 2007, Annual review of biophysics and biomolecular structure.

[25]  Mindy I. Davis,et al.  A quantitative analysis of kinase inhibitor selectivity , 2008, Nature Biotechnology.