| In modern biological science, computational methods occupy an important, ever-expanding niche. Biological scientific computing is concerned with building mathematical and statistical models to address a wide range of questions originated from biological research and applications. Motivated by actual problems in stem cell research, we propose a branching process model to study stem cell fate decisions.;Finally, we turn to theoretical aspects of structure learning and prove strong consistency property of the Bayesian scoring criterion for the case of binomial Bayesian network models. We obtained asymptotic expansions for the logarithm of the Bayesian score, as well as the logarithm of the Bayes factor comparing two models. These results are important extensions of the consistency property of the Bayesian scoring criterion providing insight into the rates at which the Bayes factor favors correct models.;Further, we investigate the machinery of stem cell signaling processes by building an appropriate Bayesian network model. We show how expert knowledge regarding the nature of the relationship between the components of cell signaling network could be encoded in terms of a novel parameter prior. Additionally, we address the question of structure learning efficiency by developing a new family of learning algorithms. |
