Biology subjects

Beccuti, M.

Publications and source records attributed to Beccuti, M..

9 recordsLinked to original sources

bioRxiv2026

A new cancer progression model: from synthetic tumors to real data and back

Intratumor heterogeneity (ITH) arises from the combined effects of genetic alterations, clonal interactions, and environmental constraints, and plays a central role in therapeutic resistance and disease progression. While ITH has been extensively documented in empirical tumor data, the scientific debate regarding the biological mechanisms underlying this heterogeneity remains complex, highlighting the need for cancer evolution models that are sufficiently flexible and sophisticated to reproduce the observed behaviors and to give insights on the unobserved ones. Here, we present a stochastic modelling framework for tumor evolution that integrates genotypic inheritance with phenotype driven functional traits and resource mediated competition. Mutational events are associated with functional capabilities such as altered proliferation, increased mutation rates, limit evasion potential or enhanced control over shared resources, allowing multiple genotypes to converge on similar phenotypes. The model explicitly tracks subclonal lineages while incorporating environmental constraints that modulate growth and competition.The framework is defined through a mathematically rigorous construction and is accompanied by an efficient simulation algorithm. To facilitate exploration and reproducibility, we provide an open-source graphical user interface that allows users to configure model parameters, run simulations, and inspect clonal genealogies and population dynamics without requiring direct interaction with the underlying code. Using this model, we illustrate how ecological feedbacks can shape clonal dynamics over time, supporting an interpretation in which early tumor growth is dominated by stochastic expansion, while later evolution increasingly reflects selection for traits that alleviate environmental constraints. Rather than constituting a new evolutionary paradigm, this behaviour demonstrates how well-documented biological patterns can emerge naturally from a unified stochastic and ecological description. Overall, our approach offers a flexible and extensible platform for investigating how chance, functional traits, and environmental interactions jointly govern tumor heterogeneity. Author summaryNot all cancerous cells are created equal: inside the same tumor, different populations of cells exist at the same time, fighting for the same resources and influencing the way the disease evolves and reacts to treatments. These groups of cells have different behaviour and abilities thanks to different genetic mutations, which might give them an advantage or bring their population to disappearance. We have built a mathematical model that mimics the evolution of a tumor over time, simulating a competition between its different populations of cells. Our simulated experiments show that tumors evolve in two distinct phases: at first, cells that grow and divide more quickly have an advantage. Once the space and nutrients are limited, cells that can survive with fewer resources have an advantage and can potentially take over the race. We use these simulations to argue that the evolution of a tumor doesnt depend on the shape of the space it expands in, but rather on the availability of nutrients.

bioinformatics↗

bioRxiv2024

UnifiedGreatMod: A New Holistic Modeling Paradigm for Studying Biological Systems on a Complete and Harmonious Scale

MotivationComputational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognisable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, for example, to the cancer evolution study. ResultsTo address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It allows for functional studies, in which the understanding of the systems multi-level stable condition and the systems fluctuating condition are combined to investigate the functional dependencies among the biological entities in the system under study. This is achieved thanks to the hybridisation of two analysis approaches that capture a systems different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposals effectiveness was demonstrated by mechanistically simulating the metabolic output of Echerichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.

systems biology↗

bioRxiv2023

OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data.

Open science and reproducibility are two key pillars of modern scientific research. Open science is making scientific research and data accessible and transparent to the broader scientific community and the public. Reproducibility, on the other hand, is the ability to replicate and confirm research results by following the same methods and procedures. Reproducibility is thus crucial because it ensures the reliability and validity of scientific findings. The relationship between open science and reproducibility is intertwined; indeed open science practices, such as sharing raw data, detailed methodologies, and code, greatly facilitate the reproducibility of research. In recent years, concerns about the reproducibility of scientific research have gained prominence, and indeed scientists still lament the lack of details in the methods sections of published papers and the unavailability of raw data from the authors. To assist cellular biologists and immunologists and to promote a more transparent, open and reproducible research practice, we developed OmniReproducibleCellAnalysis (ORCA), a new Shiny Application based in R, for the semi-automated analysis of Western Blot (WB), Reverse Transcription-quantitative PCR (RT-qPCR), Enzyme-Linked ImmunoSorbent Assay (ELISA), Endocytosis and Cytotoxicity experiments. ORCA is open-source and approachable by scientists without advanced R language knowledge. Our application automatically compiles a report containing the finalized data analysis and all its preliminary and intermediate steps, ensuring data analysis standardization and reproducibility. Furthermore, ORCA allows to upload raw data and results directly on the data repository Harvard Dataverse, a valuable tool for promoting transparency and data accessibility in scientific research. By employing ORCA, scientists will cut down analysis time and human-dependent errors, while taking a step towards a research practice compliant with Open Science and FAIR principle.

bioinformatics↗

bioRxiv2022

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

The transition from the evaluation of a single time point to the examination of the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and data clustering challenging. Here we present CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft (PDX) growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. Through the lens of a new perspective of mechanism interpretation, CONNECTOR shed light onto novel model aggregations and identified unanticipated molecular associations with response to clinically approved therapies.

bioinformatics↗

bioRxiv2022

Stardust: improving spatial transcriptomics data analysis through space aware modularity optimization based clustering.

BackgroundSpatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result. ResultsWe propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analysing ST datasets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbours when perturbations are applied. ConclusionsStardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.

bioinformatics↗

bioRxiv2020

Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Although scRNAseq has some technical challenges, it would be of great interest being able to disclose biological information out of cell subpopulations, which can be defined by cluster analysis of scRNAseq data. In this manuscript, we evaluated the efficacy of sparsely-connected autoencoder (SCA) as tool for the functional mining of single cells clusters. We show that SCA can be uses as tool to uncover hidden features associated to scRNAseq data. Our approach is strengthened by two metrics, QCF and QCM, which respectively allow to evaluate the ability of SCA to reconstruct a cells cluster and to evaluate the overall quality of the neural network model. Our data indicate that SCA encoded spaces, derived by different experimentally validated data (TFs targets, miRNAs targets, Kinases targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

bioinformatics↗

bioRxiv2018

rCASC: reproducible Classification Analysis of Single Cell sequencing data

SummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.\n\nAvailability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.\n\nSupplementary informationSupplementary data are available at rCASC github

bioinformatics↗

bioRxiv2018

Complementary information on single nucleotide variants, INDELs and functional translocations can be obtained with RNAseq using different library preparations

BackgroundRNA-seq represents an attractive methodology for the detection of functional genomic variants because it allows the integration of variant frequency and their expression. However, although specific statistic frameworks have been designed to detect SNVs/INDELS/gene fusions in RNA-seq data, very little has been done to understand the effect of library preparation protocols on transcript variant detection in RNA-seq data.\n\nResultsHere, we compared RNA-seq results obtained on short reads sequencing platform with two protocols: one based on polyA+ RNA selection protocol (POLYA) and the other based on exonic regions capturing protocol (ACCESS). Our data indicate that ACCESS detects 10% more coding SNV/INDELs with respect to POLYA, making this protocol more suitable for this goal. Furthermore, ACCESS requires less reads for coding SNV detection with respect to POLYA. On the other hand, if the analysis aims at identifying SNV/INDELs also in the 5and 3 UTRs, POLYA is definitively the preferred method. No particular advantage comes from the usage of ACCESS or POLYA in the detection of fusion transcripts.\n\nConclusionData show that a careful selection of the \"wet\" protocol adds specific features that cannot be obtained with bioinformatics alone.

genomics↗

bioRxiv2017

Reproducible Bioinformatics Project: A community for reproducible bioinformatics analysis pipelines

BackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and a list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.\n\nResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.\n\nConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

bioinformatics↗