Limits of entrainment of circadian neuronal networks.

Circadian rhythmicity lies at the center of various important physiological and behavioral processes in mammals, such as sleep, metabolism, homeostasis, mood changes, and more. Misalignment of intrinsic neuronal oscillations with the external day-night cycle can disrupt such processes and lead to numerous disorders. In this work, we computationally determine the limits of circadian synchronization to external light signals of different frequency, duty cycle, and simulated amplitude. Instead of modeling circadian dynamics with generic oscillator models (e.g., Kuramoto-type), we use a detailed computational neuroscience model, which integrates biomolecular dynamics, neuronal electrophysiology, and network effects. This allows us to investigate the effect of small drug molecules, such as Longdaysin, and connect our results with experimental findings. To combat the high dimensionality of such a detailed model, we employ a matrix-free approach, while our entire algorithmic pipeline enables numerical continuation and construction of bifurcation diagrams using only direct simulation. We, thus, computationally explore the effect of heterogeneity in the circadian neuronal network, as well as the effect of the corrective therapeutic intervention of Longdaysin. Last, we employ unsupervised learning to construct a data-driven embedding space for representing neuronal heterogeneity.

How to Achieve Reversible Electrowetting on Superhydrophobic Surfaces.

Collapse (Cassie to Wenzel) wetting transitions impede the electrostatically induced reversible modification of wettability on superhydrophobic surfaces, unless a strong external actuation (e.g., substrate heating) is applied. Here we show that collapse transitions can be prevented (the droplet remains suspended on the solid roughness protrusions) when the electrostatic force, responsible for the wetting modification, is smoothly distributed along the droplet surface. The above argument is initially established theoretically and then verified experimentally.

Neither Lippmann nor Young: enabling electrowetting modeling on structured dielectric surfaces.

Aiming to illuminate mechanisms of wetting transitions on geometrically patterned surfaces induced by the electrowetting phenomenon, we present a novel modeling approach that goes beyond the limitations of the Lippmann equation and is even relieved from the implementation of the Young contact angle boundary condition. We employ the equations of the capillary electrohydrostatics augmented by a disjoining pressure term derived from an effective interface potential accounting for solid/liquid interactions. Proper parametrization of the liquid surface profile enables efficient simulation of multiple and reconfigurable three-phase contact lines (TPL) appearing when entire droplets undergo wetting transitions on patterned surfaces. The liquid/ambient and the liquid/solid interfaces are treated in a unified context tackling the assumption that the liquid profile is wedge-shaped at any three-phase contact line. In this way, electric field singularities are bypassed, allowing for accurate electric field and liquid surface profile computation, especially in the vicinity of TPLs. We found that the invariance of the microscopic contact angle in electrowetting systems is valid only for thick dielectrics, supporting published experiments. By applying our methodology to patterned dielectrics, we computed all admissible droplet equilibrium profiles, including Cassie-Baxter, Wenzel, and mixed wetting states. Mixed wetting states are computed for the first time in electrowetting systems, and their relative stability is presented in a clear and instructive way.

Tailoring Waterborne Coating Rheology with Hydrophobically Modified Ethoxylated Urethanes (HEURs): Molecular Architecture Insights Supported by CG-MD Simulations.

A novel investigation of the effects of the hydrophilic and hydrophobic segments of hydrophobically modified ethoxylated urethanes (HEURs) on the rheological properties of their aqueous solutions, latex-based emulsions, and waterborne paints is demonstrated. Different HEUR thickeners were produced by varying the poly(ethylene glycol) (PEG) molecular weight and terminal hydrophobic size. Results reveal that the strength of hydrophobic associations and, consequently, the rheological properties of HEUR formulations can be effectively controlled by modifying the structure of the hydrophobic segment, specifically, the combination of diisocyanate and monoalcohol. This allows for the on-demand attainment of diverse rheological behaviors ranging from predominantly Newtonian profiles exhibiting lower viscosities to markedly pseudoplastic behaviors with significantly higher viscosities. The length of the hydrophilic group appears to affect viscosity only marginally up to a molecular weight of 23,000 g/mol, with more notable effects at 33,000 g/mol. Additionally, it was indicated that the rheological responses observed in water solutions provide a reliable forecast of their behavior in latex-based emulsions and waterborne paints. Coarse-grained molecular dynamics (CG-MD) simulations were also applied to gain insight into HEUR micelle dynamics in aqueous solutions. Guided by the DBSCAN algorithm, the simulations successfully captured the concentration-dependent behavior and the impact of hydrophilic chain length, aligning with the experimental viscosity trends. Various metrics were employed to provide a comprehensive analysis of the micellization process, including the hydrophobic cluster volume, the total micellar volume, the aggregation number, and the number of chains interconnecting with other micelles.

Enabling efficient energy barrier computations of wetting transitions on geometrically patterned surfaces.

Proper roughness design is important in realizing surfaces with fully tunable wetting properties. Engineering surface roughness boils down to an energy barrier optimization problem, in which the geometric features of roughness serve as the optimization parameters. Computations of energy barriers, separating admissible equilibrium wetting states on patterned surfaces, have been demonstrated utilizing fine-scale simulators (e.g., lattice-Boltzmann for mesoscale and molecular dynamics for microscale simulations), however with substantial computational requirements. Here, by solving an augmented Young­Laplace equation with a disjoining pressure term, we demonstrate accurate and efficient computations of equilibrium shapes of entire millimeter sized droplets on patterned surfaces. In particular, by adopting a natural parameterization of the Young­Laplace equation along the liquid/air and liquid/solid interfaces, the tedious implementation of the Young's contact angle boundary condition at multiple three phase contact lines is bypassed. We, thus, enable the computation of wetting transition energy barriers, separating the well-known Cassie­Baxter and Wenzel states, as well as intermediate states, but with negligible computational cost. We demonstrate the method's efficiency by computing the equilibrium of droplets on stripe-patterned surfaces, and compare the results with mesoscopic lattice Boltzmann simulations. Our computationally efficient continuum-level analysis can be readily applied to patterned surfaces with increased and unstructured geometric complexity, and straightforwardly coupled with shape optimizers towards the design of surfaces with desirable wetting behavior.

A compartmental model for the bicoid gradient.

The anterior region of the Drosophila embryo is patterned by the concentration gradient of the homeodomain transcription factor bicoid (Bcd). The Bcd gradient was the first identified morphogen gradient and continues to be a subject of intense research at multiple levels, from the mechanisms of RNA localization in the oocyte to the evolution of the Bcd-mediated patterning events in multiple Drosophila species. Critical assessment of the mechanisms of the Bcd gradient formation requires biophysical models of the syncytial embryo. Most of the proposed models rely on reaction-diffusion equations, but their formulation and applicability at high nuclear densities is a nontrivial task. We propose a straightforward alternative in which the syncytial blastoderm is approximated by a periodic arrangement of well-mixed compartments: a single nucleus and an associated cytoplasmic region. We formulate a compartmental model, constrain its parameters by experimental data, and demonstrate that it provides an adequate description of the Bcd gradient dynamics.

Effect of Intrinsic Noise on the Phenotype of Cell Populations Featuring Solution Multiplicity: An Artificial lac Operon Network Paradigm.

Heterogeneity in cell populations originates from two fundamentally different sources: the uneven distribution of intracellular content during cell division, and the stochastic fluctuations of regulatory molecules existing in small amounts. Discrete stochastic models can incorporate both sources of cell heterogeneity with sufficient accuracy in the description of an isogenic cell population; however, they lack efficiency when a systems level analysis is required, due to substantial computational requirements. In this work, we study the effect of cell heterogeneity in the behaviour of isogenic cell populations carrying the genetic network of lac operon, which exhibits solution multiplicity over a wide range of extracellular conditions. For such systems, the strategy of performing solely direct temporal solutions is a prohibitive task, since a large ensemble of initial states needs to be tested in order to drive the system--through long time simulations--to possible co-existing steady state solutions. We implement a multiscale computational framework, the so-called "equation-free" methodology, which enables the performance of numerical tasks, such as the computation of coarse steady state solutions and coarse bifurcation analysis. Dynamically stable and unstable solutions are computed and the effect of intrinsic noise on the range of bistability is efficiently investigated. The results are compared with the homogeneous model, which neglects all sources of heterogeneity, with the deterministic cell population balance model, as well as with a stochastic model neglecting the heterogeneity originating from intrinsic noise effects. We show that when the effect of intrinsic source of heterogeneity is intensified, the bistability range shifts towards higher extracellular inducer concentration values.

Mesoscopic model for microscale hydrodynamics and interfacial phenomena: slip, films, and contact-angle hysteresis.

We present a model based on the lattice Boltzmann equation that is suitable for the simulation of dynamic wetting. The model is capable of exhibiting fundamental interfacial phenomena such as weak adsorption of fluid on the solid substrate and the presence of a thin surface film within which a disjoining pressure acts. Dynamics in this surface film, tightly coupled with hydrodynamics in the fluid bulk, determine macroscopic properties of primary interest: the hydrodynamic slip; the equilibrium contact angle; and the static and dynamic hysteresis of the contact angles. The pseudo-potentials employed for fluid-solid interactions are composed of a repulsive core and an attractive tail that can be independently adjusted. This enables effective modification of the functional form of the disjoining pressure so that one can vary the static and dynamic hysteresis on surfaces that exhibit the same equilibrium contact angle. The modeled fluid-solid interface is diffuse, represented by a wall probability function that ultimately controls the momentum exchange between solid and fluid phases. This approach allows us to effectively vary the slip length for a given wettability (i.e., a given static contact angle) of the solid substrate.

Efficient coarse simulation of a growing avascular tumor.

The subject of this work is the development and implementation of algorithms which accelerate the simulation of early stage tumor growth models. Among the different computational approaches used for the simulation of tumor progression, discrete stochastic models (e.g., cellular automata) have been widely used to describe processes occurring at the cell and subcell scales (e.g., cell-cell interactions and signaling processes). To describe macroscopic characteristics (e.g., morphology) of growing tumors, large numbers of interacting cells must be simulated. However, the high computational demands of stochastic models make the simulation of large-scale systems impractical. Alternatively, continuum models, which can describe behavior at the tumor scale, often rely on phenomenological assumptions in place of rigorous upscaling of microscopic models. This limits their predictive power. In this work, we circumvent the derivation of closed macroscopic equations for the growing cancer cell populations; instead, we construct, based on the so-called "equation-free" framework, a computational superstructure, which wraps around the individual-based cell-level simulator and accelerates the computations required for the study of the long-time behavior of systems involving many interacting cells. The microscopic model, e.g., a cellular automaton, which simulates the evolution of cancer cell populations, is executed for relatively short time intervals, at the end of which coarse-scale information is obtained. These coarse variables evolve on slower time scales than each individual cell in the population, enabling the application of forward projection schemes, which extrapolate their values at later times. This technique is referred to as coarse projective integration. Increasing the ratio of projection times to microscopic simulator execution times enhances the computational savings. Crucial accuracy issues arising for growing tumors with radial symmetry are addressed by applying the coarse projective integration scheme in a cotraveling (cogrowing) frame. As a proof of principle, we demonstrate that the application of this scheme yields highly accurate solutions, while preserving the computational savings of coarse projective integration.

Coarsening in the electrohydrodynamic patterning of thin polymer films.

Periodic pillarlike microstructures can be created from initially flat polymer films via the electrohydrodynamic instabilities. Those patterns, however, are metastable. Our experimental observations show that the average pillar size increases slowly after linear growth. Major coarsening events then take place over times several orders of magnitude longer than the linear growth time. For all fill ratios, a logarithmic time dependence of the average pillar size can be identified, i.e., proportional to ln t. Thicker films, however, have faster coarsening rates than thinner films. Linear stability analysis of the pseudosteady states reveals two major coarsening mechanisms, collision and Ostwald ripening, which can also be identified from experimental images. We then reduce the original partial differential equation (PDE) into a pair of ODEs, which govern the interaction between pillars due to the above two coarsening mechanisms. From this, a logarithm scaling law is obtained for both low and high fill ratios and the coarsening rate is slower for lower fill ratios, consistent with experimental observations. We also find that arrays with more uniform sizes tend to start coarsening later, but they coarsen faster than more "disperse" arrays, which could be possibly utilized in experiments for controlling the onset and speed of coarsening. The logarithm scaling in the electrohydrodynamic coarsening phenomenon, which differs from coarsening in spinodal decomposition and dewetting of thin liquid films, is due to the significant nonlinear effect of Maxwell stresses and geometric confinement on the disjoining pressure at both top and bottom electrodes.