Modeling Viral Capsid Assembly

Hagan, Michael F.

doi:10.1002/9781118755815.ch01

Cited by 143 publications

(236 citation statements)

References 297 publications

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“…It likewise creates problems for the most popular modeling methods. Mass action differential equation (DE) models are generally unsuitable for non-trivial assemblies because they require either extensive simplifications [70, 56, 125] or enormous numbers of equations and variables to account for the many possible intermediates [90]. Brownian dynamics (BD) models, even highly coarse-grained [167, 17, 51, 10], are likewise challenged by the large numbers of reactants and long timescales typical of self-assembly systems, requiring themselves great simplifications of reaction processes that generally make them unsuitable for accurate quantitative modeling [58].…”

Section: Introductionmentioning

confidence: 99%

“…Cytoskeletal assembly (i.e., actin and microtubule assembly) has been the subject of extensive modeling work, leading to many seminal results in the basic biophysics of molecular assembly processes. Viral capsid assembly [70] has a long history as one of the primary model systems for macromolecular self assembly, both from an experimental and a computational perspective. Another key model system is amyloid aggregation, the basis for many major public health threats, including Alzheimers disease, Huntington’s disease, Parkinsons disease, prion disease, and type II diabetes.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative computational models of molecular self-assembly in systems biology

Thomas

Schwartz

2017

Phys. Biol.

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Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative computational models of molecular self-assembly in systems biology

Thomas

Schwartz

2017

Phys. Biol.

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“…Furthermore, most experiments are performed under these conditions, with concentrations of intermediates often so low as to be undetectable, because assembly is most robust against kinetic traps when nucleation is rate limiting. 4,99,128,129 The MSM approach described here enables simulating assembly at these experimentally relevant parameter values.…”

Section: Application To Non-templated Assemblymentioning

confidence: 99%

“…94 Second, a priori knowledge of the system can be used to reduce the number of unique states. Since assembly into a target structure with high fidelity generally requires weak subunit-subunit interactions, 4,96,97 subunits in a cluster with only one bond rapidly dissociate. Thus, the edges corresponding to these interactions can generally be neglected when building graphs.…”

Section: Reducing the Number Of Statesmentioning

confidence: 99%

Using Markov state models to study self-assembly

Perkett

Hagan

2014

The Journal of Chemical Physics

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Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude. © 2014 AIP Publishing LLC.

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“…The dynamics of self-assembling systems involve the evolution of hierarchical components at different time scales due to their architecture (e.g., atoms make up proteins, proteins make up capsomers, and capsomers make up viral capsids [24]). Eq.…”

Section: High-dimensional Stochastic Nonlinear Dynamicsmentioning

confidence: 99%

Control of self-assembly in micro- and nano-scale systems

Paulson

Mesbah

Zhu

et al. 2015

Journal of Process Control

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a b s t r a c tControl of self-assembling systems at the micro-and nano-scale provides new opportunities for the engineering of novel materials in a bottom-up fashion. These systems have several challenges associated with control including high-dimensional and stochastic nonlinear dynamics, limited sensors for real-time measurements, limited actuation for control, and kinetic trapping of the system in undesirable configurations. Three main strategies for addressing these challenges are described, which include particle design (active self-assembly), open-loop control, and closed-loop (feedback) control. The strategies are illustrated using a variety of examples such as the design of patchy and Janus particles, the toggling of magnetic fields to induce the crystallization of paramagnetic colloids, and high-throughput crystallization of organic compounds in nanoliter droplets. An outlook of the future research directions and the necessary technological advancements for control of micro-and nano-scale self-assembly is provided.

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Modeling Viral Capsid Assembly

Cited by 143 publications

References 297 publications

Quantitative computational models of molecular self-assembly in systems biology

Quantitative computational models of molecular self-assembly in systems biology

Using Markov state models to study self-assembly

Control of self-assembly in micro- and nano-scale systems

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