Guillaume Fertin, Anthony Labarre, Irena Rusu, Eric Tannier, and Stéphane Vialette
- Published in print:
- 2009
- Published Online:
- August 2013
- ISBN:
- 9780262062824
- eISBN:
- 9780262258753
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262062824.001.0001
- Subject:
- Mathematics, Mathematical Biology
From one cell to another, from one individual to another, and from one species to another, the content of DNA molecules is often similar. The organization of these molecules, however, differs ...
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From one cell to another, from one individual to another, and from one species to another, the content of DNA molecules is often similar. The organization of these molecules, however, differs dramatically, and the mutations that affect this organization are known as genome rearrangements. Combinatorial methods are used to reconstruct putative rearrangement scenarios in order to explain the evolutionary history of a set of species, often formalizing the evolutionary events that can explain the multiple combinations of observed genomes as combinatorial optimization problems. This book offers a comprehensive survey of this rapidly expanding application of combinatorial optimization. It can be used as a reference for experienced researchers or as an introductory text for a broader audience. Genome rearrangement problems have proved so interesting from a combinatorial point of view that the field now belongs as much to mathematics as to biology. The book takes a mathematically oriented approach, but provides biological background when necessary. It presents a series of models, beginning with the simplest (which is progressively extended by dropping restrictions), each constructing a genome rearrangement problem. The book also discusses an important generalization of the basic problem known as the median problem, surveys attempts to reconstruct the relationships between genomes with phylogenetic trees, and offers a collection of summaries and appendixes with additional information.Less
From one cell to another, from one individual to another, and from one species to another, the content of DNA molecules is often similar. The organization of these molecules, however, differs dramatically, and the mutations that affect this organization are known as genome rearrangements. Combinatorial methods are used to reconstruct putative rearrangement scenarios in order to explain the evolutionary history of a set of species, often formalizing the evolutionary events that can explain the multiple combinations of observed genomes as combinatorial optimization problems. This book offers a comprehensive survey of this rapidly expanding application of combinatorial optimization. It can be used as a reference for experienced researchers or as an introductory text for a broader audience. Genome rearrangement problems have proved so interesting from a combinatorial point of view that the field now belongs as much to mathematics as to biology. The book takes a mathematically oriented approach, but provides biological background when necessary. It presents a series of models, beginning with the simplest (which is progressively extended by dropping restrictions), each constructing a genome rearrangement problem. The book also discusses an important generalization of the basic problem known as the median problem, surveys attempts to reconstruct the relationships between genomes with phylogenetic trees, and offers a collection of summaries and appendixes with additional information.
Daniel S. Hamermesh and Daniel S. Hamermesh
- Published in print:
- 2017
- Published Online:
- March 2017
- ISBN:
- 9780198791379
- eISBN:
- 9780191833847
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198791379.003.0015
- Subject:
- Economics and Finance, Macro- and Monetary Economics
Measuring market discrimination is extremely difficult except i where physical output measures allow direct measurement of productivity. We illustrate this point with evidence on elections to offices ...
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Measuring market discrimination is extremely difficult except i where physical output measures allow direct measurement of productivity. We illustrate this point with evidence on elections to offices of the American Economic Association. Using a new technique to infer the determinants of the chances of observing a particular outcome when there are K choices out of N possibilities, we find that female candidates have a much better than random chance of victory. This advantage can be interpreted either as reverse discrimination or as reflecting voters’ beliefs that women are more productive than observationally identical men in this activity. There was a clear structural change in voting behavior in the mid-1970s. The results suggest that it is not generally possible to claim that differences in rewards for different groups measure the extent of discrimination or even its direction.Less
Measuring market discrimination is extremely difficult except i where physical output measures allow direct measurement of productivity. We illustrate this point with evidence on elections to offices of the American Economic Association. Using a new technique to infer the determinants of the chances of observing a particular outcome when there are K choices out of N possibilities, we find that female candidates have a much better than random chance of victory. This advantage can be interpreted either as reverse discrimination or as reflecting voters’ beliefs that women are more productive than observationally identical men in this activity. There was a clear structural change in voting behavior in the mid-1970s. The results suggest that it is not generally possible to claim that differences in rewards for different groups measure the extent of discrimination or even its direction.
