We present a mathematical model to study the effects of HER2 over-expression on cell proliferation in breast cancer. The model illustrates the proliferative behavior of cells as a function of HER2 and EGFR receptors numbers, and the growth factor EGF. This mathematical model comprises kinetic equations describing the cell surface binding of EGF growth factor to EGFR and HER2 receptors, coupled to a model for the dependence of cell proliferation rate on growth factor receptors binding. The simulation results from this model predict: (1) a growth advantage associated with excess HER2 receptors; (2) that HER2-over-expression is an insufficient parameter to predict the proliferation response of cancer cells to epidermal growth factors; and (3) the EGFR receptor expression level in HER2-over-expressing cells plays a key role in mediating the proliferation response to receptor-ligand signaling. This mathematical model also elucidates the interaction and roles of other model parameters in determining cell proliferation rate of HER2-over-expressing cells.
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http://dx.doi.org/10.1007/s11538-008-9315-4 | DOI Listing |
Glob Ment Health (Camb)
December 2024
Faculty of Education, Niğde Ömer Halisdemir University, Niğde, Türkiye.
One of the most popular instruments used to assess perceived social support is the Multidimensional Scale of Perceived Social Support (MSPSS). Although the original structure of the MSPSS was defined to include three specific factors (significant others, friends and family), studies in the literature propose different factor solutions. In this study, we addressed the controversial factor structure of the MSPSS using a meta-analytic confirmatory factor analysis approach.
View Article and Find Full Text PDFChem Sci
December 2024
VASP Software GmbH Berggasse 21 A-1090 Vienna Austria.
Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists. Yet, predicting even the absolute standard hydrogen electrode potential, the most fundamental reference for electrode potentials, proves to be extremely challenging. Here, we show that a hybrid functional incorporating 25% exact exchange enables quantitative predictions when statistically accurate phase-space sampling is achieved thermodynamic integrations and thermodynamic perturbation theory calculations, utilizing machine-learned force fields and Δ-machine learning models.
View Article and Find Full Text PDFMol Ecol Resour
January 2025
School of Life Sciences, Anhui University, Hefei, Anhui, China.
Emberiza buntings (Aves: Emberizidae) exhibit extensive diversity and rapid diversification within the Old World, particularly in the eastern Palearctic, making them valuable models for studying rapid radiation among sympatric species. Despite their ecological and morphological diversity, there remains a significant gap in understanding the genomic underpinnings driving their rapid speciation. To fill this gap, we assembled high-quality chromosome-level genomes of five representative Emberiza species (E.
View Article and Find Full Text PDFBull Math Biol
January 2025
School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia.
We propose a simple mathematical model to describe the mechanical relaxation of cells within a curved epithelial tissue layer represented by an arbitrary curve in two-dimensional space. This model generalises previous one-dimensional models of flat epithelia to investigate the influence of curvature for mechanical relaxation. We represent the mechanics of a cell body either by straight springs, or by curved springs that follow the curve's shape.
View Article and Find Full Text PDFAppl Microbiol Biotechnol
January 2025
NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Kent Ridge, 117456, Singapore.
Detecting alterations in plasmid structures is often performed using conventional molecular biology. However, these methods are laborious and time-consuming for studying the conditions inducing these mutations, which prevent real-time access to cell heterogeneity during bioproduction. In this work, we propose combining both flow cytometry and fluorescence-activated cell sorting, integrated with mechanistic modelling to study conditions that lead to plasmid recombination using a limonene-producing microbial system as a case study.
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