18 papers · ranked by Valyu relevance
Aaditya Prasad Gupta
A modeling is a mathematical tool, like a microscope, which allows consequences to logically follow from a set of assumptions by which a real world problem can be described by a mathematical formulation. It has become indispensable tools for integrating and interpreting heterogeneous biological data, validating…
James Holehouse
Population-level distributions of fluorescence or molecule counts are often taken to reflect the behaviors of individual cells within that population. In this article, we argue that counting sub-populations can be a misleading proxy for identifying the number of behavioral modes accessible to individual cells within a…
Maximilien Cosme, Colin Thomas, Cédric Gaucherel, Tomas Veloz
Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential…
V. L. Kalmykov, L. V. Kalmykov
Mathematical black box models, which hide the structure and behavior of the subsystems, currently dominate science. Errors and paradoxes, such as the biodiversity paradox and the limiting similarity hypothesis, often arise from subjective interpretations of these hidden mechanisms. To address these problems, we have…
Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz
Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of…
Luis U Aguilera, Lisa M Weber, Eric Ron, Connor R King + 10 more
'Alex Popinga' 'Joshua Cook' 'Michael P May' 'William S Raymond' 'Zachary R Fox' 'Linda S Forero-Quintero' 'Jack R Forman' 'Alexandre David' 'Brian Munsky'] Title: Abstract The field of quantitative biology (q-bio) seeks to provide precise and testable explanations for observed biological phenomena by applying…
Thales R. Spartalis, Wan Tang, Xun Tang
Ordinary differential equation (ODE)-based modeling is a powerful tool in the design and characterization of synthetic gene circuits. Despite its popularity, identifying the model parameters based off experimental measurement is a nontrivial task. In this study, we leverage cell-free experimental measurement of two…
Anna Wigren, Johan Wågberg, Fredrik Lindsten, Adrian Wills + 1 more
'Thomas B. Schön'] ``` @article { Wigren2022 , author ={ Wigren , Anna and W {\ aa } gberg , Johan and Lindsten , Fredrik and Wills , Adrian G . and Sch {\" o }n , Thomas B .} , journal ={ IEEE Control Systems Magazine } , title ={ Nonlinear System Identification : Learning While Respecting Physical Models Using a…
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…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Jana C. Massing, Thilo Gross
Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their…
Luca Serena, Moreno Marzolla, Gabriele D’Angelo, Stefano Ferretti
—Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming…
L. Mark Berliner, Radu Herbei, Christopher K. Wikle, Ralph F. Milliff + 1 more
'Ralph F. Milliff' 'Pablo Martin Rodriguez'] Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Kara Bocan, Nataša Miškov-Živanov
—Computational modeling of a complex system is limited by the parts of the system with the least information. While detailed models and high-resolution data may be available for parts of a system, abstract relationships are often necessary to connect the parts and model the full system. For example, modeling food…
Jessica S. Yu, Blair Lyons, Susanne Rafelski, Julie A. Theriot + 2 more
Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate…
James Koch, Pranab Roy Chowdhury, Heng Wan, Parin Bhaduri + 3 more
Socioeconomic Dynamics Authors: ['James Koch' 'Pranab Roy Chowdhury' 'Heng Wan' 'Parin Bhaduri' 'Jim Yoon' 'Vivek Srikrishnan' 'W. Brent Daniel'] We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarsegraining fine-scale observations, our modeling framework…