24 papers · ranked by Valyu relevance
Muffy Calder, Claire Craig, Dave Culley, Richard de Cani + 15 more
'Christl A. Donnelly' 'Rowan Douglas' 'Bruce Edmonds' 'Jonathon Gascoigne' 'Nigel Gilbert' 'Caroline Hargrove' 'Derwen Hinds' 'David C. Lane' 'Dervilla Mitchell' 'Giles Pavey' 'David Robertson' 'Bridget Rosewell' 'Spencer Sherwin' 'Mark Walport' 'Alan Wilson'] In order to deal with an increasingly complex world, we…
Mark Burgin, Gordana Dodig-Crnković
We need much better understanding of information processing and computation as its primary form. Future progress of new computational devices capable of dealing with problems of big data, internet of things, semantic web, cognitive robotics and neuroinformatics depends on the adequate models of computation. In this…
Konrad Hinsen
Computational techniques have revolutionized many aspects of scientific research over the last few decades. Experimentalists use computation for data analysis, processing ever bigger data sets. Theoreticians compute predictions from ever more complex models. However, traditional articles do not permit the publication…
Yasmin Z. Paterson, David Shorthouse, Markus W. Pleijzier, Nir Piterman + 3 more
In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of…
Oliver Lee, Malte Gather, Eli Zysman-Colman
We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of…
Matthias König
To address the issue of reproducibility in computational modeling we developed the concept of an executable simulation model (EXSIMO). An EXSIMO combines model, data and code with the execution environment to run the computational analysis in an automated manner using tools from software engineering. Key components are…
Maja Rudolph, Stefan Kurz, Barbara Rakitsch
Design patterns provide a systematic way to convey solutions to recurring modeling challenges. This paper introduces design patterns for hybrid modeling, an approach that combines modeling based on first principles with data-driven modeling techniques. While both approaches have complementary advantages there are often…
Niloofar Shahidi, Michael Pan, Soroush Safaei, Kenneth Tran + 2 more
Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Barak Raveh, Liping Sun, Kate L. White, Tanmoy Sanyal + 11 more
Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each…
Julio Vera, Christopher Lischer, Momchil Nenov, Svetoslav Nikolov + 2 more
'Xin Lai' 'Martin Eberhardt'] In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational…
Beatrix C. Hiesmayr, Marc‐Thorsten Hütt
A recent trend in mathematical modeling is to publish the computer code together with the research findings. Here we explore the formal question, whether and in which sense a computer implementation is distinct from the mathematical model. We argue that, despite the convenience of implemented models, a set of implicit…
Andreas Tolk, Saikou Y. Diallo, José J. Padilla, Ross Gore
Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology. The branch that deals with questions like "What is real?" and "What do we know?" as it provides these components. When using modeling and simulation, we usually imply that we are doing so to either apply…
Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg + 5 more
The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server…
Peter V. Coveney, Roger R. Highfield
Title: Highlights 1. • When machine learning is applied in ignorance of fundamental laws of nature, it is likely to deliver unreliable answers. 2. • With his colleagues, Peter Coveney has described the computational algorithms best suited for deployment on exascale architectures. 3. • In the exascale era, we will…
Sachini P. Kadaoluwa Pathirannahalage, Nastaran Meftahi, Aaron Elbourne, Alessia C. G. Weiss + 8 more
Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water…
Christopher Schölzel, Valeria Blesius, Gernot Ernst, Andreas Dominik
Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context can be challenging due to an…
Ilya Korsunsky, Kathleen McGovern, Tom LaGatta, Loes Olde Loohuis + 3 more
'Terri Grosso-Applewhite' 'Nancy Griffeth' 'Bud Mishra'] A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with…
Niloofar Shahidi, Michael Pan, Kenneth Tran, Edmund J. Crampin + 1 more
Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model’s components and assumptions. To address these challenges in…
Matthew P. Szudzik
Alan Turing [14] proposed the concept of a computer—that is, the concept of a mechanical device that can be programmed to perform any conceivable calculation—after studying the processes that humans use to perform calculations. In particular, he claimed that any function of non-negative integers which can be…
Filippo Maggioli, Toni Mancini, Enrico Tronci
Motivation: SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML…
Stefan Ivanov
In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect – the hydration free energy (HFE) of small…
Gopal Sarma, Victor Faúndez
Integrative biological simulations have a varied and controversial history in the biological sciences. From computational models of organelles, cells, and simple organisms, to physiological models of tissues, organ systems, and ecosystems, a diverse array of biological systems have been the target of large-scale…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…