ITEE Home » Complexity - Data Mining Workshops » Evol-comp » RNA Reading Group

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10th April 2003

• Jen -- networks
• Mikael -- analysis of splice site signals (Lim and Burge, 2001, see below or go directly to journal entry).

1st May

• Nic -- models of gene regulation

  H. Bolouri and E. H. Davidson (2002). Modeling transcriptional regulatory networks. BioEssays, 24:1118-1129.

• Stefan -- RNA mediated control

  D. Banerjee and F. Slack (2002). Control of developmental timing by small temporal RNAs: a paradigm for RNA-mediated regulation of gene expression. BioEssays, 24:119-129.

15th May

• James -- Solé et. al., Selection, tinkering and emergence in complex networks

  R. Solé, R. F. Cancho, J. M. Montoya, and S. Valverde (2002). Selection, tinkering and emergence in complex networks. Complexity, 8(1):20-33.

• Ben -- RBNs with different degree distributions

  J. J. Fox and C. C. Hill (2001). From topology to dynamics in biochemical networks. Chaos, 11(4):809-815.

29th May

• Janet -- networks
• Kai -- models

12th June

 

Areas of Interest

Gene regulation in biology

E. F. Keller (2000). The Century of the Gene. Harvard University Press, Cambridge, MA.

A very short (under 200 small pages) and readable (has pictures) account of changing ideas of what a 'gene' is over the last 100 years. Focuses particularly on importance of interaction between DNA, proteins and environment, and insufficiency of studying genes alone. See Richard Lewontin's "The Triple Helix" for another short introduction to this perspective.

E. Coen (1999). The Art of Genes: How Organisms Make Themselves. Oxford University Press, Oxford.

A fairly easy reading (more pictures) introduction to developmental biology. A description the developmental process of plants and animals using analogies from painting and creativity. An excellent expansion of the "DNA is a recipe, not a blueprint" argument. Beautifully written.

S. B. Carroll, J. K. Grenier, and S. D. Weatherbee (2001). From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design. Blackwell Science, Oxford.

A more "scientific" introduction to evolutionary developmental biology. The initial chapters focus on the genetic toolkit (Hox genes, etc.) used in Drosophila and how this specifies the development of the body plan. The later chapters describe how this genetic toolkit has been assembled over evolutionary time and some of the ways in which it enables evolutionary novelty. Clear and concise.

M. Ptashne (1992). A Genetic Switch: Phage Lambda and Higher Organisms. 2nd Edition. Blackwell Science, Oxford.

Another excellent book satisfying the three criteria of length (short), readability (good) and illustration (lots). Describes the lysis/lysogeny decision of phage lambda (essentially, how environmental signals result in a choice between two different patterns of gene expression. Gave me a whole new appreciation of the sensitivity of genetic processes at the level of individual molecules.

E. H. Davidson (2001). Genomic Regulatory Systems. Academic Press, San Diego, CA.

R. A. Raff (1996). The Shape of Life: Genes, Development and the Evolution of Animal Form. The University of Chicago Press, Chicago.

The above two books are on my (Nic's) reading list due to encouraging recommendations - if anyone who has read them has any comments?

 

Small RNA Review Papers

S. R. Eddy (2001). Non-coding RNA genes and the modern RNA world. Nature Reviews Genetics, 2:919-929.

A good overview of the multiple roles of non-coding RNAs (ncRNAs), including small nucleolar RNAs (snoRNAs) that modifying rRNA and tRNA molecules, micro RNAs (miRNAs) responsible for developmental timing (eg. lin-4 and let-7) and RNA interference and others. The more recent Cell linked below has better information on ncRNA detection. Concludes with view that ncRNAs are not relics of a pre-protein "RNA World", but rather are highly adapted to perform their specific tasks and hence complementary to protein regulation.

S. Gottesman (2002). Stealth regulation: biological circuits with small RNA switches. Genes & Development, 16:2829-2842.

A more focused survey of the ncRNAs involved in gene regulation, covering the regulatory mechanisms used (direct base pairing, acting as molecular decoys, etc.), how direct base pairing occurs in plasmids, bacteria and higher organisms, how small RNAs are themselves regulated (role of environmental signals) and why small RNAs may be used in preference to proteins for certain regulatory tasks.

D. Banerjee and F. Slack (2002). Control of developmental timing by small temporal RNAs: a paradigm for RNA-mediated regulation of gene expression. BioEssays, 24:119-129.

A. E. Pasquinelli and G. Ruvkun (2002). Control of developmental timing by microRNAs and their targets. Annu. Rev. Cell Dev. Biol., 18:495-513.

The above two articles review the role small RNAs in regulating developmental timing, concentrating on what is known about lin-4 and let-7 in C. elegans.

R. H. A. Plasterk (2002). RNA silencing: the genome's immune system. Science, 296:1263-1264.

The "other" role for small RNAs (besides regulating developmental timing). An overview of RNA-interference and the hypothesis that it may have evolved to defend the genome against viruses and transposable elements.

J. S. Mattick and M. J. Gagen (2001). The evolution of controlled multitasked gene networks: the role of introns and other noncoding RNAs in the development of complex organisms. Mol. Biol. Evol., 18(9):1611-1630.

J. S. Mattick (2001). Non-coding RNAs: the architects of eukaryotic complexity. EMBO reports, 2(11):986-991.

J. S. Mattick (1994). Introns: evolution and function. Current Opinion in Genetics & Development, 4:823-831.

 

Science 296, 2002 : Special Issue on RNA Silencing and Noncoding RNA.

 

Complex networks

D. J. Watts (1999). Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton Univ. Press, Princeton, NJ.

Small Worlds - the book.

S. Strogatz (2001). Exploring complex networks. Nature, 410:268-276.

A concise review of the work that has been done in SW Network theory over the last few years, less the maths. Extensive list of references.

R. Albert and A.-L Barabási (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74:47-97.

S. N. Dorogovtsev and J. F. F. Mendes (2002). Evolution of networks. Advances in Physics, 51(4):1079-1187.

From their sheer size alone, I can only presume that the above two papers represent a relatively exhaustive coverage of their respective topics. Should come with a health warning - "Not for the mathematically timid".

R. Solé, R. F. Cancho, J. M. Montoya, and S. Valverde (2002). Selection, tinkering and emergence in complex networks. Complexity, 8(1):20-33.

A good, recent overview of network research from a complexity perspective. Covers (lightly) issues of SW and SF structure, degree distribution, redundancy and degeneracy and modularity in networks from a variety of different fields - proteomics/genomics, ecology, semantic nets, electronics and software design. Advances thesis that convergence of structural properties in natural (evolved) and artificial (designed) networks is due to optimization of communication distance between nodes, from which other characteristic features, such as clustering, modularity, etc., follow. Again, an extensive list of references.

 

Complexity 8(1), 2002 : Special Issue on Complex Networks.

Complexity 8(2), 2002 : Special Issue on Complex Adaptive Systems: Part I.

 

Modelling gene regulation

H. Bolouri and E. H. Davidson (2002). Modeling transcriptional regulatory networks. BioEssays, 24:1118-1129.

Excellent paper on the issues involved in modeling genetic regulatory networks, focusing on reverse engineering of systems from gene expression data, knowledge of signaling pathways, etc. Fairly biased towards their own approach and tools developed to model sea urchin development, but also a clear description of various modeling formalisms and their advantages and disadvantages.

E. Meir, E. M. Munro, G. M. Odell, and G. von Dassow (2002). Ingeneue: A versatile tool for reconstituting genetic networks, with examples from the segment polarity network. Journal of Experimental Zoology (Mol. Dev. Evol.), 294:216-251.

Describes 'Ingeneue', a software tool for integrating molecular genetic data into an ordinary differential equation model of a gene network. Conceptually divided into: (a) Nodes, network components = mRNAs, proteins, etc.; (b) Cells - multicellularity and intracellular signalling is catered for; and (c) Affectors, representing interactions between Nodes such as transcription and translation. Focuses very much on the implementation of the tool and a particular example of its use, rather than any higher level modeling issues.

H. de Jong (2002). Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology, 9(1):67-103.

Comprehensive and well written review of GRN modeling from RBNs to partial differential equatios and much in between.

H. Kitano (2001). Foundations of Systems Biology. The MIT Press/Bradford Books, Cambridge, MA.

A fairly broad and uncohesive collection of papers on Systems Biology, however, the introduction (available from Kitano's group here) is worth reading for a comprehensive (and optimistic) overview of why the systems approach to biology is a Good Thing.

G. Marnellos and E. Mjolsness (1998). A gene network approach to modeling early neurogenesis in drosophila. In R. B. Altman, A. K. Dunker, L. Hunter, and T. E. Klein, editors. Pacific Symposium on Biocomputing '98, Singapore, World Scientific, pages 30-41.

Marnellos develops previous work by Mjolsness on using ANN-like networks (trained on biological expression patterns) to model gene regulation. More specific than the Artificial Life models while still retaining more generality than the data-driven approaches of von Dassow, Davidson, etc.

A. Wuensche (2002). Basins of attraction in network dynamics: A conceptual framework for biomolecular networks. To appear in Modularity in Development and Evolution.

The most recent in a series of reworkings (with gradual developments) of the "RBNs as a model for gene networks, where basins of attraction equal cell types" thesis.

 

Science 295, 2002 : Special Issue on Systems Biology.

 

Bioinformatics

Splicing

Lim, Lee P., and Burge, Christopher B., A computational analysis of sequence features involved in recognition of short introns, Proceedings of the National Academy of Sciences (PNAS), 98(20), pp. 11193-11198, 2001. (Journal entry).

Performance measures on recognition of short introns in various organisms. Studies sufficiency of splice signals/motifs and discusses other factors which assist in recognition.

Patterson, Donald, J., Yasuhara, K., and Ruzzo, Walter, L., Pre-mRNA secondary structure prediction aids splice site prediction, in Proceedings of the Pacific Symposium on Biocomputing 7, pp. 223-243, 2002. (Paper).

Extends investigations on splice site motifs by including secondary structure features into the recognition. Decision tress and SVMs are used for classification.

Ott, S., Tamada, Y., Bannai, H., Nakai, K., and Miyano, S., Intrasplicing - Analysis of long intron sequences. in Proceedings of the Pacific Symposium on Biocomputing 8, pp. 339-350, 2003. (Paper).

Implements Lim and Burge's short intron predictor and computationally evaluates the hypothesis that inner parts are (iteratively or recursively) spliced prior to the splicing of the long intron.

Noncoding RNA genes

Rivas, Elena, Klein, Robert J., Jones, Thomas, J., and Eddy, Sean R., Computational identification of noncoding RNAs in E. coli by comparative genomics, Current Biology, 11:1369-1373. 2001. (Journal entry).

Detects structural RNA conservation in genomic sequences to recognize noncoding RNA genes. A full screening of E. coli is reported using the approach.

Rivas, Elena, and Eddy, Sean R., Noncoding RNA gene detection using comparative sequence analysis, BMC Bioinformatics, 2:8. 2001. (Journal entry).

Describes the technique used in Rivas et al. (2001) in detail.

Carter, Richard J, Dubchak, Inna, and Holbrook, Stephen R., A computational approach to identify genes for functional RNAs in genomic sequences, Nucleic Acids Research, 29(19). 2001. (Journal entry).

Uses a neural network trained on various statistical features of genomic sequences to detect noncoding, functional RNA genes.

Eddy, Sean R., Computational genomics of noncoding RNA genes, Cell, 109(2), pp. 137-140. 2002. (Journal entry).

Discusses the results generated by the above papers (and a couple of others) on whole genomic screening.