Mesoscale modeling: solving complex flows in biology and biotechnology

MTA1, a Novel ATP Synthase Complex Modulator, Enhances Colon Cancer Liver Metastasis by Driving Mitochondrial Metabolism Reprogramming

Liver metastasis is the most fatal event of colon cancer patients. Warburg effect has been long challenged by the fact of upregulated oxidative phosphorylation (OXPHOS), while its mechanism remains unclear. Here, metastasis-associated antigen 1 (MTA1) is identified as a newly identified adenosine triphosphate (ATP) synthase modulator by interacting with ATP synthase F1 subunit alpha (ATP5A), facilitates colon cancer liver metastasis by driving mitochondrial bioenergetic metabolism reprogramming, enhancing OXPHOS; therefore, modulating ATP synthase activity and downstream mTOR pathways. High-throughput screening of an anticancer drug shows MTA1 knockout increases the sensitivity of colon cancer to mitochondrial bioenergetic metabolism-targeted drugs and mTOR inhibitors. Inhibiting ATP5A enhances the sensitivity of liver-metastasized colon cancer to sirolimus in an MTA1-dependent manner. The therapeutic effects are verified in xenograft models and clinical cases. This research identifies a new modulator of mitochondrial bioenergetic reprogramming in cancer metastasis and reveals a new mechanism on upregulating mitochondrial OXPHOS as the reversal of Warburg effect in cancer metastasis is orchestrated.

Development of Janus Particles as Potential Drug Delivery Systems for Diabetes Treatment and Antimicrobial Applications

Janus particles have emerged as a novel and smart material that could improve pharmaceutical formulation, drug delivery, and theranostics. Janus particles have two distinct compartments that differ in functionality, physicochemical properties, and morphological characteristics, among other conventional particles. Recently, Janus particles have attracted considerable attention as effective particulate drug delivery systems as they can accommodate two opposing pharmaceutical agents that can be engineered at the molecular level to achieve better target affinity, lower drug dosage to achieve a therapeutic effect, and controlled drug release with improved pharmacokinetics and pharmacodynamics. This article discusses the development of Janus particles for tailored and improved delivery of pharmaceutical agents for diabetes treatment and antimicrobial applications. It provides an account of advances in the synthesis of Janus particles from various materials using different approaches. It appraises Janus particles as a promising particulate system with the potential to improve conventional delivery systems, providing a better loading capacity and targeting specificity whilst promoting multi-drugs loading and single-dose-drug administration.

Mesoscience in cell biology and cancer research

Mesoscale characteristics and their interdimensional correlation are the focus of contemporary interdisciplinary research. Mesoscience is a discipline that has the potential to radically update the existing knowledge structure, which differs from the conventional unit-scale and system-scale research models, revealing a previously untouchable area for scientific research. Integrative biology research aims to dissect the complex problems of life systems by conducting comprehensive research and integrating various disciplines from all biological levels of the living organism. However, the mesoscientific issues between different research units are neglected and challenging. Mesoscale research in biology requires the integration of research theories and methods from other disciplines (mathematics, physics, engineering, and even visual imaging) to investigate theoretical and frontier questions of biological processes through experiments, computations, and modeling. We reviewed  integrative paradigms and methods for the biological mesoscale problems (focusing on oncology research) and prospected the potential of their multiple dimensions and upcoming challenges. We expect to establish an interactive and collaborative theoretical platform for further expanding the depth and width of our understanding on the nature of biology.

Mesoscale Modeling of Phase Separation Controlled by Hydrosilylation in Polyhydromethylsiloxane (PHMS)-Containing Blends

Controlling morphology of polysiloxane blends crosslinked by the hydrosilylation reaction followed by pyrolysis constitutes a robust strategy to fabricate polymer-derived ceramics (PDCs) for a number of applications, from water purification to hydrogen storage. Herein, we introduce a dissipative particle dynamics (DPD) approach that captures the phase separation in binary and ternary polymer blends undergoing hydrosilylation. Linear polyhydromethylsiloxane (PHMS) chains are chosen as preceramic precursors and linear vinyl-terminated polydimethylsiloxane (v-PDMS) chains constitute the reactive sacrificial component. Hydrosilylation of carbon-carbon unsaturated double bonds results in the formation of carbon-silicon bonds and is widely utilized in the synthesis of organosilicons. We characterize the dynamics of binary PHMS/v-PDMS blends undergoing hydrosilylation and ternary blends in which a fraction of the reactive sacrificial component (v-PDMS) is replaced with the non-reactive sacrificial component (methyl-terminated PDMS (m-PDMS), polyacrylonitrile (PAN), or poly(methyl methacrylate) (PMMA)). Our results clearly demonstrate that the morphology of the sacrificial domains in the nanostructured polymer network formed can be tailored by tunning the composition, chemical nature, and the degree of polymerization of the sacrificial component. We also show that the addition of a non-reactive sacrificial component introduces facile means to control the self-assembly and morphology of these nanostructured materials by varying the fraction, degree of polymerization, or the chemical nature of this component.

Mesoscale Modeling of Agglomeration of Molecular Bottlebrushes: Focus on Conformations and Clustering Criteria

Using dissipative particle dynamics, we characterize dynamics of aggregation of molecular bottlebrushes in solvents of various qualities by tracking the number of clusters, the size of the largest cluster, and an average aggregation number. We focus on a low volume fraction of bottlebrushes in a range of solvents and probe three different cutoff criteria to identify bottlebrushes belonging to the same cluster. We demonstrate that the cutoff criteria which depend on both the coordination number and the length of the side chain allows one to correlate the agglomeration status with the structural characteristics of bottlebrushes in solvents of various qualities. We characterize conformational changes of the bottlebrush within the agglomerates with respect to those of an isolated bottlebrush in the same solvents. The characterization of bottlebrush conformations within the agglomerates is an important step in understanding the relationship between the bottlebrush architecture and material properties. An analysis of three distinct cutoff criteria to identify bottlebrushes belonging to the same cluster introduces a framework to identify both short-lived transient and long-lived agglomerates; the same approach could be further extended to characterize agglomerates of various macromolecules with complex architectures beyond the specific bottlebrush architecture considered herein.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Controlling Degradation and Erosion of Polymer Networks: Insights from Mesoscale Modeling

Understanding and controlling degradation of polymer networks on the mesoscale is critical for a range of applications. We utilize dissipative particle dynamics to capture photocontrolled degradation and erosion processes in hydrogels formed by end-linking of four-arm polyethylene glycol precursors. We demonstrate that the polydispersity and the fraction of broken-off fragments scale with the relative extent of reaction. The reverse gel point measured is close to the value predicted by the bond percolation theory on a diamond lattice. We characterize the erosion process via tracking the mass loss that accounts for the fragments remaining in contact with the percolated network. We quantify the dependence of the mass loss on the extent of reaction and on the properties of the film prior to degradation. These results elucidate the main features of degradation and erosion on the mesoscale and could provide guidelines for future design of degrading materials with dynamically controlled properties.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Native-Based Dissipative Particle Dynamics Approach for α-Helical Folding

We developed a dissipative particle dynamics (DPD) approach that captures polyalanine folding into a stable helical conformation. Within the proposed native-based approach, the DPD parameters are derived based on the contact map constructed from the molecular dynamics (MD) simulations. We show that the proposed approach reproduces the folding of polypeptides of various lengths, including bundle formation for sufficiently long polypeptides. The proposed approach also allows one to capture the folding of the helical segments of the lysozyme. With further development of computationally efficient native-based DPD approaches for folding, modeling of a range of biomaterials incorporating α-helical segments could be extended to time and length scales far beyond those accessible in molecular dynamics simulations.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution.

Highly Oil-Repellent Thermoplastic Boundaries via Surface Delivery of CF3 Groups by Molecular Bottlebrush Additives

We fabricated thermoplastic surfaces possessing extremely limited water and oil wettability without employment of long-chain perfluoroalkyl (LCPFA) substances. Namely, by taking advantage of the structure and behavior of original oleophobic perfluoropolyether (PFPE) methacrylate (PFM) molecular bottlebrush (MBB) additive we obtained polymeric surfaces with oil contact angles well above 80° and surface energy on the level of 10 mN/m. Those angles and surface energies are the highest and the lowest respective values reported to date for any bulk solid flat organic surface not containing LCPFA. We show experimentally and computationally that this remarkable oil repellency is attributed to migration of small quantities of the oleophobic MBB additives to the surface of the thermoplastics. Severe mismatch in the affinity between the densely grafted long side chains of MBB and a host matrix promotes stretching and densification of mobile side chains delivering the lowest surface energy functionalities (CF₃) to the materials' boundary. Our studies demonstrate that PFM can be utilized as an effective low surface energy additive to conventional thermoplastic polymers, such as poly(methyl methacrylate) and Nylon-6. We show that films containing PFM achieve the level of oil repellency significantly higher than that of polytetrafluoroethylene (PTFE), a fully perfluorinated thermoplastic. The surface energy of the films is also significantly lower than that of PTFE, even at low concentrations of PFM additives.

Modelling lipid systems in fluid with Lattice Boltzmann Molecular Dynamics simulations and hydrodynamics

In this work we present the coupling between Dry Martini, an efficient implicit solvent coarse-grained model for lipids, and the Lattice Boltzmann Molecular Dynamics (LBMD) simulation technique in order to include naturally hydrodynamic interactions in implicit solvent simulations of lipid systems. After validating the implementation of the model, we explored several systems where the action of a perturbing fluid plays an important role. Namely, we investigated the role of an external shear flow on the dynamics of a vesicle, the dynamics of substrate release under shear, and inquired the dynamics of proteins and substrates confined inside the core of a vesicle. Our methodology enables future exploration of a large variety of biological entities and processes involving lipid systems at the mesoscopic scale where hydrodynamics plays an essential role, e.g. by modulating the migration of proteins in the proximity of membranes, the dynamics of vesicle-based drug delivery systems, or, more generally, the behaviour of proteins in cellular compartments.

Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism

We present a bottom-up coarse-graining (CG) method to establish implicit-solvent CG modeling for polymers in solution, which conserves the dynamic properties of the reference microscopic system. In particular, tens to hundreds of bonded polymer atoms (or Lennard-Jones beads) are coarse-grained as one CG particle, and the solvent degrees of freedom are eliminated. The dynamics of the CG system is governed by the generalized Langevin equation (GLE) derived via the Mori-Zwanzig formalism, by which the CG variables can be directly and rigorously linked to the microscopic dynamics generated by molecular dynamics (MD) simulations. The solvent-mediated dynamics of polymers is modeled by the non-Markovian stochastic dynamics in GLE, where the memory kernel can be computed from the MD trajectories. To circumvent the difficulty in direct evaluation of the memory term and generation of colored noise, we exploit the equivalence between the non-Markovian dynamics and Markovian dynamics in an extended space. To this end, the CG system is supplemented with auxiliary variables that are coupled linearly to the momentum and among themselves, subject to uncorrelated Gaussian white noise. A high-order time-integration scheme is used to solve the extended dynamics to further accelerate the CG simulations. To assess, validate, and demonstrate the established implicit-solvent CG modeling, we have applied it to study four different types of polymers in solution. The dynamic properties of polymers characterized by the velocity autocorrelation function, diffusion coefficient, and mean square displacement as functions of time are evaluated in both CG and MD simulations. Results show that the extended dynamics with auxiliary variables can construct arbitrarily high-order CG models to reproduce dynamic properties of the reference microscopic system and to characterize long-time dynamics of polymers in solution.

Phagocyte-Inspired Smart Microcapsules

Phagocytes protect the organism by ingesting harmful foreign particles and cells. We use mesoscale computer simulations to design a phagocyte-inspired active microcapsule that is capable of selectively capturing nanoparticles dispersed in solvent. Our fully synthetic microdevice is actuated by a temperature-sensitive microgel enclosed inside a perforated spherical shell. The shell pores are decorated with a copolymer brush that regulates the transport of solutes into the capsule interior. When exposed to an external stimulus, the microgel swells, expanding through the shell pores to make contact with the nanoparticle-rich solution surrounding the capsule. Upon removal of the external stimulus, the gel retracts back into the shell, bringing along with it captured nanoparticles. We probe how periodic application of the stimulus combined with nanoparticle-microgel adhesion enable selectivity and enhance capturing efficiency of our nature-inspired microdevice.

Stokes Flow Inside Topographically Patterned Microchannel Using Boundary Element Method

This study focuses on the investigation of two-dimensional steady Stokes flow inside topographically patterned microchannel. Boundary element method (BEM) is used to solve the Stokes equation and obtain the streamline profiles. The velocity field and pressure gradients are obtained by taking the appropriate spatial derivatives of the stream function and vorticity variables. We restrict ourselves to rectangular stepped geometries and study the effect of variation of step width, step height and step frequency. Interestingly, ‘crown-shaped’ patterns in the horizontal velocity profiles are formed when a sudden contraction is met in the flow region. Pressure gradients, together with the velocity and streamline profiles are analyzed to gain a wholesome understanding of the flow physics.

Refined energy-conserving dissipative particle dynamics model with temperature-dependent properties and its application in solidification problem

It has been observed previously that the physical behaviors of Schmidt number (Sc) and Prandtl number (Pr) of an energy-conserving dissipative particle dynamics (eDPD) fluid can be reproduced by the temperature-dependent weight function appearing in the dissipative force term. In this paper, we proposed a simple and systematic method to develop the temperature-dependent weight function in order to better reproduce the physical fluid properties. The method was then used to study a variety of phase-change problems involving solidification. The concept of the "mushy" eDPD particle was introduced in order to better capture the temperature profile in the vicinity of the solid-liquid interface, particularly for the case involving high thermal conductivity ratio. Meanwhile, a way to implement the constant temperature boundary condition at the wall was presented. The numerical solutions of one- and two-dimensional solidification problems were then compared with the analytical solutions and/or experimental results and the agreements were promising.

Asymmetric motion of magnetically actuated artificial cilia

Most microorganisms use hair-like cilia with asymmetric beating to perform vital bio-physical processes. In this paper, we demonstrate a novel fabrication method for creating magnetic artificial cilia capable of such a biologically inspired asymmetric beating pattern essential for inducing microfluidic transport at low Reynolds number. The cilia are fabricated using a lithographic process in conjunction with deposition of magnetic nickel-iron permalloy to create flexible filaments that can be manipulated by varying an external magnetic field. A rotating permanent magnet is used to actuate the cilia. We examine the kinematics of a cilium and demonstrate that the cilium motion is defined by an interplay among elastic, magnetic, and viscous forces. Specifically, the forward stroke is induced by the rotation of the magnet which bends the cilium, whereas the recovery stroke is defined by the straightening of the deformed cilium, releasing accumulated elastic potential energy. This difference in dominating forces acting during the forward stroke and the recovery stroke leads to an asymmetric beating pattern of the cilium. Such magnetic cilia can find applications in microfluidic pumping, mixing, and other fluid handling processes.

A comparative study of coarse-graining methods for polymeric fluids: Mori-Zwanzig vs. iterative Boltzmann inversion vs. stochastic parametric optimization

We construct effective coarse-grained (CG) models for polymeric fluids by employing two coarse-graining strategies. The first one is a forward-coarse-graining procedure by the Mori-Zwanzig (MZ) projection while the other one applies a reverse-coarse-graining procedure, such as the iterative Boltzmann inversion (IBI) and the stochastic parametric optimization (SPO). More specifically, we perform molecular dynamics (MD) simulations of star polymer melts to provide the atomistic fields to be coarse-grained. Each molecule of a star polymer with internal degrees of freedom is coarsened into a single CG particle and the effective interactions between CG particles can be either evaluated directly from microscopic dynamics based on the MZ formalism, or obtained by the reverse methods, i.e., IBI and SPO. The forward procedure has no free parameters to tune and recovers the MD system faithfully. For the reverse procedure, we find that the parameters in CG models cannot be selected arbitrarily. If the free parameters are properly defined, the reverse CG procedure also yields an accurate effective potential. Moreover, we explain how an aggressive coarse-graining procedure introduces the many-body effect, which makes the pairwise potential invalid for the same system at densities away from the training point. From this work, general guidelines for coarse-graining of polymeric fluids can be drawn.

Integrating Physiology and Architecture in Models of Fruit Expansion

Architectural properties of a fruit, such as its shape, vascular patterns, and skin morphology, play a significant role in determining the distributions of water, carbohydrates, and nutrients inside the fruit. Understanding the impact of these properties on fruit quality is difficult because they develop over time and are highly dependent on both genetic and environmental controls. We present a 3D functional-structural fruit model that can be used to investigate effects of the principle architectural properties on fruit quality. We use a three step modeling pipeline in the OpenAlea platform: (1) creating a 3D volumetric mesh representation of the internal and external fruit structure, (2) generating a complex network of vasculature that is embedded within this mesh, and (3) integrating aspects of the fruit's function, such as water and dry matter transport, with the fruit's structure. We restrict our approach to the phase where fruit growth is mostly due to cell expansion and the fruit has already differentiated into different tissue types. We show how fruit shape affects vascular patterns and, as a consequence, the distribution of sugar/water in tomato fruit. Furthermore, we show that strong interaction between tomato fruit shape and vessel density induces, independently of size, an important and contrasted gradient of water supply from the pedicel to the blossom end of the fruit. We also demonstrate how skin morphology related to microcracking distribution affects the distribution of water and sugars inside nectarine fruit. Our results show that such a generic model permits detailed studies of various, unexplored architectural features affecting fruit quality development.

Computational fluid dynamics in the microcirculation and microfluidics: what role can the lattice Boltzmann method play?

Patient-specific simulations, efficient parametric analyses, and the study of complex processes that are otherwise experimentally intractable are facilitated through the use of Computational Fluid Dynamics (CFD) to study biological flows. This review discusses various CFD methodologies that have been applied across different biological scales, from cell to organ level. Through this discussion the lattice Boltzmann method (LBM) is highlighted as an emerging technique capable of efficiently simulating fluid problems across the midrange of scales; providing a practical analytical tool compared to methods more attuned to the extremities of scale. Furthermore, the merits of the LBM are highlighted through examples of previous applications and suggestions for future research are made. The review focusses on applications in the midrange bracket, such as cell-cell interactions, the microcirculation, and microfluidic devices; wherein the inherent mesoscale nature of the LBM renders it well suited to the incorporation of fluid-structure interaction effects, molecular/particle interactions and interfacial dynamics. The review demonstrates that the LBM has the potential to become a valuable tool across a range of emerging areas in bio-CFD, such as understanding and predicting disease, designing lab-on-a-chip devices, and elucidating complex biological processes.

Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. However, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while the corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. However, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model.

Eyelashes divert airflow to protect the eye

Eyelashes are ubiquitous, although their function has long remained a mystery. In this study, we elucidate the aerodynamic benefits of eyelashes. Through anatomical measurements, we find that 22 species of mammals possess eyelashes of a length one-third the eye width. Wind tunnel experiments confirm that this optimal eyelash length reduces both deposition of airborne particles and evaporation of the tear film by a factor of two. Using scaling theory, we find this optimum arises because of the incoming flow's interactions with both the eye and eyelashes. Short eyelashes create a stagnation zone above the ocular surface that thickens the boundary layer, causing shear stress to decrease with increasing eyelash length. Long eyelashes channel flow towards the ocular surface, causing shear stress to increase with increasing eyelash length. These competing effects result in a minimum shear stress for intermediate eyelash lengths. This design may be employed in creating eyelash-inspired protection for optical sensors.

Numerical modeling of capillary electrophoresis - electrospray mass spectrometry interface design

Capillary electrophoresis hyphenated with electrospray mass spectrometry (CE-ESI-MS) has emerged in the past decade as one of the most powerful bioanalytical techniques. As the sensitivity and efficiency of new CE-ESI-MS interface designs are continuously improving, numerical modeling can play important role during their development. In this review, different aspects of computer modeling and simulation of CE-ESI-MS interfaces are comprehensively discussed. Relevant essentials of hydrodynamics as well as state-of-the-art modeling techniques are critically evaluated. Sheath liquid-, sheathless-, and liquid-junction interfaces are reviewed from the viewpoint of multidisciplinary numerical modeling along with details of single and multiphase models together with electric field mediated flows, electrohydrodynamics, and free fluid-surface methods. Practical examples are given to help non-specialists to understand the basic principles and applications. Finally, alternative approaches like air amplifiers are also included. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 34: 558-569, 2015.

Transport dissipative particle dynamics model for mesoscopic advection-diffusion-reaction problems

We present a transport dissipative particle dynamics (tDPD) model for simulating mesoscopic problems involving advection-diffusion-reaction (ADR) processes, along with a methodology for implementation of the correct Dirichlet and Neumann boundary conditions in tDPD simulations. tDPD is an extension of the classic dissipative particle dynamics (DPD) framework with extra variables for describing the evolution of concentration fields. The transport of concentration is modeled by a Fickian flux and a random flux between tDPD particles, and the advection is implicitly considered by the movements of these Lagrangian particles. An analytical formula is proposed to relate the tDPD parameters to the effective diffusion coefficient. To validate the present tDPD model and the boundary conditions, we perform three tDPD simulations of one-dimensional diffusion with different boundary conditions, and the results show excellent agreement with the theoretical solutions. We also performed two-dimensional simulations of ADR systems and the tDPD simulations agree well with the results obtained by the spectral element method. Finally, we present an application of the tDPD model to the dynamic process of blood coagulation involving 25 reacting species in order to demonstrate the potential of tDPD in simulating biological dynamics at the mesoscale. We find that the tDPD solution of this comprehensive 25-species coagulation model is only twice as computationally expensive as the conventional DPD simulation of the hydrodynamics only, which is a significant advantage over available continuum solvers.

Microscale technology and biocatalytic processes: opportunities and challenges for synthesis

Despite the expanding presence of microscale technology in chemical synthesis and energy production as well as in biomedical devices and analytical and diagnostic tools, its potential in biocatalytic processes for pharmaceutical and fine chemicals, as well as related industries, has not yet been fully exploited. The aim of this review is to shed light on the strategic advantages of this promising technology for the development and realization of biocatalytic processes and subsequent product recovery steps, demonstrated with examples from the literature. Constraints, opportunities, and the future outlook for the implementation of these key green engineering methods and the role of supporting tools such as mathematical models to establish sustainable production processes are discussed.

Theoretical and computational investigations of nanoparticle-biomembrane interactions in cellular delivery

With the rapid development of nanotechnology, nanoparticles have been widely used in many applications such as phototherapy, cell imaging, and drug/gene delivery. A better understanding of how nanoparticles interact with bio-system (especially cells) is of great importance for their potential biomedical applications. In this review, the current status and perspective of theoretical and computational investigations is presented on the nanoparticle-biomembrane interactions in cellular delivery. In particular, the determining parameters (including the properties of nanoparticles, cell membranes and environments) that govern the cellular uptake of nanoparticles (direct penetration and endocytosis) are discussed. Further, some special attention is paid to their interactions beyond the translocation of nanoparticles across membranes (e.g., nanoparticles escaping from endosome and entering into nucleus). Finally, a summary is given, and the challenging problems of this field in the future are identified.

Self-Propelled Microswimmer Actuated by Stimuli-Sensitive Bilayered Hydrogel

Using computational modeling, we design a microscopic swimmer made of a bilayered responsive hydrogel capable of swimming in a viscous fluid when actuated by a periodically applied stimulus. The gel has an X-shaped geometry and two bonded layers, one of which is responsive to environmental changes and the other which is passive. When the stimulus is turned on, the responsive layer swells and causes the swimmer to deform. We demonstrate that when such stimulus-induced deformations occur periodically the gel swimmer effectively propels forward through the fluid. We show that the swimming speed depends on the relative stiffness of the two gel layers composing the swimmer, and we determine the optimal stiffness ratio that maximizes the swimming speed.

Mesoscale modeling of phase transition dynamics of thermoresponsive polymers

A non-isothermal mesoscopic model is developed to investigate the thermally induced phase transition dynamics of thermoresponsive polymers.

Mesoscale modelling of environmentally responsive hydrogels: emerging applications

We review recent advances in mesoscale computational modeling, focusing on dissipative particle dynamics, used to probe stimuli-sensitive behavior of hydrogels.

Construction of dissipative particle dynamics models for complex fluids via the Mori-Zwanzig formulation

We present a bottom-up coarse-graining procedure to construct mesoscopic force fields directly from microscopic dynamics. By grouping many bonded atoms in the molecular dynamics (MD) system into a single cluster, we compute both the conservative and non-conservative interactions between neighboring clusters. In particular, we perform MD simulations of polymer melts to provide microscopic trajectories for evaluating coarse-grained (CG) interactions. Subsequently, dissipative particle dynamics (DPD) is considered as the effective dynamics resulting from the Mori-Zwanzig (MZ) projection of the underlying atomistic dynamics. The forces between finite-size clusters have, in general, both radial and transverse components and hence we employ four different DPD models to account differently for such interactions. Quantitative comparisons between these DPD models indicate that the DPD models with MZ-guided force fields yield much better static and dynamics properties, which are consistent with the underlying MD system, compared to standard DPD with empirical formulae. When the rotational motion of the particle is properly taken into account, the entire velocity autocorrelation function of the MD system as well as the pair correlation function can be accurately reproduced by the MD-informed DPD model. Since this coarse-graining procedure is performed on an unconstrained MD system, our framework is general and can be used in other soft matter systems in which the clusters can be faithfully defined as CG particles.

Phase segregation in bio-inspired multi-component vesicles encompassing double tail phospholipid species

Our aim is to investigate the phase segregation and the structure of multi-component bio-inspired phospholipid vesicles via dissipative particle dynamics. The chemical distinction in the phospholipid species arises due to different head and tail group moieties, and molecular stiffness of the hydrocarbon tails. The individual amphiphilic phospholipid molecular species are represented by a hydrophilic head group and two hydrophobic tails. The distinct chemical nature of the moieties is modeled effectively via soft repulsive interaction parameters, and the molecular rigidity is tuned via suitable three-body potential constants. We demonstrate the formation of a stable hybrid vesicle through the self-assembly of the amphiphilic phospholipid molecules in the presence of a hydrophilic solvent. We investigate and characterize the phase segregation and the structure of the binary vesicles for different phospholipid mixtures. Our results demonstrate macroscopic phase separation for phospholipid mixtures composed of species with different hydrocarbon tail groups. We also investigate the relationship between the phase segregation and thermodynamic variables such as interfacial line tension and surface tension, and obtain correspondence between existing theory and experiments, and our simulation results. We report variations in the molecular chain stiffness to have negligible contributions to the phase segregation in the mixed bilayer, and to demonstrate shape transformations of the hybrid vesicle. Our results can be used to design novel bio-inspired hybrid vehicles for potential applications in biomedicine, sensing, imaging and sustainability.