Towards the Design of Heuristics by Means of Self-Assembly

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Many decision and optimization problems arising in bioinformatics field are time demanding, and several algorithms are designed to solve these problems or to improve their current best solution approach. Modeling and implementing a new heuristic algorithm may be time‐consuming but has strong motivations: on the one hand, even a small improvement of the new solution may be worth the long time spent on the construction of a new method; on the other hand, there are problems for which good‐enough solutions are acceptable which could be achieved at a much lower computational cost. In the first case, specially designed heuristics or metaheuristics are needed, while the latter hyper‐heuristics can be proposed. The paper will describe both approaches in different domain problems

Separation of On-Column Labeled Model Proteins with Packed Capillary Electrophoresis

Protein drugs are increasingly developed in the pharmaceutical company. Under the regulation of FDA, high purity of therapeutic proteins needs to be maintained. Before putting those drugs in the market, fast and efficient method is in need to achieve homogeneity. Traditionally, polyacrylamide gel electrophoresis (PAGE), capillary electrophoresis (CE), and size-exclusion chromatography (SEC) are used for the purification process. These methods have the disadvantages of low time and cost efficiency, and this quality assurance process has become the bottleneck of production. In our group, sub-micron silica colloidal particles with polyacrylamide layer on the surface are packed inside capillaries to increase the separation efficiency. In this particular project, NanoOrange dye is non-covalently associated with the protein sample to best reserve their native conformation. Proteins were separated at a distance as short as 8.2 mm in 85 seconds with extremely low plate height. The efficiency can be improved by decrease the silica particle size. This high throughput separation has a potential to be adopted in industries. Part II of this thesis describes the cost-effective DNA microarray project. Microarray is a technology evolved from southern blotting, and it is a common tool to measure the expression levels of the DNA samples. To quantify the DNA samples, fluorescently labeled target strands hybridize with the DNA probe which binds to a solid surface. DNA microarray is frequently utilized in clinical studies, and the efficiency is highly desired to be improved. When attach the probes on a smooth surface, the amount of DNA captured will be limited. In this research, the microarray sensitivity is increased by layering silica colloidal particles on the solid surface. Silica particles obtain face-centered cubic packing which increases the surface area to bind to the DNA probe, thus improve the microarray sensitivity. To reduce the cost of the microarray and to make it more point-of-care feasible, transparent plastic sheets is going to be researched to replace quartz silica plates. This new sensitive and cost effective microarray has a great possibility to be developed into point-of-care which detects a variety of diseases

Industry 4.0 challenges to IE paradigms: A pilot study in materials handling

Industrial engineering practices are expected to be affected by, and most likely adapt to, the new paradigms of Industry 4.0. Early indications in practice, as well as extrapolations from the current technology trends, point toward a few fundamental features. Among these are further integration, leaner and hence more agile practices, and the use of real-time data. The final objective is to reduce complexity while striving for real-time supply- and production-chain optimization. We argue that the optimization of highly integrated production systems cannot be sought by simply aggregating the known operations management tools of industrial engineering. Specifically, we present evidence, gleaned from a recent industrial project, that indicates how as the systems become more integrated, the concept of operations optimization needs to be revisited. Our work has two distinct contributions to the literature. We develop and present a state-of-the-art optimization model for a joint materials handling, inventory, and scheduling model. The model incorporates aspects of the knapsack, bin packing, vehicle routing, and inventory control formulations. Further, we show that simply collecting existing industrial engineering models into larger aggregations, albeit in line with the current best practices of our profession, will not necessarily suffice to completely fulfill the ambitions of Industry 4.0

Role of Shape in the Self-Assembly of Anisotropic Colloids.

Self-assembly is the process of spontaneous organization of a set of interacting components. We examine how particle shape drives the self-assembly of colloids in three different systems. When particles interact only via their shape, entropic crystallization can occur; we discuss a design strategy using the Voronoi tesslelation to create “Voronoi particles,” (VP) which are hard particles in the shape of Voronoi cells of their target structure. Although VP stabilize their target structure in the limit of infinite pressure, the self-assembly of the same structure at moderate pressure is not guaranteed. We find that more symmetric crystals are often preferred due to entropic contributions of several kBT from configurational degeneracies. We characterize the assembly of VP in terms of their symmetries and the complexities of the target structure and demonstrate how controlling the degeneracies through modifying shape and field-directed assembly can improve the assembly propensity. With the addition of non-adsorbing, polymers, hard colloids experience an attraction dependent on polymer concentration, the form of which is dictated by the colloid shape; we study a system of oblate, spheroidal colloids that self-assemble thread-like clusters. In both simulation and experiment the colloids condense into disordered droplets at low polymer concentrations; at higher concentrations we observe kinetic arrest into primarily linear clusters of aligned colloids. We show that the mechanical stabilty of these low-valence structures results from the anisotropic particle shape. Particle surfaces can be patterned with metal coatings, introducing enthalpic attraction between particles; we study a system of prolate spheroidal colloids, half-coated in gold. We show with experiments and computer simulations that Janus ellipsoids can self-assemble into self-limiting one-dimensional fibers with shape-memory properties, and that the fibrillar assemblies can be actuated on application of an external alternating-current electric field. Actuation of the fibers occurs through a sliding mechanism (allowed by the curved ellipsoidal surface) that permits the reversible elongation of the Janus-ellipsoid chains by ~36%. In each case, we find shape plays a critical role. By understanding and isolating its impact, we enhance shape's utility as a parameter for the design of self-assembling colloids.PhDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111630/1/baschult_1.pd

Development of small-angle scattering pair distribution function analysis techniques and application to nanoparticles assemblies

With the improvement in synthesis method, a variety of nanoparticles (NPs) with nearly uniform distribution in size and morphology are now available to scientists. This progress opens a new opportunity of assembling these high quality nanoparticles into metamaterial - nanoparticle assemblies (NPAs). The properties of NPA depend on the interactions between constituent NPs, therefore NPA offer a distinct advantage in designing material properties that are not available in the bulk phase (crystal) or discrete phase (nanoparticle). Novel application of NPA in modern devices, such as solar cells and field effect transistors, had also been demonstrated. The spatial arrangements of NPs is the key factor to their interactions, therefore, it is crucial to characterize the structure of NPA quantitatively. The technique of diffraction plays an unique role for characterizing NPA structure, as it not only offers the structural type, which may also be obtained from imagine technique, but also yields structural information in three-dimension, such inter-particle distance and the range of structural coherence of the packing order. Traditionally, the diffraction analysis is based on crystallography and is carried out in reciprocal space. However, it has been known local structure is overlooked in this kind of crystallographic analysis, which places a challenge for have a comprehensive understanding of the NPA structure. The pair distribution function (PDF) analysis, which is powerful in probing local structures for atomic systems, serves as a promising tool for characterizing NPA structure. How- ever, the approach of using PDF analysis for NPA structure characterization has barely been explored. In this thesis, I will present the methodological developments of the PDF technique. Starting from presenting a machine-learning-assisted approach for predicting the space group of its structure from the PDF, I will be focusing on the aspect of accelerating the structure modeling steps with PDF. Next, the development of pair distribution function analysis in small-angle scattering domain sasPDF will be introduced, including software package PDFgetX3 which is aiming to facilitate the extraction of PDF from small-angle scattering data quickly. The approach of sasPDF is validated against three representative structures across different levels of structural order. Finally, the example of applying sasPDF method to identify the jamming transition signature in polymer-ligated NPA is introduced, followed by another example of discovering multiply-twinned structure from the reprogramming of DNA-ligated NPA

Towards Understanding the Self-assembly of Complicated Particles via Computation.

We develop advanced Monte Carlo sampling schemes and new methods of calculating thermodynamic partition functions that are used to study the self-assembly of complicated ``patchy '' particles. Patchy particles are characterized by their strong anisotropic interactions, which can cause critical slowing down in Monte Carlo simulations of their self-assembly. We prove that detailed balance is maintained for our implementation of Monte Carlo cluster moves that ameliorate critical slowing down and use these simulations to predict the structures self-assembled by patchy tetrominoes. We compare structures predicted from our simulations with those generated by an alternative learning-augmented Monte Carlo approach and show that the learning-augmented approach fails to sample thermodynamic ensembles. We prove one way to maintain detailed balance when parallelizing Monte Carlo using the checkerboard domain decomposition scheme by enumerating the state-to-state transitions for a simple model with general applicability. Our implementation of checkerboard Monte Carlo on graphics processing units enables accelerated sampling of thermodynamic properties and we use it to confirm the fluid-hexatic transition observed at high packing fractions of hard disks. We develop a new method, bottom-up building block assembly, which generates partition functions hierarchically. Bottom-up building block assembly provides a means to answer the question of which structures are favored at a given temperature and allows accelerated prediction of potential energy minimizing structures, which are difficult to determine with Monte Carlo methods. We show how the sequences of clusters generated by bottom-up building block assembly can be used to inform ``assembly pathway engineering'', the design of patchy particles whose assembly propensity is optimized for a target structure. The utility of bottom-up building block assembly is demonstrated for systems of CdTe/CdS tetrahedra, DNA-tethered nanospheres, colloidal analogues of patchy tetrominoes and shape-shifting particles.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91509/1/erjank_1.pd

Variational Methods for Biomolecular Modeling

Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs). This principle allows us to focus on the identification of essential energetic components, the optimal parametrization of energies, and the efficient computational implementation of energy variation or minimization. Given the fact that complex biomolecular systems are structurally non-uniform and their interactions occur through contact interfaces, their free energies are associated with various interfaces as well, such as solute-solvent interface, molecular binding interface, lipid domain interface, and membrane surfaces. This fact motivates the inclusion of interface geometry, particular its curvatures, to the parametrization of free energies. Applications of such interface geometry based energetic variational principles are illustrated through three concrete topics: the multiscale modeling of biomolecular electrostatics and solvation that includes the curvature energy of the molecular surface, the formation of microdomains on lipid membrane due to the geometric and molecular mechanics at the lipid interface, and the mean curvature driven protein localization on membrane surfaces. By further implicitly representing the interface using a phase field function over the entire domain, one can simulate the dynamics of the interface and the corresponding energy variation by evolving the phase field function, achieving significant reduction of the number of degrees of freedom and computational complexity. Strategies for improving the efficiency of computational implementations and for extending applications to coarse-graining or multiscale molecular simulations are outlined.Comment: 36 page