outline introduction to systems biology biological networks
TRANSCRIPT
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OUTLINE
Introduction to Systems Biology Biological Networks
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Introduction to Systems Biology
First introduced in 1934, By Austrian biologist Ludwig von
Bertalanffy, He applied the general system theory to
biology.
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Introduction to Systems Biology
To fully understand the functioning of cellular processes, whole cells, organisms, and even organisms:– it is not enough to simply assign functions to
individual genes, proteins, and other cellular organisms,
– we need an integrated way to look at the dynamic networks representing the interactions of components.
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Introduction to Systems Biology
What is a System:– dynamics of its components,– interaction of components,– we need modeling to understand the mechanism.
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Introduction to Systems Biology
The higher-order properties and functions that arise from the interaction of the parts of a system are called emergent properties.– human brain can thought by the interaction of
brain cells,– a single brain cell is incapable of the property of
thought.
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Introduction to Systems Biology
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Introduction to Systems Biology
A number of web sites make available information about the interacting proteins in a particular pathway.
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Introduction to Systems Biology
the glycolytic patway
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Introduction to Systems Biology
The interactions in networks can be represented as DEs:– all the interactions between components in a
model need to be represented mathematically,– differential equations are used for
representation of interactions
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Introduction to Systems Biology
Example:
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Introduction to Systems Biology
Example:
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Introduction to Systems Biology
Another example (Tumor Growth Simulation):
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Biological Networks
the glycolytic patway
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Biological Networks
E. coli:– a single cell,– amazing technology.
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Biological Networks
Gene regulation:– Activators increase gene production
– Repressors decrease gene production
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Biological Networks
Gene regulation:– Negative feedback loop:
– Positive feedback loop:
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Biological Networks
Nodes are proteins (or genes)
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Biological Networks
Nodes are proteins (or genes)
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Biological Networks
Network motifs:– Subgraphs: which occur in the real network
significantly more than in a suitable random ensemble of network.
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Biological Networks
Network motifs:– 3-node subgraphs:
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Biological Networks
Network motifs:– 4-node subgraphs:
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Biological Networks
Network motifs:– 5-node subgraphs:
9 364 possible subgraphs
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Biological Networks
Network motifs:– 6-node subgraphs:
1 530 843 possible subgraphs
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Biological Networks
Find network motifs (ALGORITHM):
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Biological Networks
Find network motifs (EXAMPLE):– Network motifs in E. coli
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Biological Networks
Find network motifs (EXAMPLE):– Network motifs in E. coli– only one 3-node network motif is significant.
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Biological Networks
Network motifs:– Network motifs are functional building blocks of
these information processing networks.– Each motif can be studied theoretically and
experimentally.
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Biological Networks
Other networks:– enzyme – lignad
metabolic pathways
– protein – protein cell signaling pathways,
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Biological Networks
Pathways:– Pathways are subsets of networks,– Pathways are networks of interactions,– Pathways are related to a known physiological
process or complete function.
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Biological Networks
Pathways EXAMPLE:
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Biological Networks
Problems:– Source of interaction data is basicly the
experiments,– But in these experiments:
low quality, false positive, false negative.
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Biological Networks
Problems SOLUTION:– Probabilistic networks.
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Biological Networks
Other Problems:– Network reliability:
What is the probability that some path of functioning wires connects two terminals at a given time?
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Biological Networks
Other Problems:– Finding the best simple path (each vertex is
visited once, no cycles) of length k starting from a given node in the graph:
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References
M. Zvelebil, J. O. Baum, “Understanding Bioinformatics”, 2008, Garland Science
Andreas D. Baxevanis, B.F. Francis Ouellette, “Bioinformatics: A practical guide to the analysis of genes and proteins”, 2001, Wiley.
Barbara Resch, “Hidden Markov Models - A Tutorial for the Course Computational Intelligence”, 2010.
Wang, Z., Zhang, L., Sagotsky, J., Deisboeck. T. S. (2007), Simulating non-small cell lung cancer with a multiscale agent-based model, Theoretical Biology & Medical Modelling.