Modeling and Analysis of the Buckling Phenomena in the Homogeneous and Heterogeneous Biomembranes

In this project, nonlinear behavior of biomembrane are modeled as heterogeneous elastic biological systems. In addition to the static behavior of the membranes, their dynamic behavior are modeled to be able to investigate time-dependency of the variables of the systems. Some of the available models are used and some new ones are developed to study static and dynamic analysis of monolayer and bilayer membranes as well as circular axisymmetric biomembranes. The presented models are developed based on the Euler-Bernoulli constitutive law and employed to investigate buckling phenomena in the membranes as one of the most important physical phenomena in biological environment. Static and dynamic behavior of Buckling phenomenon in biological membranes are modeled. The static model results in nonlinear ordinary di erential equation for one-dimensional approximation. In order to extend the model for circular membranes, the criteria of constant length in one-dimensional membranes is changed to constant surface. Moreover, tension-compression and bending springs are added to the model and employed to study buckling of biomembranes. Similar to the procedure of obtaining the equations of static large deformation of the membrane, the equations of motion of the membrane is obtained using free body diagram of an in finitesimal element of the membrane and employing Euler- Bernoulli constitutive law. Hence, nonlinear integro partial di erential equations are obtained t model the dynamic behavior of the membrane. All of the equations, including static and dynamic ones, are changed to the dimensionless forms so that the results can be considered general and can be employed to analyze diff erent systems with diff erent properties. The nondimensional equations of each part of the project are solved using di erent iterative and time-dependent schemes. The schemes are used to obtain the discretized forms of the equations. The discretized equations of all nodes of the domain, with due attention to the considered boundary conditions, are gathered in a matrix and the matrix solved to obtain the solution of the variables at each node and time stage. The solutions obtained for diff erent problems investigated in this project are employed to illustrate variations of diff erent dependent variables of the models with respect to the independent variables and parameters of the problems. As the important step to analyze the problems, diff erent results of the problems investigated in the project are verifi ed using the available information in literature. Membrane pro le are obtained for di erent parameter values and external forces in the stationary condition. In addition, variation of maximum deflection and slope are studied with respect to the variation of diff erent dimensionless parameters of the system. As a verifi cation of the solution, the incompressibility of bilayer membrane is shown as well. Growth of di fferent variables is shown with respect to time employing the solution of dynamic modeling of the membrane. As one of the important parts of this project, e ects of heterogeneity on dynamic behavior of the membrane under buckling is investigated. The heterogeneous region is considered to have di fferent material properties and it position is changed to also study the geometrical e ffects.1 yea

Quantitative Approaches to the Cancer Stem Cell Hypothesis

In this thesis, phenotype switching in cancer cell populations is modeled. We focus on the behavior of cells at the phenotypic level and present mathematical models to capture the results of available experiments. The models, based on the cancer stem cell hypothesis (in addition to the new concept of plasticity in tumor populations), are also employed to predict cancer cell growth in vitro and in vivo. The models are analyzed in the two limits of large and small cell numbers. We use stochastic analysis to capture the random behavior of cells in the limit of low number as observed in mammosphere formation assays (MFAs). Stochastic analysis is employed to estimate quantities such as survival rate while the deterministic solution of the models is obtained to simulate the average behavior of cells. The importance of stochastic analysis and deterministic simulations is discussed in detail. The primary purpose of the thesis is to highlight the importance of stochastic analysis in cancer stem cell experiments. The models are developed or modified based on the idea that both stochastic and deterministic behavior of cells should be considered simultaneously. In order to describe the behavior of the cells in cancer stem cell assays, the developed models are then used to investigate the possible experimental errors in the area and suggest possible filtration methods for corresponding experiments. In addition, the models are used to investigate the behavior of tumors under radiotherapy; and the effects of phenotype switching on the efficiency of therapies are investigated in the final part of the thesis

On the Study of Viscoelastic Walters' B Fluid in Boundary Layer Flows

Viscoelastic Walters' B fluid flows for three problems, stagnation-point flow, Blasius flow, and Sakiadis flow, have been investigated. In each problem, Cauchy equations are changed to a nondimensional differential equations using stream functions and with assumption of boundary layer flow. The fourth-order predictor-corrector finite-difference method for solving these nonlinear differential equations has been employed. The results that have been obtained using this method are compared with the results of the last studies, and it is clarified that this method is more accurate. It is also shown that the results of last study about Sakiadis flow of Walter's B fluid are not true. In addition, the effects of order of discretization in the boundaries are investigated. Moreover, it has been discussed about the valid region of Weissenberg numbers for the second-order approximation of viscoelastic fluids in each case of study

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.We gratefully acknowledge funding that supported this research support from the Ryerson University Faculty of Science (CNA), as well as funding support in the form of a CIFAR Catalyst Grant (JPJ and CNA), an NSERC Alliance Grant (CNA) and the Ryerson COVID-19 SRC Response Fund award (CNA). BW is partly supported by CIFAR AI Chairs Program. This work was also supported by a Mitacs award (BW), the European Union’s Horizon 2020 research and innovation program under a Marie Sklodowska-Curie grant (ER), by the CIFAR Azrieli Global Scholar program (JPJ), by the Ontario Early Researcher Awards program (JPJ and CNA), and by the Canada Research Chairs program (JPJ). We also thank Dr. James Rini (University of Toronto) for the kind gift of the 9.8E12 antibody used to detect the 229E Spike protein, and Dr. Scott Gray-Owen (University of Toronto) for the kind gift of the NL63 human coronavirus.Peer reviewe

Computational Models for Mapping Transcriptome and Epigenome to Human Phenotypes

Fate and identity of eukaryotic cells are results of the cooperative function of biological features such as transcripts, cis-regulatory elements and transcription regulators. Recent advances in sequencing technologies helped the biomedical research community to study this cooperation and identify features associated with the identity of cell types, development of mature phenotypes from embryonic stem cells, development of malignancies like cancer, poor survival of cancer patients and resistance of tumours to drugs. Despite tremendous success in using these features in applications like the development of targeted therapies and identification of signatures of stemness, there is an undeniable gap for a better understanding of the healthy and malignant phenotypes. In this dissertation, I initially propose a transcriptomic feature extraction approach, Similarity Identification in Gene Expression (SIGN), as a new method for identifying patterns of transcriptomic expression within biological pathways. I found that these patterns can be used to predict the survival of breast cancer patients as well as their response to targeted therapies. SIGN can not only provide a highly accurate but highly interpretable scheme in predicting malignant phenotypes like aggressive tumours. Despite successful applications of transcriptomic profiles of cells in building predictive models of human phenotypes, more recently epigenomic profiles of cells were introduced as a complementary source of information for these task. Hence, going beyond the transcriptomic features, I propose another approach called CREAM (Clustering of genomic Regions Analysis Method) to extract DNA level features relying on cis-regulatory elements, histone modification as well as transcription factor binding loci. These features can provide complementary information to the transcriptomic features, regarding cell identities and their fate in physiological and disease developments. This work provides new approaches in extracting transcriptomic and epigenomic features in cell types and their association with cell type identity, the survival of cancer patients as well as the resistance of tumors to drugs.Ph.D

Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy and the lack of repeatability with new datasets, mainly due to slice-wise splits of the data, creating dependency between training and test sets due to shared data across the sets. We introduce a new dataset of CT images (ISFCT Dataset) with labels indicating the subject-wise split to train and test our DL algorithms in an unbiased manner. We also use this dataset to validate the real performance of the published works in a subject-wise data split. Another key feature provides more specific labels (eight characteristic lung features) rather than being limited to COVID-19 and healthy labels. We show that the reported high accuracy of the existing models on current slice-wise splits is not repeatable for subject-wise splits, and distribution differences between data splits are demonstrated using t-distribution stochastic neighbor embedding. We indicate that, by examining subject-wise data splitting, less complicated models show competitive results compared to the exiting complicated models, demonstrating that complex models do not necessarily generate accurate and repeatable results.Applied Science, Faculty ofNon UBCEngineering, School of (Okanagan)ReviewedFacultyResearche

Biological and therapeutic implications of a unique subtype of NPM1 mutated AML

In acute myeloid leukemia (AML), molecular heterogeneity across patients constitutes a major challenge for prognosis and therapy. AML with NPM1 mutation is a distinct genetic entity in the revised World Health Organization classification. However, differing patterns of co-mutation and response to therapy within this group necessitate further stratification. Here we report two distinct subtypes within NPM1 mutated AML patients, which we label as primitive and committed based on the respective presence or absence of a stem cell signature. Using gene expression (RNA-seq), epigenomic (ATAC-seq) and immunophenotyping (CyToF) analysis, we associate each subtype with specific molecular characteristics, disease differentiation state and patient survival. Using ex vivo drug sensitivity profiling, we show a differential drug response of the subtypes to specific kinase inhibitors, irrespective of the FLT3-ITD status. Differential drug responses of the primitive and committed subtype are validated in an independent AML cohort. Our results highlight heterogeneity among NPM1 mutated AML patient samples based on stemness and suggest that the addition of kinase inhibitors to the treatment of cases with the primitive signature, lacking FLT3-ITD, could have therapeutic benefit. Molecular heterogeneity of acute myeloid leukaemia (AML) across patients is a major challenge for prognosis and therapy. Here, the authors show that NPM1 mutated AML is a heterogeneous class, consisting of two subtypes which exhibit distinct molecular characteristics, differentiation state, patient survival and drug response