Approximate inference for stochastic dynamics in large biological networks

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1 MID-TERM REVIEW Institut Henri Poincaré, Paris January 2014 Approximate inference for stochastic dynamics in large biological networks Ludovica Bachschmid Romano Supervisor: Prof. Manfred Opper Artificial Intelligence group, TU Berlin

2 The functions of most biological systems arise from complex interactions between the numerous system s components

3 protein

4 DNA protein

5 protein DNA Gene: information needed to produce one kind of proteins

6 protein RNA: copy of the genetic information TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins

7 protein RNA: copy of the genetic information TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins

8 protein Transcription factor RNA TRANSCRIPTION DNA Gene

9 protein sugar Transcription factor RNA TRANSCRIPTION DNA Gene

10 Protein that breaks down sugar into simpler molecules sugar Transcription factor RNA TRANSCRIPTION DNA Gene

11 Protein sugar Transcription factor RNA X TRANSCRIPTION DNA Gene

12 Protein Y sugar Transcription factor RNA X TRANSCRIPTION DNA Gene

13 X increases the production of Y Protein Y sugar Transcription factor RNA X TRANSCRIPTION DNA Gene

14 protein Y Envirometal signal Transcription factor RNA: copy of the genetic information X TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins

15 protein Y Envirometal signal Transcription factor RNA: copy of the genetic information X TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins

16 protein Y X decreases the production of Y Envirometal signal Transcription factor RNA: copy of the genetic information X TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins

17 X Y

18 X Y X

19 X Y X W U

20 X Y X W K U

21 E. coli regulatory network

22 The development of high-throughput data-collection techniques allows for the simultaneous interrogation of the status of cell s components

23 The development of high-throughput data-collection techniques allows for the simultaneous interrogation of the status of cell s components Big data

24 Challenge: network reconstruction

25 Challenge: network reconstruction Difficulty: large size (many components) of biological systems

26 Challenge: network reconstruction Difficulty: large size (many components) of biological systems Statistical physics find approximate methods to solve the problem

27 Challenge: network reconstruction Difficulty: large size (many components) of biological systems Statistical physics find approximate methods to solve the problem Most of the current reconstruction techniques do not exploit the temporal information of the data

28 GOAL: Statistical mechanics improve the quality of biological network reconstruction

29 GOAL: Statistical mechanics develop new inference techniques that exploit the temporal structure of the data

30 GOAL: Statistical mechanics develop new inference techniques that exploit the temporal structure of the data improve the quality of biological network reconstruction

31

32 New method to predict the state of the system knowing the value of the links (results agree well with simulations of small systems)

33 New method to predict the state of the system knowing the value of the links (results agree well with simulations of small systems) Faster algorithm

34 New method to predict the state of the system knowing the value of the links (results agree well with simulations of small systems) Faster algorithm To do: Inference problem: optimally predict the links

35 Hidden nodes

36 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems)

37 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems) Quality of the prediction on the hidden nodes varying the sistem s parameters

38 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems) Quality of the prediction on the hidden nodes varying the sistem s parameters Algorithm to infer the links

39 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems) Quality of the prediction on the hidden nodes varying the sistem s parameters Algorithm to infer the links To do: Study different kinds of networks, continuous variables

40 Courses and schools (besides those organized by NETADIS): TU Berlin: - Stochastic Systems - Monte Carlo methods in Artificial Intelligence and Machine Learning - Probabilistic and Bayesian Modelling in Machine Learning and Artificial Intelligence Bernstein Center for Computational Neuroscience, Berlin: Benjamin Lindner s seminars on "Stochastic Aspects of Neurobiological Problems" ICTP, Trieste: School on Large Scale Problems in Machine Learning and Workshop on Common Concepts in Machine Learning and Statistical Physics, August Main collaborations: Prof. Sollich s group and Dr. Roudi group

41 Thanks for your attention!

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