Muutke küpsiste eelistusi

E-raamat: Algebraic and Combinatorial Computational Biology

3.00/5 (4 hinnangut Goodreads-ist)
Edited by (Professor of Mathematical Sciences, Sweet Briar College, VA, USA), Edited by (Associate Professor of Mathematical Sciences, Clemson University, SC, USA)
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 130,97 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Algebraic and Combinatorial Computational BiologySuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.

  • 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
Contributors xi
Preface xiii
1 Multiscale Graph-Theoretic Modeling of Biomolecular Structures
1(34)
John Jungck
Debra Knisley
Greta Pangborn
Manda Riehl
Emilie Wiesner
1.1 Introduction
1(1)
1.1.1 The Molecules of Life
1(1)
1.2 Graph Theory Fundamentals
2(1)
1.3 Modeling RNA Structure
3(14)
1.3.1 RNA Secondary Structure Features
6(3)
1.3.2 Tree and Dual Graph Models of RNA Secondary Structure
9(4)
1.3.3 Homework Problems and Projects
13(4)
1.4 RNA Structure and Matchings
17(6)
1.4.1 L&P Matchings
19(2)
1.4.2 The C&C Family
21(1)
1.4.3 Homework Problems and Projects
22(1)
1.5 Hierarchical Protein Models
23(8)
1.5.1 Weighted Graph Invariants
29(2)
1.5.2 Homework Problems and Projects
31(1)
References
31(3)
Further Reading
34(1)
2 Tile-Based DNA Nanostructures
35(26)
Joanna Ellis-Monaghan
Natasa Jonoska
Greta Pangborn
2.1 Introduction
35(1)
2.2 Laboratory Process
36(3)
2.3 Graph Theoretical Formalism and Tools
39(11)
2.3.1 Flexible Tiles
42(1)
2.3.2 Flexible Tiles, Unconstrained Case
43(2)
2.3.3 Flexible Tiles, Constrained Case
45(2)
2.3.4 The Matrix of a Pot
47(3)
2.4 Rigid Tiles
50(4)
2.5 Computation by Self-Assembly
54(3)
2.6 Conclusion
57(1)
2.7 Resource Materials
57(1)
Acknowledgments
58(1)
References
58(2)
Further Reading
60(1)
3 Graphs Associated With DNA Rearrangements and Their Polynomials
61(28)
Robert Brijder
Hendrik Jan Hoogeboom
Natasa Jonoska
Masahico Saito
3.1 Introduction
61(2)
3.2 Gene Assembly in Ciliates
63(3)
3.2.1 Biological Background
63(1)
3.2.2 Motivational Example
64(2)
3.3 Mathematical Preliminaries
66(4)
3.4 Mathematical Models for Gene Rearrangement
70(4)
3.4.1 Graphs Obtained From Double Occurrence Words
70(2)
3.4.2 Double Occurrence Words Corresponding to Graphs
72(2)
3.5 Graph Polynomials
74(10)
3.5.1 Transition Polynomial
74(3)
3.5.2 Assembly Polynomial
77(2)
3.5.3 Reduction Rules for the Assembly Polynomial
79(3)
3.5.4 Rearrangement Polynomial
82(2)
3.6 Generalizations
84(1)
Acknowledgments
84(1)
References
85(4)
4 The Regulation of Gene Expression by Operons and the Local Modeling Framework
89(58)
Matthew Macauley
Andy Jenkins
Robin Davies
4.1 Basic Biology Introduction
89(9)
4.1.1 The Central Dogma and Gene Regulation
90(1)
4.1.2 Types of Operons
91(3)
4.1.3 Two Well-Known Operons in E. coli
94(4)
4.2 Continuous and Discrete Models of Biological Networks
98(8)
4.2.1 Differential Equation Models
98(4)
4.2.2 Bistability in Biological Systems
102(2)
4.2.3 Discrete Models of Biological Networks
104(2)
4.3 Local Models
106(12)
4.3.1 Polynomial Rings and Ideals for the Nonexpert
106(1)
4.3.2 Finite Fields
107(2)
4.3.3 Functions Over Finite Fields
109(3)
4.3.4 Boolean Networks and Local Models
112(3)
4.3.5 Asynchronous Boolean Networks and Local Models
115(2)
4.3.6 Phase Space Structure
117(1)
4.4 Local Models of Operons
118(6)
4.4.1 A Boolean Model of the lac Operon
118(4)
4.4.2 A Boolean Model of the ara Operon
122(2)
4.5 Analyzing Local Models With Computational Algebra
124(7)
4.5.1 Computing the Fixed Points
125(5)
4.5.2 Longer Limit Cycles
130(1)
4.6 Software for Local Models
131(9)
4.6.1 GINsim
132(3)
4.6.2 TURING: Algorithms for Computation With FDSs
135(5)
4.7 Concluding Remarks
140(3)
References
143(4)
5 Modeling the Stochastic Nature of Gene Regulation With Boolean Networks
147(28)
David Murrugarra
Boris Aguilar
5.1 Introduction
147(2)
5.2 Stochastic Discrete Dynamical Systems
149(8)
5.3 Long-Term Dynamics
157(1)
5.4 PageRank Algorithm
158(3)
5.5 Parameter Estimation Techniques
161(4)
5.6 Optimal Control for SDDS
165(5)
5.6.1 Control Actions
166(1)
5.6.2 Markov Decision Processes for SDDS
166(4)
5.7 Discussion and Conclusions
170(1)
References
171(4)
6 Inferring Interactions in Molecular Networks via Primary Decompositions of Monomial Ideals
175(38)
Matthew Macauley
Brandilyn Stigler
6.1 Introduction
175(5)
6.1.1 The Local Modeling Framework
175(3)
6.1.2 A Motivating Example of Reverse Engineering
178(2)
6.2 Stanley-Reisner Theory
180(11)
6.2.1 Monomial Ideals
180(2)
6.2.2 Square-Free Monomial Ideals
182(6)
6.2.3 Primary Decompositions
188(3)
6.3 Finding Min-Sets of Local Models
191(8)
6.3.1 Wiring Diagrams
191(1)
6.3.2 Feasible and Disposable Sets of Variables
192(6)
6.3.3 Min-Sets Over Non-Boolean Fields
198(1)
6.4 Finding Signed Min-Sets of Local Models
199(4)
6.4.1 The Pseudo-Monomial Ideal of Signed Nondisposable Sets
199(3)
6.4.2 A Non-Boolean Example
202(1)
6.5 Applications to a Real Gene Network
203(4)
6.6 Concluding Remarks
207(3)
References
210(3)
7 Analysis of Combinatorial Neural Codes: An Algebraic Approach
213(28)
Carina Curto
Alan Veliz-Cuba
Nora Youngs
7.1 Introduction
213(7)
7.1.1 Biological Motivation: Neurons With Receptive Fields
213(3)
7.1.2 Receptive Field Relationships
216(2)
7.1.3 The Simplicial Complex of a Code
218(2)
7.2 The Neural Ideal
220(6)
7.2.1 Definition of the Neural Ideal
221(2)
7.2.2 The Neural Ideal and Receptive Field Relationships
223(3)
7.3 The Canonical Form
226(6)
7.3.1 Computing the Canonical Form
227(2)
7.3.2 Alternative Computation Method: The Primary Decomposition
229(1)
7.3.3 Sage Code for Computations
230(2)
7.4 Applications: Using the Neural Ideal
232(6)
7.4.1 Convex Realizability
232(5)
7.4.2 Dimension
237(1)
7.5 Concluding Remarks
238(1)
Acknowledgments
239(1)
References
239(1)
Further Reading
240(1)
8 Predicting Neural Network Dynamics via Graphical Analysis
241(38)
Katherine Morrison
Carina Curto
8.1 Introduction
241(5)
8.1.1 Neuroscience Background and Motivation
241(1)
8.1.2 The CTLN Model
242(4)
8.2 A CTLN as a Patchwork of Linear Systems
246(6)
8.2.1 How Graph Structure Affects Fixed Points
248(4)
8.3 Graphical Analysis of Stable and Unstable Fixed Points
252(13)
8.3.1 Graph Theory Concepts
253(2)
8.3.2 Stable Fixed Points
255(2)
8.3.3 Unstable Fixed Points
257(8)
8.4 Predicting Dynamic Attractors via Graph Structure
265(9)
8.4.1 Sequence Prediction Algorithm
267(4)
8.4.2 Symmetry of Graphs Acting on the Space of Attractors
271(3)
Acknowledgments
274(1)
A.1 Review of Linear Systems of ODEs
275(1)
References
276(3)
9 Multistationarity in Biochemical Networks: Results, Analysis, and Examples
279(40)
Carsten Conradi
Casian Pantea
9.1 Introduction
279(2)
9.2 Reaction Network Terminology and Background
281(14)
9.2.1 Chemical Reaction Networks and Their Dynamics
281(2)
9.2.2 Some Useful Notation
283(1)
9.2.3 The Jacobian Matrix
284(1)
9.2.4 Stoichiometry Classes
285(1)
9.2.5 Equilibria
286(2)
9.2.6 Nondegenerate Equilibria
288(1)
9.2.7 The Reduced Jacobian
289(1)
9.2.8 General Setup and Preliminaries
289(6)
9.3 Necessary Conditions for Multistationarity I: Injective CRNs
295(5)
9.4 Necessary Conditions for Multistationarity II: The DSR Graph
300(4)
9.5 Sufficient Conditions for Multistationarity: Inheritance of Multiple Equilibria
304(4)
9.6 Sufficient Conditions for Multistationarity II: The Determinant Optimization Method
308(1)
9.7 Results Based on Deficiency Theory
309(4)
9.7.1 The Deficiency Zero and Deficiency One Theorems
310(2)
9.7.2 The Deficiency One Algorithm and the Advanced Deficiency Algorithm
312(1)
Acknowledgments
313(1)
References
313(6)
10 The Minimum Evolution Problem in Phylogenetics: Polytopes, Linear Programming, and Interpretation
319(32)
Stefan Forcey
Gabriela Hamerlinck
Logan Keefe
William Sands
10.1 Introduction
319(8)
10.1.1 Phylogenetic Reconstructions and Interpretation
319(2)
10.1.2 Balanced Minimum Evolution
321(3)
10.1.3 Definitions and Notation
324(3)
10.2 Polytopes and Relaxations
327(9)
10.2.1 What Is a Polytope?
327(1)
10.2.2 The BME Polytope
328(7)
10.2.3 The Splitohedron
335(1)
10.3 Optimizing With Linear Programming
336(10)
10.3.1 Discrete Integer Linear Programming: The Branch and Bound Algorithm
337(3)
10.3.2 Recursive Structure: Branch Selection Strategy and Fixing Values
340(2)
10.3.3 Pseudocode for the Algorithm: PolySplit
342(4)
10.4 Neighbor Joining and Edge Walking
346(1)
10.4.1 NNI and SPR Moves, and FastMe 2.0
346(1)
10.5 Summary
347(1)
References
347(4)
11 Data Clustering and Self-Organizing Maps in Biology
351(24)
Olcay Akman
Timothy Comar
Daniel Hrozencik
Josselyn Gonzales
11.1 Clustering: An Introduction
351(3)
11.2 Clustering: A Basic Procedure
354(4)
11.2.1 Data Representation
354(1)
11.2.2 Clustering Algorithm Selection
355(1)
11.2.3 Cluster Validation
356(1)
11.2.4 Interpretation of Results
357(1)
11.3 Types of Clustering
358(5)
11.3.1 Hard Clustering
358(4)
11.3.2 Exercises
362(1)
11.4 Fuzzy Clustering
363(2)
11.5 Self-Organizing Maps
365(3)
11.6 SOM Applications to Biological Data
368(5)
11.6.1 Example 1: Water Composition Data
368(2)
11.6.2 Example 2: Grasshopper Size Data
370(3)
11.6.3 Group Project: Iris Data Set
373(1)
References
373(2)
12 Toward Revealing Protein Function: Identifying Biologically Relevant Clusters With Graph Spectral Methods
375(36)
Robin Davies
Urmi Ghosh-Dastidar
Jeff Knisley
Widodo Samyono
12.1 Introduction to Proteins
375(7)
12.1.1 Protein Structures
375(3)
12.1.2 Experimental Determination of Protein Structure
378(1)
12.1.3 Isofunctional Families
379(1)
12.1.4 Sequence Motifs and Logos
380(2)
12.2 Clustering of Data
382(16)
12.2.1 An Overview
382(2)
12.2.2 Clustering Methods
384(6)
12.2.3 Network Spectral Methods
390(1)
12.2.4 Spectral Clustering
391(5)
12.2.5 Spectral Clustering With Outliers
396(2)
12.3 Clustering to Identify Isofunctional Families
398(8)
12.3.1 Similarity Scores Based on Tertiary Structure
398(8)
Appendix
406(2)
References
408(3)
Index 411
Raina Robeva was born in Sofia, Bulgaria. She holds a PhD in Mathematics from the University of Virginia and has broad research interests spanning theoretical mathematics, applied probability, and systems biology. Robeva is the founding Chief Editor of the journal Frontiers in Systems Biology and the lead author/editor of the books An Invitation to Biomathematics (2008), Mathematical Concepts and Methods in Modern Biology: Using Modern Discrete Models (2013), and Algebraic and Discrete Mathematical Methods for Modern Biology (2015), all published by Academic Press. She is Professor of Mathematical Sciences and Director of the Center for Science and Technology in Society at Sweet Briar College. 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.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97801281406972e.html
Märksõnad: