Modeling Biomolecular Networks

Modeling Biomolecular Networks in Cells shows how the interaction between the molecular components of basic living organisms can be modelled mathematically and the models used to create artificial biological entities within cells. Such forward engineering is a difficult task but the nonlinear dynamical methods espoused in this book simplify the biology so that it can be successfully understood and the synthesis of simple biological oscillators and rhythm-generators made feasible. Such simple units can then be co-ordinated using intercellular signal biomolecules. The formation of such man-made multicellular networks with a view to the production of biosensors, logic gates, new forms of integrated circuitry based on "gene-chips" and even biological computers is an important step in the design of faster and more flexible "electronics". The book also provides theoretical frameworks and tools with which to analyze the nonlinear dynamical phenomena which arise from the connection of building units in a biomolecular network.

This book describes the essentials of a mathematical description of the dynamics of biochemical networks. It covers both deterministic and stochastic aspects of the dynamics. After providing a brief introduction to basic molecular biology, the book describes fundamentals of chemical kinetics. The chapter on signal transduction makes contact with ideas from feedback circuit analysis and signal processing. The chapter on switches and oscillators analyses in detail biological examples, both natural and synthetic. Excitable systems are introduced and contrasted with oscillators. The last chapter deals with pattern formation and development and brings us to current questions of robustness of performance of developmental networks. The book provides brief introductions to some of the mathematical tools required in the main text and in a dedicated appendix. The emphasis, throughout, is on understanding of the essential dynamical aspects rather than just on recipes to build complex models.

Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.

This project under the DARPA BIOCOMP program integrated fundamental scientific investigations in the field of molecular systems biology, algorithm development for biomolecular modeling, and open source, object based software implementation. Major accomplishments were 1) experimental gene knockout strain investigations of the V.fisheri quorum sensing system that yielded a mathematical model of its regulatory proteins, 2) a model of stringent response in E.coli and M.tuberculosis describing the role of enzyme RelMtb, 3) a first example of reachability analysis applied to a biomolecular system (lactose induction), 4) a model of tetracycline resistance that discriminates between two possible mechanisms for tetracycline diffusion through the cell membrane, and 5) a new method for investigating the producibility of a metabolite by a network of chemical reactions from an available set of nutrients using sets of gene knockouts. Accomplishments in algorithm/implementation were 1) reachability and other metabolic analysis tools for non-linear biomolecular networks aiding construction of a hybrid systems-based abstraction, 2) a Systems Biology Markup Language compatible reachability algorithm using a piecewise multi-affine hybrid system method, and 3) a metabolic network producibility analysis algorithm for large scale metabolic networks predicting the possibility of producing a set of metabolites from a set of available nutrients, complementing biomass flux optimization.

By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms Sampling techniques for estimating evolutionary rates and generating molecular structures Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations End-of-chapter exercises

The chemical and biological sciences face unprecedented opportunities in the 21st century. A confluence of factors from parallel universes - advances in experimental techniques in biomolecular structure determination, progress in theoretical modeling and simulation for large biological systems, and breakthroughs in computer technology - has opened new avenues of opportunity as never before. Now, experimental data can be interpreted and further analysed by modeling, and predictions from any approach can be tested and advanced through companion methodologies and technologies. This two volume set describes innovations in biomolecular modeling and simulation, in both the algorithmic and application fronts. With contributions from experts in the field, the books describe progress and innovation in areas including: simulation algorithms for dynamics and enhanced configurational sampling, force field development, implicit solvation models, coarse-grained models, quantum-mechanical simulations, protein folding, DNA polymerase mechanisms, nucleic acid complexes and simulations, RNA structure analysis and design and other important topics in structural biology modeling. The books are aimed at graduate students and experts in structural biology and chemistry and the emphasis is on reporting innovative new approaches rather than providing comprehensive reviews on each subject.

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).

This two volume set describes innovations in biomolecular modeling and simulation, in both the algorithmic and application fronts.

Models of biomolecular structure and dynamics are often obtained by combining simulation or prediction approaches (e.g., comparative modeling, Molecular Dynamics (MD) simulations, Normal Mode Analysis (NMA), etc.) with experimental approaches (e.g., Nuclear Magnetic Resonance (NMR), X-ray crystallography, Small-Angle X-ray Scattering (SAXS), Electron Microscopy (EM), etc.). Such hybrid modeling extends the capabilities of experimental techniques, by enriching structural information and facilitating dynamics studies of biomolecules. This eBook contains articles on methodological developments, applications, and challenges of hybrid biomolecular modeling that have been collected in the framework of the Frontiers Research Topic entitled “Hybrid Biomolecular Modeling”.