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El. knyga: Systems Genetics: Linking Genotypes and Phenotypes

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Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.

Recenzijos

'Since the completion of the Human Genome Project we hold the 'book of life' in our hands, but for the most part, we do not understand how to interpret it. We lack an understanding of the grammar that it is written in. With this book the authors put together an impressive collection of chapters that provide insights into our current efforts to understand how genetic information is integrated, coordinated and ultimately assembled into biological systems. If you are interested in how to decipher the grammar of life this is a must read!' Frank Buchholz, Technische Universität Dresden, Germany

Daugiau informacijos

The first book to comprehensively cover the field of systems genetics, gathering contributions from leading scientists.
List of contributors
ix
1 An introduction to systems genetics
1(11)
Florian Markowetz
Michael Boutros
1.1 Definition of systems genetics
1(2)
1.2 History of systems genetics
3(4)
1.3 Future challenges
7(1)
1.4 What is covered in the book
8(4)
2 Computational paradigms for analyzing genetic interaction networks
12(24)
Carles Pons
Michael Costanzo
Charles Boone
Chad L. Myers
2.1 Definition of genetic interaction
12(3)
2.2 Toward the first reference global genetic interaction network: Synthetic Genetic Array analysis in yeast
15(2)
2.3 Computational paradigms for genetic interaction networks
17(12)
2.4 Perspectives
29(7)
3 Mapping genetic interactions across many phenotypes in metazoan cells
36(15)
Christina Laufer
Maximilian Billmann
Michael Boutros
3.1 A short history of genetic interaction analysis
36(1)
3.2 Perturbation-based genetic interaction studies in yeast
37(2)
3.3 Genetic interaction analysis in Drosophila
39(3)
3.4 Expanding genetic interaction mapping towards the genomic scale
42(3)
3.5 Towards genetic interaction mapping in human cells
45(3)
3.6 Conclusions
48(3)
4 Genetic interactions and network reliability
51(14)
Edgar Delgado-Eckert
Niko Beerenwinkel
4.1 Biological networks
51(1)
4.2 Epistasis
52(2)
4.3 Network reliability
54(3)
4.4 Epistasis on networks
57(2)
4.5 Inferring function from observed genetic interactions
59(2)
4.6 Conclusions
61(4)
5 Synthetic lethality and chemoresistance in cancer
65(18)
Kimberly Maxfield
Angelique Whitehurst
5.1 Cancer chemotherapy
65(3)
5.2 Employing small interfering RNA (siRNA) to identify modifiers of chemotherapeutic responsiveness
68(6)
5.3 Mobilizing new therapeutic opportunities with large-scale RNAi screens
74(3)
5.4 Conclusions
77(6)
6 Joining the dots: network analysis of gene perturbation data
83(25)
Xin Wang
Ke Yuan
Florian Markowetz
6.1 Scenario 1: Genome-wide screens with single reporters
83(3)
6.2 Scenario 2: Single gene silenced, multi-level dynamic phenotype
86(1)
6.3 Scenario 3a: Pathway components perturbed with global transcriptional phenotypes
86(6)
6.4 Scenario 3b: Capturing rewiring events during network evolution
92(4)
6.5 Scenario 4: Multi-parametric screen, up to genome-wide
96(6)
6.6 Conclusions
102(6)
7 High-content screening in infectious diseases: new drugs against bugs
108(31)
Andre P. Maurer
Peter R. Braun
Kate Holden-Dye
Thomas F. Meyer
7.1 The challenge of fighting infectious diseases
108(1)
7.2 Classic strategies for antimicrobial drug development and their limitations
109(6)
7.3 Post-genomic approaches for investigating host-pathogen interactions
115(6)
7.4 Advanced high-content screening in pathogen research
121(8)
7.5 Single-cell population analyses in high-content screening
129(2)
7.6 Future directions
131(8)
8 Inferring genetic architecture from systems genetics studies
139(22)
Xiaoyun Sun
Stephanie Mohr
Arunachalam Vinayagam
Pengyu Hong
Norbert Perrimon
8.1 Introduction
139(2)
8.2 Identification of network components by RNAi
141(4)
8.3 Identification of network components using proteomics
145(3)
8.4 Integration of RNAi and proteomic data sets
148(1)
8.5 Network modeling: the next step
149(6)
8.6 Applications of network reconstruction
155(6)
9 Bayesian inference for model selection: an application to aberrant signalling pathways in chronic myeloid leukaemia
161(30)
Lisa E. M. Hopcroft
Ben Calderhead
Paolo Gallipoli
Tessa L. Holyoake
Mark A. Girolami
9.1 The oncology of chronic myeloid leukaemia
161(9)
9.2 Introduction to model comparison
170(1)
9.3 Modelling the JAK/STAT pathway in response to TKI and/or JakI
171(3)
9.4 The statistical methodology: Riemannian manifold population MCMC
174(4)
9.5 A proof-of-concept study with synthetic data
178(3)
9.6 Beyond a proof of concept: considering a more biologically realistic dataset
181(6)
9.7 Discussion
187(4)
10 Dynamic network models of protein complexes
191(23)
Yongjin Park
Joel S. Bader
10.1 Dynamic network data
191(4)
10.2 Block models of a network
195(2)
10.3 Learning algorithms
197(6)
10.4 Results
203(6)
10.5 Discussion
209(5)
11 Phenotype state spaces and strategies for exploring them
214(20)
Andreas Hadjiprocopis
Rune Linding
11.1 Introduction
214(1)
11.2 Phenotype: a constructive generality
215(1)
11.3 Cellular noise
216(1)
11.4 Genome evolution, protein families, and phenotype
217(5)
11.5 Complex networks
222(1)
11.6 Random Boolean networks
223(6)
11.7 Genomic state spaces
229(5)
12 Automated behavioural fingerprinting of Caenorhabditis elegans mutants
234(23)
Andre E. X. Brown
William R. Schafer
12.1 The worm as a model organism
234(4)
12.2 High-throughput data collection and information extraction
238(5)
12.3 Linking behaviours and genes
243(3)
12.4 Outlook
246(5)
12.5 Conclusions
251(6)
Index 257
Florian Markowetz is a Group Leader at Cancer Research UK's Cambridge Research Institute. His research is concerned with developing statistical and mathematical models of complex biological systems and analysing large-scale molecular data. His research interests range from the analysis of molecular clinical data to inference of cellular networks from high-throughput gene perturbation screens and integration of heterogeneous data sources using machines learning techniques and probabilistic graphic models. Michael Boutros is a group leader at the German Cancer Research Centre (DKFZ) in Heidelberg where he heads the Division of Signalling and Functional Genomics. He also holds a Professorship at the University of Heidelberg. His research focuses on the systematic dissection signalling pathways in Drosophila and mammalian cells, which are important during development and cancer. He attempts to define key components of signalling pathways, discovering interaction between pathways, and characterisation of signalling networks under normal and perturbed conditions.
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