A time discretization scheme for a nonlocal degenerate problem modelling resistance spot welding

This is the author's PDF version of an article published in Mathematical Modelling of Natural Phenomena© 2015. The definitive version is available at http://www.mmnp-journal.org/articles/mmnp/abs/2015/06/mmnp2015106p90/mmnp2015106p90.htmlIn the current work we construct a nonlocal mathematical model describing the phase transition occurs during the resistance spot welding process in the industry of metallurgy. We then consider a time discretization scheme for solving the resulting nonlocal moving boundary problem. The scheme consists of solving at each time step a linear elliptic partial differential equation and then making a correction to account for the nonlinearity. The stability and error estimates of the developed scheme are investigated. Finally some numerical results are presented confirming the efficiency of the developed numerical algorithm

A Novel Averaging Principle Provides Insights in the Impact of Intratumoral Heterogeneity on Tumor Progression

Typically stochastic differential equations (SDEs) involve an additive or multiplicative noise term. Here, we are interested in stochastic differential equations for which the white noise is nonlinearly integrated into the corresponding evolution term, typically termed as random ordinary differential equations (RODEs). The classical averaging methods fail to treat such RODEs. Therefore, we introduce a novel averaging method appropriate to be applied to a specific class of RODEs. To exemplify the importance of our method, we apply it to an important biomedical problem, in particular, we implement the method to the assessment of intratumoral heterogeneity impact on tumor dynamics. Precisely, we model gliomas according to a well-known Go or Grow (GoG) model, and tumor heterogeneity is modeled as a stochastic process. It has been shown that the corresponding deterministic GoG model exhibits an emerging Allee effect (bistability). In contrast, we analytically and computationally show that the introduction of white noise, as a model of intratumoral heterogeneity, leads to monostable tumor growth. This monostability behavior is also derived even when spatial cell diffusion is taken into account

Delineating the role of βIV-Tubulins in pancreatic cancer: βIVb-tubulin inhibition sensitizes pancreatic cancer cells to vinca alkaloids

Pancreatic cancer (PC) is a lethal disease which is characterized by chemoresistance. Components of the cell cytoskeleton are therapeutic targets in cancer. βIV-tubulin is one such component that has two isotypes—βIVa and βIVb. βIVa and βIVb isotypes only differ in two amino acids at their C-terminus. Studies have implicated βIVa-tubulin or βIVb-tubulin expression with chemoresistance in prostate, breast, ovarian and lung cancer. However, no studies have examined the role of βIV-tubulin in PC or attempted to identify isotype specific roles in regulating cancer cell growth and chemosensitivity. We aimed to determine the role of βIVa- or βIVb-tubulin on PC growth and chemosensitivity. PC cells (MiaPaCa-2, HPAF-II, AsPC1) were treated with siRNA (control, βIVa-tubulin or βIVb-tubulin). The ability of PC cells to form colonies in the presence or absence of chemotherapy was measured by clonogenic assays. Inhibition of βIVa-tubulin in PC cells had no effect chemosensitivity. In contrast, inhibition of βIVb-tubulin in PC cells sensitized to vinca alkaloids (Vincristine, Vinorelbine and Vinblastine), which was accompanied by increased apoptosis and enhanced cell cycle arrest. We show for the first time that βIVb-tubulin, but not βIVa-tubulin, plays a role in regulating vinca alkaloid chemosensitivity in PC cells. The results from this study suggest βIVb-tubulin may be a novel therapeutic target and predictor of vinca alkaloid sensitivity for PC and warrants further investigation

How can we use the endocytosis pathways to design nanoparticle drug-delivery vehicles to target cancer cells over healthy cells?

Targeted drug delivery in cancer typically focuses on maximising the endocytosis of drugs into the diseased cells. However, there has been less focus on exploiting the differences in the endocytosis pathways of cancer cells versus non-cancer cells. An understanding of the endocytosis pathways in both cancer and non-cancer cells allows for the design of nanoparticles to deliver drugs to cancer cells whilst restricting healthy cells from taking up anticancer drugs, thus efficiently killing the cancer cells. Herein we compare the differences in the endocytosis pathways of cancer and healthy cells. Second, we highlight the importance of the physicochemical properties of nanoparticles (size, shape, stiffness, and surface chemistry) on cellular uptake and how they can be adjusted to selectively target the dominated endocytosis pathway of cancer cells over healthy cells and to deliver anticancer drug to the target cells. The review generates new thought in the design of cancer-selective nanoparticles based on the endocytosis pathways

99pharnessing copper in cancer to enhance anti tumor immune response

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Computational analysis of image-based drug profiling predicts synergistic drug combinations: Applications in triple-negative breast cancer

© 2014 Federation of European Biochemical Societies. An imaged-based profiling and analysis system was developed to predict clinically effective synergistic drug combinations that could accelerate the identification of effective multi-drug therapies for the treatment of triple-negative breast cancer and other challenging malignancies. The identification of effective drug combinations for the treatment of triple-negative breast cancer (TNBC) was achieved by integrating high-content screening, computational analysis, and experimental biology. The approach was based on altered cellular phenotypes induced by 55 FDA-approved drugs and biologically active compounds, acquired using fluorescence microscopy and retained in multivariate compound profiles. Dissimilarities between compound profiles guided the identification of 5 combinations, which were assessed for qualitative interaction on TNBC cell growth. The combination of the microtubule-targeting drug vinblastine with KSP/Eg5 motor protein inhibitors monastrol or ispinesib showed potent synergism in 3 independent TNBC cell lines, which was not substantiated in normal fibroblasts. The synergistic interaction was mediated by an increase in mitotic arrest with cells demonstrating typical ispinesib-induced monopolar mitotic spindles, which translated into enhanced apoptosis induction. The antitumour activity of the combination vinblastine/ispinesib was confirmed in an orthotopic mouse model of TNBC. Compared to single drug treatment, combination treatment significantly reduced tumour growth without causing increased toxicity. Image-based profiling and analysis led to the rapid discovery of a drug combination effective against TNBC invitro and invivo, and has the potential to lead to the development of new therapeutic options in other hard-to-treat cancers

A high-throughput 3D bioprinted cancer cell migration and invasion model with versatile and broad biological applicability

Understanding the underlying mechanisms of migration and metastasis is a key focus of cancer research. There is an urgent need to develop in vitro 3D tumor models that can mimic physiological cell-cell and cell-extracellular matrix interactions, with high reproducibility and that are suitable for high throughput (HTP) drug screening. Here, we developed a HTP 3D bioprinted migration model using a bespoke drop-on-demand bioprinting platform. This HTP platform coupled with tunable hydrogel systems enables (i) the rapid encapsulation of cancer cells within in vivo tumor mimicking matrices, (ii) in situ and real-time measurement of cell movement, (iii) detailed molecular analysis for the study of mechanisms underlying cell migration and invasion, and (iv) the identification of novel therapeutic options. This work demonstrates that this HTP 3D bioprinted cell migration platform has broad applications across quantitative cell and cancer biology as well as drug screening

A 3D Bioprinter Specifically Designed for the High-Throughput Production of Matrix-Embedded Multicellular Spheroids

Biotechnology; Cell Biology; Biomaterial

On a degenerate non-local parabolic problem describing infinite dimensional replicator dynamics

We establish the existence of locally positive weak solutions to the homogeneous Dirichlet problem for $$u_t = u \Delta u + u \int_\Omega |\nabla u|^2$$ in bounded domains \Om\sub\R^n which arises in game theory. We prove that solutions converge to $0$ if the initial mass is small, whereas they undergo blow-up in finite time if the initial mass is large. In particular, it is shown that in this case the blow-up set coincides with $\overline{\Omega}$, i.e. the finite-time blow-up is global

Precise, high-throughput production of multicellular spheroids with a bespoke 3D bioprinter

3D in vitro cancer models are important therapeutic and biological discovery tools, yet formation of multicellular spheroids in a throughput and highly controlled manner to achieve robust and statistically relevant data, remains challenging. Here, we developed an enabling technology consisting of a bespoke drop-on-demand 3D bioprinter capable of high-throughput printing of 96-well plates of spheroids. 3D-multicellular spheroids are embedded inside a tissue-like matrix with precise control over size and cell number. Application of 3D bioprinting for high-throughput drug screening was demonstrated with doxorubicin. Measurements showed that IC 50 values were sensitive to spheroid size, embedding and how spheroids conform to the embedding, revealing parameters shaping biological responses in these models. Our study demonstrates the potential of 3D bioprinting as a robust high-throughput platform to screen biological and therapeutic parameters. Significance Statement In vitro 3D cell cultures serve as more realistic models, compared to 2D cell culture, for understanding diverse biology and for drug discovery. Preparing 3D cell cultures with defined parameters is challenging, with significant failure rates when embedding 3D multicellular spheroids into extracellular mimics. Here, we report a new 3D bioprinter we developed in conjunction with bioinks to allow 3D-multicellular spheroids to be produced in a high-throughput manner. High-throughput production of embedded multicellular spheroids allowed entire drug-dose responses to be performed in 96-well plate format with statistically relevant numbers of data points. We have deconvoluted important parameters in drug responses including the impact of spheroid size and embedding in an extracellular matrix mimic on IC 50 values