Bond graphs can be used to build thermodynamically-compliant hierarchical models of biomolecular systems. As bond graphs have been widely used to model, analyse and synthesise engineering systems, this study suggests that they can play the same rôle in the modelling, analysis and synthesis of biomolecular systems. The particular structure of bond graphs arising from biomolecular systems is established and used to elucidate the relation between thermodynamically closed and open systems. Block diagram representations of the dynamics implied by these bond graphs are used to reveal implicit feedback structures and are linearised to allow the application of control-theoretical methods. Two concepts of modularity are examined: computational modularity where physical correctness is retained and behavioural modularity where module behaviour (such as ultrasensitivity) is retained. As well as providing computational modularity, bond graphs provide a natural formulation of behavioural modularity and reveal the sources of retroactivity. A bond graph approach to reducing retroactivity, and thus inter-module interaction, is shown to require a power supply such as that provided by the ATP ⇌ ADP + Pi reaction. The mitogen-activated protein kinase cascade (Raf-MEK-ERK pathway) is used as an illustrative example.
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http://dx.doi.org/10.1049/iet-syb.2015.0083 | DOI Listing |
J Chem Theory Comput
January 2025
The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Metal-organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective in exploring structure-property relationships and discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity and patterns.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
More than 50 % of proteins bind to metal ions. Interactions between metal ions and proteins, especially coordinated interactions, are essential for biological functions, such as maintaining protein structure and signal transport. Physiological metal-ion binding prediction is pivotal for both elucidating the biological functions of proteins and for the design of new drugs.
View Article and Find Full Text PDFJ Environ Manage
February 2025
Australian Rivers Institute, Griffith University, Nathan, Queensland, Australia.
In-channel persistent surface water provides critical refuge habitat for aquatic organisms in intermittently flowing rivers. Quantifying the flows that maintain connectivity among persistent waterholes is important for managing river flows to maintain refuges, improve their quality and facilitate connectivity and nutrient and energy transport. This study aimed to quantify spatial and temporal waterhole persistence and connectivity in a 664 km reach of the Darling River in Australia's Murray-Darling Basin.
View Article and Find Full Text PDFNat Commun
January 2025
School of Life Sciences, Northwestern Polytechnical University, Xi'an, China.
Inferring appropriate synthesis reaction (i.e., retrosynthesis) routes for newly designed molecules is vital.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
School of Information Science and Engineering, Yunnan University, Kunming650091,China.
Liquid chromatography retention time (RT) prediction plays a crucial role in metabolite identification, a challenging and essential task in untargeted metabolomics. Accurate molecular representation is vital for reliable RT prediction. To address this, we propose a novel molecular representation learning framework, ABCoRT(tom-ond -learning for etention ime prediction), designed for predicting metabolite retention times.
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