## Advanced Technologies (Cambridge) Limited

### BibTeX

@MISC{Thaliana_advancedtechnologies,

author = {Of Arabidopsis Thaliana and Rónán Daly and Kieron D. Edwards and John S. O’neill and Stuart Aitken and Andrew J. Millar and Mark Girolami},

title = {Advanced Technologies (Cambridge) Limited},

year = {}

}

### OpenURL

### Abstract

Abstract. Modelling gene regulatory networks in organisms is an important task that has recently become possible due to large scale assays using technologies such as microarrays. In this paper, the circadian clock of Arabidopsis thaliana is modelled by fitting dynamic Bayesian networks to luminescence data gathered from experiments. This work differs from previous modelling attempts by using higher-order dynamic Bayesian networks to explicitly model the time lag between the various genes being expressed. In order to achieve this goal, new techniques in preprocessing the data and in evaluating a learned model are proposed. It is shown that it is possible, to some extent, to model these time delays using a higher-order dynamic Bayesian network.

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Citation Context ...onsist of a prior network, used to model initial conditions, and a transition network, used to model the effect of interactions over time. Whilst DBNs have been used to model gene regulatory networks =-=[3,4,5,6,7]-=-, the method presented here differs in how the data are treated and in how it can infer interactions at multiple time periods. To test the methods proposed, a set of gene expression data obtained from... |

48 |
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Citation Context ...onsist of a prior network, used to model initial conditions, and a transition network, used to model the effect of interactions over time. Whilst DBNs have been used to model gene regulatory networks =-=[3,4,5,6,7]-=-, the method presented here differs in how the data are treated and in how it can infer interactions at multiple time periods. To test the methods proposed, a set of gene expression data obtained from... |

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Citation Context ...refore, methods involving graphical representations have become more widely used. These include Boolean networks, networks similar to neural networks, stochastic process calculi and Bayesian networks =-=[10]-=-. 2.1 Data from Arabidopsis Thaliana Experiments The experimental study in this chapter will involve luminescence data obtained from experiments on Arabidopsis thaliana. The luminescence is an indirec... |

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Citation Context ...a obtained from experiments observing particular genes of the plant Arabidopsis thaliana will be used. These genes are known to behave in a clock-like fashion that regulates the function of the plant =-=[8,9]-=-. The data will be preprocessed and experiments will be conducted to learn the structure of DBNs using these preprocessed data. Using a new technique, these networks will be compared to a standard net... |

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24 |
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8 |
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Citation Context ...a obtained from experiments observing particular genes of the plant Arabidopsis thaliana will be used. These genes are known to behave in a clock-like fashion that regulates the function of the plant =-=[8,9]-=-. The data will be preprocessed and experiments will be conducted to learn the structure of DBNs using these preprocessed data. Using a new technique, these networks will be compared to a standard net... |

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Citation Context ...hould perform well in most cases [1]. It has been known for some time that the value of the equivalent sample size parameter N ′ for the BDeu scoring function has a large effect on learning structure =-=[15,16,17]-=-. In a sense, the value of N ′ can be seen as a regularisation parameter - the larger the value of N ′ , the more edges are supported in the learned graph [18]. This can be contrasted to scores like t... |

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4 |
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Citation Context ...on known as ACO-E [1] to build dynamic Bayesian networks (DBNs). DBNs are an extension of Bayesian networks, graphical models that can be useful in modelling probabilistic relations between variables =-=[2]-=-. They consist of a prior network, used to model initial conditions, and a transition network, used to model the effect of interactions over time. Whilst DBNs have been used to model gene regulatory n... |

4 | Learning the bayesian network structure: Dirichlet prior vs. data
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Citation Context ...large effect on learning structure [15,16,17]. In a sense, the value of N ′ can be seen as a regularisation parameter - the larger the value of N ′ , the more edges are supported in the learned graph =-=[18]-=-. This can be contrasted to scores like the BIC and AIC, where regularisation is implicit in the function and cannot be adjusted. Therefore, inUsing Higher-Order Dynamic Bayesian Networks to Model Pe... |

3 |
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Citation Context ...tic model. This enables large scale, non-linear interactions between many genes to be represented and simulated. E.g., recent studies have looked at learning Bayesian networks with thousands of genes =-=[12]-=-. The large amounts of data present in microarray studies can be used to learn both the structure and parameters of the network – missing values and noise can also be taken care of. Also, Bayesian net... |

2 |
D.: Modeling multiple time units delayed gene regulatory network using dynamic Bayesian network
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(Show Context)
Citation Context ...ossible dynamic Bayesian network was set to 9, i.e. the time slice at t and 8 slices back. Other authors show using DBNs with multiple layers to model gene regulatory networks with multiple time lags =-=[14]-=-. However, their methods consider arcs between arbitrary layers that are not at time t. This method is incorrect, as it means that arcs could be added between nodes without taking into account the oth... |

1 |
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(Show Context)
Citation Context ...30 AT0031b AT0032 AT0033 AT0047 Ratio 6:18 9:15 12:12 15:9 18:6 3:21 the ‘plant circadian rhythms’ of this organism, i.e. the oscillating behaviour of plants as they respond to the change in sunlight =-=[11]-=-. Briefly, the expression levels of the genes in plants tend to synchronise with the rising and setting of the sun, oscillating between high and low levels. However, when the light source is removed, ... |