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Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Dr. Sergio Decherchi and Dr. Andrea Cavalli are co-founders of BiKi Technologies s.r.l. - a company that commercializes a Molecular Dynamics-based software suite for drug discovery. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Very broad overview of the field intended for an interdisciplinary audience; Lively discussion of current challenges written in a colloquial style; Author is a rising star in this discipline; Suitably accessible for beginners and suitably rigorous for experts; Features extensive four-color illustrations; Appendices featuring homework assignments and reading lists complement the material in the main text
This book provides an accessible introduction to the principles and tools for modeling, analyzing, and synthesizing biomolecular systems. It begins with modeling tools such as reaction-rate equations, reduced-order models, stochastic models, and specific models of important core processes. It then describes in detail the control and dynamical systems tools used to analyze these models. These include tools for analyzing stability of equilibria, limit cycles, robustness, and parameter uncertainty. Modeling and analysis techniques are then applied to design examples from both natural systems and synthetic biomolecular circuits. In addition, this comprehensive book addresses the problem of modular composition of synthetic circuits, the tools for analyzing the extent of modularity, and the design techniques for ensuring modular behavior. It also looks at design trade-offs, focusing on perturbations due to noise and competition for shared cellular resources. Featuring numerous exercises and illustrations throughout, Biomolecular Feedback Systems is the ideal textbook for advanced undergraduates and graduate students. For researchers, it can also serve as a self-contained reference on the feedback control techniques that can be applied to biomolecular systems. Provides a user-friendly introduction to essential concepts, tools, and applications Covers the most commonly used modeling methods Addresses the modular design problem for biomolecular systems Uses design examples from both natural systems and synthetic circuits Solutions manual (available only to professors at press.princeton.edu) An online illustration package is available to professors at press.princeton.edu
The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models. 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation.
Dr. Elif Ozkirimli is a full time employee of F. Hoffmann-La Roche AG, Switzerland and Dr. Artur Yakimovich is a full time employee of Roche Products Limited, UK. All other Topic Editors declare no competing interests with regards to the Research Topic.
This textbook provides an introduction to the fundamental and applied aspects of biophysics for advanced undergraduate and graduate students of physics, chemistry, and biology. The application of physics principles and techniques in exploring biological systems has long been a tradition in scientific research. Biological systems hold naturally inbuilt physical principles and processes which are popularly explored. Systematic discoveries help us understand the structures and functions of individual biomolecules, biomolecular systems, cells, organelles, tissues, and even the physiological systems of animals and plants. Utilizing a physics- based scientific understanding of biological systems to explore disease is at the forefront of applied scientific research. This textbook covers key breakthroughs in biophysics whilst looking ahead to future horizons and directions of research. It contains models based on both classical and quantum mechanical treatments of biological systems. It explores diseases related to physical alterations in biomolecular structures and organizations alongside drug discovery strategies. It also discusses the cutting- edge applications of nanotechnologies in manipulating nanoprocesses in biological systems. Key Features: • Presents an accessible introduction to how physics principles and techniques can be used to understand biological and biochemical systems. • Addresses natural processes, mutations, and their purposeful manipulation. • Lays the groundwork for vitally important natural scientific, technological, and medical advances. Mohammad Ashrafuzzaman, a biophysicist and condensed matter scientist, is passionate about investigating biological and biochemical processes utilizing physics principles and techniques. He is a professor of biophysics at King Saud University’s Biochemistry Department in the College of Science, Riyadh, Saudi Arabia; the co- founder of MDT Canada Inc., and the founder of Child Life Development Institute, Edmonton, Canada. He has authored Biophysics and Nanotechnology of Ion Channels, Nanoscale Biophysics of the Cell, and Membrane Biophysics. He has also published about 50 peer- reviewed articles and several patents, edited two books, and has been serving on the editorial boards of Elsevier and Bentham Science journals. Dr. Ashrafuzzaman has held research and academic ranks at Bangladesh University of Engineering & Technology, University of Neuchatel (Switzerland), Helsinki University of Technology (Finland), Weill Medical College of Cornell University (USA), and University of Alberta (Canada). During 2013– 2018 he also served as a Visiting Professor at the Departments of Oncology, and Medical Microbiology and Immunology, of the University of Alberta. Dr. Ashrafuzzaman earned his highest academic degree, Doctor of Science (D.Sc.) in condensed matter physics from the University of Neuchatel, Switzerland in 2004.
Protein Simulation focuses on predicting how protein will act in vivo. These studies use computer analysis, computer modeling, and statistical probability to predict protein function.* Force Fields* Ligand Binding* Protein Membrane Simulation* Enzyme Dynamics* Protein Folding and unfolding simulations
This book is the third volume in this highly successful series. Since the first volume in 1989 and the second in 1993, many exciting developments have occurred in the development of simulation techniques and their application to key biological problems such as protein folding, protein structure prediction and structure-based design, and in how, by combining experimental and theoretical approaches, very large biological systems can be studied at the molecular level. This series attempts to capture that progress. Volume 3 includes contributions that highlight developments in methodology which enable longer and more realistic simulations (e.g. multiple time steps and variable reduction techniques), a study of force fields for proteins and new force field development, a novel approach to the description of molecular shape and the use of molecular shape descriptors, the study of condensed phase chemical reactions, the use of electrostatic techniques in the study of protonation, equilibria and flexible docking studies, structure refinement using experimental data (X-ray, NMR, neutron, infrared) and theoretical methods (solvation models, normal mode analysis, MD simulations, MC lattice dynamics, and knowledge-based potentials). There are several chapters that show progress in the development of methodologies for the study of folding processes, binding affinities, and the prediction of ligand-protein complexes. The chapters, contributed by experienced researchers, many of whom are leaders in their field of study, are organised to cover developments in: simulation methodology the treatment of electrostatics protein structure refinement the combined experimental and theoretical approaches to the study of very large biological systems applications and methodology involved in the study of protein folding applications and methodology associated with structure-based design.