NERDSS: A Nonequilibrium Simulator for Multibody Self-Assembly at the Cellular Scale

Varga, Matthew J.; Fu, Yiben; Loggia, Spencer; Yogurtcu, Osman N.; Johnson, Margaret E.

doi:10.1016/j.bpj.2020.05.002

Cited by 32 publications

(52 citation statements)

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“…Smoldyn simulations were run either through Virtual Cell, version 7, or using the stand-alone Smoldyn software, version 2.60/2.61 (Andrews, 2017). FPR or NERDSS simulations were run using the NERDSS software, version 1 (Varga et al, 2020), which uses the FPR algorithms (Johnson, 2018;Johnson and Hummer, 2014;Yogurtcu and Johnson, 2015) to solve the single-particle reaction-diffusion model. GFRD simulations were run using eGFRD software (Sokolowski et al, 2019) from August 3 rd 2019.…”

Section: Methodsmentioning

confidence: 99%

“…In practice, this turns out to be feasible for many interesting biological problems. Several computational frrameworks have been developed to take advantage of this approach, including FPR (Johnson and Hummer, 2014), NERDSS (Varga et al, 2020), GFRD (van Zon and ten Wolde, 2005) and eGFRD (Sokolowski et al, 2019) ( Fig. 2c).…”

Section: Box 4: Microscopic Vs Macroscopic Rates and The Sensitivitymentioning

confidence: 99%

“…microtubules or clathrin-coated vesicles) and as disordered condensates, will be essential for bridging biochemical interactions with mechanics. Recent advances in extending single-particle methods to include rigid (Varga et al, 2020) or polymer-like structures (Hoffmann et al, 2019;Michalski and Loew, 2016) have enabled simulations of highly ordered clathrin lattices and viral shells (Varga et al, 2020), or disordered condensate-like assemblies (Chattaraj et al, 2019), respectively. For cytoskeletal mechanics, in many cases a simple approximation of cytoskeletal filaments as elastic rods and motor proteins as discrete localized generators of quantized directional force can give impressively realistic simulation outcomes (Nedelec and Foethke, 2007b).…”

Section: It May Not Be Possible To Address Distinct Model Features Rementioning

confidence: 99%

“…D A = D A = D c = 10 ߤ m 2 /s. Sim parameters: For FPR/NERDSS (Varga et al, 2020), a maximal ∆ t for each system was limited by the density, values for each crowding fraction: [10 -11 s, 10 -11 s , 10 -11 s, 5x10 -12 s, 5x10 -12 s, 10 -12 s ]. N traj =40-80 for each crowding fraction.…”

mentioning

confidence: 99%

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The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Johnson

Chen

Faeder

et al. 2020

Preprint

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ABSTRACTMost of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures and rapidly propagating molecular signals embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computational simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However it is often difficult to reconcile conflicting computational results that use different simulation approaches (for example partial differential equations versus particle-based stochastic methods) to describe the same phenomenon. Moreover, the details of the computational implementation of any particular algorithm may give rise to quantitatively or even qualitatively different results for the same set of starting assumptions and parameters. In an effort to address this issue systematically, we have defined a series of computational test cases ranging from very simple (bimolecular binding in solution) to moderately complex (spatial and temporal oscillations generated by proteins binding to membranes) that represent building blocks for comprehensive three-dimensional models of cellular function. Having used two or more distinct computational approaches to solve each of these test cases with consistent parameter sets, we generally find modest but measurable differences in the solutions of the same problem, and a few cases where significant deviations arise. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers wishing to develop new models for cell biology. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

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Section: Methodsmentioning

confidence: 99%

Section: Box 4: Microscopic Vs Macroscopic Rates and The Sensitivitymentioning

confidence: 99%

Section: It May Not Be Possible To Address Distinct Model Features Rementioning

confidence: 99%

mentioning

confidence: 99%

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The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Johnson

Chen

Faeder

et al. 2020

Preprint

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“…Stochastic effects are most accurately captured using particle-based (microscopic) simulations [16,26,27,[36][37][38] or a convergent reaction-diffusion master equation [39].…”

Section: Efficient and Accurate Stochastic Simulationsmentioning

confidence: 99%

A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement

Ramirez

Pablo

Burk

et al. 2020

Preprint

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Cells polarize their movement or growth toward external directional cues in many different contexts. For example, budding yeast cells grow toward potential mating partners in response to pheromone gradients. Directed growth is controlled by polarity factors that assemble into clusters at the cell membrane. The clusters assemble, disassemble, and move between different regions of the membrane before eventually forming a stable polarity site directed toward the pheromone source. Pathways that regulate clustering have been identified but the molecular mechanisms that regulate cluster mobility are not well understood. To gain insight into the contribution of chemical noise to cluster behavior we simulated clustering within the reaction-diffusion master equation (RDME) framework to account for molecular-level fluctuations. RDME simulations are a computationally efficient approximation, but their results can diverge from the underlying microscopic dynamics. We implemented novel concentration-dependent rate constants that improved the accuracy of RDME-based simulations of cluster behavior, allowing us to efficiently investigate how cluster dynamics might be regulated. Molecular noise was effective in relocating clusters when the clusters contained low numbers of limiting polarity factors, and when Cdc42, the central polarity regulator, exhibited short dwell times at the polarity site. Cluster stabilization occurred when abundances or binding rates were altered to either lengthen dwell times or increase the number of polarity molecules in the cluster. We validated key results using full 3D particle-based simulations. Understanding the mechanisms cells use to regulate the dynamics of polarity clusters should provide insights into how cells dynamically track external directional cues.

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Secretion‐Catalyzed Assembly of Protein Biomaterials on a Bacterial Membrane Surface

Xie,

On Lee,

Vissamsetti

et al. 2023

Angewandte Chemie

Self Cite

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Protein‐based biomaterials have played a key role in tissue engineering, and additional exciting applications as self‐healing materials and sustainable polymers are emerging. Over the past few decades, recombinant expression and production of various fibrous proteins from microbes have been demonstrated; however, the resulting proteins typically must then be purified and processed by humans to form usable fibers and materials. Here, we show that the Gram‐positive bacterium Bacillus subtilis can be programmed to secrete silk through its translocon via an orthogonal signal peptide/peptidase pair. Surprisingly, we discover that this translocation mechanism drives the silk proteins to assemble into fibers spontaneously on the cell surface, in a process we call secretion‐catalyzed assembly (SCA). Secreted silk fibers form self‐healing hydrogels with minimal processing. Alternatively, the fibers retained on the membrane provide a facile route to create engineered living materials from Bacillus cells. This work provides a blueprint to achieve autonomous assembly of protein biomaterials in useful morphologies directly from microbial factories.

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NERDSS: A Nonequilibrium Simulator for Multibody Self-Assembly at the Cellular Scale

Cited by 32 publications

References 100 publications

The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement

Secretion‐Catalyzed Assembly of Protein Biomaterials on a Bacterial Membrane Surface

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