Simulating blood cells for biomedical applications is a challenging goal. Whether you want to investigate blood flow behavior on the cell scale, or use a blood cell model for fast computational prototyping in microfluidics, Computational Blood Cell Mechanics will help you get started, and show you the path forward. The text presents a step-by-step approach to cell model building that can be adopted when developing and validating models for biomedical applications, such as filtering and sorting cells, or examining flow and deformations of individual cells under various conditions. It starts with basic building-blocks that, together, model the red blood cell membrane according to its physical properties, before moving on to discuss several issues that may pose problems along the way, and finally leads to suggestions on how to set up computational experiments. More details available at www.compbloodcell.eu
Simulating blood cells for biomedical applications is a challenging goal. Whether you want to investigate blood flow behavior on the cell scale, or use a blood cell model for fast computational prototyping in microfluidics, Computational Blood Cell Mechanics will help you get started, and show you the path forward. The text presents a step-by-step approach to cell model building that can be adopted when developing and validating models for biomedical applications, such as filtering and sorting cells, or examining flow and deformations of individual cells under various conditions. It starts with basic building-blocks that, together, model the red blood cell membrane according to its physical properties, before moving on to discuss several issues that may pose problems along the way, and finally leads to suggestions on how to set up computational experiments. More details available at www.compbloodcell.eu
Computational biology has developed rapidly during the last two decades following the genomic revolution which culminated in the sequencing of the human genome. More than ever it has developed into a field which embraces computational methods from different branches of the exact sciences: pure and applied mathematics, computer science, theoretical physics. This Second Edition provides a solid introduction to the techniques of statistical mechanics for graduate students and researchers in computational biology and biophysics. Material has been reorganized to clarify equilbrium and nonequilibrium aspects of biomolecular systems Content has been expanded, in particular in the treatment of the electrostatic interactions of biomolecules and the application of non-equilibrium statistical mechanics to biomolecules New network-based approaches for the study of proteins are presented. All treated topics are put firmly in the context of the current research literature, allowing the reader to easily follow an individual path into a specific research field. Exercises and Tasks accompany the presentations of the topics with the intention of enabling the readers to test their comprehension of the developed basic concepts.
This book gathers selected, extended and revised contributions to the 16th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, and the 4th Conference on Imaging and Visualization (CMBBE 2019), held on August 14-16, 2019, in New York City, USA. It reports on cutting-edge models and algorithms for studying various tissues and organs in normal and pathological conditions; innovative imaging and visualization techniques; and the latest diagnostic tools. Further topics addressed include: numerical methods, machine learning approaches, FEM models, and high-resolution imaging and real-time visualization methods applied for biomedical purposes. Given the scope of its coverage, the book provides graduate students and researchers with a timely and insightful snapshot of the latest research and current challenges in biomedical engineering, computational biomechanics and biological imaging, as well as a source of inspiration for future research and cross-disciplinary collaborations.
Red blood cells in humans—and most other mammals—have a tendency to form aggregates with a characteristic face-to-face morphology, similar to a stack of coins. Known as rouleaux, these aggregates are a normally occurring phenomenon and have a major impact on blood rheology. What is the underlying mechanism that produces this pattern? Does this really happen in blood circulation? And do these rouleaux formations have a useful function? The first book to offer a comprehensive review of the subject, Red Blood Cell Aggregation tackles these and other questions related to red blood cell (RBC) aggregates. The book covers basic, clinical, and physiological aspects of this important biophysical phenomenon and integrates these areas with concepts in bioengineering. It brings together state-of-the-art research on the determinants, mechanisms, and measurement and effects of RBC aggregation as well as on variations and comparative aspects. After an introductory overview, the book outlines factors and conditions that affect RBC aggregation. It presents the two hypotheses—the bridging model and the depletion model—that provide potential mechanisms for the adhesive forces that lead to the regular packing of the cells in rouleaux formations. The book also reviews the methods used to quantify RBC aggregation in vitro, focusing on their importance in clinical practice. Chapters discuss the effect of RBC aggregation on the in vitro rheology of blood as well as on tube flow. The book also looks at what happens in the circulation when red blood cells aggregate and examines variations due to physiological and pathophysiological challenges. The concluding chapter explores the formation of red blood cell aggregates in other mammals. Written by leading researchers in the field, this is an invaluable resource for basic science, medical, and clinical researchers; graduate students; and clinicians interested in mammalian red blood cells.
This volume constitutes the proceedings of the 9th International Work-Conference on IWBBIO 2020, held in Maspalomas, Gran Canaria, Spain, in June 2022. The total of 75 papers presented in the proceedings, was carefully reviewed and selected from 212 submissions. The papers cover the latest ideas and realizations in the foundations, theory, models, and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, biology, bioinformatics, and biomedicine.
Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline" of analytical methods with concrete examples starting from raw gene expression and DNA methylation data at the genome scale. Methods on quality control, data pre-processing, data mining, and further assessments are presented in the book, and R programs based on simulated data and real data are included. Codes with example data are all reproducible. Features: • Provides a sequence of analytical tools for genome-scale gene expression data and DNA methylation data, starting from quality control and pre-processing of raw genome-scale data. • Organized by a parallel presentation with explanation on statistical methods and corresponding R packages/functions in quality control, pre-processing, and data analyses (e.g., clustering and networks). • Includes source codes with simulated and real data to reproduce the results. Readers are expected to gain the ability to independently analyze genome-scaled expression and methylation data and detect potential biomarkers. This book is ideal for students majoring in statistics, biostatistics, and bioinformatics and researchers with an interest in high dimensional genetic and epigenetic studies.
In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the ability to plan suitable analyses of biological datasets, and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained, including linear and rank-based correlation, distance metrics and hierarchical clustering, hypothesis testing using linear regression, proportional hazards regression for survival data, and principal component analysis. These methods are then applied as appropriate throughout the case studies, illustrating how they can be used to answer research questions. Key Features: · Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. · Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles · Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. · Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq, genome sequencing, ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. · Gives worked-out examples geared towards problems encountered in cancer research, which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills.
Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.
