Biology subjects

Sommariva, S.

Publications and source records attributed to Sommariva, S..

3 recordsLinked to original sources

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↗