Algebraic and Combinatorial Computational Biology
Published on 13. September 2018
Book
Paperback/Softback
434 pages
978-0-12-814066-6 (ISBN)
Description
Algebraic and Combinatorial Computational Biology introduces students and researchers to a panorama of powerful and current methods for mathematical problem-solving in modern computational biology. Presented in a modular format, each topic introduces the biological foundations of the field, covers specialized mathematical theory, and concludes by highlighting connections with ongoing research, particularly open questions. The work addresses problems from gene regulation, neuroscience, phylogenetics, molecular networks, assembly and folding of biomolecular structures, and the use of clustering methods in biology. A number of these chapters are surveys of new topics that have not been previously compiled into one unified source. These topics were selected because they highlight the use of technique from algebra and combinatorics that are becoming mainstream in the life sciences.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
College/higher education
Upper division undergraduate and graduate students. Early career researchers in biology or mathematics, particularly those transitioning into the field of mathematical and computational biology. Some practitioners seeking a methods-based primer for the field.
Dimensions
Height: 229 mm
Width: 152 mm
Weight
790 gr
ISBN-13
978-0-12-814066-6 (9780128140666)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Raina Robeva | Matthew Macauley
Algebraic and Combinatorial Computational Biology
E-Book
10/2018
Academic Press
€109.00
Available for download
Persons
Raina Robeva was born in Sofia, Bulgaria. She holds a Ph.D. in Mathematics from the University of Virginia and is a Professor of Mathematics and a Karl E. Peace Fellow in Mathematics at Randolph-Macon College in the United States. Her current research is in the broad fields of mathematical and systems biology. In addition to editing this second edition, she has authored and edited several books, including A Bridge to Higher Mathematics (CRC Press, 2024), Algebraic and Combinatorial Computational Biology (Academic Press, 2019), and Algebraic and Discrete Mathematical Methods for Modern Biology (Academic Press, 2015). She has chaired the Advisory Board of the National Institute for Mathematical and Biological Synthesis (NIMBioS), led the Committee on Special Interest Groups of the Mathematical Association of America, and served for over a decade as the founding Editor-in-Chief of Frontiers in Systems Biology. Matthew Macauley is an Associate Professor at Clemson University in South Carolina. Since finishing his PhD in Mathematics from the University of California, Santa Barbara, he has been a research visitor at the Biocomplexity Institute of Virginia Tech, the Institute for Systems Biology in Seattle, and the University of Southern Denmark. He has also taught internationally in both South Africa and Taiwan. Macauley has supervised two PhD and five MS students, as well as a number of undergraduate research students. With Raina Robeva, he has co-organized three faculty development workshops on teaching discrete and algebraic methods in mathematical biology to undergraduates.
Content
1. Multi-scale graph-theoretic modeling of bimolecular structures
2. DNA nanostructures: Mathematical design and problem encoding
3. Graphs associated with DNA rearrangements and their polynomials
4. Regulation of gene expression by operons: Boolean, logical, and local models
5. Modeling the stochastic nature of gene regulation: probabilistic Boolean networks
6. Inferring interactions in molecular networks via primary decompositions of monomial ideals
7. Analysis of combinatorial neural codes: an algebraic approach
8. Predicting neural network dynamics: insights from graph theory
9. Multistationarity in biochemical networks: Results, analysis, and examples
10. Optimization problems in phylogenetics: Polytopes, programming and interpretation
11. Clustering via self-organizing maps on biology and medicine
12. Toward revealing protein function: Identifying biologically relevant clusters with graph spectral methods
2. DNA nanostructures: Mathematical design and problem encoding
3. Graphs associated with DNA rearrangements and their polynomials
4. Regulation of gene expression by operons: Boolean, logical, and local models
5. Modeling the stochastic nature of gene regulation: probabilistic Boolean networks
6. Inferring interactions in molecular networks via primary decompositions of monomial ideals
7. Analysis of combinatorial neural codes: an algebraic approach
8. Predicting neural network dynamics: insights from graph theory
9. Multistationarity in biochemical networks: Results, analysis, and examples
10. Optimization problems in phylogenetics: Polytopes, programming and interpretation
11. Clustering via self-organizing maps on biology and medicine
12. Toward revealing protein function: Identifying biologically relevant clusters with graph spectral methods