User:Malloy.65/sandbox

This is my sandbox. URL: https://en.wikipedia.org/wiki/Systems_biology 3 suggestions: It might be useful to include an applications section at the end of the article so that readers may better understand what to do with the information presented. Also, it is important to define terms such as "reductionism" on this page so that it is accessible to a wide variety of readers. Lastly, including a section on the Human Genome Project may be of interest as it pertains to the systems biology of genetics. Sentence addition: For example, the Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics. [1]

EDITS MADE TO "THE HUMAN GENOME PROJECT" PAGE:

The project has inspired and paved the way for genomic work in other fields, such as agriculture. For example, by studying the genetic composition of Tritium aestivum, the world’s most commonly used bread wheat, great insight has been gained into the ways that domestication has impacted the evolution of the plant[50]. Which loci are most susceptible to manipulation, and how does this play out in evolutionary terms? Genetic sequencing has allowed these questions to be addressed for the first time, as specific loci can be compared in wild and domesticated strains of the plant. This will allow for advances in genetic modification in the future which could yield healthier, more disease-resistant wheat crops.

Generally speaking, advances in genome sequencing technology have followed Moore’s Law, a concept from computer science which states that integrated circuits can increase in complexity at an exponential rate[29]. This means that the speeds at which whole genomes can be sequenced can increase at a similar rate, as was seen during the development of the above-mentioned Human Genome Project.

Joseph Felsenstein laid the foundation for describing how these techniques might be applied to create evolutionary trees in 1981. He describes the “maximum likelihood technique” which is in many ways superior to the methods of parsimony traditionally used by researchers. The maximum likelihood technique uses likelihood ratios from the raw data—nucleotide bases themselves—to determine the strength of the relationship between species [30]. In the traditional method, parsimony, morphologic characteristics have often been used which can be misleading due to homologous evolution. As more and more organisms have their information sequenced, we can begin to make sense of phylogenetic relationships on a broad evolutionary scale using genetic techniques rather than the traditional methods.

Changed organization of the above sections accordingly.

TERM PAPER BEGINS HERE

The Application of Gene Sequencing to Evolutionary Problems SM4381 Thursday 9:10 A.M. Abstract The application of novel computing methods to ancient biological questions promises to be a systematic leap towards understanding the evolutionary processes of life. This paper will examine the current technologies being applied to evolutionary issues, as well as the implications these technologies will have for both the study of evolution and the evolutionary processes themselves. Background In 1990, the U.S. Department of Energy and the National Institutes of Health formally began the Human Genome Project (NIH 2014). The goal was simple: map the entirety of the nucleotide bases which comprise the human genetic structure, although at the time researchers could only estimate the number of these bases and were relatively uncertain of their quantity (2014). This work spanned more than a decade, and the project’s goals were met in 2003 when the genome was determined to be fully sequenced, and over 20,000 human genes were discovered in the small number of test subjects whose genomes were sequenced (2014). Interestingly, of the more than three billion nucleotides sequenced, only about two percent were found to code for functional proteins (Clamp et al. 2007). The incredible amount of information presented by the sequencing of the genome, therefore, has led to perhaps more answers than questions with regard to the human genome. The project was international in scope, and has led to a great deal of research which has branched out from the original goals of the program. Importantly, breakthroughs in sequencing technology have reduced the time and financial costs required to do genome sequencing, which is what will allow these technologies to be applied to evolutionary biology projects in the future. It is now affordable to apply genomics sequencing technology to solve problems outside of the context of the human genome, such as the evolution of bacteria or fungi. Generally speaking, advances in genome sequencing technology have followed Moore’s Law, a concept from computer science which states that integrated circuits can increase in complexity at an exponential rate (Mardis 2008). This means that the speeds at which whole genomes can be sequenced can increase at a similar rate, as was seen during the development of the above-mentioned Human Genome Project. Expanding the Uses of Sequencing The applications of genome sequencing in other contexts are only beginning to be discovered and advanced. Because the genomes of microorganisms such as E. coli have also been fully sequenced, many experimental methods not previously available are now used by researchers interested in understanding how these species change over time. For example, an interdisciplinary project described in Nature Genetics in 2006 explains the spontaneous mutations, and subsequent fixation, of several critical genes in a strain of microorganisms which occurred in real-time in the laboratory (Herring et al. 2006). This means that instead of simply observing phenotypic changes in the bacteria (which would be quite difficult for subtle changes on such a microscopic level), researchers can observe point mutations in nucleotide sequences, which cause entire shifts in genes. Because the kinds of bacteria observed reproduce asexually, these mutations quickly became fixed in the population (2006). The new computational tools of gene sequencing are not limited to humans and their microbial counterparts; many opportunities exist for improvements to agricultural crops and other types of plant life via these technologies. Rajeev Varshney and his colleagues describe, for example, the use of sequencing technologies to map great numbers of crop plants in order to determine how these populations are interbreeding and what the diversity of a specific site looks like (Varshney et al. 2009). This information could be used to determine the risk for widespread infection: if the genetic information demonstrates little diversity, a high amount of inbreeding likely took place. If a high amount of inbreeding has taken place, there is a greater risk of heterozygote elimination in a population, which means that a pest could easily attack susceptible homozygotes. A crop researcher, therefore, can use this genetic information to gain better insight about the diversity of her field and adopt preventative measures accordingly. Researchers can also genetically modify these plants to prevent them from being susceptible to pest attacks in the first place (2009). Evolutionary Techniques Nucleic acid sequence comparisons have been thought of as a useful tool for evolutionary biologists even before the technology to conduct them became widely available in the 21st century. For example, Joseph Felsenstein in 1981 laid the foundation for describing how these techniques might be applied to create evolutionary trees. He describes the “maximum likelihood technique” which is in many ways superior to the methods of parsimony traditionally used by researchers. The maximum likelihood technique uses likelihood ratios from the raw data—nucleotide bases themselves—to determine the strength of the relationship between species (Felsenstein 1981). In the traditional method, parsimony, morphologic characteristics have often been used which can be misleading due to homologous evolution. As more and more organisms have their information sequenced, we can begin to make sense of phylogenetic relationships on a broad evolutionary scale using genetic techniques rather than the traditional methods. Gene Banks and Genetic Modification Gene banks are the result of these efforts to centralize knowledge about genes and the genomes of distinct, but related, organisms. Concentrated in nationally-funded labs and international research centers, gene banks are both physical and digital preservations of unique species—physical in the sense that germ plasm (in the case of plants) is isolated and stored for future use, and digital in the sense that the sequenced genome is maintained for evolutionary comparisons (Tanskley and McCouch 1997). This practice has become common in the case of plants which have a great deal of agricultural value; the relationship between the thousands of species of maize, for example, has been discovered as these species have been entered into vast gene banks where their information can be collected and stored (1997). Recent work on wheat has also had important implications. By studying the genetic composition of Tritium aestivum, the world’s most commonly used bread wheat, great insight has been gained into the ways that domestication has impacted the evolution of the plant (Peng, Sun and Nevo 2011). Which loci are most susceptible to manipulation, and how does this play out in evolutionary terms? Genetic sequencing has allowed these questions to be addressed for the first time, as specific loci can be compared in wild and domesticated strains of the plant. This will allow for advances in genetic modification in the future which could yield healthier, more disease-resistant wheat crops. Concluding Remarks Genetic information has caused a paradigm-shift in evolutionary research. Relationships between species which were once determined through parsimony are now being revisited using the mathematics of maximum likelihood techniques and the direct comparison of single nucleotide base changes among related organisms. The speed of doing sequencing projects has increased exponentially in agreement with Moore’s law; this has led to applications of genomics to projects outside of the original Human Genome Project, such as gene banks for crops and studies of the evolution of microorganisms. In the future, it may be possible to use a combination of environmental data and an organism’s genetic composition to determine what its evolutionary fate will be using computer software. This could mean understanding what future generations of mutating microbes will look like before they have mutated and predicting diseases associated with these mutations faster than currently available.

References Clamp, M., Fry, B., Kamal, M., Xie, X., Cuff, J., Lin, M., ... Lander, E. (2007). From the Cover: Distinguishing protein-coding and noncoding genes in the human genome. Proceedings of the National Academy of Sciences, 19428-19433.

Felsenstein, J. (1981). Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution, 17(6), 368-376.

Herring, C., Raghunathan, A., Honisch, C., Patel, T., Applebee, M., Joyce, A., ... Palsson, B. (2006). Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nature Genetics, 38, 1406-1412.

Mardis, E. (2008). The impact of next-generation sequencing technology on genetics. Trends in Genetics, 24(3), 133-141.

Peng, J., Sun, D., & Nevo, E. (2011). Domestication Evolution, Genetics And Genomics In Wheat. Molecular Breeding, 28(3), 281-301.

Tanksley, S. (1997). Seed Banks and Molecular Maps: Unlocking Genetic Potential from the Wild. Science, 277(5329), 1063-1066.

The Human Genome Project. (2014, October 1). Retrieved October 29, 2014. http://ghr.nlm.nih.gov/handbook/hgp?show=all

Varshney, R., Nayak, S., May, G., & Jackson, S. (2009). Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends in Biotechnology, 27(9), 522-530.