Model Checking of Metabolic Networks: Application to Metabolic Diseases

The prominent Model Checking technology is used to build executable models of Metabolic Networks. These models are verified or simulated to extract relevant information on the dynamics of the underlying biological system. The proposed methodology is tested on a genome-scale metabolic network model of the human hepatocyte to investigate on a mendelian disease called Primary Hyperoxaluria type I

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer

Abstract Background Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states. Results In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study. Conclusions Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena

Semi-supervised generalized eigenvalues classification

Supervised classification is one of the most powerful techniques to analyze data, when a-priori information is available on the membership of data samples to classes. Since the labeling process can be both expensive and time-consuming, it is interesting to investigate semi-supervised algorithms that can produce classification models taking advantage of unlabeled samples. In this paper we propose LapReGEC, a novel technique that introduces a Laplacian regularization term in a generalized eigenvalue classifier. As a result, we produce models that are both accurate and parsimonious in terms of needed labeled data. We empirically prove that the obtained classifier well compares with other techniques, using as little as 5% of labeled points to compute the models

A generalized eigenvalues classifier with embedded feature selection

Supervised classification is one of the most used methods in machine learning. In case of data characterized by a large number of features, a critical issue is to deal with redundant or irrelevant information. To this extent, an effective algorithm needs to identify a suitable subset of features, as small as possible, for the classification. In this work we present ReGEC_L1, a classifier with embedded feature selection based on the Regularized Generalized Eigenvalue Classifier (ReGEC) and equipped with a L1-norm regularization term. We detail the mathematical formulation and the numerical algorithm. Numerical results, obtained on some de facto standard benchmark data sets, show that the approach we propose produces a remarkable selection of the features, without losing accuracy in the classification. In that respect, our algorithm seems to compare favorably with the SVM_L1 method. A MATLAB implementation of ReGEC_L1 is available at http://www.na.icar.cnr.it/~mariog/regec_l1.html

Combining Flux Balance Analysis and Model Checking for Metabolic Network Validation and Analysis.

Several human diseases are caused by metabolism defects. Discovering the mechanisms that govern the onset and progression of human metabolism-related diseases is not a straightforward process. Computational approaches, such as the flux balance analysis, have been successfully used to extract useful knowledge on the metabolic dysregulation processes from genome-scale network models. In this work, we propose a novel approach which integrates constraint-based techniques with model checking methods, with the aim to extract relevant qualitative information from a metabolic network model. As a case study, we applied our methodology to the simulation and analysis of the primary hyperoxaluria type I, an inherited disease in which the lack of a particular liver enzyme causes the kidney to accumulate excessive amounts of oxalate

On the regularization of generalized eigenvalues classifiers

Generalized Eigenvalues Classifiers (GEC), which originated from the GEPSVM algorithm by Mangasarian, proved to be an efficient alternative to the Support Vector Machines (SVMs) in the solution of supervised classification tasks. However real-life datasets are often characterized by a large number of redundant features and by a great number of points whose labels are difficult (or too expensive) to assign. In this work we start from the Regularized Generalized Eigenvalue Classifier (ReGEC) and show how regularization terms can be used to enable the classifier to solve two different problems, strictly connected to that of supervised classification: feature selection and semi-supervised classification. Numerical results, obtained on some standard benchmark data sets, show the efficiency of the proposed solution

pATsi: Paralogs and Singleton Genes from

Arabidopsis thaliana is widely accepted as a model species in plant biology. Its genome, due to its small size and diploidy, was the first to be sequenced among plants, making this species also a reference for plant comparative genomics. Nevertheless, the evolutionary mechanisms that shaped the Arabidopsis genome are still controversial. Indeed, duplications, translocations, inversions, and gene loss events that contributed to the current organization are difficult to be traced. A reliable identification of paralogs and single-copy genes is essential to understand these mechanisms. Therefore, we implemented a dedicated pipeline to identify paralog genes and classify single-copy genes into opportune categories. PATsi, a web-accessible database, was organized to allow the straightforward access to the paralogs organized into networks and to the classification of single-copy genes. This permits to efficiently explore the gene collection of Arabidopsis for evolutionary investigations and comparative genomics

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources