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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).
Mathematical models have become invaluable tools for understanding the intricate dynamic behavior of complex biochemical and biological systems. Among computational strategies, logical modeling has been recently gaining interest as an alternative approach to address network dynamics. Due to its advantages, including scalability and independence of kinetic parameters, the logical modeling framework is becoming increasingly popular to study the dynamics of highly interconnected systems, such as cell cycle progression, T cell differentiation and gene regulation. Novel tools and standards have been developed to increase the interoperability of logical models, which can now be employ to respond a variety of biological questions. This Research Topic brings together the most recent and cutting-edge approaches in the area of logical modeling including, among others, novel biological applications, software development and model analysis techniques.
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 modelling and the study of phase transition phenomena are capital issues as they have essential applications in material sciences and in biological and industrial processes. We can mention, e.g., phase separation in alloys, ageing of materials, microstructure evolution, crystal growth, solidification in complex alloys, surface diffusion in the presence of stress, evolution of the surface of a thin flow in heteroepitaxial growth, motion of voids in interconnects in integrated circuits, treatment of airway closure disease by surfactant injection, fuel injection, fire extinguishers etc., This book consists of 11 contributions from specialists in the mathematical modelling and analysis of phase transitions. The content of these contributions ranges from the modelling to the mathematical and numerical analysis. Furthermore, many numerical simulations are presented. Finally, the contributors have tried to give comprehensive and accurate reference lists. This book should thus serve as a reference book for researchers interested in phase transition phenomena.
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 book describes the dynamics of biological cells and their mathematical modeling. The topics cover the dynamics of RNA polymerases in transcription, construction of vascular networks in angiogenesis, and synchronization of cardiomyocytes. Statistical analysis of single cell dynamics and classification of proteins by mathematical modeling are also presented. The book provides the most up-to-date information on both experimental results and mathematical models that can be used to analyze cellular dynamics. Novel experimental results and approaches to understand them will be appealing to the readers. Each chapter contains 1) an introductory description of the phenomenon, 2) explanations about the mathematical technique to analyze it, 3) new experimental results, 4) mathematical modeling and its application to the phenomenon. Elementary introductions for the biological phenomenon and mathematical approach to them are especially useful for beginners. The importance of collaboration between mathematics and biological sciences has been increasing and providing new outcomes. This book gives good examples of the fruitful collaboration between mathematics and biological sciences.
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.