ECS289A - Modeling Gene Regulation
Reading List of Research Papers for the Lectures:
Golub et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression
Monitoring, 1999. Science, v. 286, 531-537.
Brown et al., Knowledge-based analysis of microarray gene expression data by using support vector machines, 2000. PNAS, v. 97, 262-267.
Eisen et al., Cluster analysis and display of genome-wide expression
patterns, 1998. PNAS, v. 95, 14863-14868.
Tamayo et al., Interpreting patterns of gene expression with self organizing maps, 1999. PNAS, v. 96, 2907-2912.
Chen G, et al., Cluster analysis of microarray gene expression data, 2001. Statistica Sinica, 12:241-262.
Troyanskaya et al., Missing value estimation methods for DNA microarrays, Bioinformatics 2001 Jun;17(6):520-5.
Talked in class:
Haefner, Modeling Biological Systems, Chapman & Hall, 1996.
Yuh, Moore, and Davidson, Quantitative functional interrelations within the cis-regulatory system of the S. purpuratus Endo16 gene, 1996, Development 122.
Chen, Filkov, Skiena, Identifying Gene Regulatory Networks from Experimental Data, RECOMB 1999, Lyon, France.
Hidde de Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal
of Computational Biology 9(1): 67-103 (2002).
Additional reading:
Eric H. Davidson, Genomic Regulatory Systems, Academic Press, 2001.
Alvis Brazma and Thomas Schlitt, Reverse Engineering of Gene Regulatory Networks: a Finite State Linear
Model, BITS 2000, Heidelberg, Germany.
Trey Ideker et al., Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network, (2001) Science, v.292, pp 929-934.
Bas Dutilh, Analysis of Data from Microarray Experiments, the State of the Art in Gene Network Reconstruction, 1999, Literature Thesis, Utrecht University, Utrecht, The Nederlands.
V. Filkov and S. Skiena and J. Zhi, Methods for Analysis of Microarray
Time-Series Data, Journal of Computational Biology, v. 9, p. 317-330,
2002.
A. Wagner, Estimating Coarse Gene Network Structure from Large-Scale
Gene Perturbation Data, Genome Research, v. 12, p. 309-315, 2002.
D. C. Weaver and C. T. Workman and G. D. Stromo, Modeling
regulatory networks with weight matrices, Pacific Symposium on
Biocomputing, 1999.
E.P. van Someren and L.F.A. Wessels and M.J.T. Reinders, Linear
Modeling of Genetic Networks from Experimental Data, Intelligent
Systems for Molecular Biology, 2000.
P. D’Haeseleer and X. Wen and S. Fuhrman and R. Somogyi, Linear
Modeling of mRNA Expression Levels During CNS Development and Injury,
Pacific Symposium on Biocomputing, 1999.
Akutsu et al., Identification of Genetic Networks From a Small Number of Gene Expression Patterns Under the Boolean Network
Model, Pacific Symposium on Biocomputing, 1999.
Akutsu et al., Algorithms for Identifying Boolean Networks and Related Biological Networks Based on Matrix Multiplication and Fingerprint Function, RECOMB 00, 2000.
Liang et al., REVEAL, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures, Pacific Symposium on Biocomputing, 1998.
Wuensche, Genomic Regulation Modeled as a Network With Basins of Attraction, Pacific Symposium on Biocomputing, 1998.
D’Haeseleer et al., Tutorial on Gene Expression, Data Analysis, and Modeling, PSB, 1999.
Friedman et al., Using Bayesian Networks to Analyze
Expression Data, RECOMB 2000, 127-135.
Pe’er et al., Inferring Subnetworks from Perturbed
Expression Profiles, Bioinformatics, v.1, 2001, 1-9.
Ron Shamir's course, Analysis of Gene Expression Data,
DNA Chips and Gene Networks, at Tel Aviv University, lecture 10.
Data Set Papers:
Spellman et al., Mol. Bio. Cell, v. 9, 3273-3297, 1998.
Hughes et al., Cell, v. 102, 109-26, 2000.
Chen et al., Modeling Gene Expression with Differential Equations,
PSB 1999.
Haefner, Modeling Biological Systems, Chapman & Hall, 1996.
Hidde de Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal
of Computational Biology 9(1): 67-103 (2002).