1
|
Jakubowski HV, Agnew H, Jardine B, Sauro HM. Use of interactive mathematical simulations in fundamentals of biochemistry, a LibreText online educational resource, to promote understanding of dynamic reactions. Biochem Mol Biol Educ 2024. [PMID: 38516799 DOI: 10.1002/bmb.21830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 02/09/2024] [Accepted: 03/09/2024] [Indexed: 03/23/2024]
Abstract
Biology is perhaps the most complex of the sciences, given the incredible variety of chemical species that are interconnected in spatial and temporal pathways that are daunting to understand. Their interconnections lead to emergent properties such as memory, consciousness, and recognition of self and non-self. To understand how these interconnected reactions lead to cellular life characterized by activation, inhibition, regulation, homeostasis, and adaptation, computational analyses and simulations are essential, a fact recognized by the biological communities. At the same time, students struggle to understand and apply binding and kinetic analyses for the simplest reactions such as the irreversible first-order conversion of a single reactant to a product. This likely results from cognitive difficulties in combining structural, chemical, mathematical, and textual descriptions of binding and catalytic reactions. To help students better understand dynamic reactions and their analyses, we have introduced two kinds of interactive graphs and simulations into the online educational resource, Fundamentals of Biochemistry, a LibreText biochemistry book. One is available for simple binding and kinetic reactions. The other displays progress curves (concentrations vs. time) for simple reactions and complex metabolic and signal transduction pathways. Users can move sliders to change dissociation and kinetic constants as well as initial concentrations and see instantaneous changes in the graphs. They can also export data into a spreadsheet for further processing, such as producing derivative Lineweaver-Burk and traditional Michaelis-Menten graphs of initial velocity (v0) versus substrate concentration.
Collapse
Affiliation(s)
- Henry V Jakubowski
- Department of Chemistry, College of Saint Benedict/Saint John's University, Saint Joseph, Minnesota, USA
| | - Henry Agnew
- Department of Chemistry and Biochemistry, University of California San Diego, San Diego, California, USA
| | - Bartholomew Jardine
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| |
Collapse
|
2
|
Savoj R, Agnew H, Zhou R, Paesani F. Molecular Insights into the Influence of Ions on the Water Structure. I. Alkali Metal Ions in Solution. J Phys Chem B 2024; 128:1953-1962. [PMID: 38373140 DOI: 10.1021/acs.jpcb.3c08150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
In this study, we explore the impact of alkali metal ions (Li+, Na+, K+, Rb+, and Cs+) on the hydration structure of water using molecular dynamics simulations carried out with MB-nrg potential energy functions (PEFs). Our analyses include radial distribution functions, coordination numbers, dipole moments, and infrared spectra of water molecules, calculated as a function of solvation shells. The results collectively indicate a highly local influence of all of the alkali metal ions on the hydrogen-bond network established by the surrounding water molecules, with the smallest and most densely charged Li+ ion exerting the most pronounced effect. Remarkably, the MB-nrg PEFs demonstrate excellent agreement with available experimental data for the position and size of the first solvation shells, underscoring their potential as predictive models for realistic simulations of ionic aqueous solutions across various thermodynamic conditions and environments.
Collapse
Affiliation(s)
- Roya Savoj
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Henry Agnew
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Ruihan Zhou
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States
- Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California 92093, United States
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States
| |
Collapse
|
4
|
Riera M, Knight C, Bull-Vulpe EF, Zhu X, Agnew H, Smith DGA, Simmonett AC, Paesani F. MBX: A many-body energy and force calculator for data-driven many-body simulations. J Chem Phys 2023; 159:054802. [PMID: 37526156 PMCID: PMC10550339 DOI: 10.1063/5.0156036] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/11/2023] [Indexed: 08/02/2023] Open
Abstract
Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.
Collapse
Affiliation(s)
- Marc Riera
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Christopher Knight
- Argonne National Laboratory, Computational Science Division, Lemont, Illinois 60439, USA
| | - Ethan F. Bull-Vulpe
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Xuanyu Zhu
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Henry Agnew
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | | | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | | |
Collapse
|
6
|
Shaikh B, Smith LP, Vasilescu D, Marupilla G, Wilson M, Agmon E, Agnew H, Andrews SS, Anwar A, Beber ME, Bergmann FT, Brooks D, Brusch L, Calzone L, Choi K, Cooper J, Detloff J, Drawert B, Dumontier M, Ermentrout G, Faeder J, Freiburger A, Fröhlich F, Funahashi A, Garny A, Gennari J, Gleeson P, Goelzer A, Haiman Z, Hasenauer J, Hellerstein J, Hermjakob H, Hoops S, Ison J, Jahn D, Jakubowski H, Jordan R, Kalaš M, König M, Liebermeister W, Sheriff RM, Mandal S, McDougal R, Medley J, Mendes P, Müller R, Myers C, Naldi A, Nguyen TVN, Nickerson D, Olivier B, Patoliya D, Paulevé L, Petzold L, Priya A, Rampadarath A, Rohwer JM, Saglam A, Singh D, Sinha A, Snoep J, Sorby H, Spangler R, Starruß J, Thomas P, van Niekerk D, Weindl D, Zhang F, Zhukova A, Goldberg A, Schaff J, Blinov M, Sauro H, Moraru I, Karr J. BioSimulators: a central registry of simulation engines and services for recommending specific tools. Nucleic Acids Res 2022; 50:W108-W114. [PMID: 35524558 PMCID: PMC9252793 DOI: 10.1093/nar/gkac331] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/07/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.
Collapse
Affiliation(s)
- Bilal Shaikh
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Dan Vasilescu
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | | - Michael Wilson
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Eran Agmon
- Stanford University, Stanford, CA 94305, USA
| | | | | | - Azraf Anwar
- New York University, Brooklyn, NY 11201, USA
| | | | | | - David Brooks
- University of Auckland, 1010 Auckland, New Zealand
| | - Lutz Brusch
- Technical University of Dresden, 01187 Dresden, Germany
| | | | - Kiri Choi
- Korea Institute for Advanced Study, 02455 Seoul, South Korea
| | - Joshua Cooper
- University of North Carolina, Asheville, Ashville, NC 28804, USA
| | | | - Brian Drawert
- University of North Carolina, Asheville, Ashville, NC 28804, USA
| | | | | | | | | | | | | | - Alan Garny
- University of Auckland, 1010 Auckland, New Zealand
| | | | | | - Anne Goelzer
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Zachary Haiman
- University of California, San Diego, La Jolla, CA 92093, USA
| | | | | | - Henning Hermjakob
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Stefan Hoops
- University of Virginia, Charlottesville, VA 22904, USA
| | - Jon C Ison
- CNRS, UMS 3601, Institut Français de Bioinformatique, IFB-core, 91000 Évry-Courcouronnes, France
| | - Diego Jahn
- Technical University of Dresden, 01187 Dresden, Germany
| | - Henry V Jakubowski
- College of Saint Benedict and Saint John’s University, St. Joseph, MN 56374, USA
| | - Ryann Jordan
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | | | - Rahuman S Malik Sheriff
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | | | | | - Pedro Mendes
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Robert Müller
- Technical University of Dresden, 01187 Dresden, Germany
| | - Chris J Myers
- University of Colorado at Boulder, Boulder CO, 80309, USA
| | - Aurelien Naldi
- Inria Saclay - Île-de-France Research Centre, 91120 Palaiseau, France
| | - Tung V N Nguyen
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | - Brett G Olivier
- Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, Netherlands
| | - Drashti Patoliya
- Sarvajanik College of Engineering & Technology, Surat, Gujarat 395001, India
| | - Loïc Paulevé
- Centre National de la Recherche Scientifique, 33400 Talence, France
| | - Linda R Petzold
- University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Ankita Priya
- Birla Institute of Technology, Mesra, Jharkhand 835215, India
| | | | | | - Ali S Saglam
- University of Pittsburgh, Pittsburgh, PA 15260, USA
| | | | - Ankur Sinha
- University College London, London, WC1E 6BT, UK
| | - Jacky Snoep
- Stellenbosch University, Stellenbosch, 7600, South Africa
| | - Hugh Sorby
- University of Auckland, 1010 Auckland, New Zealand
| | - Ryan Spangler
- Allen Institute for Cell Science, Seattle, WA 98109, USA
| | - Jörn Starruß
- Technical University of Dresden, 01187 Dresden, Germany
| | | | | | - Daniel Weindl
- Helmholtz Zentrum München GmbH and German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Fengkai Zhang
- National Institutes of Health, Bethesda, MD 20892, USA
| | | | | | - James C Schaff
- University of Connecticut School of Medicine, Farmington, CT 06030, USA,Applied BioMath LLC, Concord, MA 01742, USA
| | - Michael L Blinov
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | | - Ion I Moraru
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | |
Collapse
|