1
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Trifan A, Gorgun D, Salim M, Li Z, Brace A, Zvyagin M, Ma H, Clyde A, Clark D, Hardy DJ, Burnley T, Huang L, McCalpin J, Emani M, Yoo H, Yin J, Tsaris A, Subbiah V, Raza T, Liu J, Trebesch N, Wells G, Mysore V, Gibbs T, Phillips J, Chennubhotla SC, Foster I, Stevens R, Anandkumar A, Vishwanath V, Stone JE, Tajkhorshid E, A. Harris S, Ramanathan A. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2022; 36:603-623. [PMID: 38464362 PMCID: PMC10923581 DOI: 10.1177/10943420221113513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.
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Affiliation(s)
- Anda Trifan
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | - Defne Gorgun
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | | | | | | | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ian Foster
- Argonne National Laboratory
- University of Chicago
| | - Rick Stevens
- Argonne National Laboratory
- University of Chicago
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2
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Integrative modeling of the cell. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1213-1221. [PMID: 36017893 PMCID: PMC9909318 DOI: 10.3724/abbs.2022115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.
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3
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Wu Y, Pegoraro AF, Weitz DA, Janmey P, Sun SX. The correlation between cell and nucleus size is explained by an eukaryotic cell growth model. PLoS Comput Biol 2022; 18:e1009400. [PMID: 35180215 PMCID: PMC8893647 DOI: 10.1371/journal.pcbi.1009400] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 03/03/2022] [Accepted: 01/12/2022] [Indexed: 12/19/2022] Open
Abstract
In eukaryotes, the cell volume is observed to be strongly correlated with the nuclear volume. The slope of this correlation depends on the cell type, growth condition, and the physical environment of the cell. We develop a computational model of cell growth and proteome increase, incorporating the kinetics of amino acid import, protein/ribosome synthesis and degradation, and active transport of proteins between the cytoplasm and the nucleoplasm. We also include a simple model of ribosome biogenesis and assembly. Results show that the cell volume is tightly correlated with the nuclear volume, and the cytoplasm-nucleoplasm transport rates strongly influence the cell growth rate as well as the cell/nucleus volume ratio (C/N ratio). Ribosome assembly and the ratio of ribosomal proteins to mature ribosomes also influence the cell volume and the cell growth rate. We find that in order to regulate the cell growth rate and the cell/nucleus volume ratio, the cell must optimally control groups of kinetic and transport parameters together, which could explain the quantitative roles of canonical growth pathways. Finally, although not explicitly demonstrated in this work, we point out that it is possible to construct a detailed proteome distribution using our model and RNAseq data, provided that a quantitative cell division mechanism is known.
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Affiliation(s)
- Yufei Wu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - David A. Weitz
- Department of Physics, Harvard University, Boston, Massachusetts, United States of America
| | - Paul Janmey
- Department of Cell and Developmental Biology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Sean X. Sun
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Center for Cell Dynamics, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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4
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Gilbert BR, Thornburg ZR, Lam V, Rashid FZM, Glass JI, Villa E, Dame RT, Luthey-Schulten Z. Generating Chromosome Geometries in a Minimal Cell From Cryo-Electron Tomograms and Chromosome Conformation Capture Maps. Front Mol Biosci 2021; 8:644133. [PMID: 34368224 PMCID: PMC8339304 DOI: 10.3389/fmolb.2021.644133] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 05/14/2021] [Indexed: 12/31/2022] Open
Abstract
JCVI-syn3A is a genetically minimal bacterial cell, consisting of 493 genes and only a single 543 kbp circular chromosome. Syn3A’s genome and physical size are approximately one-tenth those of the model bacterial organism Escherichia coli’s, and the corresponding reduction in complexity and scale provides a unique opportunity for whole-cell modeling. Previous work established genome-scale gene essentiality and proteomics data along with its essential metabolic network and a kinetic model of genetic information processing. In addition to that information, whole-cell, spatially-resolved kinetic models require cellular architecture, including spatial distributions of ribosomes and the circular chromosome’s configuration. We reconstruct cellular architectures of Syn3A cells at the single-cell level directly from cryo-electron tomograms, including the ribosome distributions. We present a method of generating self-avoiding circular chromosome configurations in a lattice model with a resolution of 11.8 bp per monomer on a 4 nm cubic lattice. Realizations of the chromosome configurations are constrained by the ribosomes and geometry reconstructed from the tomograms and include DNA loops suggested by experimental chromosome conformation capture (3C) maps. Using ensembles of simulated chromosome configurations we predict chromosome contact maps for Syn3A cells at resolutions of 250 bp and greater and compare them to the experimental maps. Additionally, the spatial distributions of ribosomes and the DNA-crowding resulting from the individual chromosome configurations can be used to identify macromolecular structures formed from ribosomes and DNA, such as polysomes and expressomes.
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Affiliation(s)
- Benjamin R Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Zane R Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Vinson Lam
- Division of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Fatema-Zahra M Rashid
- Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands.,Center for Microbial Cell Biology, Leiden University, Leiden, Netherlands
| | - John I Glass
- Synthetic Biology Group, J. Craig Venter Institute, La Jolla, CA, United States
| | - Elizabeth Villa
- Division of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Remus T Dame
- Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands.,Center for Microbial Cell Biology, Leiden University, Leiden, Netherlands
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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5
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Lévy D, Di Cicco A, Bertin A, Dezi M. [Cryo-electron microcopy for a new vision of the cell and its components]. Med Sci (Paris) 2021; 37:379-385. [PMID: 33908856 DOI: 10.1051/medsci/2021034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) is a technique for imaging biological samples that plays a central role in structural biology, with high impact on research fields such as cell and developmental biology, bioinformatics, cell physics and applied mathematics. It allows the determination of structures of purified proteins within cells. This review describes the main recent advances in cryo-EM, illustrated by examples of proteins of biomedical interest, and the avenues for future development.
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Affiliation(s)
- Daniel Lévy
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
| | - Aurélie Di Cicco
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
| | - Aurélie Bertin
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
| | - Manuela Dezi
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR 168, Laboratoire Physico- Chimie Curie, 11 rue Pierre et Marie Curie, 75005 Paris, France
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6
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Goetz SK, Mahamid J. Visualizing Molecular Architectures of Cellular Condensates: Hints of Complex Coacervation Scenarios. Dev Cell 2021; 55:97-107. [PMID: 33049214 DOI: 10.1016/j.devcel.2020.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/15/2020] [Accepted: 09/05/2020] [Indexed: 02/09/2023]
Abstract
In the last decade, liquid-liquid phase separation has emerged as a fundamental principle in the organization of crowded cellular environments into functionally distinct membraneless compartments. It is now established that biomolecules can condense into various physical phases, traditionally defined for simple polymer systems, and more recently elucidated by techniques employed in life sciences. We review pioneering cryo-electron tomography studies that have begun to unravel a wide spectrum of molecular architectures, ranging from amorphous to crystalline assemblies, that underlie cellular condensates. These observations bring into question current interpretations of microscopic phase behavior. Furthermore, by examining emerging concepts of non-classical phase separation pathways in small-molecule crystallization, we draw parallels with biomolecular condensation that highlight aspects not yet fully explored. In particular, transient and metastable intermediates that might be challenging to capture experimentally inside cells could be probed through computational simulations and enable a multi-scale understanding of the subcellular organization governed by distinct phases.
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Affiliation(s)
- Sara Kathrin Goetz
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany; Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Faculty of Biosciences, Im Neuenheimer Feld 234, 69120 Heidelberg, Germany
| | - Julia Mahamid
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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7
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Goodsell DS, Olson AJ, Forli S. Art and Science of the Cellular Mesoscale. Trends Biochem Sci 2020; 45:472-483. [PMID: 32413324 PMCID: PMC7230070 DOI: 10.1016/j.tibs.2020.02.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 02/12/2020] [Accepted: 02/27/2020] [Indexed: 12/22/2022]
Abstract
Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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8
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Leng J, Shoura M, McLeish TCB, Real AN, Hardey M, McCafferty J, Ranson NA, Harris SA. Securing the future of research computing in the biosciences. PLoS Comput Biol 2019; 15:e1006958. [PMID: 31095554 PMCID: PMC6521984 DOI: 10.1371/journal.pcbi.1006958] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Improvements in technology often drive scientific discovery. Therefore, research requires sustained investment in the latest equipment and training for the researchers who are going to use it. Prioritising and administering infrastructure investment is challenging because future needs are difficult to predict. In the past, highly computationally demanding research was associated primarily with particle physics and astronomy experiments. However, as biology becomes more quantitative and bioscientists generate more and more data, their computational requirements may ultimately exceed those of physical scientists. Computation has always been central to bioinformatics, but now imaging experiments have rapidly growing data processing and storage requirements. There is also an urgent need for new modelling and simulation tools to provide insight and understanding of these biophysical experiments. Bioscience communities must work together to provide the software and skills training needed in their areas. Research-active institutions need to recognise that computation is now vital in many more areas of discovery and create an environment where it can be embraced. The public must also become aware of both the power and limitations of computing, particularly with respect to their health and personal data.
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Affiliation(s)
- Joanna Leng
- School of Computing, University of Leeds, Leeds, United Kingdom
| | - Massa Shoura
- School of Pathology, Stanford University, Palo Alto, California, United States of America
| | | | - Alan N. Real
- Advanced Research Computing, University of Durham, Durham, United Kingdom
| | - Mariann Hardey
- Advanced Research Computing, University of Durham, Durham, United Kingdom
- School of Business, University of Durham, Durham, United Kingdom
| | | | - Neil A. Ranson
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Sarah A. Harris
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
- * E-mail:
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9
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Visualizing Biological Membrane Organization and Dynamics. J Mol Biol 2019; 431:1889-1919. [DOI: 10.1016/j.jmb.2019.02.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 02/02/2019] [Accepted: 02/13/2019] [Indexed: 11/22/2022]
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10
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Earnest TM, Cole JA, Luthey-Schulten Z. Simulating biological processes: stochastic physics from whole cells to colonies. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2018; 81:052601. [PMID: 29424367 DOI: 10.1088/1361-6633/aaae2c] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.
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Affiliation(s)
- Tyler M Earnest
- Department of Chemistry, University of Illinois, Urbana, IL, 61801, United States of America. National Center for Supercomputing Applications, University of Illinois, Urbana, IL, 61801, United States of America
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11
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Amaro RE, Mulholland AJ. Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures. Nat Rev Chem 2018; 2:0148. [PMID: 30949587 PMCID: PMC6445369 DOI: 10.1038/s41570-018-0148] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.
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Affiliation(s)
- Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0304
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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12
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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13
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Trovato F, Fumagalli G. Molecular simulations of cellular processes. Biophys Rev 2017; 9:941-958. [PMID: 29185136 DOI: 10.1007/s12551-017-0363-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 11/19/2017] [Indexed: 12/12/2022] Open
Abstract
It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cell-cycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms. Graphical abstract.
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Affiliation(s)
- Fabio Trovato
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
| | - Giordano Fumagalli
- Nephrology and Dialysis Unit, USL Toscana Nord Ovest, 55041, Lido di Camaiore, Lucca, Italy
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