Diamond-Blackfan anemia (DBA) is a constitutional disease characterized by a specific maturation defect in cells of erythroid lineage. We have assembled a registry of 229 DBA patients, which includes 151 patients from France, 70 from Germany, and eight from other countries. Presence of malformations was significantly and independently associated with familial history of DBA, short stature at presentation (before any steroid therapy), and absence of hypotrophy at birth. Two hundred twenty-two patients were available for long-term follow-up analysis (median, 111.5 mo). Of these individuals, 62.6% initially responded to steroid therapy. Initial steroid responsiveness was found significantly and independently associated with older age at presentation, familial history of DBA, and a normal platelet count at the time of diagnosis. Severe evolution of the disease (transfusion dependence or death) was significantly and independently associated with a younger age at presentation and with a history of premature birth. In contrast, patients with a familial history of the disease experienced a better outcome. Outcome analysis revealed the benefit of reassessing steroid responsiveness during the course of the disease for initially nonresponsive patients. Bone marrow transplantation was successful in 11/13 cases; HLA typing of probands and siblings should be performed early if patients are transfusion dependent, and cord blood should be preserved. Incidence of DBA (assessed for France over a 13-y period) is 7.3 cases per million live births without effect of seasonality on incidence of the disease or on malformative status. Similarly, no parental imprinting effect or anticipation phenomenon could be documented in families with dominant inheritance.
Improved mechanistic understanding of biochemical networks is one of the driving ambitions of Systems Biology. Computational modeling allows the integration of various sources of experimental data in order to put this conceptual understanding to the test in a quantitative manner. The aim of computational modeling is to obtain both predictive as well as explanatory models for complex phenomena, hereby providing useful approximations of reality with varying levels of detail. As the complexity required to describe different system increases, so does the need for determining how well such predictions can be made. Despite efforts to make tools for uncertainty analysis available to the field, these methods have not yet found widespread use in the field of Systems Biology. Additionally, the suitability of the different methods strongly depends on the problem and system under investigation. This review provides an introduction to some of the techniques available as well as gives an overview of the state-of-the-art methods for parameter uncertainty analysis.
Motivation: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity.Results: We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions.Availability and implementation: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.htmlContact: j.vanlier@tue.nl; N.A.W.v.Riel@tue.nlSupplementary information: Supplementary data are available at Bioinformatics online.
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