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Probabilistic modelling in computational biology Dirk Husmeier Biomathematics & Statistics Scotland

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James Watson & Francis Crick, 1953

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Frederick Sanger, 1980

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Network reconstruction from postgenomic data

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Model Parameters q

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Friedman et al. (2000), J. Comp. Biol. 7, 601-620 Marriage between graph theory and probability theory

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Bayes net ODE model

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Model Parameters q Probability theory Likelihood

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Model Parameters q Bayesian networks: integral analytically tractable!

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UAI 1994

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Identify the best network structure Ideal scenario: Large data sets, low noise

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Uncertainty about the best network structure Limited number of experimental replications, high noise

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Sample of high-scoring networks

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Feature extraction, e.g. marginal posterior probabilities of the edges High-confident edge High-confident non-edge Uncertainty about edges

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Number of structures Number of nodes Sampling with MCMC

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Madigan & York (1995), Guidici & Castello (2003)

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Overview Introduction Limitations Methodology Application to morphogenesis Application to synthetic biology

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Homogeneity assumption Interactions don’t change with time

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Limitations of the homogeneity assumption

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Example: 4 genes, 10 time points t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 t7t7 t8t8 t9t9 t 10 X (1) X 1,1 X 1,2 X 1,3 X 1,4 X 1,5 X 1,6 X 1,7 X 1,8 X 1,9 X 1,10 X (2) X 2,1 X 2,2 X 2,3 X 2,4 X 2,5 X 2,6 X 2,7 X 2,8 X 2,9 X 2,10 X (3) X 3,1 X 3,2 X 3,3 X 3,4 X 3,5 X 3,6 X 3,7 X 3,8 X 3,9 X 3,10 X (4) X 4,1 X 4,2 X 4,3 X 4,4 X 4,5 X 4,6 X 4,7 X 4,8 X 4,9 X 4,10

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Supervised learning. Here: 2 components t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 t7t7 t8t8 t9t9 t 10 X (1) X 1,1 X 1,2 X 1,3 X 1,4 X 1,5 X 1,6 X 1,7 X 1,8 X 1,9 X 1,10 X (2) X 2,1 X 2,2 X 2,3 X 2,4 X 2,5 X 2,6 X 2,7 X 2,8 X 2,9 X 2,10 X (3) X 3,1 X 3,2 X 3,3 X 3,4 X 3,5 X 3,6 X 3,7 X 3,8 X 3,9 X 3,10 X (4) X 4,1 X 4,2 X 4,3 X 4,4 X 4,5 X 4,6 X 4,7 X 4,8 X 4,9 X 4,10

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Changepoint model Parameters can change with time

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Changepoint model Parameters can change with time

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t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 t7t7 t8t8 t9t9 t 10 X (1) X 1,1 X 1,2 X 1,3 X 1,4 X 1,5 X 1,6 X 1,7 X 1,8 X 1,9 X 1,10 X (2) X 2,1 X 2,2 X 2,3 X 2,4 X 2,5 X 2,6 X 2,7 X 2,8 X 2,9 X 2,10 X (3) X 3,1 X 3,2 X 3,3 X 3,4 X 3,5 X 3,6 X 3,7 X 3,8 X 3,9 X 3,10 X (4) X 4,1 X 4,2 X 4,3 X 4,4 X 4,5 X 4,6 X 4,7 X 4,8 X 4,9 X 4,10 Unsupervised learning. Here: 3 components

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Extension of the model q

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q

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q k h Number of components (here: 3) Allocation vector

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Analytically integrate out the parameters q k h Number of components (here: 3) Allocation vector

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P(network structure | changepoints, data) P(changepoints | network structure, data) Birth, death, and relocation moves RJMCMC within Gibbs

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Dynamic programming, complexity N 2

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Collaboration with the Institute of Molecular Plant Sciences at Edinburgh University (Andrew Millar’s group) - Focus on: 9 circadian genes: LHY, CCA1, TOC1, ELF4, ELF3, GI, PRR9, PRR5, and PRR3 - Transcriptional profiles at 4*13 time points in 2h intervals under constant light for - 4 experimental conditions Circadian rhythms in Arabidopsis thaliana

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Comparison with the literature Precision Proportion of identified interactions that are correct Recall = Sensitivity Proportion of true interactions that we successfully recovered Specificity Proportion of non-interactions that are successfully avoided

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CCA1 LHY PRR9 GI ELF3 TOC1 ELF4 PRR5 PRR3 False negative Which interactions from the literature are found? True positive Blue: activations Red: Inhibitions True positives (TP) = 8 False negatives (FN) = 5 Recall= 8/13= 62%

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Which proportion of predicted interactions are confirmed by the literature? False positives Blue: activations Red: Inhibitions True positive True positives (TP) = 8 False positives (FP) = 13 Precision = 8/21= 38%

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Precision= 38% CCA1 LHY PRR9 GI ELF3 TOC1 ELF4 PRR5 PRR3 Recall= 62%

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True positives (TP) = 8 False positives (FP) = 13 False negatives (FN) = 5 True negatives (TN) = 9²-8-13-5= 55 Sensitivity = TP/[TP+FN] = 62% Specificity = TN/[TN+FP] = 81% Recall Proportion of avoided non-interactions

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Model extension So far: non-stationarity in the regulatory process

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Non-stationarity in the network structure

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Flexible network structure.

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Model Parameters q

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Use prior knowledge!

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Flexible network structure.

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Flexible network structure with regularization Hyperparameter Normalization factor

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Flexible network structure with regularization Exponential prior versus Binomial prior with conjugate beta hyperprior

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NIPS 2010

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Overview Introduction Limitations Methodology Application to morphogenesis Application to synthetic biology

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Morphogenesis in Drosophila melanogaster Gene expression measurements at 66 time points during the life cycle of Drosophila (Arbeitman et al., Science, 2002). Selection of 11 genes involved in muscle development. Zhao et al. (2006), Bioinformatics 22

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Can we learn the morphogenetic transitions: embryo larva larva pupa pupa adult ?

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Average posterior probabilities of transitions Morphogenetic transitions: Embryo larva larva pupa pupa adult

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Can we learn changes in the regulatory network structure ?

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Overview Introduction Limitations Methodology Application to morphogenesis Application to synthetic biology

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Can we learn the switch Galactose Glucose? Can we learn the network structure?

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Task 1: Changepoint detection Switch of the carbon source: Galactose Glucose

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Task 2: Network reconstruction Precision Proportion of identified interactions that are correct Recall Proportion of true interactions that we successfully recovered

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BANJO: Conventional homogeneous DBN TSNI: Method based on differential equations Inference: optimization, “best” network

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Sample of high-scoring networks

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Marginal posterior probabilities of the edges P=1 P=0 P=0.5

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P=1 True network Thresh0.9 Prec1 Recall1/2 Precision Recall

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P=1 P=0.5 True network Thresh0.90.4 Prec12/3 Recall1/21 Precision Recall

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P=1 P=0 P=0.5 True network Thresh0.90.4-0.01 Prec12/31/2 Recall1/211 Precision Recall

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Future work

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How are we getting from here …

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… to there ?!

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Input: Learn: MCMC Prior knowledge

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