Integrated computational extraction of cross-cancer poly-omic signatures

Understanding the interplay between metabolism and genetic regulation is considered key to shed light on the mechanisms underlying cancer onset and progression. In thiswork, we reconstruct a number of tumor-specific genome-scale metabolic models and inspect estimated flux profiles via statistical analysis, characterizing the detailed metabolicresponse associated to altered regulation in various tissues. We thus demonstrate that combining complementary computational techniques it is possible to identify polyomicdifferences and commonalities across cancer types

A data- and model-driven analysis reveals the multi-omic landscape of ageing

Altered expression of a number of genes has been correlated to biological ageing in humans. The biological age predicted from gene expression levels is known as transcriptomic age. This di ers from chronological age which is measured as the time that an individual has lived since their date of birth. Transcrip- tomic age can be older or younger than an individual's chronological age. At present, studies have focused on using transcriptomic data to predict transcrip- tomic age. However, this approach largely does not consider the e ect that genes have on the metabolic network and therefore on the observable cellular pheno- type. This research takes the current understanding of transcriptomic ageing a step further by generating and investigating genome-scale metabolic models of ageing, using machine learning methods and a multi-omic approach based on constraint-based modelling. We combine these models with a transcriptomic age predictor and gene expression data from CD4 T-Cells from human peripheral blood mononuclear cells in healthy individuals. We show that metabolic models augmented with transcriptomics data of ageing can generate greater metabolic insights into the di erences between chronological and transcriptomic age. Com- pared to standard transcriptomic-only approaches, our method provides a more comprehensive analysis of transcriptomic ageing and paves the way for a multi- omic understanding of ageing mechanisms in human cells

Predictive analytics of environmental adaptability in multi-omic network models.

Bacterial phenotypic traits and lifestyles in response to diverse environmental conditions depend on changes in the internal molecular environment. However, predicting bacterial adaptability is still difficult outside of laboratory controlled conditions. Many molecular levels can contribute to the adaptation to a changing environment: pathway structure, codon usage, metabolism. To measure adaptability to changing environmental conditions and over time, we develop a multi-omic model of Escherichia coli that accounts for metabolism, gene expression and codon usage at both transcription and translation levels. After the integration of multiple omics into the model, we propose a multiobjective optimization algorithm to find the allowable and optimal metabolic phenotypes through concurrent maximization or minimization of multiple metabolic markers. In the condition space, we propose Pareto hypervolume and spectral analysis as estimators of short term multi-omic (transcriptomic and metabolic) evolution, thus enabling comparative analysis of metabolic conditions. We therefore compare, evaluate and cluster different experimental conditions, models and bacterial strains according to their metabolic response in a multidimensional objective space, rather than in the original space of microarray data. We finally validate our methods on a phenomics dataset of growth conditions. Our framework, named METRADE, is freely available as a MATLAB toolbox

Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments

In this work, a genome-scale metabolic model of Synechococcus sp. PCC 7002 which utilizes flux balance analysis across multiple layers is analyzed to observe flux response between 23 growth conditions. This is achieved by setting reactions involved in biomass accumulation and energy production as objectives for bi-level linear optimization, thus serving to improve the characterization of mechanisms underlying these processes in photoautotrophic microalgae. Additionally, the incorporation of statistical techniques such as k-means clustering and principal component analysis (PCA) contribute to reducing dimensionality and inferring latent patterns

Mechanistic effects of influenza in bronchial cells through poly-omic genome-scale modelling

In this work we propose regularised bi-level constraint-based modelling to determine the fluxomic profiles for four different influenza viruses, H7N9, H7M7, H3N2 and H5N1. We report here the first step of the analysis of the flux data usingAutoSOME clustering, where we identify novel biomarkers of infection. This is a work in progress that can directly lead to novel therapeutic targets

Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science

To paraphrase Stan Ulam, a Polish mathematician who became a leading figure in the Manhattan Project, in this dissertation I focus not only on how computer science can help biologists, but also on how biology can inspire computer scientists. On one hand, computer science provides powerful abstraction tools for metabolic networks. Cell metabolism is the set of chemical reactions taking place in a cell, with the aim of maintaining the living state of the cell. Due to the intrinsic complexity of metabolic networks, predicting the phenotypic traits resulting from a given genotype and metabolic structure is a challenging task. To this end, mathematical models of metabolic networks, called genome-scale metabolic models, contain all known metabolic reactions in an organism and can be analyzed with computational methods. In this dissertation, I propose a set of methods to investigate models of metabolic networks. These include multi-objective optimization, sensitivity, robustness and identifiability analysis, and are applied to a set of genome-scale models. Then, I augment the framework to predict metabolic adaptation to a changing environment. The adaptation of a microorganism to new environmental conditions involves shifts in its biochemical network and in the gene expression level. However, gene expression profiles do not provide a comprehensive understanding of the cellular behavior. Examples are the cases in which similar profiles may cause different phenotypic outcomes, while different profiles may give rise to similar behaviors. In fact, my idea is to study the metabolic response to diverse environmental conditions by predicting and analyzing changes in the internal molecular environment and in the underlying multi-omic networks. I also adapt statistical and mathematical methods (including principal component analysis and hypervolume) to evaluate short term metabolic evolution and perform comparative analysis of metabolic conditions. On the other hand, my vision is that a biomolecular system can be cast as a “biological computer”, therefore providing insights into computational processes. I therefore study how computation can be performed in a biological system by proposing a map between a biological organism and the von Neumann architecture, where metabolism executes reactions mapped to instructions of a Turing machine. A Boolean string represents the genetic knockout strategy and also the executable program stored in the “memory” of the organism. I use this framework to investigate scenarios of communication among cells, gene duplication, and lateral gene transfer. Remarkably, this mapping allows estimating the computational capability of an organism, taking into account also transmission events and communication outcomes

Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data

Motivation: High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modeling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multisource and multiomic nature of these data types while preserving mechanistic interpretation. Results: Here, we investigate different regularization techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularization frameworks including group, view-specific and principal component regularization and experimentally compare them using data from 1143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularization employed. In multiomic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularized linear models compared to data-hungry methods based on neural networks. Availability and implementation: All data, models and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso. Supplementary information: Supplementary data are available at Bioinformatics online