29 papers · ranked by Valyu relevance
Patrik Christen, Olivier Del Fabbro
Many computer models have been developed and successfully applied. However, in some cases, these models might be restrictive on the possible solutions or their solutions might be difficult to interpret. To overcome this problem, we outline a new approach, the so-called allagmatic method, that automatically programs and…
Axel Loewe, Peter J. Hunter, Peter Kohl
Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of ‘reductionism and integrationism’ seems less controversial today, a new…
Marvin van Aalst, Oliver Ebenhöh, Anna Matuszyńska
Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of…
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…
Judith B. Rommel
Reliable predictions of the behaviour of chemical systems are essential across many industries, from nanoscale engineering over validation of advanced materials to nanotoxicity assessment in health and medicine. For the future we therefore envision a paradigm shift for the design of chemical simulations across all…
Jacek A Kopec, Philippe Finès, Douglas G Manuel, David L Buckeridge + 9 more
Background Computer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models. Methods We developed a comprehensive…
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…
Natal van Riel, Ralph Müller, Enrico Dall’Ara
Computational models can be used to study the mechanistic phenomena of disease. Current mechanistic computer simulation models mainly focus on (patho)physiology in humans. However, often data and experimental findings from preclinical studies are used as input to develop such models. Biological processes underlying…
Nathalie Mejean Perrot, Severine Layec, Alberto Tonda, Nadia Boukhelifa + 2 more
In this paper, we present a test of an interactive modelling scheme in real conditions. The aim is to use this scheme to identify the physiological responses of microorganisms at different scales in a real industrial application context. The originality of the proposed tool, Biosys-LiDeOGraM, is to generate through a…
Axel Loewe, Peter J. Hunter, Peter Köhl
tools and visions from the beginning of the century Authors: ['Axel Loewe' 'Peter J. Hunter' 'Peter Köhl'] Abstract. Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial…
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…
N. Furian, M. O’Sullivan, C. Walker, S. Vössner + 1 more
Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for…
Thaleia Ntiniakou, James Osborne, Jieling Zhao, Jules Dichamp + 30 more
The emergence of virtual human twins (VHT) in biomedical research has sparked interest in multiscale in silico modelling frameworks, particularly in their application bridging cellular to tissue levels. Among the diverse array of multiscale modelling tools, off-lattice center-based agent-based models (CBM) offer a…
Juan M. Durán
The book is organized as follows. In chapter 1, I address the question 'what are computer simulations?' by giving an historical overview of the concept. Tracking back the concept of computer simulation to the early 1960s, we will soon realize that many contemporary definitions owe much to these early attempts. A proper…
Szymon Wasik, Tomasz Prejzendanc, Jacek Blazewicz
Computational modeling is an important element of systems biology. One of its important applications is modeling complex, dynamical, and biological systems, including viral infections. This type of modeling usually requires close cooperation between biologists and mathematicians. However, such cooperation often faces…
Lesley A. Ogilvie, Aleksandra Kovachev, Christoph Wierling, Bodo M. H. Lange + 1 more
'Bodo M. H. Lange' 'Hans Lehrach'] Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of…
Janina Wellmann
Over the course of the last three decades, computer simulations have become a major tool of doing science and engaging with the world, not least in an effort to predict and intervene in a future to come. Born in the context of the Second World War and the discipline of physics, simulations have long spread into most…
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…
Alan Aspuru-Guzik, Roland Lindh, Markus Reiher
To date, the program for the development of methods and models for atomistic and continuum simulation directed toward chemicals and materials has reached an incredible degree of sophistication and maturity. Currently, one can witness an increasingly rapid emergence of advances in computing, artificial intelligence, and…
Niloofar Shahidi, Michael Pan, Soroush Safaei, Kenneth Tran + 3 more
'Edmund J. Crampin' 'David P. Nickerson' 'Daniel A. Beard'] 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…
Anna Matuszyńska, Oliver Ebenhöh, Matias D. Zurbriggen, Daniel C. Ducat + 1 more
Synthetic biology designs and constructs new biological parts, devices and systems with predetermined functionalities. With the unlimited ability to synthesise any DNA and RNA and transfer it to almost any organism, we are at the dawn of a new era in which biology is being recreated in ways never before possible. It…
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…
Jan Hasenauer, Nick Jagiella, Sabrina Hroß, Fabian J. Theis
Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual…
Michael Pan, Peter J. Gawthrop, Joseph Cursons, Edmund J. Crampin
It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the…
Wei Zhang, Jonathan A Fine, Christopher Sculley, Jordon McGraw + 1 more
The representation of complex biomolecular structures and interactions is a difficult challenge across life sciences. Researchers and students use unintuitive 2D representations to gain an intuitive understanding of 3D space and molecular interactions. Since this is cumbersome for complex structures, such as…
Authors not listed
As a prove of concept for experimental geochemistry, an advanced 3D numerical framework, here and after called Digital Twin (DT), of a diffusion experiment conducted at a synchrotron beamline, has been implemented using in-situ measurements data, physics-based modelling, a machine learning (ML) model, and parameter…
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…
Daiki Erikawa, Nobuaki Yasuo, Masakazu Sekijima
Automatic optimization methods for compounds in the vast compound space are important for drug discovery and material design. Several machine learning-based molecular generative models for drug discovery have been proposed, but most of these methods generate compounds from scratch and are not suitable for exploring and…
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…