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ОбложкаSystems genetics: linking genotypes and phenotypes / ed. by F.Markowetz, M.Boutros. - Cambridge: Cambridge university press, 2015. - xi, 258 p.: ill. - (Cambridge series in systems genetics). - Bibliogr. at the end of the chapters. - Ind.: p.257-258. - ISBN 978-1-107-01384-1
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Оглавление / Contents
 
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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