List of contributors ........................................... ix
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
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 Angelique 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. Mäurer, 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
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 JakI ........................................... 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 Hadjiprocopis and Rune Linding
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.
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