ECS289A, Spring 2004,  Gene Network Inference
Reading List

The papers that are presented in detail during lectures are highlighted in red.
The other papers can be used for presentations. The papers highlighted in white
are the ones I recommend for presentation.

Lectures 1, 2, and 3: Intro to Molecular Biology, Biotechnologies, Microarray Data Analysis

Lectures 1 - 8 from last year's course

They are available from this page. Most of these lecture notes will have more material than we will cover in class,
and can be used as additional material and pointers to further reading.

Microarrays

  • Lockhat and Winzeler, Genomics, gene expression and DNA arrays, 2000. Nature, v. 405, 827-836.
  • Huber, Heydebreck, and Vingron, Analysis of Microarray Gene Expression Data, available here.
  • 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.
  • A. Tanay, R. Sharan, R. Shamir, Discovering Statistically Significant Biclusters in Gene Expression Data Proceedings of ISMB 2002 (Bioinformatics Vol. 18 Suppl. 1 S136--S144. [PDF]

Network example handout comes from the following two papers

  • Davidson, E.H. et al., A genomic regulatory network for development. Science 295, 1669-1678, 2002. [PDF]
  • Albert, R. and Othmer, H.G. The topology of the regulatory interactions predicts the expression pattern of the Drosophila segment polarity genes Journal of Theoretical Biology 223, 1-18 (2003). [PDF]

TF-DNA Data Network and Data

  • Lee et al., Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 298: 799-804 (2002).[PDF]

Lecture 3, 4: Graph Theoretic Models (week of 4-11-04)

  • Wagner, A. (2003) Reconstructing pathways in large genetic networks from genetic perturbations. (in press). [PDF]
  • 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. [PDF]
  • A. Wagner, Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation Data, Genome Research, v. 12, p. 309-315, 2002.
  • Wagner, A. (2001) How to reconstruct a large genetic network from n gene perturbation in n^2 easy steps. Bioinformatics 17, 1183-1197. [PDF]
  • Chen, T., Filkov, V., Skiena, S. Identifying Gene Regulatory Networks from Experimental Data, RECOMB 1999. [PDF]

Lecture 6, 7: Bayesian Nets (week of 4-18-04)

  • Friedman et al., Using Bayesian Networks to Analyze Expression Data, RECOMB 2000, 127-135. [PDF]
  • Pe’er et al., Inferring Subnetworks from Perturbed Expression Profiles, Bioinformatics, v.1, 2001, 1-9. [PDF]
  • Friedman, Inferring Cellular Networks Using Probabilistic Graphical Models, Science 303, 799-805, 2004 (in-class handout)
  • Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2002) “Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks.” International Conference on Systems Biology 2002. [PDF] (presentation paper)
  • Ott et al., Finding Optimal Models for Small Gene Networks, PSB 2004 [PDF] (presentation paper)
  • Ron Shamir's course, Analysis of Gene Expression Data, DNA Chips and Gene Networks, at Tel Aviv University [link]

Lecture 8, 9: Boolean Nets (week of 4-26-04)

I used the Boolean Nets slides from last years course as an introduction.
They are available here. The slides are based on material from the following papers.

  • Akutsu et al., Identification of genetic networks by strategic gene disruptions and gene overexpressions under a Boolean model. Theoretical Computer Science (298): 235-251 (2003). [PDF] (extended version from SODA 1998)
  • Akutsu et al., Algorithms for Identifying Boolean Networks and Related Biological Networks Based on Matrix Multiplication and Fingerprint Function, Journal of Computational Biology, (7) 331-343 (2000). [PDF]
  • 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. [PDF]
  • Wuensche, Genomic Regulation Modeled as a Network With Basins of Attraction, PSB 1998. [PDF]

The following is a paper I presented in more detail in class. Note the combinatorial problem in it and its relationship to Wagner 2001 above.

  • Ideker et al., Discovery of Regulatory Interactions Through Perturbation: Inference and Experimental Design, PSB 2000. [PDF]

The next two papers are algorithmic, relating experiments with network identification. Similar to some of the above.

  • Karp et al., Algorithms for choosing differential gene expression experiments. RECOMB 1999, 208-217. [PDF]
  • Liang et al., REVEAL, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures, PSB 1998. [PDF]

The following address the issue of narrowing down the search space of possible Boolean functions, by defining a subclass of Chain Functions.

  • Gat-Viks, I., and Shamir, R. Chain Functions and Scoring Functions in Genetic Networks, Bioinformatics 19 (S1), 108-117, 2003. [PDF]
  • Gat-Viks, I. et al. Reconstructing Chain Functions in Genetic Networks, PSB 2004. [PDF]

The next papers are more biological, but they follow the Boolean formalism. I've been mentioning results from these papers throughout
the course. They are all very important papers and worth the read.

  • Buchler, N.E. et al., On Schemes of Combinatorial Transcriptional Logic, PNAS 100 (9), 5136-5141, 2003. [PDF]
  • von Dassow et al., The Segment Polarity Network is a Robust Developmental Module, Nature 406: 188-92, 2000. [PDF] (a more detailed analysis is here)
  • Setty, Y. et al., Detailed map of a cis-regulatory input function, PNAS, 100:7702-7707 (2003). [PDF]
  • Albert and Othmer paper from lectures 1, 2 and 3 above... (for a Boolean Network version of the segment polarity network in D. Melanogaster)

Additional papers (5-1-04):

×           Shmulevich et al., The Role of Certain Post Classes in Boolean Network Models of Genetic Networks, PNAS, 100:10734-10739 (2003). [link] (restricting Boolean functions)

×           Hashimoto et al, Growing Genetic Regulatory Networks from Seed Genes, Bioinformatics (2004). [link]

×           Tanay and Shamir, Computational Expansion of Genetic Networks, Bioinformatics, 17 (S1): S270-278. [link]

Lecture 10, 11: Linear Additive Models (week of 5-2-04)

  • D. C. Weaver and C. T. Workman and G. D. Stromo, Modeling regulatory networks with weight matrices, Pacific Symposium on Biocomputing, 1999. [PDF]
  • 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. [PDF]
  • 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. [PDF]
  • Hertz, J. Statistical Issues in Reverse Engineering Genetic Networks, PSB 1998, (poster) [PDF]

The following are all from the same group of people, and should be read together:

  • Yeung et al., Reverse engineering gene networks using singular value decomposition and robust regression, PNAS 99, 6163-6168, 2002. [PDF]
  • Tegner et al, Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling, PNAS 100: 5944-5949, 2003. [PDF]
  • Gardner et al., Inferring genetic networks and identifying compound mode of action via expression profiling, Science 301, 102-105, 2003. [PDF]
  • di Bernardo et al., Robust identification of large genetic networks, PSB 2004. [PDF]

This paper is a lead-in to diff. equations:

  • de la Fuente et al., A quantitative method for reverse engineering gene networks from microarray experiments using regulatory strengths, ICSB, 213-221, 2001. [PDF]

Lecture 12: Differential Equations

  • Chen et al., Modeling Gene Expression with Differential Equations, PSB 1999. [PDF]
  • Stark et al, Reconstructing Gene Networks: What are the limits? Biochem Soc Trans, 2003. [link]
  • Sontag, E.D. For Differential Equations with r Parameters, 2r+1 Experiments Are Enough for Identification, J. Nonlinear Sci. 12, 553–583, 2002 [link] (this one is dense but rewarding)

General:

Hidde de Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal of Computational Biology 9(1): 67-103 (2002). [PDF]

D’Haeseleer et al., Tutorial on Gene Expression, Data Analysis, and Modeling, PSB 1999. [PDF]