Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study.

Interactions between biological systems and nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Molecular Dynamics Simulations of Deformable Viral Capsomers.

Most coarse-grained models of individual capsomers associated with viruses employ rigid building blocks that do not exhibit shape adaptation during self-assembly. We develop a coarse-grained general model of viral capsomers that incorporates their stretching and bending energies while retaining many features of the rigid-body models, including an overall trapezoidal shape with attractive interaction sites embedded in the lateral walls to favor icosahedral capsid assembly. Molecular dynamics simulations of deformable capsomers reproduce the rich self-assembly behavior associated with a general T=1 icosahedral virus system in the absence of a genome. Transitions from non-assembled configurations to icosahedral capsids to kinetically-trapped malformed structures are observed as the steric attraction between capsomers is increased. An assembly diagram in the space of capsomer-capsomer steric attraction and capsomer deformability reveals that assembling capsomers of higher deformability into capsids requires increasingly large steric attraction between capsomers. Increasing capsomer deformability can reverse incorrect capsomer-capsomer binding, facilitating transitions from malformed structures to symmetric capsids; however, making capsomers too soft inhibits assembly and yields fluid-like structures.

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates.

Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (106-1010s-1) for which substantial shear thinning is observed under pressures P∈0.1-955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435×6 dimensions and the intermolecular atom pair orientation tensors of 61×6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P=0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P≥400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures.

Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations.

The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. The link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains.

Electrical properties of tissues from a microscopic model of confined electrolytes.

Objective. In the presence of oscillatory electric fields, the motion of electrolyte ions in biological tissues is often limited by the confinement created by cell and organelle walls. This confinement induces the organization of the ions into dynamic double layers. This work determines the contribution of these double layers to the bulk conductivity and permittivity of tissues.Approach. Tissues are modeled as repeated units of electrolyte regions separated by dielectric walls. Within the electrolyte regions, a coarse-grained model is used to describe the associated ionic charge distribution. The model emphasizes the role of the displacement current in addition to the ionic current and enables the evaluation of macroscopic conductivities and permittivities.Main results. We obtain analytical expressions for the bulk conductivity and permittivity as a function of the frequency of the oscillatory electric field. These expressions explicitly include the geometric information of the repeated structure and the contribution of the dynamic double layers. The low-frequency limit of the conductivity expression yields a result predicted by the Debye permittivity form. The model also provides a microscopic interpretation of the Maxwell-Wagner effect.Significance. The results obtained contribute to the interpretation of the macroscopic measurements of electrical properties of tissues in terms of their microscopic structure. The model enables a critical assessment of the justification for the use of macroscopic models to analyze the transmission of electrical signals through tissues.

Shape control of deformable charge-patterned nanoparticles.

Deformable nanoparticles (NPs) offer unprecedented opportunities as dynamic building blocks that can spontaneously reconfigure during assembly in response to environmental cues. Designing reconfigurable materials based on deformable NPs hinges on an understanding of the shapes that can be engineered in these NPs. We solve for the low-energy shapes of charge-patterned deformable NPs by using molecular dynamics-based simulated annealing to minimize a coarse-grained model Hamiltonian characterized with NP elastic and electrostatic energies subject to a volume constraint. We show that deformable spherical NPs of radius 50 nm whose surface is tailored with octahedrally distributed charged patches and double-cap charged patches adapt their shape differently in response to changes in surface charge coverage and ionic strength. We find shape transitions to rounded octahedra, faceted octahedra, faceted bowls, oblate spheroids, spherocylinders, dented beans, and dimpled rounded bowls. We demonstrate that similar shape transitions can be achieved in deformable NPs of different sizes. The effects of counterion condensation on the free-energetic drive associated with the observed deformations are examined via Manning model calculations that utilize simulation-derived estimates for the NP Coulomb energy under salt-free conditions. The charge-pattern-based shape control of deformable NPs has implications for the design of responsive nanocontainers and for assembling reconfigurable materials whose functionality hinges on the shape-shifting properties of their nanoscale building blocks.

Multilayered Ordered Protein Arrays Self-Assembled from a Mixed Population of Virus-like Particles.

Biology shows many examples of spatially controlled assembly of cells and biomacromolecules into hierarchically organized structures, to which many of the complex biological functions are attributed. While such biological structures have inspired the design of synthetic materials, it is still a great challenge to control the spatial arrangement of individual building blocks when assembling multiple types of components into bulk materials. Here, we report self-assembly of multilayered, ordered protein arrays from mixed populations of virus-like particles (VLPs). We systematically tuned the magnitude of the surface charge of the VLPs via mutagenesis to prepare four different types of VLPs for mixing. A mixture of up to four types of VLPs selectively assembled into higher-order structures in the presence of oppositely charged dendrimers during a gradual lowering of the ionic strength of the solution. The assembly resulted in the formation of three-dimensional ordered VLP arrays with up to four distinct layers including a central core, with each layer comprising a single type of VLP. A coarse-grained computational model was developed and simulated using molecular dynamics to probe the formation of the multilayered, core-shell structure. Our findings establish a simple and versatile bottom-up strategy to synthesize multilayered, ordered materials by controlling the spatial arrangement of multiple types of nanoscale building blocks in a one-pot fabrication.

Designing Surface Charge Patterns for Shape Control of Deformable Nanoparticles.

Designing reconfigurable materials based on deformable nanoparticles (NPs) hinges on an understanding of the energetically favored shapes these NPs can adopt. Using simulations, we show that hollow, deformable, patchy NPs tailored with surface charge patterns such as Janus patches, stripes, and polyhedrally distributed patches differently adapt their shape in response to changes in patterns and ionic strength, transforming into capsules, hemispheres, variably dimpled bowls, and polyhedra. The links between anisotropy in NP surface charge, shape, and the elastic energy density are discussed.

Computational studies of shape control of charged deformable nanocontainers.

Biological matter is often compartmentalized by soft membranes that dynamically change their shape in response to chemical and mechanical cues. Deformable soft-matter-based nanoscale membranes or nanocontainers that mimic this behavior can be used as drug-delivery carriers that can adapt to evolving physiological conditions, or as dynamic building blocks for the design of novel hierarchical materials via assembly engineering. Here, we connect the intrinsic features of charged deformable nanocontainers such as their size, charge, surface tension, and elasticity with their equilibrium shapes for a wide range of solution conditions using molecular dynamics simulations. These links identify the fundamental mechanisms that establish the chemical and materials design control strategies for modulating the equilibrium shape of these nanocontainers. We show that flexible nanocontainers of radii ranging from 10-20 nm exhibit sphere-to-rod-to-disc shape transitions yielding rods and discs over a wide range of aspect ratio λ (0.3 < λ < 5). The shape transitions can be controlled by tuning salt and/or surfactant concentration as well as material elastic parameters. The shape changes are driven by reduction in the global electrostatic energy and are associated with dramatic changes in local surface elastic energy distributions. To illustrate the shape transition mechanisms, exact analytical calculations for idealized spheroidal nanocontainers in salt-free conditions are performed. Explicit counterion simulations near nanocontainers and associated Manning model calculations provide an assessment of the stability of observed shape deformations in the event of ion condensation.

Linker-Mediated Assembly of Virus-Like Particles into Ordered Arrays via Electrostatic Control.

Nanoscale virus-like particles (VLPs), which are self-assembled from protein subunits, offer the possibility of generating hierarchically assembled functional materials such as biomimetic catalytic systems and optical metamaterials. We explore the capacity to control and tune a higher-order assembly of VLPs into ordered array materials over a wide range of ionic conditions using a combination of experimental and computational methods. The integrated methodology demonstrates that P22 VLP variants, genetically engineered to exhibit different surface charges, self-assemble into ordered arrays in the presence of PAMAM dendrimers acting as oppositely charged, macromolecular linkers. Ordered assembly occurs at an optimal ionic strength that strongly correlates with the VLP surface charge. The ordered VLP arrays exhibit the same long-range order and a similar configuration of VLP-bound dendrimers, regardless of the VLP surface charge. The experimentally validated model identifies key electrostatic and kinetic mechanisms underlying the self-assembly process and guides the modulation of dendrimer concentration as a control parameter to tune the assembly of VLPs. The integrated approach opens new design and control strategies to fabricate active functional materials via the self-assembly of engineered VLPs.

Probing large viscosities in glass-formers with nonequilibrium simulations.

For decades, scientists have debated whether supercooled liquids stop flowing below a glass transition temperature [Formula: see text] or whether motion continues to slow gradually down to zero temperature. Answering this question is challenging because human time scales set a limit on the largest measurable viscosity, and available data are equally well fit to models with opposite conclusions. Here, we use short simulations to determine the nonequilibrium shear response of a typical glass-former, squalane. Fits of the data to an Eyring model allow us to extrapolate predictions for the equilibrium Newtonian viscosity [Formula: see text] over a range of pressures and temperatures that change [Formula: see text] by 25 orders of magnitude. The results agree with the unusually large set of equilibrium and nonequilibrium experiments on squalane and extend them to higher [Formula: see text] Studies at different pressures and temperatures are inconsistent with a diverging viscosity at finite temperature. At all pressures, the predicted viscosity becomes Arrhenius with a single temperature-independent activation barrier at low temperatures and high viscosities ([Formula: see text] Pa[Formula: see text]s). Possible experimental tests of our results are outlined.

Ionic structure in liquids confined by dielectric interfaces.

The behavior of ions in liquids confined between macromolecules determines the outcome of many nanoscale assembly processes in synthetic and biological materials such as colloidal dispersions, emulsions, hydrogels, DNA, cell membranes, and proteins. Theoretically, the macromolecule-liquid boundary is often modeled as a dielectric interface and an important quantity of interest is the ionic structure in a liquid confined between two such interfaces. The knowledge gleaned from the study of ionic structure in such models can be useful in several industrial applications, such as in the design of double-layer supercapacitors for energy storage and in the extraction of metal ions from wastewater. In this article, we compute the ionic structure in a model system of electrolyte confined by two planar dielectric interfaces using molecular dynamics simulations and liquid state theory. We explore the effects of high electrolyte concentrations, multivalent ions, dielectric contrasts, and external electric field on the ionic distributions. We observe the presence of non-monotonic ionic density profiles leading to a layered structure in the fluid which is attributed to the competition between electrostatic and steric (entropic) interactions. We find that thermal forces that arise from symmetry breaking at the interfaces can have a profound effect on the ionic structure and can oftentimes overwhelm the influence of the dielectric discontinuity. The combined effect of ionic correlations and inhomogeneous dielectric permittivity significantly changes the character of the effective interaction between the two interfaces.

Coulomb energy of uniformly charged spheroidal shell systems.

We provide exact expressions for the electrostatic energy of uniformly charged prolate and oblate spheroidal shells. We find that uniformly charged prolate spheroids of eccentricity greater than 0.9 have lower Coulomb energy than a sphere of the same area. For the volume-constrained case, we find that a sphere has the highest Coulomb energy among all spheroidal shells. Further, we derive the change in the Coulomb energy of a uniformly charged shell due to small, area-conserving perturbations on the spherical shape. Our perturbation calculations show that buckling-type deformations on a sphere can lower the Coulomb energy. Finally, we consider the possibility of counterion condensation on the spheroidal shell surface. We employ a Manning-Oosawa two-state model approximation to evaluate the renormalized charge and analyze the behavior of the equilibrium free energy as a function of the shell's aspect ratio for both area-constrained and volume-constrained cases. Counterion condensation is seen to favor the formation of spheroidal structures over a sphere of equal area for high values of shell volume fractions.

Electrostatics-driven shape transitions in soft shells.

Manipulating the shape of nanoscale objects in a controllable fashion is at the heart of designing materials that act as building blocks for self-assembly or serve as targeted drug delivery carriers. Inducing shape deformations by controlling external parameters is also an important way of designing biomimetic membranes. In this paper, we demonstrate that electrostatics can be used as a tool to manipulate the shape of soft, closed membranes by tuning environmental conditions such as the electrolyte concentration in the medium. Using a molecular dynamics-based simulated annealing procedure, we investigate charged elastic shells that do not exchange material with their environment, such as elastic membranes formed in emulsions or synthetic nanocontainers. We find that by decreasing the salt concentration or increasing the total charge on the shell's surface, the spherical symmetry is broken, leading to the formation of ellipsoids, discs, and bowls. Shape changes are accompanied by a significant lowering of the electrostatic energy and a rise in the surface area of the shell. To substantiate our simulation findings, we show analytically that a uniformly charged disc has a lower Coulomb energy than a sphere of the same volume. Further, we test the robustness of our results by including the effects of charge renormalization in the analysis of the shape transitions and find the latter to be feasible for a wide range of shell volume fractions.

Generating true minima in constrained variational formulations via modified Lagrange multipliers.

Variational principles are important in the investigation of large classes of physical systems. They can be used both as analytical methods as well as starting points for the formulation of powerful computational techniques such as dynamical optimization methods. Systems with charged objects in dielectric media and systems with magnetically active particles are important examples. In these examples and other important cases, the variational principles describing the system are required to obey a number of constraints. These constraints are implemented within the variational formulation by means of Lagrange multipliers. Such constrained variational formulations are in general not unique. For the application of efficient simulation methods, one must find specific formulations that satisfy a number of important conditions. An often required condition is that the functional be positive-definite, in other words, its extrema be actual minima. In this article, we present a general approach to attack the problem of finding, among equivalent variational functionals, those that generate true minima. The method is based on the modification of the Lagrange multiplier which allows us to generate large families of effective variational formulations associated with a single original constrained variational principle. We demonstrate its application to different examples and, in particular, to the important cases of Poisson and Poisson-Boltzmann equations. We show how to obtain variational formulations for these systems with extrema that are always minima.

Free-energy functionals of the electrostatic potential for Poisson-Boltzmann theory.

In simulating charged systems, it is often useful to treat some ionic components of the system at the mean-field level and solve the Poisson-Boltzmann (PB) equation to get their respective density profiles. The numerically intensive task of solving the PB equation at each step of the simulation can be bypassed using variational methods that treat the electrostatic potential as a dynamic variable. But such approaches require the access to a true free-energy functional: a functional that not only provides the correct solution of the PB equation upon extremization, but also evaluates to the true free energy of the system at its minimum. Moreover, the numerical efficiency of such procedures is further enhanced if the free-energy functional is local and is expressed in terms of the electrostatic potential. Existing PB functionals of the electrostatic potential, while possessing the local structure, are not free-energy functionals. We present a variational formulation with a local free-energy functional of the potential. In addition, we also construct a nonlocal free-energy functional of the electrostatic potential. These functionals are suited for employment in simulation schemes based on the ideas of dynamical optimization.

A variational formulation of electrostatics in a medium with spatially varying dielectric permittivity.

In biological and synthetic materials, many important processes involve charges that are present in a medium with spatially varying dielectric permittivity. To accurately understand the role of electrostatic interactions in such systems, it is important to take into account the spatial dependence of the permittivity of the medium. However, due to the ensuing theoretical and computational challenges, this inhomogeneous dielectric response of the medium is often ignored or excessively simplified. We develop a variational formulation of electrostatics to accurately investigate systems that exhibit this inhomogeneous dielectric response. Our formulation is based on a true energy functional of the polarization charge density. The defining characteristic of a true energy functional is that at its minimum it evaluates to the actual value of the energy; this is a feature not found in many commonly used electrostatic functionals. We explore in detail the charged systems that exhibit sharp discontinuous change in dielectric permittivity, and we show that for this case our functional reduces to a functional of only the surface polarization charge density. We apply this reduced functional to study model problems for which analytical solutions are well known. We demonstrate, in addition, that the functional has many properties that make it ideal for use in molecular dynamics simulations.

Simulation of charged systems in heterogeneous dielectric media via a true energy functional.

For charged systems in heterogeneous dielectric media, a key obstacle for molecular dynamics (MD) simulations is the need to solve the Poisson equation in the media. This obstacle can be bypassed using MD methods that treat the local polarization charge density as a dynamic variable, but such approaches require access to a true free energy functional, one that evaluates to the equilibrium electrostatic energy at its minimum. In this Letter, we derive the needed functional. As an application, we develop a Car-Parrinello MD method for the simulation of free charges present near a spherical emulsion droplet separating two immiscible liquids with different dielectric constants. Our results show the presence of nonmonotonic ionic profiles in the dielectric with a lower dielectric constant.

Iterative Monte Carlo path integral with optimal grids from whole-necklace sampling.

The efficiency of the iterative Monte Carlo (IMC) path integral methodology for complex time correlation functions is increased through the use of optimal grids, which are sampled from paths that span the entire path integral necklace. The two-bead marginal distributions required in each step of the IMC iteration are obtained from a recursive procedure. Applications to one-dimensional and multi-dimensional model systems illustrate the enhancement in stability effected by the use of grids based on whole-necklace sampling.