(6hg) Non-Equilibrium Self-Assembly and Structures

My research focuses on non-equilibrium self-assemblies (NESAs) that require energy supply to maintain their order or, as cells, to "survive". Non-equilibrium assemblies are energy (external field) driven systems that reside in steady states or long-lived metastable structures, some of which can be highly dissipative and are directed by production of the entropy within the system. DNA transcription machinery, self-propelling micelles, liquid crystals under external fields, cytoskeletal fibers, and molecular motors are all examples of various aspects NESAs. The ability to control the ordered non-equilibrium state paves the way for new types of materials that can be reassembled when needed. This shifting ability between different configurations offers variety of applications in several branches: they can be implemented in energy storage purposes due to the high energy stored in their metastable conditions, they can be utilized as sensors responding to outside stimuli by adapting their mode of organization, and in biological applications such as drug delivery. Theoretical studies are promising for these systems due to the presence of time varying different length scales that changes from molecular and nanoscale to mesoscopic and macroscopic ones. In my research, these complex assemblies are studied using the tools of non- equilibrium thermodynamics and molecular simulation techniques. The NESA structures are influenced by the intra- and intermolecular forces imposed by their building blocks, external forces such as magnetic and electromagnetic fields, capillary interactions, and also their solvent media or solid interfaces in the surrounding environment. Therefore, the proposed mesoscopic model should capture the relation between microscopic structure and macroscopic behavior. These dissipative structures have many applications in biological, electronics, catalysis, material science, and chemical worlds with multi-tasking and self-repair properties that can be predicted by the analytical tool of the molecular simulation. Until now, NESA in artificial systems has been achieved by modifying the interactions between the constituent objects/parts. In the proposed methodology, I will look at the problem from a fundamentally different perspective – namely, I will keep the interactions between the components intact but instead change the agitation mode to achieve different structures. For this purpose, I aim to control the particle self- assembly (SA) in non-equilibrium regime by applying local anisotropic vibrations (for the solvent or particles or both), which effectively translating into anisotropic temperature that can be considered through hydrodynamic effects. The hydrodynamic effects along with dynamic anisotropy bias the particles toward the desired configuration. Also, the role of the external fields such as static and alternative magnetic fields in SA of magnetic fluids to develop a complete theoretical approach for dynamic SA far from equilibrium is investigated. Molecular dynamics simulation has employed to test the above hypothesis during my postdoctoral time.

Sample proposed Project: Reaction Networks in Non-Equilibrium Self- Assembled Structures

In biology and chemistry complex biochemical networks displaced far from equilibrium coupled with transport phenomena maintain the NESA structures. These out-of-equilibrium chemical reactions provide a strong tool for controlling NESA systems through a signaling network. The network is a mathematical toolbox composed of several nodes that are responsible for the chemical reactions that control the NESA behavior. By implementing a step-by-step approach the most critical nodes of the network for controlling the NESA systems are identified. Also, the nodes that have most synergistic effects on the network functioning are investigated. These networks have important influences on switching behavior (by imposing molecular stimuli), motility, and memory storage. The challenge here is to combine reaction network with dynamic (motion) behavior of the system that provide the necessary information about the evolution of the particles. As a start we will consider bioinspired simple chemical oscillators that generate waves inside NESA such as a pH-driven self-assembly coupled to a chemical oscillator that cycles the pH of the solution. This switching behavior causes the surface molecules to protonate/deprotonate and self-assemble/disassemble as their attractive forces are turned on and off. Also, pH change can cause coil-globule transition and dramatic volume changes in polymers and biology. A good example of pH responsive materials can be found in polyelectrolytes namely polyacids and polybases. Polyacids are protonated at low pH and is charge neutral. In this situation polymer adopts a hydrophobic collapsed conformation. By increasing the pH (above the pKa) of the acidic monomer, the polymer becomes ionized and deprotonated. Moreover, the negative charges on the polymer chain makes counter ions and associated water molecules to increase the total volume of the polymer. This volume change can be switched "on" and "off" by cycling the pH above and below the pKa of the polyacid. Polybases also protonate at low pH and deprotonate at high pH. However, they swell and ionize at low pH due to protonation of the basic groups. Examples of this behavior is observed in triblock copolymers comprising poly(methacrylic acid) and poly[(2-diethylamino)ethyl methacrylate] as polyacid and polybase pairs. The pH intensive endblocks used in each case includes poly(methyl methacrylate). The advantage of using polyacid and polybase pairs is the device fabricated from these two building blocks is bipolar. Under the same pH condition one polymer is swollen while the other is collapsed. This phenomenon can be used in artificial muscles that need contraction and extraction. Another application is in cell biology where protein-protein interactions (PPI) inside cytoskeleton affect the NESA structure through a network of chemical reactions. It has been well established that in metastatic cancer cells not only a single protein but also entire network of interacting proteins—specifically, those regulating actin filament assembly and disassembly— are up- and/or downregulated resulting in deregulated cell motility and reaction network patterns. The NESA of the cytoskeleton causes constant destruction, self-repair, and/or construction in the structure is the key factor for cellular motions. In addition, cell interactions with surrounding extracellular matrix also influence the NESA. What is not known is how small changes in functioning of the complex intracellular PPI networks translate into global changes inoverall NESA patterns. The situation becomes even more complex when one starts to consider the combined effects of intracellular protein networks and cell interaction with extracellular matrix, surrounding cells and/or soluble factors. Non-equilibrium thermodynamics has been promising in modeling biological systems such as transport through membranes and protein- folding/unfolding. Hence, it has the ultimate tools for complex cellular systems that have both PPI and membrane transport. Considering that the cells are composed of cytoskeleton dynamic events that span a large spectrum of time and length scales, the mechanism and stochastic character of cell movements are difficult to understand based solely on experimental observations. To tackle this complex problem, I propose an advanced theoretical and molecular simulation approach. The physical insight in a complex nonlinear dissipative biological system can be coherently expressed in a mathematical framework in non-equilibrium thermodynamics. To minimize the simulation effort but still develop an efficient technique, I propose a thermodynamically guided simulation/theoretical approach, which will use experimental and simulation data of network PPIs between actin regulators cell surface/membrane proteins and soluble peptide. Specifically, we will implement a non-equilibrium thermodynamic approach that utilizes the experimental/simulation data of the functional protein interaction networks as additional potential wells (intra- and intermolecular interactions) to guide the coarse-graining approach in the simulation technique. We will focus on the protein interaction networks that control polymerization and turnover of actin and thus act as a driving force for cell movements and is a key feature, which is upregulated in metastatic cancer. We will develop a model/technique that allows determining the most important nodes/their combinations of the network for controlling the actin polymerization and consequently NESA and dynamic patterns specific to metastatic cancer cells. Non-equilibrium molecular dynamic simulations will also consider the external interactions with matrix and soluble factors. The proposed research will be the first attempt, to the best of our knowledge, to combine the network module relevant/specific to cancer metastasis with non-equilibrium molecular dynamics approach. Our hope is that understanding of how intracellular interacting protein networks and NESA of the cytoskeleton control overall cell motility patterns in the context of complex microenvironments will enable managing of the cancer cell motility and ultimately would provide rationale for stopping cancer progression. Our theoretical approach would be validated experimentally in collaboration with scientists in experimental research groups while supported by the National Institutes of Health (NIH) and National Science Foundation (NSF).

Outlook and Conclusions:

The goal of this proposal is to develop the theory of NESA based on the experimental data and molecular simulation observations. The projects that I proposed are for the first 7 years of my academic career. However, the methodology that I suggested can be extended to other branches of research that is suitable for the next 20 years. These areas include: electrorheological fluids, cell agglomeration in lysosome-rich cells by light scattering ability at near-IR wavelength that create a long range force for the cells to attract each other at distances up to 200 μm, and light- switch aggregation of magnetic colloids, nanoparticle self-assembly, micellar surfactants or surface chemical reactors, design of catalysts by assembly of metal colloids dispersed in block copolymers, self-replicating micelles, and reactions in vesicle membranes.