Identifying Interventional and Pathogenic Mechanisms by Generative Inverse Modeling of Gene Expression Profiles

MOTIVATION The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level. RESULTS We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.

[1]  Mathäus Dejori,et al.  Bayesian Inference of Genetic Networks from Gene-Expression-Data: Convergence and Reliability , 2003, IC-AI.

[2]  Rina Dechter,et al.  Bucket elimination: A unifying framework for probabilistic inference , 1996, UAI.

[3]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[4]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[5]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[6]  C. Murre,et al.  Pbx1 is converted into a transcriptional activator upon acquiring the N-terminal region of E2A in pre-B-cell acute lymphoblastoid leukemia. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Harald Steck,et al.  Constraint-based structural learning in Bayesian networks using finite data sets , 2001 .

[8]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[9]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[10]  R Dulbecco,et al.  A turning point in cancer research: sequencing the human genome. , 1986, Science.

[11]  M. Stetter,et al.  Hunting drug targets by systems-level modeling of gene expression profiles , 2004, IEEE Transactions on NanoBioscience.

[12]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[13]  C. Murre,et al.  The role of E2A-PBX1 in leukemogenesis , 2001, Oncogene.

[14]  S. P. Fodor,et al.  Multiplexed biochemical assays with biological chips , 1993, Nature.

[15]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[16]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[17]  Tommi S. Jaakkola,et al.  Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks , 2000, Pacific Symposium on Biocomputing.

[18]  S. Smith,et al.  A new fused transcript in Philadelphia chromosome positive acute lymphocytic leukaemia , 1987, Nature.

[19]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[20]  Ross D. Shachter.  Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams) , 1998, UAI.

[21]  Gustavo Deco,et al.  Large-Scale Computational Modeling of Genetic Regulatory Networks , 2003, Artificial Intelligence Review.

[22]  Nir Friedman,et al.  Inferring subnetworks from perturbed expression profiles , 2001, ISMB.

[23]  Gregory F. Cooper,et al.  A Bayesian Method for Constructing Bayesian Belief Networks from Databases , 1991, UAI.

[24]  Satoru Miyano,et al.  Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[25]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[26]  Satoru Miyano,et al.  Estimation of Genetic Networks and Functional Structures Between Genes by Using Bayesian Networks and Nonparametric Regression , 2001, Pacific Symposium on Biocomputing.

[27]  Abdelhak M. Zoubir,et al.  Bootstrap: theory and applications , 1993, Optics & Photonics.

[28]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.