Mitra Shojania Feizabadi

A two-compartment model interacting with dynamic drugs

Abstract By combining the total cell evolution curve and a two-compartment model interacting with dynamic anti-cancer agents, the evolution of subpopulations has been analytically obtained and investigated in this work.

Measuring the persistence length of MCF7 cell microtubules in vitro

The dynamic and mechanical properties of mammalian neural microtubules have been widely studied; however, similar knowledge about these properties is limited for non-neural microtubules, which, unlike neural microtubules, consist of different β-tubulin isotypes. In this study, we report, for the first time, an estimated value for the persistence length of a single non-neural microtubule polymerized from purified tubulin from human breast cancer cell lines (MCF7 tubulin). The method of measurement is based on an analysis of the local curvature of a microtubule as a result of thermal fluctuations. In parallel, we measured the persistence length of a single bovine brain microtubule under similar conditions. The results of our measurements indicate a higher value for the persistence length of MCF7 microtubules in vitro as compared to the persistence length of a neural microtubule. The difference can be associated with different β-tubulin isotypes in the structure of MCF7 microtubules.

Kinesin-1 translocation: Surprising differences between bovine brain and MCF7-derived microtubules.

While there have been many single-molecule studies of kinesin-1, most have been done along microtubules purified from bovine or porcine brain, and relatively little is known about how variations in tubulin might alter motor function. Of particular interest is transport along microtubules polymerized from tubulin purified from MCF7 breast cancer cells, both because these cells are a heavily studied model system to help understand breast cancer, and also because the microtubules are already established to have interesting polymerization/stability differences from bovine tubulin, suggesting that perhaps transport along them is also different. Thus, we carried out paired experiments to allow direct comparison of in vitro kinesin-1 translocation along microtubules polymerized from either human breast cancer cells (MCF7) or microtubules from bovine brain. We found surprising differences: on MCF7 microtubules, kinesin-1s processivity is significantly reduced, although its velocity is only slightly altered.

The Contribution of the C-Terminal Tails of Microtubules in Altering the Force Production Specifications of Multiple Kinesin-1

The extent to which beta tubulin isotypes contribute to the function of microtubules and the microtubule-driven transport of molecular motors is poorly understood. The major differences in these isotypes are associated with the structure of their C-terminal tails. Recent studies have revealed a few aspects of the C-terminal tails’ regulatory role on the activities of some of the motor proteins on a single-molecule level. However, little attention is given to the degree to which the function of a team of motor proteins can be altered by the microtubule’s tail. In a set of parallel experiments, we investigated this open question by studying the force production of several kinesin-1 (kinesin) molecular motors along two groups of microtubules: regular ones and those microtubules whose C-terminals are cleaved by subtilisin digestion. The results indicate that the difference between the average of the force production of motors along two types of microtubules is statistically significant. The underlying mechanism of such production is substantially different as well. As compared to untreated microtubules, the magnitude of the binding time of several kinesin-1 is almost three times greater along subtilisin-treated microtubules. Also, the velocity of the group of kinesin molecules shows a higher sensitivity to external loads and reduces significantly under higher loads along subtilisin-treated microtubules. Together, this work shows the capacity of the tails in fine-tuning the force production characteristics of several kinesin molecules.

Two-compartment model interacting with proliferating regulatory factor

In this short work, by combining the total cell evolution curve and the two-compartment model, the evolution of one of the subpopulations is simulated while the system interacts with a proliferating regulatory factor.

Chemotherapy in conjoint aging-tumor systems: some simple models for addressing coupled aging-cancer dynamics

BackgroundIn this paper we consider two approaches to examining the complex dynamics of conjoint aging-cancer cellular systems undergoing chemotherapeutic intervention. In particular, we focus on the effect of cells growing conjointly in a culture plate as a precursor to considering the larger multi-dimensional models of such systems. Tumor cell growth is considered from both the logistic and the Gompertzian case, while normal cell growth of fibroblasts (WI-38 human diploid fibroblasts) is considered as logistic only.ResultsWe demonstrate, in a simple approach, how the interdependency of different cell types in a tumor, together with specifications of for treatment, can lead to different evolutionary patterns for normal and tumor cells during a course of therapy.ConclusionsThese results have significance for understanding appropriate pharmacotherapy for elderly patients who are also undergoing chemotherapy.

Modeling drug resistance in a conjoint normal-tumor setting

BackgroundIn this paper, we modify our previously developed conjoint tumor-normal cell model in order to make a distinction between tumor cells that are responsive to chemotherapy and those that may show resistance.ResultsUsing this newly developed core model, the evolution of three cell types: normal, tumor, and drug-resistant tumor cells, is studied through a series of numerical simulations. In addition, we illustrate critical factors that cause different dynamical patterns for normal and tumor cells. Among these factors are the co-dependency of the normal and tumor cells, the cells’ response mechanism to a single or multiple chemotherapeutic treatment, the drug administration sequence, and the treatment starting time.ConclusionThe results provide us with a deeper understanding of the possible evolution of normal, drug-responsive, and drug-resistant tumor cells during the cancer progression, which may contribute to improving the therapeutic strategies.

Modeling multi-mutation and drug resistance: analysis of some case studies

BackgroundDrug-induced resistance is one the major obstacles that may lead to therapeutic failure during cancer treatment. Different genetic alterations occur when tumor cells divide. Among new generations of tumor cells, some may express intrinsic resistance to a specific chemotherapeutic agent. Also, some tumor cells may carry a gene that can develop resistance induced by the therapeutic drug. The methods by which the therapeutic approaches need to be revised in the occurrence of drug induced resistance is still being explored. Previously, we introduced a model that expresses only intrinsic drug resistance in a conjoint normal-tumor cell setting. The focus of this work is to expand our previously reported model to include terms that can express both intrinsic drug resistance and drug-induced resistance. Additionally, we assess the response of the cell population as a function of time under different treatment strategies and discuss the outcomes.MethodsThe model introduced is expressed in the format of coupled differential equations which describe the growth pattern of the cells. The dynamic of the cell populations is simulated under different treatment cases. All computational simulations were executed using Mathematica v7.0.ResultsThe outcome of the simulations clearly demonstrates that while some therapeutic strategies can overcome or control the intrinsic drug resistance, they may not be effective, and are even to some extent damaging, if the administered drug creates resistance by itself.ConclusionIn the present study, the evolution of the cells in a conjoint setting, when the system expresses both intrinsic and induced resistance, is mathematically modeled. Followed by a set of computer simulations, the different growing patterns that can be created based on choices of therapy were examined. The model can still be improved by considering other factors including, but not limited to, the nature of the cancer growth, the level of toxicity that the body can tolerate, or the strength of the patient’s immune system.