Biology subjects

Piana, M.

Publications and source records attributed to Piana, M..

10 recordsLinked to original sources

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

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

Feeding recombinant Royalactin: Measures of improved lifespan and mitochondrial function in Caenorhabditis elegans

Ageing, the decline of biological functions over time, is inherent to eukaryotes. Female honeybees attain a long-lived queen phenotype upon continuous consumption of royal jelly, whereas restricted supply of this nutritional substance promotes the development of worker bees, which are short-lived. An abundant protein found within royal jelly is major royal jelly protein 1 (MRJP1), also known as Royalactin. Health- and lifespan promoting effects have been attributed to Royalactin in species from diverse animal taxa, suggesting it acts on phylogenetically conserved physiological processes. Here, we explore the effects of feeding the nematode Caenorhabditis elegans with Escherichia coli that express a recombinant form of Royalactin (RArec). We confirm that consumption of RArec increases body size, improves locomotion and extends lifespan. We discover a link between Royalactin and mitochondria, organelles which play a key part in the ageing process: both spare respiratory capacity and morphology indicate improved mitochondrial function in RArec fed C. elegans. These results demonstrate the feasibility of using recombinant Royalactin to gain further insight into processes of healthy ageing in many species. RArec production allows insight into potential beneficial effects across species.

physiology↗

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↗

bioRxiv2019

Computational approaches for parametric imaging of dynamic PET data

Parametric imaging of nuclear medicine data exploits dynamic functional images in order to reconstruct maps of kinetic parameters related to the metabolism of a specific tracer injected in the biological tissue. From a computational viewpoint, the realization of parametric images requires the pixel-wise numerical solution of compartmental inverse problems that are typically ill-posed and nonlinear. In the present paper we introduce a fast numerical optimization scheme for parametric imaging relying on a regularized version of the standard affine-scaling Trust Region method. The validation of this approach is realized in a simulation framework for brain imaging and comparison of performances is made with respect to a regularized Gauss-Newton scheme and a standard nonlinear least-squares algorithm.

bioengineering↗

bioRxiv2017

Parametric Imaging Of FDG-PET Data Using Physiology And Iterative Regularization: Application To The Hepatic And Renal Systems

I. AIMS I. AIMS II. METHODS III. RESULTS IV. CONCLUSIONS REFERENCES Positron Emission Tomography (PET) [1] is a nuclear medicine imaging technique capable of detecting the time dependent spatial distribution of pico-molar quantities of a labelled tracer, which is diffused into a living body. FDG-PET is a PET modality in which the radiopharmaceutical [18F]Fluoro-2-deoxy-D-glucose (FDG) is used as a tracer to observe metabolic processes related to glucose consumption. In order to overcome the limits of spatial resolution in PET and to improve the quality of information achievable from PET images, one approach is to develop parametric imaging methods able of showing the tracer metabolism at a local level. Starting from the design of compartmental models appropriate to describe the tracer kinetics in a predefined physiol ...

physiology↗

bioRxiv2017

Inverse Modeling For MEG/EEG Data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.

neuroscience↗