The application of methodological approaches and mathematical formalisms proper to Physics and Engineering to investigate and describe biological processes and design biological structures has led to the development of many disciplines in the context of computational biology and biotechnology. The best known applicative domain is tissue engineering and its branches. Recent domains of interest are in the field of biophysics, e.g.: multiscale mechanics of biological membranes and films and filaments; multiscale mechanics of adhesion; biomolecular motors and force generation.

Modern hypotheses, models, and tools are currently emerging and resulting from the convergence of the methods and phylosophycal apporaches of the different research areas and disciplines. All these emerging approaches share the purpose of disentangling the complexity of organisms, tissues, and cells and mimiking the function of living systems.

The contributions presented in this book are current research highlights of six challenging and representative applicative domains of phyisical, engineering, and computational approaches in medicine and biology, i.e tissue engineering, modelling of molecular structures, cell mechanics and cell adhesion processes, cancer physics, and physico-chemical processes of metabolic interactions. Each chapter presents a compendium or a review of the original results achieved by authors in the last years. Furthermore, the book also wants to pinpoint the questions that are still open and that could propel the future research.

The convergence of biology and computer science was initially motivated by the need to organize and process a growing number of biological observations resulting from rapid advances in experimental techniques. Today, however, close collaboration between biologists, biochemists, medical researchers, and computer scientists has also generated remarkable benefits for the field of computer science. Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances presents new techniques that have resulted from the application of computer science methods to the organization and interpretation of biological data. The book covers three subject areas: bioinformatics, computational biology, and computational systems biology. It focuses on recent, systemic approaches in computer science and mathematics that have been used to model, simulate, and more generally, experiment with biological phenomena at any scale.

This introductory guide provides a thorough explanation of the mathematics and algorithms used in standard data analysis techniques within systems biology, biochemistry, and biophysics. Each part of the book covers the mathematical background and practical applications of a given technique. Readers will gain an understanding of the mathematical and algorithmic steps needed to use these software tools appropriately and effectively, as well how to assess their specific circumstance and choose the optimal method and technology. Ideal for students planning for a career in research, early-career researchers, and established scientists undertaking interdisciplinary research. 

Quantum physics provides the concepts and their mathematical formalization that lend themselves to describe important properties of biological networks topology, such as vulnerability to external stress and their dynamic response to changing physiological conditions. A theory of networks enhanced with mathematical concepts and tools of quantum physics opens a new area of biological physics, the one of systems biological physics.

The application of methodological approaches and mathematical formalisms proper to Physics and Engineering to investigate and describe biological processes and design biological structures has led to the development of many disciplines in the context of computational biology and biotechnology. The best known applicative domain is tissue engineering and its branches. Recent domains of interest are in the field of biophysics, e.g.: multiscale mechanics of biological membranes and films and filaments; multiscale mechanics of adhesion; biomolecular motors and force generation. Modern hypotheses, models, and tools are currently emerging and resulting from the convergence of the methods and phylosophycal apporaches of the different research areas and disciplines. All these emerging approaches share the purpose of disentangling the complexity of organisms, tissues, and cells and mimiking the function of living systems. The contributions presented in this book are current research highlights of six challenging and representative applicative domains of phyisical, engineering, and computational approaches in medicine and biology, i.e tissue engineering, modelling of molecular structures, cell mechanics and cell adhesion processes, cancer physics, and physico-chemical processes of metabolic interactions. Each chapter presents a compendium or a review of the original results achieved by authors in the last years. Furthermore, the book also wants to pinpoint the questions that are still open and that could propel the future research.

Quantum physics provides the concepts and their mathematical formalization that lend themselves to describe important properties of biological networks topology, such as vulnerability to external stress and their dynamic response to changing physiological conditions. A theory of networks enhanced with mathematical concepts and tools of quantum physics opens a new area of biological physics, the one of systems biological physics.

This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems. This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling. The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.

Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics.
Introduces mathematical concepts and formalisms of deterministic and stochastic modelling through clear and simple examplesPresents recently developed discrete stochastic formalisms for modelling biological systems and processesDescribes and applies stochastic simulation algorithms to implement a stochastic formulation of biochemical and biological kinetics