Biology subjects

Maini, P.

Publications and source records attributed to Maini, P..

9 recordsLinked to original sources

bioRxiv2024

A new mechanochemical vertex model with Ca2+ signalling, for apical constriction in neural tube closure

Apical constriction during neural tube closure is driven by cell contractions which are preceded by asynchronous and cell-autonomous Ca2+ flashes, as demonstrated in recent experiments. Disruption of these Ca2+ signals and contractions leads to neural tube defects, such as anencephaly and spina bifida. A good understanding of the two-way mechanochemical coupling of Ca2+ signalling and mechanics remains elusive, while live-cell imaging is difficult. Thus, mathematical modelling is essential but existing models do not exhibit good agreement with experiments. We present two new mechanochemical vertex models of apical constriction during neural tube closure and simulate them using CelluLink, a new user-friendly open-source Python package for vertex modelling. The first, one-way mechanochemical model only studies the effect of Ca2+ signalling on cell mechanics. It improves previous models, reproducing some key experimental observations, such as the reduction of the neural plate size to 2%-8% of its initial area. Other novel features of the one-way model is the incorporation of the surface ectoderm and of the experimental amplitude and frequency profiles of the Ca2+ flashes. Furthermore, guided by experiments, the damping coefficient of the vertices and cell-cell adhesion are modelled as functions of the actomyosin concentration and cell size. Furthermore, we develop a two-way model which improves the one-way model by capturing the two-way coupling between Ca2+ signalling and cell mechanics, through the incorporation of stretch-sensitive Ca2+ channels. These channels enable cells to sense mechanical stimuli and encode them into Ca2+ signals. In the two-way model, the Ca2+ flash frequency and amplitude profiles are model outputs and are not inputs as in the one-way model. Finally, we use both models to propose a series of hypotheses for future experiments. Author summaryAs a baby is growing in the womb, its neural tube closes to form the brain and the spinal cord. During neural tube closure, cells are contracting in a ratchet-like way while experiencing a choreography of Ca2+ flashes. If the Ca2+ flashes or the contractions go wrong, serious birth defects like spina bifida and anencephaly may arise. Understanding how Ca2+ flashes and contractions work together is complex, especially since studying living cells is challenging. To address this challenge, we developed two new mathematical models. The first model captures how Ca2+ flashes affect contractions, improving previous models and accurately capturing some experimental results. For example, it incorporates recent experimental measurements of the amplitude of Ca2+ flashes (brightness) and their frequency (how frequently the flashes appear). The second model builds on the first model by additionally capturing the effect contractions have on the Ca2+ flashes. We capture this two-way coupling by enabling cells to sense mechanical stimuli through stretch-sensitive Ca2+ channels. In this case, the amplitude and frequency of the Ca2+ flashes arise as outputs. Both models inform future experiments that will further elucidate embryo malformations.

systems biology↗

bioRxiv2024

Growth rate-driven modelling reveals how phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma

Phenotypic adaptation, the ability of cells to change phenotype in response to external pressures, has been identified as a driver of drug resistance in cancer. To quantify phenotypic adaptation in BRAFV600E-mutant melanoma, we develop a theoretical model informed by growthrate data of WM239A-BRAFV600E cells challenged with the BRAF-inhibitor encorafenib. We use an individual-based model (IBM) in which each cell is described by one of multiple discrete and plastic phenotype states that are directly linked to drug-dependent net growth rates and, by extension, drug resistance. To describe how cells transition between phenotype states, we explore a gamut of candidate models common in the mathematical biology literature. Comparing these on their ability to reproduce in vitro growth curves, data-matched simulations suggest that phenotypic adaptation is directed towards states of high net growth rates, enabling the evasion of drug-effects. The model subsequently provides an explanation for when and why intermittent treatments outperform continuous treatments in the studied system, and demonstrates the benefits of not only targeting, but also leveraging, phenotypic adaptation in treatment protocols. Building on the IBM, we present a flexible mathematical methodology based on ordinary differential equations to compare responses to continuous and intermittent treatments through long-term effective net growth rates.

cancer biology↗

bioRxiv2023

Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer

Standard-of-care treatment regimes have long been designed to for maximal cell kill, yet these strategies often fail when applied to treatment-resistant tumors, resulting in patient relapse. Adaptive treatment strategies have been developed as an alternative approach, harnessing intra-tumoral competition to suppress the growth of treatment resistant populations, to delay or even prevent tumor progression. Following recent clinical implementations of adaptive therapy, it is of significant interest to optimise adaptive treatment protocols. We propose the application of deep reinforcement learning models to provide generalised solutions within adaptive drug scheduling, and demonstrate this framework can outperform the current adaptive protocols, extending time to progression by up to a quarter. This strategy is robust to varying model parameterisations, and the underlying tumor model. We demonstrate the deep learning framework can produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing a novel, analytically-derived optimal treatment strategy with no knowledge of the underlying mathematical tumor model. This approach is highly relevant beyond the simple, analytically-tractable tumor model considered here, demonstrating the capability of deep learning frameworks to help inform and develop treatment strategies in complex settings. Finally, we propose a pathway to integrate mechanistic modelling with DRL to tailor generalist treatment strategies to individual patients in the clinic, generating personalised treatment schedules that consistently outperform clinical standard-of-care protocols.

cancer biology↗

bioRxiv2023

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumors treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

cancer biology↗

bioRxiv2020

Turnover modulates the need for a cost of resistance in adaptive therapy

"Control and conquer" - this is the philosophy behind adaptive therapy, which seeks to exploit intra-tumoural competition to avoid, or at least, delay the emergence of therapy resistance in cancer. Motivated by promising results from theoretical, experimental and, most recently, a clinical study in prostate cancer, there is an increasing interest in extending this approach to other cancers. As such, it is urgent to understand the characteristics of a cancer which determine whether it will respond well to adaptive therapy, or not. A plausible candidate for such a selection criterion is the fitness cost of resistance. In this paper, we study a simple competition model between sensitive & resistant cell populations to investigate whether the presence of a cost is a necessary condition for adaptive therapy to extend the time to progression beyond that of a standard-of-care continuous therapy. We find that for tumours close to their environmental carrying capacity such a cost of resistance is not required. However, for tumours growing far from carrying capacity, a cost may be required to see meaningful gains. Notably, we show that in such cases it is important to consider the cell turnover in the tumour and we discuss its role in modulating the impact of a cost of resistance. Overall, our work helps to clarify under which circumstances adaptive therapy may be beneficial, and suggests that turnover may play an unexpectedly important role in the decision making process.

cancer biology↗

bioRxiv2018

The Goldilocks Window of Personalized Chemotherapy: An Immune Perspective

The immune system is increasingly being recognized for its untapped potential in being recruited to attack tumors in cancer therapy. The main challenge, however, is that most tumors exist in a state of immune tolerance where the patients immune system has become insensitive to the cancer cells. In order to investigate the ability to use chemotherapy to break immune tolerance, we created a mathematical modeling framework for tumor-immune dynamics. Our results suggest that optimal chemotherapy scheduling must balance two opposing objectives: maximal tumor reduction and preserving patient immune function. Successful treatment requires therapy to operate in a Goldilocks Window where patient immune health is not overly compromised. By keeping therapy just right, we show that the synergistic effects of immune activation and chemotherapy can maximize tumor reduction and control.\n\nStatement of SignificanceIn order to maximize the synergy between chemotherapy and anti-tumor immune response, lymphodepleting therapy must be balanced in a Goldilocks Window of optimal dosing.

cancer biology↗

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↗