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In this study the effect of providing explicit information in a visuomotor adaptation task was investigated using two computational models. Both models were able to reproduce the observed behavior in previous studies. However, these models make different prediction about a localization task after adaptation. A relevant experiment may be designed and performed to test whether either of these predictions is valid or the models need to be improved.

Gene regulatory network is a set of genes which interact with each other indirectly and thereby rates of gene expression to mRNA are controlled. DNA microarrays can measure the expression levels of thousands of genes. Because of noise in microarray data and probabilistic nature of Bayesian networks (BN), we used them to model causal relations between genes. One difficulty with this technique is that learning the BN structure is an NP-hard problem, as the number of possible structures is superexponential in the number of genes. So in this paper, genes are clustered based on gene ontology and then BN is applied to model relationships between co-clustered genes. On the other hand since time delay information exists in a real regulatory network and BN is a static network, we proposed a novel method that uses cross-correlation between co-clustered genes to incorporate time information in BN. This method is applied to reconstruct the regulatory network of 84 yeast genes from Saccharomyces cerevisiae cell cycle dataset. Comparing the simulations results with the KEGG pathway map show that using cross-correlation increases accuracy from 66% to 72%. Sensitivity of model is improved too, as the number of inferred links is increased from 70 to 101.

For understanding the function of an organism, it is necessary to know "which", "how fast", and "when" the genes are expressed. A gene regulatory network represents how and when the genes interact with each other. Using genetic network modeling, it is possible to explain the cell functions at molecular level. DNA microarrays can measure the expression levels of thousands of genes simultaneously. Most of methodologies have proposed so far for modeling gene networks from microarray data take into account only a small number of genes. In this paper, a two steps method is proposed that can model large-scale Gene Regulatory Networks using time series microarray data. Firstly, genes are clustered based on existing biological knowledge (Gene Ontology annotations) and then a higher-order Markov dynamic Bayesian network is applied in order to model causal relationships between genes in each cluster. Finally the learned subnetworks are integrated to make a global network. This method is applied to reconstruct the regulatory network of 75 yeast genes from cell cycle gene expression dataset collected by Spellman et al. (1998). Comparing the results with the KEGG pathway map, indicates that this approach is capable of finding 31% of true relationships between genes (69% if directionality and time delay is not considered).