Algebraic and Combinatorial Computational Biology: Mathematics in Science and Engineering
Editat de Raina Robeva, Matthew Macauleyen Limba Engleză Paperback – 12 sep 2018
- Integrates a comprehensive selection of tools from computational biology into educational or research programs
- Emphasizes practical problem-solving through multiple exercises, projects and spinoff computational simulations
- Contains scalable material for use in undergraduate and graduate-level classes and research projects
- Introduces the reader to freely-available professional software
- Supported by illustrative datasets and adaptable computer code
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Specificații
ISBN-13: 9780128140666
ISBN-10: 0128140666
Pagini: 434
Dimensiuni: 152 x 229 mm
Greutate: 0.58 kg
Editura: ELSEVIER SCIENCE
Seria Mathematics in Science and Engineering
ISBN-10: 0128140666
Pagini: 434
Dimensiuni: 152 x 229 mm
Greutate: 0.58 kg
Editura: ELSEVIER SCIENCE
Seria Mathematics in Science and Engineering
Public țintă
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.Cuprins
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