The gap between introductory level textbooks and highly specialized monographs is filled by this modern textbook. It provides in one comprehensive volume the in-depth theoretical background for molecular modeling and detailed descriptions of the applications in chemistry and related fields like drug design, molecular sciences, biomedical, polymer and materials engineering. Special chapters on basic mathematics and the use of respective software tools are included. Numerous numerical examples, exercises and explanatory illustrations as well as a web site with application tools (http://www.amrita.edu/cen/ccmm) support the students and lecturers.

Theoretical tools and insights from discrete mathematics, theoretical computer science, and topology now play essential roles in our understanding of vital biomolecular processes. The related methods are now employed in various fields of mathematical biology as instruments to "zoom in" on processes at a molecular level. This book contains expository chapters on how contemporary models from discrete mathematics – in domains such as algebra, combinatorics, and graph and knot theories – can provide perspective on biomolecular problems ranging from data analysis, molecular and gene arrangements and structures, and knotted DNA embeddings via spatial graph models to the dynamics and kinetics of molecular interactions. The contributing authors are among the leading scientists in this field and the book is a reference for researchers in mathematics and theoretical computer science who are engaged with modeling molecular and biological phenomena using discrete methods. It may also serve as a guide and supplement for graduate courses in mathematical biology or bioinformatics, introducing nontraditional aspects of mathematical biology.

With the increasing complexity of software systems and their widespread growth into many aspects of our lives, the need to search for new models, paradigms, and ultimately, technologies, to manage this problem is evident. The way nature solves various problems through processes evolving during billions of years was always an inspiration to many computational paradigms; on the other hand, the complexity of the problems posed by the investigation of biological systems challenged the research of new tractable models. Molecular Computational Models: Unconventional Approaches is looking into new computational paradigms from both a theoretical perspective which offers a solid foundation of the models developed, as well as from a modeling angle, in order to reveal their effectiveness in modeling and simulating, especially biological systems. Tools and programming concepts and implementation issues are also discussed in the context of some experiments and comparative studies.

This book constitutes the refereed proceedings of the 25th International Conference on DNA Computing and Molecular Programming, DNA 25, held in Seattle, WA, USA, in August 2019. The 12 full papers presented were carefully selected from 19 submissions. The papers cover a wide range of topics relating to biomolecular computing such as algorithms and models for computation on biomolecular systems; computational processes in vitro and in vivo; molecular switches, gates, devices, and circuits; molecular folding and self-assembly of nanostructures; analysis and theoretical models of laboratory techniques; molecular motors and molecular robotics; information storage; studies of fault-tolerance and error correction; software tools for analysis, simulation, anddesign; synthetic biology and in vitro evolution; and applications in engineering, physics, chemistry, biology, and medicine.

• Assumes no background in statistics or computers • Covers most major types of molecular biological data • Covers the statistical and machine learning concepts of most practical utility (P-values, clustering, regression, regularization and classification) • Intended for graduate students beginning careers in molecular biology, systems biology, bioengineering and genetics

This book covers applications of computational techniques to biological problems. These techniques are based by an ever-growing number of researchers with different scientific backgrounds - biologists, chemists, and physicists.The rapid development of molecular biology in recent years has been mirrored by the rapid development of computer hardware and software. This has resulted in the development of sophisticated computational techniques and a wide range of computer simulations involving such methods. Among the areas where progress has been profound is in the modeling of DNA structure and function, the understanding at a molecular level of the role of solvents in biological phenomena, the calculation of the properties of molecular associations in aqueous solutions, computationally assisted drug design, the prediction of protein structure, and protein - DNA recognition, to mention just a few examples. This volume comprises a balanced blend of contributions covering such topics. They reveal the details of computational approaches designed for biomoleucles and provide extensive illustrations of current applications of modern techniques.A broad group of readers ranging from beginning graduate students to molecular biology professions should be able to find useful contributions in this selection of reviews.

This textbook originated from the course 'Simulation, Modeling, and Computations in Biophysics' that I have taught at the University of Chicago since 2011. The students typically came from a wide range of backgrounds, including biology, physics, chemistry, biochemistry, and mathematics, and the course was intentionally adapted for senior undergraduate students and graduate students. This is not a highly technical book dedicated to specialists. The objective is to provide a broad survey from the physical description of a complex molecular system at the most fundamental level, to the type of phenomenological models commonly used to represent the function of large biological macromolecular machines.The key conceptual elements serving as building blocks in the formulation of different levels of approximations are introduced along the way, aiming to clarify as much as possible how they are interrelated. The only assumption is a basic familiarity with simple mathematics (calculus and integrals, ordinary differential equations, matrix linear algebra, and Fourier-Laplace transforms).