Abstract
Increasing evidence has been gathered for p53-dependent apoptosis, but it is still unclear how p53 initiates apoptosis by employing its transcriptional program. Pair-wise interactions of p53 with expression of other genes fail to predict p53 levels or rate of apoptosis. A more sophisticated approach, using neural networks, permits prediction of interaction among three or more genes (p53, bax, and ING1). These interactions are decidedly nonlinear. Careful measurements and advanced mathematical treatments will permit us not only to understand how expression of pro- and anti-apoptotic genes is regulated, but also to integrate cross-platform and cross-experimental data for the validation of predicted interactions.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms
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Animals
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Apoptosis / drug effects*
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Dexamethasone / pharmacology
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Gene Dosage
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Gene Expression Regulation / drug effects
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Inhibitor of Growth Protein 1
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Intracellular Signaling Peptides and Proteins / metabolism*
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Mice
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Models, Biological
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Neural Networks, Computer
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Nonlinear Dynamics*
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Nuclear Proteins / metabolism*
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Oligonucleotide Array Sequence Analysis
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Protein Binding / drug effects
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Reproducibility of Results
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Reverse Transcriptase Polymerase Chain Reaction
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S-Nitrosoglutathione / pharmacology*
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Thymus Gland / cytology*
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Thymus Gland / drug effects
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Tumor Suppressor Protein p53 / metabolism*
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Tumor Suppressor Proteins / metabolism*
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bcl-2-Associated X Protein / metabolism*
Substances
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Bax protein, mouse
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Ing1 protein, mouse
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Inhibitor of Growth Protein 1
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Intracellular Signaling Peptides and Proteins
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Nuclear Proteins
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Tumor Suppressor Protein p53
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Tumor Suppressor Proteins
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bcl-2-Associated X Protein
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S-Nitrosoglutathione
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Dexamethasone