1. Modeling Genetic Network: Boolean Network Yongyeol Ahn 2004.08.18. KAIST

2. Genetic Network Genes interact with each other via proteins, RNAs and themselves.

3. Main Objectives To infer genetic network from biological data To explain and predict the behaviors of genetic regulatory network

4. Modeling Genetic Networks Statistics rules!  Bayesian network Hmm.. We need some dynamics. Let’s be realistic!  Differential equation approach Simple is the best!  Boolean network

5. Bayesian Network : Information Theory Shannon entropy: Joint probability: Pr(x,y) Conditional probability: Pr(y|x) = Pr(x,y)/Pr(x) Mutual information:

6. Bayesian Network Find a directed acyclic graph which shows the relationships of nodes well. X i : Expression level

7. Differential Equation Approach Gene A Gene R A A 1 A A 1 50 0.01 A 50 R 5 C + 2 10 0.5 500 50 100 1 0.2 1 ‘ The Clock ’

8. Differential Equation Approach Expressed genes mRNAs R A A C R R C

9. Why Boolean network? It tells about the dynamics (vs. bayesian network) ‘Gene switch’ : There are attempts to make a ‘genetic computer’ using genetic ‘logic gates’.  Binary state approximation is fine. In many cases, the exact timing may not be important. Simple, general, easy to implement, …

10. The Boolean Network Nodes, Directed links Synchronous dynamics Binary states: ‘on’ or ‘off’ A node’s state is determined by states of other nodes which have a link to the node(by assigned boolean functions).

11. Example 0 1 2 1200101010111112001010101111 Node 0 Node 2 1 01101 0110 012 000 101 0 01 t

12. Boolean Network Variations Multi-Valued model Different updating scheme (asynchronous, …) Probabilistic model

13. Classification of boolean networks (Gershenson2004)

14. Tools DDLab –http://www.ddlab.comhttp://www.ddlab.com RBNLab –http://rbn.sourceforge.nethttp://rbn.sourceforge.net BN/PBN toolbox: –http://www2.mdanderson.org/app/ilya/PBN/PBN.htmhttp://www2.mdanderson.org/app/ilya/PBN/PBN.htm

15. (Classical) Random Boolean Network Parameter: N, K, p –N: number of nodes –K: average in-degree –p: probability of ‘1’ in each boolean function Large ensemble,state space(2^N)  So big!  Very high standard deviations

16. Phase Transition Stable (K<=2) Critical Chaotic (K>=3) Visualization method –Active nodes: ‘green’ –Frozen nodes: ‘red’

17. Phase Transition Islands –Chaotic: green sea percolate & red islands –Stable: frozen red sea & green islands Robustness –Chaotic: damage spreads –Stable: robust Convergence and divergence of traj. (Lyapunov exponent) –Chaotic: similar states tend to diverge –Stable: tend to converge

18. Loops  Trees For active dynamics, network needs Loops. Loops activate other parts (trees). Active wave propagates from loops to trees.

19. G – Density G-density : Garden of Eden states density Ordered: very high G-density, high in- degree frequency Critical: power-law in-degree distribution Chaotic: lower G-density,

20. Analytical Result of Phase Transition Derrida’s annealed approx. : Assuming connections and boolean functions are randomly reshuffled at each time step. Define overlap = 1 – Normalized Hamming distance between two states What will happen at t  inf ?

21. Analytical Result of Phase Transition For a network with in-degree k, Transforming with Hamming distance and consider bias

22. Derrida Curves K=2 K=5 Critical connectivity

23. Phase diagram (In practice, the size of the network can play a role in the phase transitions)

24. Topology of boolean network In reality, genetic networks have very inhomogeneous degree distribution Using Derrida’s annealed approximation, the phase diagram for scale-free network can be obtained.

25. Derrida’s Annealed Approx. For Power-law Degree Distribution By the assumption, x(t) obeys the equation Where,

26. Contd.

28. Topology of boolean network: Scale-free boolean network

29. Attractors

30. The Number of Attractors The number of attractors grows faster than any power law with system size. (Samuelsson2003)

31. The Length of Attractor For K=1, root(N/2) At critical phase, it is long believed that the length proportional to root N (  Kauffman argued that this is related to the number of cell types ) But it is linear

32. Applications Reverse Engineering Morphogenesis model Segment polarity development Yeast transcriptional network

33. Reverse engineering: REVEAL REVerse Engineering Algorithm It finds a minimal solution for a boolean network given any set of time-series. Use entropy, mutual information

34. Neutral Mutation and Punctuated Equilibrium (Bornholdt1998) The model evolves under robustness principle (look for silent mutations) Threshold networks (restricted set of the boolean networks) Weight = ± 1, 0

35. Evolutionary Rule Create a daughter network by ‘adding’, ‘removing’, ‘adding and removing a weight in the coupling matrix’ at random. (each p = 1/3) With a random initial state, if mother & daughter reach the same attractor, replace the mother with the daughter. In other case, keep the mother network.

36. Punctuated Equilibrium The evolution shows punctuated network connectivity (lifetime ~ 1/t^2) Evolved networks have much shorter attractors, large frozen components

37. Model for Morphogenesis(Sole2003) Modeling an organism with one dimensional cell array. Each cells have the same set of genes and hormones. Genes interact within the cell. Hormones communicate with neighboring cells. Threshold model.

38. Morphogenesis Model

39. Development

40. Adaptive Walks ‘Toward more complex organism’ Complexity measure: the number of cell types Rule –Evolve many organism in parallel –Addition, Removal, randomization of link, link’s weight (each p=1/3) –Check the complexity

41. Logarithmic Increase of the Number of Patterns Consistent with Kauffman’s ‘rugged landscape’ explanation of Cambrian Explosion

42. Segment Polarity Network in Fly (Albert2003) The genetic network of Fly development This network is simulated with ODE(Dassow, 2002) and has shown a good result.

43. Boolean Network Construction Rules The effect of transcriptional activators and inhibitors is never additive, but rather, inhibitors are dominant. Transcription and translation are ON/OFF functions of the state If transcription/translation is ON, mRNAs/proteins are synthesized in one time step mRNAs decay in one time step if not transcribed Transcription factors and proteins undergoing post- translational modification decay in one time step if their mRNA is not present.

44. Constructed Boolean Network

45. Results Stable state is same as the real fly. Essential gene deletion results also agree with real data. There are six distinct steady states in the model. Three of these are well- known experimentally

46. Yeast transcriptional network(Kauffman2003) For a given network structure, they generate boolean network ensembles with nested canalyzing functions. The ensemble of networks are very stable.

47. Canalyzing Function Canalyzing boolean function has at least one input which the output value is fixed. In most cases, genetic networks consist of canalyzing functions.

48. Yeast Transcriptional Network Find topological transition point (to determine the confidence p value) Remove genes that have no output to other genes

49. Yeast Transcriptional Network For a given network structure.. –Functions with null hypothesis –Functions based on literature (canalyzing)

50. Nested Canalyzing Function Inputs i m, outputs O m, in degree k Assume i 1 is canalyzing input, then we can define a new rule without i 1 (with indegree k-1) In most cases, this new rule is also canalyzing.

51. Conclusion Boolean network model is simple, abstract, general. But, it’s powerful.