Biology subjects

Faria, M.

Publications and source records attributed to Faria, M..

9 recordsLinked to original sources

bioRxiv2026

A 3D Tumor-on-a-chip Platform to Identify Drugs that Block Breast Cancer Cell Intravasation

Metastasis is the leading cause of death in breast cancer patients, yet there are no drugs specifically designed to block cancer cell intravasation, an early step of the metastatic cascade that originates circulating tumour cells (CTCs). A major challenge in developing anti-intravasation drugs is the scarcity of relevant in vitro platforms suitable for predictable drug discovery. Intravasation is a fundamental step of metastasis and involves the crossing of cancer cells through an endothelial barrier to enter the blood circulation. Here we developed an intravasation-on-a-chip model with controlled extracellular matrix composition, fluid flow and shear stress, which mimics the dynamic tumour-endothelium interface. The systems allows real-time imaging of intravasation and the isolation and quantification of intravasated cancer cells. As a proof-of-concept for drug testing, we show that perfusion with the PI3K/mTOR inhibitor Dactolisib, significantly reduced intravasation without compromising endothelial cell viability. The system also provides the capability to evaluate inhibitor on-target activity via imaging analysis. This intravasation-on-a-chip model offers a powerful, scalable, and imaging-compatible platform for discovering and evaluating anti-intravasation compounds.

cancer biology↗

bioRxiv2025

A multilevel hierarchical framework for quantification of experimental heterogeneity

Biological systems exhibit substantial heterogeneity: that is, variation in specific characteristics of individuals within a population. As a result, it is of critical importance to appropriately account for biological heterogeneity when calibrating mathematical models to infer cellular processes and predict behaviour. Recent approaches consider ordinary differential equations with random parameters to quantify heterogeneity in dynamical processes of cells. In this setting, statistical inference is performed to characterise the distribution of these random parameters within a cell population. One significant limitation of this approach is the tacit assumption that there are no substantial deviations in these distributions across experimental replicates. In this work, we propose a flexible Bayesian hierarchical differential equation modelling framework that quantifies and distinguishes both inter-experimental heterogeneity (heterogeneity between experimental replicates) and intra-experimental heterogeneity (biological heterogeneity within replicate populations). We consider two recent studies that employ mathematical models to interpret flow cytometry snap-shot data and quantify heterogeneity in nano-particle cell interactions and cell internalisation processes. Using simulation data, we demonstrate that substantial inaccuracy in the inferred dynamics can arise when experimental heterogeneity is not accounted for. By contrast, our hierarchical approach is robust to variability in inter-experimental and intra-experimental heterogeneity and our method simplifies to previous methods when inter-experimental heterogeneity is negligible. Our approach is flexible and widely applicable to applications involving replicate populations and snapshot data.

cell biology↗

bioRxiv2025

Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments

Nano-engineered particles are a promising tool for medical diagnostics, biomedical imaging, and targeted drug delivery. Fundamental to the assessment of particle performance are in vitro particle-cell interaction experiments. These experiments can be summarised with key parameters that facilitate objective comparisons across various cell and particle pairs, such as the particle-cell association rate. Previous studies often focus on point estimates of such parameters and neglect heterogeneity in routine measurements. In this study, we develop an ordinary differential equation-based mechanistic mathematical model that incorporates and exploits the heterogeneity in routine measurements. Connecting this model to data using approximate Bayesian computation parameter inference and prediction tools, we reveal the significant role of heterogeneity in parameters that characterise particle-cell interactions. We then generate predictions for key quantities, such as the time evolution of the number of particles per cell. Finally, by systematically exploring how the choice of experimental time points influences estimates of key quantities, we identify optimal experimental time points that maximise the information that is gained from particle-cell interaction experiments.

biophysics↗

bioRxiv2024

Protein Coronas Derived from Cerebrospinal Fluid Enhance the Interactions Between Nanoparticles and Brain Cells

Neuronanomedicine harnesses nanoparticle technology for the treatment of neurological disorders. An unavoidable consequence of nanoparticle delivery to biological systems is the formation of a protein corona on the nanoparticle surface. Despite the well-established influence of the protein corona on nanoparticle behavior and fate, as well as FDA approval of neuro-targeted nanotherapeutics, the effect of a physiologically relevant protein corona on nanoparticle-brain cell interactions is insufficiently explored. Indeed, less than 1% of protein corona studies have investigated protein coronas formed in cerebrospinal fluid (CSF), the fluid surrounding the brain. Herein, we utilize two clinically relevant polymeric nanoparticles (PLGA and PLGA-PEG) to evaluate the formation of serum and CSF protein coronas. LC-MS analysis revealed distinct protein compositions, with selective enrichment/depletion profiles. Following incubation with brain cells, serum and CSF coronas on PLGA particles showed enhanced associations with all cell types as compared to their corresponding corona on PLGA-PEG particles. CSF-derived protein coronas on PLGA nanoparticles, specifically, showed the greatest nanoparticle-cell interactions, with Pearsons correlation analysis revealing that proteins associated with enhanced nanoparticle-cell interactions were exclusively enriched in this protein corona. This study demonstrates the importance of correct choice of physiologically relevant biological fluids, and its influence on the formation of the protein corona, subsequent nanoparticle-cell interactions.

bioengineering↗

bioRxiv2023

Thermodynamically-consistent, reduced models of gene regulatory networks

Synthetic biology aims to engineer novel functionalities into biological systems. While the approach has to date been predominantly applied to single cells, a richer set of biological phenomena can be engineered by applying synthetic biology to cell populations. To rationally design cell populations, we require mathematical models that link between intracellular biochemistry and intercellular interactions. In this study, we develop a kinetic model of gene expression that is suitable for incorporation into agent-based models of cell populations. To be scalable to large cell populations, models of gene expression should be both computationally efficient and compliant with the laws of physics. We satisfy the first requirement by applying a model reduction scheme to translation, and the second requirement by formulating models using bond graphs. Our reduced model is significantly faster to simulate than the full model, and faithfully reproduces important behaviours of the full model. We couple separate models of gene expression to build models of the toggle switch and repressilator. With these models, we explore the effects of resource availability and cell-to-cell heterogeneity on circuit behaviour. The modelling approaches developed in this study are a bridge towards engineering collective cell behaviours such as synchronisation and division of labour.

synthetic biology↗

bioRxiv2022

On predicting heterogeneity in nanoparticle dosage

Nanoparticles are increasingly employed as a vehicle for the targeted delivery of therapeutics to specific cell types. However, much remains to be discovered about the fundamental biology that dictates the interactions between nanoparticles and cells. Accordingly, few nanoparticle-based targeted therapeutics have succeeded in clinical trials. One element that hinders our understanding of nanoparticle-cell interactions is the presence of heterogeneity in nanoparticle dosage data obtained from standard experiments. It is difficult to distinguish between heterogeneity that arises from stochasticity in nanoparticle behaviour, and that which arises from heterogeneity in the cell population. Mathematical investigations have revealed that both sources of heterogeneity contribute meaningfully to the heterogeneity in nanoparticle dosage. However, these investigations have relied on simplified models of nanoparticle internalisation. Here we present a stochastic mathematical model of nanoparticle internalisation that incorporates a suite of relevant biological phenomena such as multistage internalisation, cell division, asymmetric nanoparticle inheritance and nanoparticle saturation. Critically, our model provides information about nanoparticle dosage at an individual cell level. We perform model simulations to examine the influence of specific biological phenomena on the heterogeneity in nanoparticle dosage. Under certain modelling assumptions, we derive analytic approximations of the nanoparticle dosage distribution. We demonstrate that the analytic approximations are accurate, and show that nanoparticle dosage can be described by a Poisson mixture distribution with rate parameters that are a function of Beta-distributed random variables. We discuss the implications of the analytic results with respect to parameter estimation and model identifiability from standard experimental data. Finally, we highlight extensions and directions for future research.

biophysics↗

bioRxiv2022

A photoswitchable ligand targeting β1-adrenoceptor enables light-control of the cardiac rhythm

Catecholamine-triggered {beta}-adrenoceptor ({beta}-AR) signaling is essential for the correct functioning of the heart. Although both {beta}1- and {beta}2-AR subtypes are expressed in cardiomyocytes, drugs selectively targeting {beta}1-AR have proven this receptor as the main target for the therapeutic effects of beta blockers in heart. Here, we report a new strategy for the spatiotemporal control of {beta}1-AR activation by means of light-regulated drugs with a high level of {beta}1-/{beta}2-AR selectivity. All reported molecules allow for an efficient real time optical control of receptor function in vitro. Moreover, using confocal microscopy we demonstrate that the binding of our best hit, pAzo-2, can be reversibly photocontrolled. Strikingly, pAzo-2 also enables a dynamic cardiac rhythm management on alive zebrafish larvae using light, thus highlighting the therapeutic and research potential of the developed photoswitches. Overall, this work provides the first proof of precise control of the therapeutic target {beta}1-AR in native environments using light.

pharmacology and toxicology↗

bioRxiv2020

Improving water quality does not guarantee fish health: effects of ammonia pollution on the behaviour of wild-caught pre-exposed fish.

Ammonia is a pollutant frequently found in aquatic ecosystems. In fish, ammonia can cause physical damage, alter its behaviour and even cause death. Exposure to ammonia also increases fish physiological stress, which can be measured through biomarkers. In this study, we analysed the effect of sublethal ammonia concentrations on the behaviour and the oxidative stress of Barbus meridionalis that had been pre-exposed to this compound in the wild. Wild-caught fish from a polluted site (pre-exposed fish) and from an unpolluted site (non-pre-exposed fish) were exposed, under experimental conditions, to total ammonia concentrations (TAN) of 0, 1, 5 and 8 mg/L. Swimming activity, feeding behaviour and oxidative stress response based on biomarkers were analysed. Pre-exposed fish showed both an altered behaviour and an altered oxidative stress response in the control treatment (0 mg/L). Differences in swimming activity were also found as pre-exposed fish swam less. Lower feeding activity (voracity and satiety) and altered response to oxidative stress were also observed at [≥] 1 mg/L TAN. Biomarker results confirmed pre-exposed fish suffer from a reduction in their antioxidant defences and, hence, showed increased oxidative tissue damage. In summary, pre-exposed fish showed more sensitivity to ammonia exposure than fish from a pristine site.

animal behavior and cognition↗

bioRxiv2019

Isolating the sources of heterogeneity in nanoparticle-cell interactions

Nanoparticles have the potential to enhance therapeutic success and reduce toxicity-based treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of nanoparticles inside each cell, further inhibits understanding. Here we present a mathematical framework that is capable of examining the impact of individual processes during nanoparticle delivery. We demonstrate that variation in experimental nanoparticle uptake data can be explained by three factors: random nanoparticle motion; variation in nanoparticle-cell interactions; and variation in the maximum nanoparticle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mecha-nisms that cause heterogeneous responses to treatment, and enables precise identification of treatment-resistant cell subpopulations.

biophysics↗