Download Free A Mathematical Modeling Framework To Simulate And Analyze Cell Type Transitions Book in PDF and EPUB Free Download. You can read online A Mathematical Modeling Framework To Simulate And Analyze Cell Type Transitions and write the review.

The quantitative understanding of changes in cell types, referred to as cell type transitions, is fundamental to advance fields such as stem cell research, immunology, and cancer therapies. This thesis provides a mathematical modeling framework to simulate and analyze cell type transitions. The novel methodological approaches and models presented here address diverse levels which are essential in this context: Gene regulatory network models represent the cell type-determining gene expression dynamics. Here, a novel construction method for gene regulatory network models is introduced, which allows to transfer results from generic low-dimensional to realistic high-dimensional gene regulatory network models. For populations of cells, a generalized model class is proposed that accounts for multiple cell types, division numbers, and the full label distribution. Analysis and solution methods are presented for this new model class, which cover common cell population experiments and allow to exploit the full information from data. The modeling and analysis methods presented here connect formerly isolated approaches, and thereby contribute to a holistic framework for the quantitative understanding of cell type transitions.
Whole new areas of immunological research are emerging from the analysis of experimental data, going beyond statistics and parameter estimation into what an applied mathematician would recognise as modelling of dynamical systems. Stochastic methods are increasingly important, because stochastic models are closer to the Brownian reality of the cellular and sub-cellular world.
Currently, the assessment of functional immunological relevance is mainly done in animal models. Motivation to work on non-animal methods, or new approach methods (NAM), stems from economical and ethical considerations, and is supported by public pressure. Importantly, the translational gap between results obtained in animal studies and clinical trials in humans (the ‘valley of death’), combined with the reproducibility crisis in science, also provide strong scientific arguments to work on novel, robust, human-based methodology. The field of immunology confronts NAM scientists with specific challenges. Firstly, immunological responses require several cell types in different locations for proper development and take considerable time to develop. Secondly, immunological responses in outbred humans are characterized by genetic and functional variability. Still, the development and application of NAM are increasing rapidly, and the field is moving at such a fast pace that a special issue is timely. Our goal is to provide an overview of the current state-of-the-art regarding new approach methods or non-animal methods (NAM) in immunology. These should be inspired by the desire to mimic in vivo biology and describe e.g. challenges in mimicking immunological structures (like lymph nodes, bone marrow, local immune structures), immunological responses (systemic and local, innate and adaptive, B cells and T cells) and/or immunological processes (like maturation, trafficking, extravasation, immunotoxicity, affinity maturation).
This book discusses the mathematical simulation of biological systems, with a focus on the modeling of gene expression, gene regulatory networks and stem cell regeneration. The diffusion of morphogens is addressed by introducing various reaction-diffusion equations based on different hypotheses concerning the process of morphogen gradient formation. The robustness of steady-state gradients is also covered through boundary value problems. The introduction gives an overview of the relevant biological concepts (cells, DNA, organism development) and provides the requisite mathematical preliminaries on continuous dynamics and stochastic modeling. A basic understanding of calculus is assumed. The techniques described in this book encompass a wide range of mechanisms, from molecular behavior to population dynamics, and the inclusion of recent developments in the literature together with first-hand results make it an ideal reference for both new students and experienced researchers in the field of systems biology and applied mathematics.
The vast amount of knowledge in Cell Signaling gathered through reductionist efforts and omics technology is poised to approach a Systems Biology understanding of precise representations of cell structure and function and predictions at multi-scale levels despite the complexity. Super-resolution microscopy and single cell analysis are also providing opportunities to explore both spatial and temporal landscapes. Notably, many basic biological processes have been studied capturing mechanistic detail with the goal to understand cellular proliferation and differentiation, gene regulation, morphogenesis, metabolism, and cell-cell communication. Similarly, at the intracellular level, addressing functions such as self-assembly, phase separation, and transport is leading to insights not readily understood as linear pathways. Therefore, network-based mathematical modeling, delineating dynamic biochemical reactions through ordinary and partial differential equations, promises to discover emergent biological properties not heretofore expected.
This volume LNCS 14243 constitutes the refereed proceedings of the Second International Workshop, CMMCA 2023, Held in Conjunction with MICCAI 2023, on October 8, 2023, in Vancouver, BC, Canada. The 17 full papers presented were carefully reviewed and selected from 25 submissions. The conference focuses on the discovery of cutting-edge techniques addressing trends and challenges in theoretical, computational, and applied aspects of mathematical cancer data analysis.
Cancer is a complex adaptive dynamic system that causes both local and systemic failures in the patient. Cancer is caused by a number of gain-of-function and loss-of-function events, that lead to cells proliferating without control by the host organism over time. In cancer, the immune system modulates cancer cell population heterogeneity and plays a crucial role in disease outcomes. The immune system itself also generates multiple clones of different cell types, with some clones proliferating quickly and maturing into effector cells. By creating regulatory signals and their networks, and generating effector cells and molecules, the immune system recognizes and kills abnormal cells. Anti-cancer immune mechanisms are realized as multi-layer, nonlinear cellular and molecular interactions. A number of factors determine the outcome of immune system-tumor interactions, including cancer-associated antigens, immune cells, and host organisms.
Urban development and migration from rural to urban areas are impacting prime agricultural land and natural landscapes, particularly in the less developed countries. These phenomena will persist and require serious study by those monitoring global environmental change. To address this need, various models have been devised to analyze urbanization a
Mathematical modeling, analysis and simulation are set to play crucial roles in explaining tumor behavior, and the uncontrolled growth of cancer cells over multiple time and spatial scales. This book, the first to integrate state-of-the-art numerical techniques with experimental data, provides an in-depth assessment of tumor cell modeling at multiple scales. The first part of the text presents a detailed biological background with an examination of single-phase and multi-phase continuum tumor modeling, discrete cell modeling, and hybrid continuum-discrete modeling. In the final two chapters, the authors guide the reader through problem-based illustrations and case studies of brain and breast cancer, to demonstrate the future potential of modeling in cancer research. This book has wide interdisciplinary appeal and is a valuable resource for mathematical biologists, biomedical engineers and clinical cancer research communities wishing to understand this emerging field.