Biology subjects

Maini, P.

Publications and source records attributed to Maini, P..

3 recordsLinked to original sources

bioRxiv2018

Chronic inflammation delays cell migration to villi in the intestinal epithelium

The intestinal epithelium is a single layer of cells which provides the first line of defence of the intestinal mucosa to bacterial infection. Cohesion of this physical barrier is supported by renewal of epithelial stem cells, residing in invaginations called crypts, and by crypt cell migration onto protrusions called villi; dysregulation of such mechanism may render the gut susceptible to chronic inflammation. The impact that excessive or misplaced epithelial cell death may have on villus cell migration is currently unknown. We integrated cell-tracking methods with computational models to determine how epithelial homeostasis is affected by acute and chronic inflammatory cell death. Parameter inference reveals that acute inflammatory cell death has a transient effect on epithelial cell dynamics, whereas cell death caused by chronic inflammation causes a delay in the accumulation of labelled cells onto the villus compared to control. Such a delay may be reproduced by using a cell-based model to simulate the dynamics of each cell in a crypt-villus geometry, showing that a prolonged increase in cell death slows the migration of cells from the crypt to the villus. This investigation highlights which injuries (acute or chronic) may be regenerated and which cause disruption of healthy epithelial homeostasis.

systems biology↗

bioRxiv2017

Mathematical modelling of cortical neurogenesis reveals that the founder population does not necessarily scale with neuronal output

The mammalian cerebral neocortex has a unique structure, composed of layers of different neuron types, interconnected in a stereotyped fashion. While the overall developmental program seems to be conserved, there are divergent developmental factors generating cortical diversity amongst mammalian species. In terms of cortical neuronal numbers some of the determining factors are the size of the founder population, the duration of cortical neurogenesis, the proportion of different progenitor types, and the fine-tuned balance between self-renewing and differentiative divisions. We develop a mathematical model of neurogenesis that, accounting for these factors, aims at explaining the high diversity in neuronal numbers found across mammalian species. By framing our hypotheses in rigorous mathematical terms, we are able to identify paths of neurogenesis that match experimentally observed patterns in mouse, macaque and human. Additionally, we use our model to identify key parameters that would particularly benefit from accurate experimental investigation. We find that the timing of a switch in favor of symmetric neurogenic divisions produces the highest variation in cortical neuronal numbers. Surprisingly, in our model the increase in cortical neuronal numbers does not necessarily reflect a larger size of founder population, a prediction that identified a specific need for experimental quantifications.

neuroscience↗

bioRxiv2017

Dissecting targeted therapy resistance: Integrating models to quantify environment mediated drug resistance

Drug resistance is the single most important driver of cancer treatment failure for modern targeted therapies. This resistance may be due to the presence of dormant or aggressive tumor cell phenotypes or to context-driven protection. Non-malignant cells and other factors, constituting the microenvironment in which the tumor grows (the stroma), are now thought to play a crucial role in both therapeutic response and resistance. Specifically, the dialogue between the tumor and stroma has been shown to modulate the response to molecularly targeted therapies, through proliferative and survival signaling. The goal of this work is to investigate interactions between a growing tumor and its surrounding stroma in facilitating the emergence of drug resistance. We use mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating the intrinsic and extrinsic components of resistance in BRAF mutated melanoma. The model describes tumor-stroma dynamics both with and without treatment. Calibration of our model, through the integration of experimental data, revealed significant variation across animal replicates in either the intensity of stromal promotion or intrinsic tissue carrying capacity. Furthermore our study highlights the need to account for this variation in the design of treatment strategies. Major Findings. Through the integration of a simple mathematical model with in vitro and in vivo experimental growth dynamics of melanoma cell lines (both with and without drug), we were able to dissect the relative contributions of intrinsic versus environmental resistance. Our study revealed significant heterogeneity in vivo, indicating that there is a diversity of either stromal promotion or tumor carrying capacity under targeted therapy. We believe this variation may be one possible explanation for the heterogeneity observed across patients and within individual patients with multiple metastases. Therefore, quantifying this variation both within in vivo model systems and in individual patients could have a significant impact on the design of future treatment strategies that target both the tumor and stroma. Further, we present guidelines for building more effective and longer lasting therapeutic strategies utilizing our experimentally calibrated model. These strategies explicitly consider the protective nature of the stroma and utilize inhibitors that modulate it.\n\nPrecisQuantification of the environmental contribution to drug resistance reveals heterogeneity that significantly alters treatment dynamics that can be exploited for therapeutic gain.\n\nFinancial SupportPicco and Anderson: US National Cancer Institute grant U01CA151924.\n\nPicco: UK Engineering and Physical Sciences Research Council (EPSRC grant number EP/G037280/1).\n\nConflict of Interest DisclosureThe authors declare no potential conflicts of interest.

cancer biology↗