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Husar A, Ordyan M, Garcia GC, Yancey JG, Saglam AS, Faeder JR, Bartol TM, Kennedy MB, Sejnowski TJ. MCell4 with BioNetGen: A Monte Carlo simulator of rule-based reaction-diffusion systems with Python interface. PLoS Comput Biol 2024; 20:e1011800. [PMID: 38656994 PMCID: PMC11073787 DOI: 10.1371/journal.pcbi.1011800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/06/2024] [Accepted: 01/03/2024] [Indexed: 04/26/2024] Open
Abstract
Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4's Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.
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Affiliation(s)
- Adam Husar
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Mariam Ordyan
- Institute for Neural Computations, University of California, San Diego, La Jolla, California, United States of America
| | - Guadalupe C. Garcia
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Joel G. Yancey
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Ali S. Saglam
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Thomas M. Bartol
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Mary B. Kennedy
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Terrence J. Sejnowski
- Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, California, United States of America
- Institute for Neural Computations, University of California, San Diego, La Jolla, California, United States of America
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2
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Bartol TM, Ordyan M, Sejnowski TJ, Rangamani P, Kennedy MB. A spatial model of autophosphorylation of CaMKII in a glutamatergic spine suggests a network-driven kinetic mechanism for bistable changes in synaptic strength. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578696. [PMID: 38352446 PMCID: PMC10862815 DOI: 10.1101/2024.02.02.578696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Activation of N-methyl-D-aspartate-type glutamate receptors (NMDARs) at synapses in the CNS triggers changes in synaptic strength that underlie memory formation in response to strong synaptic stimuli. The primary target of Ca2+ flowing through NMDARs is Ca2+/calmodulin-dependent protein kinase II (CaMKII) which forms dodecameric holoenzymes that are highly concentrated at the postsynaptic site. Activation of CaMKII is necessary to trigger long-term potentiation of synaptic strength (LTP), and is prolonged by autophosphorylation of subunits within the holoenzyme. Here we use MCell4, an agent-based, stochastic, modeling platform to model CaMKII holoenzymes placed within a realistic spine geometry. We show how two mechanisms of regulation of CaMKII, 'Ca2+-calmodulin-trapping (CaM-trapping)' and dephosphorylation by protein phosphatase-1 (PP1) shape the autophosphorylation response during a repeated high-frequency stimulus. Our simulation results suggest that autophosphorylation of CaMKII does not constitute a bistable switch. Instead, prolonged but temporary, autophosphorylation of CaMKII may contribute to a biochemical-network-based 'kinetic proof-reading" mechanism that controls induction of synaptic plasticity.
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Affiliation(s)
| | - Mariam Ordyan
- The Salk Institute for Biological Studies, La Jolla, CA
| | | | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA
| | - Mary B Kennedy
- Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
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3
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Bogetti AT, Leung JMG, Russo JD, Zhang S, Thompson JP, Saglam AS, Ray D, Mostofian B, Pratt AJ, Abraham RC, Harrison PO, Dudek M, Torrillo PA, DeGrave AJ, Adhikari U, Faeder JR, Andricioaei I, Adelman JL, Zwier MC, LeBard DN, Zuckerman DM, Chong LT. A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2023; 5:1655. [PMID: 37200895 PMCID: PMC10191340 DOI: 10.33011/livecoms.5.1.1655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.
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Affiliation(s)
| | | | - John D. Russo
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | | | | | - Ali S. Saglam
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Dhiman Ray
- Department of Chemistry, University of California Irvine, Irvine, CA
| | - Barmak Mostofian
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - AJ Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Rhea C. Abraham
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Page O. Harrison
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Max Dudek
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Paul A. Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Alex J. DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Upendra Adhikari
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - James R. Faeder
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, Irvine, CA
| | - Joshua L. Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | | | | | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - Lillian T. Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
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4
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Stochastic model of ERK-mediated progesterone receptor translocation, clustering and transcriptional activity. Sci Rep 2022; 12:11791. [PMID: 35821038 PMCID: PMC9276744 DOI: 10.1038/s41598-022-13821-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/27/2022] [Indexed: 11/26/2022] Open
Abstract
Progesterone receptor (PR) transcriptional activity is a key factor in the differentiation of the uterine endometrium. By consequence, progestin has been identified as an important treatment modality for endometrial cancer. PR transcriptional activity is controlled by extracellular-signal-regulated kinase (ERK) mediated phosphorylation, downstream of growth factor receptors such as EGFR. However, phosphorylation of PR also targets it for ubiquitination and destruction in the proteasome. Quantitative studies of these opposing roles are much needed toward validation of potential new progestin-based therapeutics. In this work, we propose a spatial stochastic model to study the effects of the opposing roles for PR phosphorylation on the levels of active transcription factor. Our numerical simulations confirm earlier in vitro experiments in endometrial cancer cell lines, identifying clustering as a mechanism that amplifies the ability of progesterone receptors to influence gene transcription. We additionally show the usefulness of a statistical method we developed to quantify and control variations in stochastic simulations in general biochemical systems, assisting modelers in defining minimal but meaningful numbers of simulations while guaranteeing outputs remain within a pre-defined confidence level.
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5
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Russo JD, Zhang S, Leung JMG, Bogetti AT, Thompson JP, DeGrave AJ, Torrillo PA, Pratt AJ, Wong KF, Xia J, Copperman J, Adelman JL, Zwier MC, LeBard DN, Zuckerman DM, Chong LT. WESTPA 2.0: High-Performance Upgrades for Weighted Ensemble Simulations and Analysis of Longer-Timescale Applications. J Chem Theory Comput 2022; 18:638-649. [PMID: 35043623 DOI: 10.1021/acs.jctc.1c01154] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The weighted ensemble (WE) family of methods is one of several statistical mechanics-based path sampling strategies that can provide estimates of key observables (rate constants and pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on the major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE methods. The key features of the new WESTPA 2.0 software enhance the efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation data sets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and "binless" WE strategies.
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Affiliation(s)
- John D Russo
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - She Zhang
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Jeremy M G Leung
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Anthony T Bogetti
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Jeff P Thompson
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Alex J DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Paul A Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - A J Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Kim F Wong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Junchao Xia
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - Joshua L Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Matthew C Zwier
- Department of Chemistry, Drake University, Des Moines, Iowa 50311-4505, United States
| | - David N LeBard
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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6
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Raveh B, Sun L, White KL, Sanyal T, Tempkin J, Zheng D, Bharath K, Singla J, Wang C, Zhao J, Li A, Graham NA, Kesselman C, Stevens RC, Sali A. Bayesian metamodeling of complex biological systems across varying representations. Proc Natl Acad Sci U S A 2021; 118:e2104559118. [PMID: 34453000 PMCID: PMC8536362 DOI: 10.1073/pnas.2104559118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.
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Affiliation(s)
- Barak Raveh
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190416, Israel
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
| | - Tanmoy Sanyal
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Jeremy Tempkin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Dongqing Zheng
- Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Kala Bharath
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
- Epstein Department of Industrial and Systems Engineering, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
- Information Science Institute, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Chenxi Wang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jihui Zhao
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Carl Kesselman
- Epstein Department of Industrial and Systems Engineering, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
- Information Science Institute, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158;
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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7
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FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins. Sci Rep 2020; 10:19954. [PMID: 33203884 PMCID: PMC7672080 DOI: 10.1038/s41598-020-75814-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 09/28/2020] [Indexed: 12/14/2022] Open
Abstract
Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these issues, we present here a robust computer program, called FluoSim, which is an interactive simulator of membrane protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. FluoSim integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the localization and intensity of thousands of independent molecules in 2D cellular geometries, providing simulated data directly comparable to actual experiments. FluoSim was thoroughly validated against experimental data obtained on the canonical neurexin-neuroligin adhesion complex at cell-cell contacts. This unified software allows one to model and predict membrane protein dynamics and localization at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments.
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8
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Troják M, Šafránek D, Mertová L, Brim L. Executable biochemical space for specification and analysis of biochemical systems. PLoS One 2020; 15:e0238838. [PMID: 32915842 PMCID: PMC7485897 DOI: 10.1371/journal.pone.0238838] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 08/25/2020] [Indexed: 01/28/2023] Open
Abstract
Computational systems biology provides multiple formalisms for modelling of biochemical processes among which the rule-based approach is one of the most suitable. Its main advantage is a compact and precise mechanistic description of complex processes. However, state-of-the-art rule-based languages still suffer several shortcomings that limit their use in practice. In particular, the elementary (low-level) syntax and semantics of rule-based languages complicate model construction and maintenance for users outside computer science. On the other hand, mathematical models based on differential equations (ODEs) still make the most typical used modelling framework. In consequence, robust re-interpretation and integration of models are difficult, thus making the systems biology paradigm technically challenging. Though several high-level languages have been developed at the top of rule-based principles, none of them provides a satisfactory and complete solution for semi-automated description and annotation of heterogeneous biophysical processes integrated at the cellular level. We present the second generation of a rule-based language called Biochemical Space Language (BCSL) that combines the advantages of different approaches and thus makes an effort to overcome several problems of existing solutions. BCSL relies on the formal basis of the rule-based methodology while preserving user-friendly syntax of plain chemical equations. BCSL combines the following aspects: the level of abstraction that hides structural and quantitative details but yet gives a precise mechanistic view of systems dynamics; executable semantics allowing formal analysis and consistency checking at the level of the language; universality allowing the integration of different biochemical mechanisms; scalability and compactness of the specification; hierarchical specification and composability of chemical entities; and support for genome-scale annotation.
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Affiliation(s)
- Matej Troják
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - David Šafránek
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Lukrécia Mertová
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Luboš Brim
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
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9
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Ordyan M, Bartol T, Kennedy M, Rangamani P, Sejnowski T. Interactions between calmodulin and neurogranin govern the dynamics of CaMKII as a leaky integrator. PLoS Comput Biol 2020; 16:e1008015. [PMID: 32678848 PMCID: PMC7390456 DOI: 10.1371/journal.pcbi.1008015] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 07/29/2020] [Accepted: 06/04/2020] [Indexed: 01/10/2023] Open
Abstract
Calmodulin-dependent kinase II (CaMKII) has long been known to play an important role in learning and memory as well as long term potentiation (LTP). More recently it has been suggested that it might be involved in the time averaging of synaptic signals, which can then lead to the high precision of information stored at a single synapse. However, the role of the scaffolding molecule, neurogranin (Ng), in governing the dynamics of CaMKII is not yet fully understood. In this work, we adopt a rule-based modeling approach through the Monte Carlo method to study the effect of Ca2+ signals on the dynamics of CaMKII phosphorylation in the postsynaptic density (PSD). Calcium surges are observed in synaptic spines during an EPSP and back-propagating action potential due to the opening of NMDA receptors and voltage dependent calcium channels. Using agent-based models, we computationally investigate the dynamics of phosphorylation of CaMKII monomers and dodecameric holoenzymes. The scaffolding molecule, Ng, when present in significant concentration, limits the availability of free calmodulin (CaM), the protein which activates CaMKII in the presence of calcium. We show that Ng plays an important modulatory role in CaMKII phosphorylation following a surge of high calcium concentration. We find a non-intuitive dependence of this effect on CaM concentration that results from the different affinities of CaM for CaMKII depending on the number of calcium ions bound to the former. It has been shown previously that in the absence of phosphatase, CaMKII monomers integrate over Ca2+ signals of certain frequencies through autophosphorylation (Pepke et al, Plos Comp. Bio., 2010). We also study the effect of multiple calcium spikes on CaMKII holoenzyme autophosphorylation, and show that in the presence of phosphatase, CaMKII behaves as a leaky integrator of calcium signals, a result that has been recently observed in vivo. Our models predict that the parameters of this leaky integrator are finely tuned through the interactions of Ng, CaM, CaMKII, and PP1, providing a mechanism to precisely control the sensitivity of synapses to calcium signals. Author Summary not valid for PLOS ONE submissions.
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Affiliation(s)
- Mariam Ordyan
- Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, California, United States of America
| | - Tom Bartol
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, California, United States of America
| | - Mary Kennedy
- The Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, United States of America
- * E-mail: (PR), (TS)
| | - Terrence Sejnowski
- Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, California, United States of America
- * E-mail: (PR), (TS)
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10
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Argentati C, Tortorella I, Bazzucchi M, Morena F, Martino S. Harnessing the Potential of Stem Cells for Disease Modeling: Progress and Promises. J Pers Med 2020; 10:E8. [PMID: 32041088 PMCID: PMC7151621 DOI: 10.3390/jpm10010008] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 01/18/2020] [Accepted: 02/01/2020] [Indexed: 12/11/2022] Open
Abstract
Ex vivo cell/tissue-based models are an essential step in the workflow of pathophysiology studies, assay development, disease modeling, drug discovery, and development of personalized therapeutic strategies. For these purposes, both scientific and pharmaceutical research have adopted ex vivo stem cell models because of their better predictive power. As matter of a fact, the advancing in isolation and in vitro expansion protocols for culturing autologous human stem cells, and the standardization of methods for generating patient-derived induced pluripotent stem cells has made feasible to generate and investigate human cellular disease models with even greater speed and efficiency. Furthermore, the potential of stem cells on generating more complex systems, such as scaffold-cell models, organoids, or organ-on-a-chip, allowed to overcome the limitations of the two-dimensional culture systems as well as to better mimic tissues structures and functions. Finally, the advent of genome-editing/gene therapy technologies had a great impact on the generation of more proficient stem cell-disease models and on establishing an effective therapeutic treatment. In this review, we discuss important breakthroughs of stem cell-based models highlighting current directions, advantages, and limitations and point out the need to combine experimental biology with computational tools able to describe complex biological systems and deliver results or predictions in the context of personalized medicine.
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Affiliation(s)
- Chiara Argentati
- Department of Chemistry, Biology and Biotechnologies, University of Perugia, Via del Giochetto, 06126 Perugia, Italy; (C.A.); (I.T.); (M.B.); (F.M.)
| | - Ilaria Tortorella
- Department of Chemistry, Biology and Biotechnologies, University of Perugia, Via del Giochetto, 06126 Perugia, Italy; (C.A.); (I.T.); (M.B.); (F.M.)
| | - Martina Bazzucchi
- Department of Chemistry, Biology and Biotechnologies, University of Perugia, Via del Giochetto, 06126 Perugia, Italy; (C.A.); (I.T.); (M.B.); (F.M.)
| | - Francesco Morena
- Department of Chemistry, Biology and Biotechnologies, University of Perugia, Via del Giochetto, 06126 Perugia, Italy; (C.A.); (I.T.); (M.B.); (F.M.)
| | - Sabata Martino
- Department of Chemistry, Biology and Biotechnologies, University of Perugia, Via del Giochetto, 06126 Perugia, Italy; (C.A.); (I.T.); (M.B.); (F.M.)
- CEMIN, Center of Excellence on Nanostructured Innovative Materials, Via del Giochetto, 06126 Perugia, Italy
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