outline introduction to systems biology biological networks
TRANSCRIPT
OUTLINE
Introduction to Systems Biology Biological Networks
Introduction to Systems Biology
First introduced in 1934, By Austrian biologist Ludwig von
Bertalanffy, He applied the general system theory to
biology.
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.
Introduction to Systems Biology
What is a System:– dynamics of its components,– interaction of components,– we need modeling to understand the mechanism.
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.
Introduction to Systems Biology
Introduction to Systems Biology
A number of web sites make available information about the interacting proteins in a particular pathway.
Introduction to Systems Biology
the glycolytic patway
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
Introduction to Systems Biology
Example:
Introduction to Systems Biology
Example:
Introduction to Systems Biology
Another example (Tumor Growth Simulation):
Biological Networks
the glycolytic patway
Biological Networks
E. coli:– a single cell,– amazing technology.
Biological Networks
Gene regulation:– Activators increase gene production
– Repressors decrease gene production
Biological Networks
Gene regulation:– Negative feedback loop:
– Positive feedback loop:
Biological Networks
Nodes are proteins (or genes)
Biological Networks
Nodes are proteins (or genes)
Biological Networks
Network motifs:– Subgraphs: which occur in the real network
significantly more than in a suitable random ensemble of network.
Biological Networks
Network motifs:– 3-node subgraphs:
Biological Networks
Network motifs:– 4-node subgraphs:
Biological Networks
Network motifs:– 5-node subgraphs:
9 364 possible subgraphs
Biological Networks
Network motifs:– 6-node subgraphs:
1 530 843 possible subgraphs
Biological Networks
Find network motifs (ALGORITHM):
Biological Networks
Find network motifs (EXAMPLE):– Network motifs in E. coli
Biological Networks
Find network motifs (EXAMPLE):– Network motifs in E. coli– only one 3-node network motif is significant.
Biological Networks
Network motifs:– Network motifs are functional building blocks of
these information processing networks.– Each motif can be studied theoretically and
experimentally.
Biological Networks
Other networks:– enzyme – lignad
metabolic pathways
– protein – protein cell signaling pathways,
Biological Networks
Pathways:– Pathways are subsets of networks,– Pathways are networks of interactions,– Pathways are related to a known physiological
process or complete function.
Biological Networks
Pathways EXAMPLE:
Biological Networks
Problems:– Source of interaction data is basicly the
experiments,– But in these experiments:
low quality, false positive, false negative.
Biological Networks
Problems SOLUTION:– Probabilistic networks.
Biological Networks
Other Problems:– Network reliability:
What is the probability that some path of functioning wires connects two terminals at a given time?
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:
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.