Autonomous learning of generative models with chemical reaction network ensembles
Journal of The Royal Society InterfaceCan a micron-sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment? We draw insights from control theory, machine learning theory, chemical reaction network theory and statistical physics to develop a general architecture whereby a broad class of chemical systems can autonomously learn complex
William Poole +2 more
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Improving Chemical Reaction Prediction with Unlabeled Data
Molecules, 2022 Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data.
Yu Xie +4 more
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Model discrimination of chemical reaction networks by linearization [PDF]
Proceedings of the 2010 American Control Conference, 2010 Systems biologists are often faced with competing models for a given experimental system. Performing experiments can be time-consuming and expensive. Therefore, a method for designing experiments that, with high probability, discriminate between competing models is desired.
D Georgiev, M Fazel, E Klavins
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Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks [PDF]
Combustion and Flame, 2022 Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior. Despite its importance, the burning of solid fuels remains poorly understood, which can be partly attributed to the unknown chemical kinetics of most solid fuels.
Weiqi Ji +3 more
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Identification of functional differences in metabolic networks using comparative genomics and constraint-based models. [PDF]
PLoS ONE, 2012 Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms.
Joshua J Hamilton, Jennifer L Reed
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The Gompertz model revisited and modified using reaction networks: Mathematical analysis
Biomath, 2021 In the present work we discuss the?usage of the framework of chemical reaction networks for the construction of dynamical models and their mathematical analysis.
Svetoslav Marinov Markov
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Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case
Molecules, 2023 Ab initio kinetic studies are important to understand and design novel chemical reactions. While the Artificial Force Induced Reaction (AFIR) method provides a convenient and efficient framework for kinetic studies, accurate explorations of reaction path
Ruben Staub +4 more
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Mathematical Methods for Modeling Chemical Reaction Networks [PDF]
, 2016 AbstractCancer’s cellular behavior is driven by alterations in the processes that cells use to sense and respond to diverse stimuli. Underlying these processes are a series of chemical processes (enzyme-substrate, protein-protein, etc.). Here we introduce a set of mathematical techniques for describing and characterizing these processes.
Carden Jr-PSOC Me, Justin +4 more
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Modeling Chemical Reaction Networks Using Neural Ordinary Differential Equations [PDF]
Journal of Chemical Information and ModelingIn chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordinary differential equations systems is derived from an empirical model of the reaction network, it may be incomplete.
Anna C. M. Thöni +4 more
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Persistence and stability of generalized ribosome flow models with time-varying transition rates.
PLoS ONE, 2023 In this paper some important qualitative dynamical properties of generalized ribosome flow models are studied. Ribosome flow models known from the literature are generalized by allowing an arbitrary directed network structure between compartments, and by
Mihály A Vághy, Gábor Szederkényi
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