Biology subjects

Sommariva, S.

Publications and source records attributed to Sommariva, S..

8 recordsLinked to original sources

bioRxiv2026

Benchmarking Clustering Strategies for High-Dimensional Spike Time-Windows Data from Multi-Electrode Arrays

From a statistical point of view, clustering spike time-windows from multi-electrode arrays (MEAs) recordings is a challenging high-dimensional, unsupervised clustering task where stationarity, commonly assumed in standard time-series analysis, is often violated and for which a gold standard is currently unavailable. Here we aim at providing practical guidance on how to cluster this type of data by systematically comparing 108 clustering pipelines differing along three main dimensions: (i) the data feature space; (ii) the metrics used to quantify distance between data points; and (iii) the clustering algorithm. For our benchmark we used both labeled synthetic data, mimicking four physiologically-inspired classes of spike time-windows, and a set of real MEAs recordings from a two-dimensional in-vitro neural culture. The performance of the competing pipelines was evaluated in terms of balanced accuracy and computational time when analyzing the synthetic datasets, while the Silhouette score was used for the real dataset, where no ground-truth is available. Overall, our analysis shows that the best combination is formed by k-means with Euclidean distance applied after Principal Component Analysis (PCA) of the spike time-windows. Conversely, hierarchical clustering showed the highest computational burden, while Independent Component Analysis and kernel PCA provided less effective noise suppression.

neuroscience↗

bioRxiv2025

From Michaelis-Menten parameters to microscopic rate constants: an inversion approach for enzyme kinetics

A basic model of enzyme kinetics is the enzyme-catalyzed reaction, in which a single enzyme E binds a substrate S to form a complex C, which subsequently releases a product P and regenerates the enzyme. According to the mass-action law, this process can be described by a system of four ordinary differential equations for the four unknown concentrations, governed by the microscopic rate constants: forward (kf), reverse (kr), and catalytic (kcat). Under suitable assumptions, the full system reduces to the classical Michaelis-Menten model, depending on two experimentally measurable parameters: the half saturation constant (Km), and the limiting rate Vmax. This paper addresses the inverse problem of recovering kf and kr from Km and Vmax (or equivalently kcat, retrievable from Vmax). The proposed method exploits a new identity satisfied by any substrate concentration s(t) in the full mass-action system, which provides the starting point for the inversion procedure. Here, a detailed presentation of an algorithm for reaching this goal is provided, along with an assessment of reconstruction accuracy and an outline of simulations and applications. Beyond enzyme kinetics, the approach supports the construction of novel chemical reaction networks, with potential applications to modeling complex biochemical pathways involved in diseases such as cancer.

bioinformatics↗

bioRxiv2023

In-silico modelling of the mitogen-activated kinase (MAPK) pathway in colorectal cancer: mutations and targeted therapy

Chemical reaction networks are powerful tools for computing the complex nature of cancers onset, progression, and therapy. The main reason for their effectiveness is in the fact that these networks can be rather naturally encoded as a dynamical system whose asymptotic solution mimics the proteins concentration profile at equilibrium. The paper relies on this mathematical approach to investigate global and local effects on the chemical reaction network of the colorectal cancer, triggered by partial and complete mutations occurring in its mitogen-activated kinase (MAPK) pathway. Further, this same approach allowed the in-silico modelling and dosage of a multi-target therapeutic intervention that utilizes MAPK as its molecular target.

systems biology↗

bioRxiv2023

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses. HighlightsO_LIThe regularization parameter of the Minimum Norm Estimate of neural activity impacts connectivity estimation C_LIO_LIWe study empirically the optimal parameter for connectivity estimation using realistic synthetic datasets C_LIO_LIWe find the optimal parameter for connectivity estimation is systematically smaller than the optimal parameter for source imaging; different connectivity metrics yield the same result C_LIO_LICode and data are available open source. C_LI

neuroscience↗

bioRxiv2023

SSI: A Statistical Sensitivity Index for Chemical Reaction Networks in cancer

SO_SCPLOWUMMARYC_SCPLOWAt the cellular level, cancer is triggered by mutations of the proteins involved in signalling networks made of hundreds of reacting species. The corresponding mathematical model consists of a large system of non-linear Ordinary Differential Equations for the unknown proteins concentrations depending on a consistently large number of kinetic parameters and initial concentrations. For this model, the present paper considers the problem of assessing the impact of each parameter and initial concentration on the systems output. More specifically, we introduced a statistical sensitivity index whose values can be easily computed by means of principal component analysis, and which leads to the partition of the parameters and initial concentrations sets into sensible and non-sensible families. This approach allows the identification of those kinetic parameters and initial concentrations that mostly impact the mutation-driven modification of the proteomic profile at equilibrium, and of those pathways in the network that are mostly affected by the presence of mutations in the cancer cell.

systems biology↗

bioRxiv2021

Transfreq: a Python package for computing the theta-to-alpha transition frequency from resting state EEG data

A classic approach to estimate the individual theta-to-alpha transition frequency requires two electroencephalographic (EEG) recordings, one acquired in restingstate condition and one showing an alpha de-synchronisation due e.g. to task execution. This translates into longer recording sessions that my be cumbersome in studies involving patients. Moreover, incomplete de-synchronisation of the alpha rhythm may compromise the final estimation of the transition frequency. Here we present transfreq, a Python library that allows the computation of the transition frequency from resting-state data by clustering the spectral profiles at different EEG channels based on their content in the alpha and theta bands. We first provide an overview of the transfreq core algorithm and of the software architecture. Then we demonstrate its feasibility and robustness across different experimental setups on a publicly available EEG data set and on in-house recordings. A detailed documentation of transfreq and the codes for reproducing the analysis of the paper with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/

neuroscience↗

bioRxiv2020

Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells.

Colorectal cancer (CRC) is one of the most deadly and commonly diagnosed tumors worldwide. Several genes are involved in its development and progression. The most frequent mutations concern APC, KRAS, SMAD4, and TP53 genes, suggesting that CRC relies on the alteration of different pathways. However, with classic molecular approaches, it is not easy to simultaneously analyze the interconnections between these pathways. For this reason, we propose a computational model based on a huge chemical reaction network to simulate the effects induced on the global signaling associated with CRC by single or multiple concurrent mutations or by drug treatment. This approach displays several advantages. The model can quantify the alteration in the concentration of the proteins connected with the examined mutation. Moreover, working on the global signaling of CRC, it is possible to disclose unexpected interactions between the involved pathways, representing new therapeutic targets. HighlightsO_LIColorectal cancer relates to defects in many different pathways within cell signaling C_LIO_LICell signaling is modeled as a chemical ration network with 10 interacting pathways C_LIO_LIGlobal effects induced by single or multiple concurrent mutations are quantified C_LIO_LIA possible extension of the model to account for a targeted drug is discussed C_LI

cancer biology↗

bioRxiv2020

Gain and Loss of Function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells

This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Function mutations found in cancer cells. More specifically, from a theoretical perspective, our approach focuses on the determination of moiety conservation laws for the system and their relation with the corresponding stoichiometric surfaces. Then we show that Loss of Function mutations can be implemented in the model via modification of the initial conditions in the system, while Gain of Function mutations can be implemented by eliminating specific reactions. Finally, the model is utilized to examine in detail the G1-S phase of a colorectal cancer cell.

systems biology↗