Publications (32) View all
-
Article: Knowledge Driven Variable Selection (KDVS) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data.
[show abstract] [hide abstract]
ABSTRACT: BACKGROUND: High-throughput (HT) technologies provide huge amount of gene expression data that can be used toidentify biomarkers useful in the clinical practice. The most frequently used approaches first select aset of genes (i.e. gene signature) able to characterize differences between two or more phenotypicalconditions, and then provide a functional assessment of the selected genes with an a posteriori enrichmentanalysis, based on biological knowledge. However, this approach comes with some drawbacks.First, gene selection procedure often requires tunable parameters that affect the outcome, typicallyproducing many false hits. Second, a posteriori enrichment analysis is based on mapping betweenbiological concepts and gene expression measurements, which is hard to compute because of constantchanges in biological knowledge and genome analysis. Third, such mapping is typically used in theassessment of the coverage of gene signature by biological concepts, that is either score-based orrequires tunable parameters as well, limiting its power. RESULTS: We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biologicalknowledge in HT data analysis. The expression data matrix is transformed, according to priorknowledge, into smaller matrices, easier to analyze and to interpret from both computational and biologicalviewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any functionor process potentially relevant for the biological question under investigation. Differently from thestandard approach where gene selection and functional assessment are applied independently, KDVSembeds these two steps into a unified statistical framework, decreasing the variability derived fromthe threshold-dependent selection, the mapping to the biological concepts, and the signature coverage.We present three case studies to assess the usefulness of the method. CONCLUSIONS: We showed that KDVS not only enables the selection of known biological functionalities with accuracy,but also identification of new ones. An efficient implementation of KDVS was devised to obtainresults in a fast and robust way. Computing time is drastically reduced by the effective use of distributedresources. Finally, integrated visualization techniques immediately increase the interpretabilityof results. Overall, KDVS approach can be considered as a viable alternative to enrichment-basedapproaches.Source Code for Biology and Medicine 01/2013; 8(1):2. -
Article: MORE: Mixed Optimization for Reverse Engineering-An Application to Modeling Biological Networks Response via Sparse Systems of Nonlinear Differential Equations.
[show abstract] [hide abstract]
ABSTRACT: Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 09/2012; 9(5):1459-71. · 2.25 Impact Factor -
Article: Integrating literature-constrained and data-driven inference of signalling networks.
[show abstract] [hide abstract]
ABSTRACT: Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks. We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling. We obtain a model with superior data fit in the human liver cancer HepG2 and propose potential missing pathways. CNORfeeder is in the process of being submitted to Bioconductor and in the meantime available at www.cellnopt.org. saezrodriguez@ebi.ac.uk Supplementary data are available at Bioinformatics online.Bioinformatics 06/2012; 28(18):2311-7. · 5.47 Impact Factor -
Article: Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments.
[show abstract] [hide abstract]
ABSTRACT: The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present SPQR (Systematic Perturbation - Qualitative Reasoning), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modelled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 05/2012; · 2.25 Impact Factor -
Article: Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis.
[show abstract] [hide abstract]
ABSTRACT: Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.Proceedings of the National Academy of Sciences 02/2012; 109(7):2672-7. · 9.68 Impact Factor
