We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the deadliest cancers in the world. All-trans retinoic acid (ATRA) is the major physiologically active form of vitamin A, regulating expression of many genes. Disturbances of vitamin A metabolism are prevalent in some cancer cells. The main aim of this work was to investigate deeply the components of retinoid signaling in PDAC compared to in the normal pancreas and to prove the clinical importance of retinoid receptor expression. For the study, human tumor tissues obtained from PDAC patients and murine tumors from the orthotopic Panc02 model were used for the analysis of retinoids, using high performance liquid chromatography mass spectrometry and real-time RT-PCR gene expression analysis. Survival probabilities in univariate analysis were estimated using the Kaplan-Meier method and the Cox proportional hazards model was used for the multivariate analysis. In this work, we showed for the first time that the ATRA and all-trans retinol concentration is reduced in PDAC tissue compared to their normal counterparts. The expression of RARα and β as well as RXRα and β are down-regulated in PDAC tissue. This reduced expression of retinoid receptors correlates with the expression of some markers of differentiation and epithelial-to-mesenchymal transition as well as of cancer stem cell markers. Importantly, the expression of RARα and RXRβ is associated with better overall survival of PDAC patients. Thus, reduction of retinoids and their receptors is an important feature of PDAC and is associated with worse patient survival outcomes.

Urothelial cancers of the bladder (UC) comprise biologically heterogeneous group of tumors and display complex genetic alterations. Several genetic changes have been analyzed in detail and some of them are associated with the development and progression of UCs. Only a few studies, however, are focused on identifying the order in which the aberrations may appear during UC tumorigenesis. We have analyzed 123 papillary UCs of the bladder by microsatellites for each of the chromosomal regions that have been suggested to be specifically involved in this type of tumor. We used Bayesian network modeling that enables to uncover multivariate probabilistic dependencies between variables. This methodology applied to LOH data allowed us to discover patterns of losses in UCs. Exploiting the mechanism of probabilistic reasoning in Bayesian networks we suggest primary and secondary events in tumor pathogenesis and reconstruct the possible flow of progression of allelic changes. Key words: urothelial cancer; loss of heterozygosity; Bayesian networks; network inference; genetic pathwaysUrothelial carcinoma of the bladder (UC) comprise biologically and morphologically heterogenous groups of neoplasms. During the last decade a broad spectrum of genetic alterations has been described in UCs. Cytogenetic, CGH and microsatellite analyses showed loss, gain and amplification of DNA sequences at several chromosomal regions. 1 Hemi-and homozygous deletion at and methylation/mutation of the CDKN2A gene at chromosome 9p21 is considered to be an early genetic event. 2 The vast majority of UCs acquire several additional genetic alterations during progression, including deletion of chromosome 2q, 5q and 8p, deletion/ mutation of the p53 and Rb genes or amplification and overexpression of the ERBB-2 gene. [3][4][5][6][7][8] Although some of the genetic alterations occur at random, the recurrent changes may refer to a network of genes that are specifically involved in tumor development and progression.Several models indicating a step-by-step order of genetic changes as a single pathway from normal urothelial cell to malignant tumor have been proposed. [9][10][11] Desper et al. 12 and Schäffer et al. 13 applied mathematical models to infer the order of genetic changes during progression of UCs. They described the progression of alterations as a tree with a root representing the normal cell and used 2 different tree models. In a distance-based tree leaf nodes represent genetic aberrations. The distance function between the nodes in the tree was defined based on probabilities of the co-occurrence of genetic events. A distance-based phylogenetic tree-building algorithm was used to infer the tree structure that fits the pairwise distances at best. Alternatively, a maximum weight branching algorithm was used to reconstruct a tree in which both internal nodes and leaf nodes correspond to aberrations. A major limitation of these models is that they describe the progression of genetic events as trees, whereas the biological intuition suggests that this...