Biology subjects

Zahran, S.

Publications and source records attributed to Zahran, S..

4 recordsLinked to original sources

bioRxiv2024

Finite Element Modeling of Electrical Activity in Human Uterine Tissue: Advances in Simulation Techniques

Simulating the electrical activity of the human uterus has become a crucial tool for understanding the underlying biophysical phenomena, such as uterine contractions during pregnancy. Finite element models (FEM) offer valuable insights into these dynamics by providing a scalable framework to explore the propagation of electrical signals at the cellular and tissue levels. This study presents a finite element-based bidomain model to simulate the excitation propagation across the human uterus. Our model integrates cellular-level electrophysiological properties with tissue-level electrical propagation using the FEniCS Python library. A three-dimensional, realistic representation of uterine tissue is employed to simulate excitation patterns, contributing to a deeper understanding of uterine electrophysiology. The model can also be adapted to investigate pathological conditions such as preterm labor and test potential interventions. The developed simulation framework provides a scalable solution for the numerical challenges posed by solving complex, non-linear ordinary differential equations (ODEs) associated with uterine electrical activity. These simulations could offer a foundation for future research on uterine function and its related disorders.

bioengineering↗

bioRxiv2024

Modeling the Effects of Propofol on Brain Activity: Insights from Computational EEG Studies

The effects of anesthetic agents like propofol on brain activity provide critical insights into how consciousness is altered during anesthesia. This article presents a computational approach to modeling the impact of propofol on the brains electrical activity using the COALIA model, a large-scale neural mass model of human EEG designed for consciousness research. Propofol enhances GABA_A-mediated synaptic inhibition, leading to characteristic EEG signatures such as alpha oscillations (8-12 Hz) and slow-wave oscillations (0.1-1.5 Hz). We model these effects by modulating the parameters of postsynaptic potentials and synaptic strength, simulating the action of propofol at both the molecular and network levels. COALIAs detailed representation of thalamo-cortical and cortico-cortical networks provides realistic simulations of brain dynamics under anesthesia, closely replicating experimental EEG data. Our findings highlight the importance of network-level interactions in producing the distinctive EEG changes seen during propofol-induced unconsciousness. This research paves the way for future applications in personalized anesthesia monitoring and consciousness assessment through EEG modeling.

neuroscience↗

bioRxiv2024

Designing Experimental Protocols to Elicit Vigilance Variations in Controlled Laboratory Settings

Vigilance plays a vital role in numerous high-risk professions, where real-time monitoring of vigilance levels is highly beneficial. Brain-Computer Interfaces (BCIs) utilizing EEG signals, along with machine learning and deep learning algorithms, present a promising solution for the classification and monitoring of vigilance levels. This study aimed to create a dataset for training such models by evaluating four experimental paradigms: the Hitchcock Air Traffic Controller Task (ATC), the simultaneous line task, the successive line task, and the Oddball task. Subjective reports and behavioral performance were analyzed to determine the effectiveness of these tasks in inducing vigilance decrements over time. The findings reveal that both the ATC and simultaneous line tasks effectively induced significant declines in subjective vigilance ratings and behavioral performance. In contrast, the Oddball task was less successful in generating a noticeable vigilance decrement. This research demonstrates the potential of the ATC and simultaneous line tasks to induce vigilance variations, providing valuable datasets for training vigilance detection algorithms. Additionally, it highlights the importance of considering non-linear fluctuations in vigilance and the need for more advanced techniques to accurately classify different vigilance states. Such improvements in vigilance monitoring could substantially enhance safety and well-being in critical work environments.

neuroscience↗

bioRxiv2017

Demonstration of de novo chemotaxis in E. coli using a real-time, quantitative, and digital-like approach

Chemotaxis is the movement of an organism in response to an external chemical stimulus. This system enables bacteria to sense their immediate environment and adapt to changes in its chemical composition. Bacterial chemotaxis is mediated by chemoreceptors, membrane proteins that bind an effector and transduce the signal to the downstream proteins. From a synthetic biology perspective, the natural chemotactic repertoire is of little use since bacterial chemoreceptors have evolved to sense specific ligands that either benefit or harm the cell. Here we demonstrate that using a combined computational design approach together with a quantitative, real-time, and digital detection approach, we can rapidly design, manufacture, and characterize a synthetic chemoreceptor in E. coli for histamine (a ligand for which there are no known chemoreceptors). First, we employed a computational protocol that uses the Rosetta bioinformatics software together with high threshold filters to design mutational variants to the native Tar ligand binding domain that target histamine. Second, we tested different ligand-chemoreceptors pairs with a novel chemotaxis assay, based on optical reflectance interferometry of porous silicon (PSi) optical transducers, enabling label-free quantification of chemotaxis by monitoring real-time changes in the optical readout (expressed as the effective optical thickness, EOT). We found that different ligands can be characterized by an individual set of fingerprints in our assay. Namely, a binary, digital-like response in EOT change (i.e. positive or negative) that differentiates between attractants and repellants, the amplitude of change of EOT response, and the rate by which steady state in EOT change is reached. Using this assay, we were able to positively identify and characterize a single mutational chemoreceptor variant for histamine that mediated chemotaxis comparably to the natural Tar-aspartate system. Our results demonstrate the possibility of not only expanding the natural chemotaxis repertoire, but also provide a new quantitative assay by which to characterize the efficacy of the chemotactic response.

synthetic biology↗