Empirical data indicate that biological gene networks are sparsely connected, and that the average number of upstream-regulators per gene is less than two.[21] Theoretical results show that selection for robust gene networks will favor minimally complex, more sparsely connected, networks.[21] These results suggest that a sparse, minimally connected, genetic architecture may be a fundamental design constraint shaping the evolution of gene network complexity.
See also
• Operon • Systems biology • Synexpression • Cis-regulatory module • Body plan • MorphogenReferences
[1] http://web.wi.mit.edu/young/regulator_network/ [2] http://www.pnas.org/cgi/content/full/102/14/4935 [3] Kauffman, Stuart (1993). The origins of Order.
[4] Vohradsky, J. (2001). Neural model of the genetic network. The Journal of Biological Chemistry, 276, 36168–36173.
[5] Geard, N. and Wiles, J. A Gene Network Model for Developing Cell Lineages. In Artificial Life, 11 (3): 249–268, 2005.
[6] Schilstra, M. J. and Bolouri, H. The Logic of Gene Regulation., http://strc.herts.ac.uk/bio/maria/NetBuilder/ Theory/NetBuilderModelling.htm
[7] Knabe, J. F., Nehaniv, C. L., Schilstra, M. J. and Quick, T. Evolving Biological Clocks using Genetic Regulatory Networks. In Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems, pages 15–21, MIT Press, 2006.
[8] Knabe, J. F., Nehaniv, C. L. and Schilstra, M. J. Evolutionary Robustness of Differentiation in Genetic Regulatory Networks. In Proceedings of the 7th German Workshop on Artificial Life 2006 (GWAL-7), pages 75–84, Akademische Verlagsgesellschaft Aka, Berlin, 2006.
[9] Knabe, J. F., Schilstra, M. J. and Nehaniv, C. L. Evolution and Morphogenesis of Differentiated Multicellular Organisms: Autonomously Generated Diffusion Gradients for Positional Information. In Artificial Life XI:
Gene regulatory network 57
Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, MIT Press, 2008.
[10] Elowitz, M.B., Levine, A.J., Siggia, E.D., and Swain, P.S. 2002. Stochastic gene expression in a single cell. Science 297: 1183–1186
[11] Blake, W.J., Kaern, M., Cantor, C.R., and Collins, J.J. 2003. Noise in eukaryotic gene expression. (http://www. bu.edu/abl/publications.html) Nature 422: 633–637
[12] Arkin, A. and McAdams, H.H. 1998. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149: 1633–1648.
[13] Raser, J.M., and O'Shea, E.K., (2005) Noise in gene expression: origins, consequences, and control, Science, 309, 2010–2013
[14] Elowitz, M. B., and Leibler, S., (2000) A synthetic oscillatory network of transcriptional regulators., Nature, 403, 335–338
[15] Gardner, T. S., Cantor, C. R., and Collins., J. J., (2000) Construction of a genetic toggle switch in Escherichia coli., Nature, 403, 339–342
[16] Gillespie, D.T., A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, 1976, J. Comput. Phys., 22, 403–434.
[17] Roussel, M.R., and Zhu, R., Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression, 2006, Phys. Biol. 3, 274–284
[18] Ribeiro, Andre S., Zhu, R., Kauffman, S.A. (2006). "A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics", Journal of Computational Biology, 13(9), 1630–1639.
[19] Andre S. Ribeiro and Jason Lloyd-Price, (2007) "SGN Sim, a Stochastic Genetic Networks Simulator", Bioinformatics, 23(6):777–779. doi:10.1093/bioinformatics/btm004., doi:10.1093/bioinformatics/btm004. [20] Y. N. Kaznessis, (2007) "Models for Synthetic Biology", BMC Systems Biology, 2007, 1:47
doi:10.1186/1752-0509-1-47 (http://www.biomedcentral.com/1752-0509/1/47).
[21] Leclerc R. (August 2008). " Survival of the sparsest: robust gene networks are parsimonious (http://www. nature.com/msb/journal/v4/n1/full/msb200852.html)". Mol Syst Biol. 4 (213).
• James M. Bower, Hamid Bolouri (editors), (2001) Computational Modeling of Genetic and
Biochemical NetworksComputational Molecular Biology Series, MIT Press, ISBN 0-262-02481-0
• L. Franke, H. van Bakel, L. Fokkens, E. de Jong, M. Egmont-Petersen, C. Wijmenga, (2006) Reconstruction of a probabilistic human gene network, with an application for prioritizing positional candidate genes, Amer. J. of Human Genetics, 78(6), 1011–25. Human gene network (http://www.genenetwork.nl),Prioritizer software application
(http://www.prioritizer.nl).
• S. A. Kauffman, "Metabolic stability and epigenesisin randomly constructed genetic nets", J. Theoret. Biol (1969) 22, 434–467
External links
• Gene Regulatory Networks (http://www.doegenomestolife.org/science/ generegulatorynetwork.shtml) — Short introduction
• BIB: Yeast Biological Interaction Browser (http://sergi5.com/bio)
• Graphical Gaussian models for genome data (http://strimmerlab.org/notes/ggm.html) — Inference of gene association networks with GGMs
• A bibliography on learning causal networks of gene interactions (http://www.molgen. mpg.de/~markowet/docs/network-bib.pdf) - regularly updated, contains hundreds of links to papers from bioinformatics, statistics, machine learning.
• http://mips.gsf.de/proj/biorel/ BIOREL is a web-based resource for quantitative
estimation of the gene network bias in relation to available database information about gene activity/function/properties/associations/interactio.
• Evolving Biological Clocks using Genetic Regulatory Networks (http://panmental.de/ GRNclocks) - Information page with model source code and Java applet.
Gene regulatory network 58
• Engineered Gene Networks (http://www.bu.edu/abl)
• Tutorial: Genetic Algorithms and their Application to the Artificial Evolution of Genetic Regulatory Networks (http://panmental.de/ICSBtut/)
Genomics
Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.
For the United States Environmental Protection Agency, "the term "genomics" encompasses a broader scope of scientific inquiry associated technologies than when genomics was initially considered. A genome is the sum total of all an individual organism's genes. Thus, genomics is the study of all the genes of a cell, or tissue, at the DNA (genotype), mRNA (transcriptome), or protein (proteome) levels."[1]
History
Genomics was established by Fred Sanger when he first sequenced the complete genomes of a virus and a mitochondrion. His group established techniques of sequencing, genome mapping, data storage, and bioinformatic analyses in the 1970-1980s. A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics. Study of the full set of proteins in a cell type or tissue, and the changes during various conditions, is called proteomics. A related concept is materiomics, which is defined as the study of the material properties of biological materials (e.g. hierarchical protein structures and materials, mineralized biological tissues, etc.) and their effect on the macroscopic function and failure in their biological context, linking processes, structure and properties at multiple scales through a materials science approach. The actual term 'genomics' is thought to have been coined by Dr. Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, ME) over beer at a meeting held in Maryland on the mapping of the human genome in 1986.
In 1972, Walter Fiersand his team at the Laboratory of Molecular Biology of the University of Ghent (Ghent, Belgium) were the first to determine the sequence of a gene: the gene for Bacteriophage MS2 coat protein.[2] In 1976, the team determined the complete nucleotide-sequence of bacteriophage MS2-RNA.[3] The first DNA-based genome to be sequenced in its entirety was that of bacteriophage Φ-X174; (5,368 bp), sequenced by Frederick Sangerin 1977.[4]
The first free-living organism to be sequenced was that of Haemophilus influenzae(1.8 Mb) in 1995, and since then genomes are being sequenced at a rapid pace. A rough draft of the
Genomics 59 human genome was completed by the Human Genome Projectin early 2001, creating much fanfare.
As of September 2007, the complete sequence was known of about 1879 viruses [5] , 577 bacterial species and roughly 23 eukaryote organisms, of which about half are fungi. [6]
Most of the bacteria whose genomes have been completely sequenced are problematic disease-causing agents, such as Haemophilus influenzae. Of the other sequenced species, most were chosen because they were well-studied model organisms or promised to become good models. Yeast (Saccharomyces cerevisiae) has long been an important model organism for the eukaryotic cell, while the fruit fly Drosophila melanogaster has been a very important tool (notably in early pre-molecular genetics). The worm Caenorhabditis
elegans is an often used simple model for multicellular organisms. The zebrafish
Brachydanio rerio is used for many developmental studies on the molecular level and the flower Arabidopsis thaliana is a model organism for flowering plants. The Japanese pufferfish (Takifugu rubripes) and the spotted green pufferfish (Tetraodon nigroviridis) are interesting because of their small and compact genomes, containing very little non-coding DNA compared to most species. [7] [8] The mammals dog (Canis familiaris), [9] brown rat (Rattus norvegicus), mouse (Mus musculus), and chimpanzee (Pan troglodytes) are all important model animals in medical research.