Thomas Sauter System Biology group University of Luxembourg Efficient algorithms for the contextualization of molecular network models 1. Introduction: Network modeling / Systems Biology 2. Specific metabolic network models with fastcore / fastcormics 3. Specific signalling network models with optPBN / Falcon
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Thomas SauterSystem Biology group
University of Luxembourg
Efficient algorithmsfor the contextualization
of molecular network models
1. Introduction: Network modeling / Systems Biology
2. Specific metabolic network models with fastcore / fastcormics
3. Specific signalling network models with optPBN / Falcon
[S. Subramaniam, 2004, Jones, Research Trends, 2010Su, sulab.org, 2013;Gilbert et al, Stand. Genomic Sci., 2012]
“We are witnessing the emergence of
the "data rich” era in biology…”
“The bottleneck … has shifted from generating
the data to interpreting results so as
to derive insights into biological mechanisms.”
[O. Wolkenhauer, Front Physiol, 2014, “Why model?”]
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“We cannot understand complex systems without modeling.”
“The role of the model is to make something complex intelligible or
understandable.”
“Why model?
The role of the model is to make something complex intelligible or
understandable.”
[O. Wolkenhauer, Front Physiol, 2014, “Why model?”]
[O. Wolkenhauer, Front Physiol, 2014, “Why model?”]
“Why model?
The role of the model is to make something complex intelligible or
understandable.”
“Systems biology is the science that studies how biological function
emerges from the interactions between the components of living
systems and how these emergent properties enable and constrain the
behavior of those components.”
[O. Wolkenhauer, Front Physiol, 2014, “Why model?”]
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Example Network Motif: Function?
Systems approach / Function:
Output behavior as a function of input and time
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(System) RS
Output as a function of input and time
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0 10 200
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time
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Output as a function of input and time
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0 10 200
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0 10 200
5
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Output as a function of input and time
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Output as a function of input and time
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0 10 200
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S R
Output as a function of input and time
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0 10 200
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0 10 200
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Output as a function of input and time
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0 10 20 300
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0 10 20 300
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0 50 1000
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Function:
Change detection / Adaptation
(System) RS
Systems approach:
Second example: Different wiring
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Input S
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Output R
Function: Filter – removes short inputs & act upon long inputs
Structure: Feedforward loops
incoherent: coherent:
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Functions:
Change detection Short input filter
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Model based Data Integration and
Analysis of Disease specific Networks
Data integration Network analysis
Model based Data Integration and
Analysis of Disease specific Networks
Data integration Network analysis
• Omics data
visualization
• Metabolic network reconstruction
• Signalling network
curation
• Gene regulatory networks from epigenetic data
• Data mining / machine learning
• Disease specificmetabolic and signalling networks