Transcript Introduction into MATBRAC objectives

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
Distributed
robustness
Robust control?
Critical
parameters
Robust
biological
system design
Robustness:
feedbacks and
redundancy
Robustness
and resource
limits
Robustness
and modules
Biological
robustness
Identifiably
of robust
systems
How robust
systems can
evolve?
Robustness vs
adaptability
Robustness
and
multiscaleness
(in time and
space)
Functional
robustness vs
stability
Robustnes
vs fragility
Cancer, cell cycle and developmental
program as robust systems
Mathematical frameworks
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Sensitivity analysis
Model reduction
Law of big numbers
Measure concentration phenomenon
Canalization
Robustness conservation
Evolutionary models
HOT, SOC, CAS, etc., etc.
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
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
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