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3 Publications visible to you, out of a total of 3
Metabolic thermodynamics: pertinent reference state and energy potentials

Abstract (Expand)

Chemical potentials (molar Gibbs energies) are usually extrapolated to the remote physical–chemical reference state and then stored. Subsequent use under in vivo conditions requires a similarly … conditions requires a similarly substantial, reverse extrapolation, again with significant potential errors. In order to shrink both extrapolations drastically and thereby enhance both biological meaning and accuracy, we propose a transformation to a more biological reference state: pH = 7, pMg = 3, 99.5% water, with 1 m m each of the additional ‘precursors’ inorganic phosphate, sulfate, ammonium, and bicarbonate, and with twin temperatures 37 and 25 °C, ionic strength 0.15  m and m m as concentration unit. These precursors substitute for reference compounds alien to biology such as H 2 at 1 bar, and solid graphite, sulfur, and phosphorus. The standard chemical potentials are herewith increased by the magnitudes of the chemical potentials of protons, Mg 2+ , water, and the four precursors, each multiplied by the number of corresponding atoms in the molecule. This defines standard ‘metabolic potentials’. We make these potentials findable and accessible as 1360 collated standard chemical potentials for 320 compounds of biochemical interest at the twin metabolic reference states. We do this for 3 reference pH's: We present the metabolic reference state as a convenient anchor , not a universal intracellular milieu. All datasets must continue to report the actual experimental state ( T , pH, pMg, I , osmolarity, concentrations), yet aim at (also) reporting parameter values for this anchor state; we supply algorithms to transform between states. This preserves interoperability across diverse organelles, media and between enzymology and chemical engineering, while facilitating reuse.

Authors: Hans V. Westerhoff, Barbara M. Bakker, Andreas S. Bommarius, Maria Luz Cárdenas, Athel Cornish‐Bowden, Paul Fitzpatrick, Peter J. Halling, Vassily Hatzimanikatis, Carsten Kettner, Yanhua Liu, Andrew G. McDonald, Elad Noor, Jürgen Pleiss, Frank M. Raushel, Johann M. Rohwer, Santiago Schnell, Keith F. Tipton, Ming‐Daw Tsai, Urs von Stockar, Ulrike Wittig, Roland Wohlgemuth, John M. Woodley

Date Published: 5th Mar 2026

Publication Type: Journal Article

Simulation Foundry: Automated and F.A.I.R. Molecular Modeling

Abstract (Expand)

The Simulation Foundry (SF) is a modular workflow for the automated creation of molecular modeling (MM) data. MM allows for the reliable prediction of the microscopic and macroscopic properties of … multicomponent systems from first principles. The SF makes MM repeatable, replicable, and findable, accessible, interoperable, and reusable (F.A.I.R.). The SF uses a standardized data structure and file naming convention, allowing for replication on different supercomputers and re-entrancy. We focus on keeping the SF simple by basing it on scripting languages that are widely used by the MM community (bash, Python) and making it reusable and re-editable. The SF was developed to assist expert users in performing parameter studies of multicomponent systems by high throughput molecular dynamics simulations. The usability of the SF is demonstrated by simulations of thermophysical properties of binary mixtures. A standardized data exchange format enables the integration of simulated data with data from experiments. The SF also provides a complete documentation of how the results were obtained, thus assigning provenance. Increasing computational power facilitates the intensification of the simulation process and requires automation and modularity. The SF provides a community platform on which to integrate new methods and create data that is reproducible and transparent (https://fairdomhub.org/studies/639/snapshots/1, https://fairdomhub.org/studies/639/snapshots/2).

Authors: Gudrun Gygli, Juergen Pleiss

Date Published: 27th Apr 2020

Publication Type: Journal Article

Analysis of Thermophysical Properties of Deep Eutectic Solvents by Data Integration

Abstract

Not specified

Authors: Xinmeng Xu, Jan Range, Gudrun Gygli, Jürgen Pleiss

Date Published: 12th Mar 2020

Publication Type: Journal Article

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