Estimating the joint distribution of independent categorical variables via model selection

Assume one observes independent categorical variables or, equivalently, one observes the corresponding multinomial variables. Estimating the distribution of the observed sequence amounts to estimating the expectation of the multinomial sequence. A new estimator for this mean is proposed that is nonparametric, non-asymptotic and implementable even for large sequences. It is a penalized least-squares estimator based on wavelets, with a penalization term inspired by papers of Birg\'{e} and Massart. The estimator is proved to satisfy an oracle inequality and to be adaptive in the minimax sense over a class of Besov bodies. The method is embedded in a general framework which allows us to recover also an existing method for segmentation. Beyond theoretical results, a simulation study is reported and an application on real data is provided.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ155 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

CycSim—an online tool for exploring and experimenting with genome-scale metabolic models

Summary: CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deletions mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on metabolic maps. The web interface is designed for simplicity, putting constraint-based modelling techniques within easier reach of biologists. CycSim also functions as an online repository of genome-scale metabolic models

A prognostic index predicting survival in transformed Waldenström macroglobulinemia

Histological transformation into diffuse large B-cell lymphoma is a rare complication in patients with Waldenström macroglobulinemia (WM) usually associated with a poor prognosis. The objective of this study was to develop and validate a prognostic index for survival in transformed WM patients. Through this multicenter, international collaborative effort, we developed a scoring system based on data from 133 patients with transformed WM who were evaluated between 1995 and 2016 (training cohort). Univariate and multivariate analyses were used to propose a prognostic index with 2-year survival after transformation as an end-point. For external validation, a data set of 67 patients was used to evaluate the performance of the model (validation cohort). By multivariate analysis, three adverse covariates were identified as independent predictors of 2-year survival after transformation: elevated serum LDH (2 points), platelet count < 100 x 109/L (1 point) and any previous treatment for WM (1 point). Three risk groups were defined: low-risk (0-1 point, 24% of patients), intermediate-risk (2-3 points, 59%, hazard ratio (HR) = 3.4) and high-risk (4 points, 17%, HR = 7.5). Two-year survival rates were 81%, 47%, and 21%, respectively (P < 0.0001). This model appeared to be a better discriminant than the International Prognostic Index (IPI) and the revised IPI (R-IPI). We validated this model in an independent cohort. This easy-to-compute scoring index is a robust tool that may allow identification of groups of transformed WM patients with different outcomes and could be used for improving the development of risk-adapted treatment strategies

Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways

To date, it is widely recognized that Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) can exert considerable anti-tumor effects regarding many types of cancers. The prolonged use of NSAIDs is highly associated with diverse side effects. Therefore, tailoring down the NSAID application onto individual patients has become a necessary and relevant step towards personalized medicine. This study conducts the systemsbiological approach to construct a molecular model (NSAID model) containing a cyclooxygenase (COX)-pathway and its related signaling pathways. Four cancer hallmarks are integrated into the model to reflect different developmental aspects of tumorigenesis. In addition, a Flux-Comparative-Analysis (FCA) based on Petri net is developed to transfer the dynamic properties (including drug responsiveness) of individual cellular system into the model. The gene expression profiles of different tumor-types with available drug-response information are applied to validate the predictive ability of the NSAID model. Moreover, two therapeutic developmental strategies, synthetic lethality and microRNA (miRNA) biomarker discovery, are investigated based on the COX-pathway. In conclusion, the result of this study demonstrates that the NSAID model involving gene expression, gene regulation, signal transduction, protein interaction and other cellular processes, is able to predict the individual cellular responses for different therapeutic interventions (such as NS-398 and COX-2 specific siRNA inhibition). This strongly indicates that this type of model is able to reflect the physiological, developmental and pathological processes of an individual. The approach of miRNA biomarker discovery is demonstrated for identifying miRNAs with oncogenic and tumor suppressive functions for individual cell lines of breast-, colon- and lung-tumor. The achieved results are in line with different independent studies that investigated miRNA biomarker related to diagnostics of cancer treatments, therefore it might shed light on the development of biomarker discovery at individual level. Particular results of this study might contribute to step further towards personalized medicine with the systemsbiological approach

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists

Estimating the joint distribution of independent categorical variables via model selection

Assume one observes independent categorical variables or, equivalently, one observes the corresponding multinomial variables. Estimating the distribution of the observed sequence amounts to estimating the expectation of the multinomial Sequence. A new estimator for this mean is proposed that is nonparametric, non-asymptotic and implementable even for large sequences. It is a penalized least-squares estimator based oil wavelets, with a penalization term inspired by papers of Birge and Massart. The estimator is proved to satisfy ail oracle inequality and to be adaptive in the minimax sense over a class of Besov bodies. The method is embedded in a general framework which allows us to recover also an existing method for segmentation. Beyond theoretical results, a simulation Study is reported and an application on real data is provided