2. Biological robustness
observation that biological systems stably perform their functions in highly variable external (environment) and internal (cellular content, including genetic variations) conditions
robustness is one of the most ubiquitous emergent properties of living organisms as complex systems

3. Biological robustness
Distributed robustness
Robust control?
Critical parameters
Identifiably
of robust systems
Robust biological system design
How robust systems can evolve? Robustness vs adaptability
Biological
robustness
Robustness:
feedbacks and redundancy
Robustness and multiscaleness
(in time and space)
Robustness and resource limits
Functional robustness vs stability
Robustnes vs fragility
Robustness
and modules

4. Cancer, cell cycle and developmental program as robust systems

5. Mathematical frameworks
Sensitivity analysis
Model reduction
Law of big numbers
Measure concentration phenomenon
Canalization
Robustness conservation
Evolutionary models
HOT, SOC, CAS, etc., etc.

6. MATBRAC project deliverables
Established contacts and common projects with the group of Hiroaki Kitano (Japan)
Methodology of model reduction and identification of critical parameters (Radulescu et al. BMC Systems Biology), presentation of this methodology on national and international levels (INSERM workshop on modeling networks, BGRS-2008, ICSB-2008)
Established contacts with the group of Upi Bhala (India), work on the robustness of biological switches
Submission of two SYSCOMM ANR projects (CALAMAR has been financed, on studying modules and robust behavior of RB/E2F pathway)
UK-France research consortium for preparing project on studying robustness

7. Questions for discussion
What is biological intuition to distinguish robust/non robust?
What could be the robust features: gene expression, phenotype, but in between?
What are the experimental plans to test robustness?
Do we have right methodology to create mechanistical models of robust systems? Bottom-up or top-down modeling?
Robustness and high-throughput data

8. Genomic plasticity in BRCA1 mutated breast cancers
We believe that few casual genetic events lead to genomic instabilities which increase after at random
SNP profiles for measuring gene copy numbers are highly correlated
Everything that can be lost, will be lost,
Everything that can’t be lost, will be amplified
abyss
hill