1. Microarrays

2. OUTLINE
Introduction to Systems Biology
Biological Networks

3. Introduction to Systems Biology
First introduced in 1934,
By Austrian biologist Ludwig von Bertalanffy,
He applied the general system theory to biology.

4. 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.

5. Introduction to Systems Biology
What is a System:
dynamics of its components,
interaction of components,
we need modeling to understand the mechanism.

6. 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.

7. Introduction to Systems Biology

8. Introduction to Systems Biology
A number of web sites make available information about the interacting proteins in a particular pathway.

9. Introduction to Systems Biology
the glycolytic patway

10. 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

11. Introduction to Systems Biology
Example:

12. Introduction to Systems Biology
Example:

13. Introduction to Systems Biology
Another example (Tumor Growth Simulation):

14. Biological Networks
the glycolytic patway

15. Biological Networks
E. coli:
a single cell,
amazing technology.

16. Biological Networks Gene regulation:
Activators increase gene production
Repressors decrease gene production

17. Biological Networks Gene regulation: Negative feedback loop:
Positive feedback loop:

18. Biological Networks
Nodes are proteins (or genes)

19. Biological Networks
Nodes are proteins (or genes)

20. Biological Networks Network motifs:
Subgraphs: which occur in the real network significantly more than in a suitable random ensemble of network.

21. Biological Networks
Network motifs:
3-node subgraphs:

22. Biological Networks
Network motifs:
4-node subgraphs:

23. Biological Networks Network motifs: 5-node subgraphs:
9 364 possible subgraphs

24. Biological Networks Network motifs: 6-node subgraphs:
possible subgraphs

25. Biological Networks
Find network motifs (ALGORITHM):

26. Biological Networks Find network motifs (EXAMPLE):
Network motifs in E. coli

27. Biological Networks Find network motifs (EXAMPLE):
Network motifs in E. coli
only one 3-node network motif is significant.

28. Biological Networks Network motifs:
Network motifs are functional building blocks of these information processing networks.
Each motif can be studied theoretically and experimentally.

29. Biological Networks Other networks: enzyme – lignad protein – protein
metabolic pathways
protein – protein
cell signaling pathways,

30. Biological Networks Pathways: Pathways are subsets of networks,
Pathways are networks of interactions,
Pathways are related to a known physiological process or complete function.

31. Biological Networks
Pathways EXAMPLE:

32. Biological Networks Problems:
Source of interaction data is basicly the experiments,
But in these experiments:
low quality,
false positive,
false negative.

33. Biological Networks
Problems SOLUTION:
Probabilistic networks.

34. Biological Networks Other Problems: Network reliability:
What is the probability that some path of functioning wires connects two terminals at a given time?

35. 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:

36. 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.