Papers by Giancarlo Mauri
It is well known that tumors originating from the same tissue have different prognosis and sensit... more It is well known that tumors originating from the same tissue have different prognosis and sensitivity to treatments, depending on their molecular features. Over the last decade, cancer genomics consortia like the Cancer Genome Atlas (TCGA; https://cancergenome.nih.gov) have been generating thousands of cross-sectional data, spanning from genetic and epigenetic mutations to proteome profiles, for thousands of human primary tumors originated from various tissues. Thanks to the public access provided by such consortia to their datasets, it is today possible to analyze a broad range of relevant information such as gene sequences, expression profiles or metabolite footprints , to capture tumor molecular heterogeneity and improve patient stratification and clinical management. To this aim, it is common practice to analyze datasets grouped into clusters based on clinical observations and/or molecular features. However, the identification of specific properties of each cluster that may be effectively targeted by therapeutic drugs still represents a challenging task. In this perspective, characterization of the metabolism of stratified patient cohorts may greatly help to select the best pharmacological treatment to prevent biomass formation and hence tumor growth. In this work, we define a method to generate an activity score for the metabolic reactions of different clusters of patients based on their transcriptional profile. This approach reduces the number of variables from many genes to few reactions, by aggregating tran-scriptional information associated to the same enzymatic reaction according to gene-enzyme and enzyme-reaction rules. As a proof of concept, we applied the methodology to a dataset of 244 RNAseq transcriptional profiles taken from patients with colorectal cancer (CRC), the second cause of cancer death in USA. CRC samples are typically divided into two sub-types: (i) tumors with microsatellite instability (MSI), associated with hyper-mutation and with CpG island methylation phenotype, and (ii) microsatellite stable (MSS) tumors, typically endowed with chromosomal instability. We report some key differences in the central carbon metabolism of the two clusters. We also show how the method can be used to describe the metabolism of individual patients and cluster them exclusively based on metabolic features.
Background: An important challenge in cancer biology is to understand the complex aspects of the ... more Background: An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell.
Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordinatio... more Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA–gene–gene and miRNA–protein–protein interactions, and to analyze miRNA GRNs in order to identify miRNA–gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.
Computing Research Repository, 2009
The chemotactic pathway allows bacteria to respond and adapt to environmental changes, by tuning ... more The chemotactic pathway allows bacteria to respond and adapt to environmental changes, by tuning the tumbling and running motions that are due to clockwise and counterclockwise rotations of their flagella. The pathway is tightly regulated by feedback mechanisms governed by the phosphorylation and methylation of several proteins. In this paper, we present a detailed mechanistic model for chemotaxis, that considers
Lecture Notes in Computer Science, 1987
ABSTRACT Without Abstract
Lecture Notes in Computer Science, 1998
... 1995. [LLR95] Nathan Linial, Eran London, and Yuri Rabinovich. The geometry of graphs and som... more ... 1995. [LLR95] Nathan Linial, Eran London, and Yuri Rabinovich. The geometry of graphs and some of its algorithmic applications. Combinatorica, 15(2):215-245, 1995. [MS96] Zbigniew Michalewicz and Marc Schoenauer. ...
Stochastic simulations based on the tau leaping method are applicable to well stirred chemical re... more Stochastic simulations based on the tau leaping method are applicable to well stirred chemical reaction systems occurring inside a single xed volume. In this paper we propose a novel method, based on the tau leaping procedure, for the simulation of complex systems com- posed by several communicating regions. The new method is here applied to dynamical probabilistic P systems, which
Lecture Notes in Physics, 1984
ABSTRACT
Lecture Notes in Computer Science, 1982
Without Abstract
Electronic Proceedings in Theoretical Computer Science, 2009
The chemotactic pathway allows bacteria to respond and adapt to environmental changes, by tuning ... more The chemotactic pathway allows bacteria to respond and adapt to environmental changes, by tuning the tumbling and running motions that are due to clockwise and counterclockwise rotations of their flagella. The pathway is tightly regulated by feedback mechanisms governed by the phosphorylation and methylation of several proteins. In this paper, we present a detailed mechanistic model for chemotaxis, that considers all of its transmembrane and cytoplasmic components, and their mutual interactions. Stochastic simulations of the dynamics of a pivotal protein, CheYp, are performed by means of tau leaping algorithm. This approach is then used to investigate the interplay between the stochastic fluctuations of CheYp amount and the number of cellular flagella. Our results suggest that the combination of these factors might represent a relevant component for chemotaxis. Moreover, we study the pathway under various conditions, such as different methylation levels and ligand amounts, in order to test its adaptation response. Some issues for future work are finally discussed.
Electronic Proceedings in Theoretical Computer Science, 2013
Cancer informatics, 2015
We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (G... more We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behavior of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste's modular simulation framework provides a powerful tool to model multicellular systems, possibly allowing...
SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 1998
... Journal of Com-putational Chemistry, 16( 11): 1434-1444, 1995. Nathan Linial, Eran London, an... more ... Journal of Com-putational Chemistry, 16( 11): 1434-1444, 1995. Nathan Linial, Eran London, and Yuri Ra-binovich. ... In Surveys in combi-natorics, pages 148-188. Cambridge Uni-versity Press, Cambridge, 1989. Zbigniew Michalewicz and Marc Schoe-nauer. ...
BioMed research international, 2015
The economic burden of chronic diseases and conditions (such as cardiovascular pathologies, diabe... more The economic burden of chronic diseases and conditions (such as cardiovascular pathologies, diabetes, obesity, chronic obstructive pulmonary disease, chronic pain, and traumatic brain injuries) requires new solutions not only in traditional clinical settings (in-patient treatments), but also in innovative healthcare scenarios (out-patient long-term monitoring).
The spectral-based characterization of inkjet printers is often based on a physical description o... more The spectral-based characterization of inkjet printers is often based on a physical description of the printing process. The objective of our work is to see whether an approach based on the use of neural networks is an effective strategy for spectral printer characterization without requiring a deep knowledge of the printing process. In our experiments, we treat the printers as
International Colloquium on Automata, Languages and Programming, 1977
Without Abstract
Lecture Notes in Computer Science, 2004
One of the main challenges in modern biology and genome research is to understand the complex mec... more One of the main challenges in modern biology and genome research is to understand the complex mechanisms that regulate gene expression. Being able to tell when, why, and how one or more genes are activated could provide information of inestimable value for the understanding of the mechanisms of life. The wealth of genomic data now available opens new opportunities to researchers. We present how a method based on genetic algorithms has been applied to the characterization of two regulatory signals in DNA sequences, that help the cellular apparatus to locate the beginning of a gene along the genome, and to start its transcription. The signals have been derived from the analysis of a large number of genomic sequences. Comparisons with related work show that our method presents different improvements, both from the computational viewpoint, and in the biological relevance of the results obtained.
Fundamenta Informaticae, 1983
Artificial Evolution, 1997
... 1995. [LLR95] Nathan Linial, Eran London, and Yuri Rabinovich. The geometry of graphs and som... more ... 1995. [LLR95] Nathan Linial, Eran London, and Yuri Rabinovich. The geometry of graphs and some of its algorithmic applications. Combinatorica, 15(2):215-245, 1995. [MS96] Zbigniew Michalewicz and Marc Schoenauer. ...
Lecture Notes in Computer Science, 2007
P-systems, or membrane systems [1], were introduced by George Păun as a class of unconventional c... more P-systems, or membrane systems [1], were introduced by George Păun as a class of unconventional computing devices of distributed, parallel and nondeterministic type, inspired by the compartmental structure and the functioning of living cells. The basic model consists of a membrane structure, described by a finite string of well matching parentheses, and graphically represented as regions on the plane, hierarchically
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Papers by Giancarlo Mauri
Motivation: Several cancer-related genomic data have become available (e.g., The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis.
Our goal is to infer cancer “progression” models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of “selectivity” relations, where a mutation in a gene A “selects” for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices.
Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy, and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data.
We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events.