Systems genetics: linking genotypes and phenotypes
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
[2015]
|
| Schriftenreihe: | Cambridge series in systems genetics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xi, 258 Seiten Illustrationen, Diagramme 25 cm |
| ISBN: | 9781107013841 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804176160086556672 |
|---|---|
| adam_text | Contents
R
List of contributors Pa8e
1 An introduction to systems genetics 1
Florian Markowetz and Michael Boutros
1.1 Definition of systems genetics 1
1.2 History of systems genetics 3
1.3 Future challenges 7
1.4 What is covered in the book . 8
2 Computational paradigms for analyzing genetic interaction networks 12
Carles Pons, Michael Costanzo, Charles Boone, and Chad L. Myers
2.1 Definition of genetic interaction 12
2.2 Toward the first reference global genetic interaction network: Synthetic
Genetic Array analysis in yeast 15
2.3 Computational paradigms for genetic interaction networks 17
2.4 Perspectives 29
3 Mapping genetic interactions across many phenotypes in metazoan cells 36
Christina Laufer, Maximilian Billmann, and Michael Boutros
3.1 A short history of genetic interaction analysis 36
3.2 Perturbation-based genetic interaction studies in yeast 37
3.3 Genetic interaction analysis in Drosophila 39
3.4 Expanding genetic interaction mapping towards the genomic scale 42
3.5 Towards genetic interaction mapping in human cells 45
3.6 Conclusions 48
4 Genetic interactions and network reliability 51
Edgar Delgado-Eckert and Niko Beerenwinkel
4.1 Biological networks 51
4.2 Epistasis 52
vi Contents
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4.3 Network reliability 54
4.4 Epistasis on networks 57
4.5 Inferring function from observed genetic interactions 59
4.6 Conclusions 61
5 Synthetic lethality and chemoresistance in cancer 65
Kimberly Maxfield and Angélique Whitehurst
5.1 Cancer chemotherapy 65
5.2 Employing small interfering RNA (siRNA) to identify modifiers of
chemotherapeutic responsiveness 68
5.3 Mobilizing new therapeutic opportunities with large-scale RNAi
screens 74
5.4 Conclusions 77
6 Joining the dots: network analysis of gene perturbation data 83
Xin Wang, Ke Yuan, and Florian Markowetz
6.1 Scenario 1 : Genome-wide screens with single reporters 83
6.2 Scenario 2: Single gene silenced, multi-level dynamic phenotype 86
6.3 Scenario 3a: Pathway components perturbed with global
transcriptional phenotypes 86
6.4 Scenario 3b: Capturing rewiring events during network evolution 92
6.5 Scenario 4: Multi-parametric screen, up to genome-wide 96
6.6 Conclusions 102
7 High-content screening in infectious diseases: new drugs against bugs 108
André P. Maurer, Peter R. Braun, Kate Holden-Dye, and Thomas F. Meyer
7.1 The challenge of fighting infectious diseases 108
7.2 Classic strategies for antimicrobial drug development and their
limitations 109
7.3 Post-genomic approaches for investigating host-pathogen interactions 115
7.4 Advanced high-content screening in pathogen research 121
7.5 Single-cell population analyses in high-content screening 129
7.6 Future directions 131
8 Inferring genetic architecture from systems genetics studies 139
Xiaoyun Sun, Stephanie Mohr, Arunachalam Vinayagam, Pengyu Hong, and Norbert Perrimon
8.1 Introduction 139
8.2 Identification of network components by RNAi 141
8.3 Identification of network components using proteomics 145
8.4 Integration of RNAi and proteomic data sets 148
8.5 Network modeling: the next step 149
8.6 Applications of network reconstruction 155
Contents vii
9 Bayesian inference for model selection: an application to aberrant signalling
pathways in chronic myeloid leukaemia 161
Lisa E. M. Hopcroft, Ben Calderhead, Paolo Gallipoli, Tessa L Holyoake, and Mark A. Girolami
9.1 The oncology of chronic myeloid leukaemia 161
9.2 Introduction to model comparison 170
9.3 Modelling the JAK/STAT pathway in response to TKI and/or Jakl 171
9.4 The statistical methodology: Riemannian manifold population MCMC 174
9.5 A proof-of-concept study with synthetic data 178
9.6 Beyond a proof of concept: considering a more biologically
realistic dataset 181
9.7 Discussion 187
10 Dynamic network models of protein complexes 191
Yongjin Park and Joel S. Bader
10.1 Dynamic network data 191
10.2 Block models of a network 195
10.3 Learning algorithms 197
10.4 Results 203
10.5 Discussion 209
11 Phenotype state spaces and strategies for exploring them 214
Andreas Hadjlprocopis and Rune Unding
11.1 Introduction 214
11.2 Phenotype: a constructive generality 215
11.3 Cellular noise 216
11.4 Genome evolution, protein families, and phenotype 217
11.5 Complex networks 222
11.6 Random Boolean networks 223
11.7 Genomic state spaces 229
12 Automated behavioural fingerprinting of Caenorhabditis elegans mutants 234
André E. X. Brown and William R. Schafer
12.1 The worm as a model organism 234
12.2 High-throughput data collection and information extraction 238
12.3 Linking behaviours and genes 243
12.4 Outlook 246
12.5 Conclusions 251
Index 257
The colour plate section can be found between pages 148 and 149.
CAMBRIDGE SERIES IN SYSTEMS GENETICS
... an impressive collection of chapters that
provide insights into our current efforts to un-
derstand how genetic information is integrated,
coordinated and ultimately assembled into biologi-
cal systems.
Frank Buchholz, Technische Universität Dresden,
Germany
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 to
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 quantita-
tive 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 is provided, including work from model
organisms such as Saccharomices cerevisiae,
Drosophila melanogaster, as well as from human
studies.
Researchers and graduate students in genetics,
functional genomics, bioinformatics, compu-
tational biology, systems biology and biotech-
nology will find this a valuable and timely resource.
Florian Markowetz is a Group Leader
at Cancer Research UK s Cambridge
Research Institute. His research is con-
cerned with developing statistical and
mathematical models of complex bio-
logical systems and analysing large-scale
molecular data. His research interests
range from the analysis of molecular clin-
ical 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 dis-
section signalling pathways in Drosophila
and mammalian cells, which are import-
ant during development and cancer. He
attempts to define key components of
signalling pathways, discovering interac-
tion between pathways, and character-
isation of signalling networks under
normal and perturbed conditions.
ISBN 978-1-107-01384-1
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| isbn | 9781107013841 |
| language | English |
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| series2 | Cambridge series in systems genetics |
| spelling | Systems genetics linking genotypes and phenotypes edited by Florian Markowetz, Cancer Research UK Cambridge Institute, Michael Boutros, German Cancer Research Center, Heidelberg Cambridge Cambridge University Press [2015] xi, 258 Seiten Illustrationen, Diagramme 25 cm txt rdacontent n rdamedia nc rdacarrier Cambridge series in systems genetics Phenotype Functional genomics Genexpression (DE-588)4020136-3 gnd rswk-swf Phänotyp (DE-588)4248244-6 gnd rswk-swf Genexpression (DE-588)4020136-3 s Phänotyp (DE-588)4248244-6 s DE-604 Markowetz, Florian 1976- (DE-588)131967967 edt Boutros, Michael edt Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028932300&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028932300&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Systems genetics linking genotypes and phenotypes Phenotype Functional genomics Genexpression (DE-588)4020136-3 gnd Phänotyp (DE-588)4248244-6 gnd |
| subject_GND | (DE-588)4020136-3 (DE-588)4248244-6 |
| title | Systems genetics linking genotypes and phenotypes |
| title_auth | Systems genetics linking genotypes and phenotypes |
| title_exact_search | Systems genetics linking genotypes and phenotypes |
| title_full | Systems genetics linking genotypes and phenotypes edited by Florian Markowetz, Cancer Research UK Cambridge Institute, Michael Boutros, German Cancer Research Center, Heidelberg |
| title_fullStr | Systems genetics linking genotypes and phenotypes edited by Florian Markowetz, Cancer Research UK Cambridge Institute, Michael Boutros, German Cancer Research Center, Heidelberg |
| title_full_unstemmed | Systems genetics linking genotypes and phenotypes edited by Florian Markowetz, Cancer Research UK Cambridge Institute, Michael Boutros, German Cancer Research Center, Heidelberg |
| title_short | Systems genetics |
| title_sort | systems genetics linking genotypes and phenotypes |
| title_sub | linking genotypes and phenotypes |
| topic | Phenotype Functional genomics Genexpression (DE-588)4020136-3 gnd Phänotyp (DE-588)4248244-6 gnd |
| topic_facet | Phenotype Functional genomics Genexpression Phänotyp |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028932300&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028932300&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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