(Downloads - 0)
For more info about our services contact : help@bestpfe.com
Table of contents
1 INTRODUCTION
1.1 MOTIVATIONS
1.2 APOPTOSIS SIGNALING NETWORK
1.2.1 TRAIL, a death-inducing molecular agent initiating apoptosis
1.2.2 DR4 and DR5, decoy death receptors, and clustering modes with TRAIL
1.2.3 DISC complex, the basic unit structure for activation of Caspase-8, and the role of FLIP as an inhibitor of apoptosis
1.2.4 Caspase-8, a threshold for cell-fate decision
1.2.5 Bcl-2 like proteins, pro- and anti-apoptotic roles
1.2.6 MOMP, an irreversible commitment to cell-death
1.2.7 Global overview on the extrinsic apoptosis pathway
1.3 MODELLING IN APOPTOSIS
1.3.1 Fussenegger’s model, 2000
1.3.2 Albeck’s EARM1 model, 2008
1.3.3 Schlatter’s & Calzone’s model, two Boolean modeling approaches
1.3.4 Bertaux’s model, a stochastic protein-turnover approach 2014
1.3.5 A summary list of models of apoptosis
1.4 HETEROGENEITY IN BIOLOGY, INTRINSIC AND EXTRINSIC NOISE
2 MODELING RECEPTOR-LIGAND INTERACTIONS FOR THE EXTRINSIC APOPTOSIS PATHWAY
2.1 A NETWORK OF REACTIONS DEFINING A MODEL STRUCTURE FOR THE RECEPTOR LAYER OF THE EXTRINSIC APOPTOSIS NETWORK
2.2 AVAILABLE EXPERIMENTAL DATA
2.3 CONVERSION OF FRET-SIGNAL INTO NUMBER OF ICRP-CLEAVED MOLECULES
2.4 ARROM1: INITIAL CONDITIONS AND PARAMETER VALUES
2.5 ARROM2: A LIGAND-RECEPTOR MODEL WITH AN EXTRA SET OF PROPOSED REACTIONS
3 VALIDATION OF ARROM2: A RECEPTOR-LIGAND MODEL IN AGREEMENT WITH EXPERIMENTAL DATA
3.1 FLIP, A STRONG ANTI-APOPTOTIC PROTEIN WITH IRREVERSIBLE BINDING AT THE DISC STRUCTURE
3.2 DIMERIC VS. TRIMERIC LIGAND VALENCY, AN UNEQUAL RECEPTOR BINDING RATE
4 SOURCES OF HETEROGENEITY IN APOPTOTIC CELL-FATE DECISION
4.1 INTRINSIC NOISE: A COMPUTATIONAL APPROACH WITH THE GILLESPIE ALGORITHM .
4.2 EXTRINSIC NOISE: A COMPUTATIONAL APPROACH WITH INITIAL CONDITION VARIATION.
4.3 PARAMETER NOISE: A COMPUTATIONAL APPROACH WITH VARIATION IN REACTION RATES
5 FITTING THE ENTIRE CELL POPULATION AND SEARCHING FOR SIGNATURES IN SENSITIVE AND RESISTANT POPULATIONS
6 DISCUSSION AND PERSPECTIVES
7 APPENDIX
7.1 APPENDIX 1: ARROM1 MODEL INPUTS
7.2 APPENDIX 2: ARROM1 FIT TO THE MEDIAN_CELL
7.3 APPENDIX 3: ARROM2 FIT TO THE MEDIAN_CELL
7.4 APPENDIX 4 : ARROM1 MODEL EQUATIONS
7.5 APPENDIX 5: A NEW METHOD TO QUANTIFY PARAMETER VARIABILITY AMONG DIFFERENT PARAMETER POPULATIONS
8 BIBLIOGRAPHY
