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    Perfect absorption in complex scattering systems with or without hidden symmetries
    (Springer Nature, 2020-11-17) Chen, Lei; Kottos, Tsampikos; Anlage, Steven M.
    Wavefront shaping (WFS) schemes for efficient energy deposition in weakly lossy targets is an ongoing challenge for many classical wave technologies relevant to next-generation telecommunications, long-range wireless power transfer, and electromagnetic warfare. In many circumstances these targets are embedded inside complicated enclosures which lack any type of (geometric or hidden) symmetry, such as complex networks, buildings, or vessels, where the hypersensitive nature of multiple interference paths challenges the viability of WFS protocols. We demonstrate the success of a general WFS scheme, based on coherent perfect absorption (CPA) electromagnetic protocols, by utilizing a network of coupled transmission lines with complex connectivity that enforces the absence of geometric symmetries. Our platform allows for control of the local losses inside the network and of the violation of time-reversal symmetry via a magnetic field; thus establishing CPA beyond its initial concept as the time-reversal of a laser cavity, while offering an opportunity for better insight into CPA formation via the implementation of semiclassical tools.
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    Dataset for work presented in "Perfect Absorption in Complex Scattering Systems with or without Hidden Symmetries"
    (2020) Chen, Lei; Anlage, Steven
    Dataset supports: Chen, L. et al (2020). "Perfect Absorption in Complex Scattering Systems with or without Hidden Symmetries,” Nature Communications.
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    UNCOVERING FUNDAMENTAL MECHANISMS OF ACTOMYOSIN CONTRACTILITY USING ANALYTICAL THEORY AND COMPUTER SIMULATION
    (2018) Komianos, James Eric; Papoian, Garegin A; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Actomyosin contractility is a ubiquitous force-generating function of almost all eukaryotic organisms. While more understanding of its dynamic non-equilibrium be- havior has been uncovered in recent years, little is known regarding its self-emergent structures and phase transitions that are observed in vivo. With this in mind, this thesis aims to develop a state-of-the-art computational model for the simulation of actomyosin assemblies, containing detailed cytosolic reaction-diffusion processes such as actin filament treadmilling, cross-linker (un)binding, and molecular motor walking. This is explicitly coupled with novel mechanical potentials for semi-flexible actin filaments. Then, using this simulation framework combined with other ana- lytical approaches, we propose a novel mechanism of contractility in a fundamental actomyosin structural element, derived from a thermodynamic free energy gradi- ent favoring overlapped actin filament states when passive cross-linkers are present. With this spontaneous cross-linking, transient motors such as non-muscle myosin II can generate robust network contractility in a collective myosin II-cross-linker ratcheting mechanism. Finally, we map the phases of contractile behavior of disor- dered actomyosin using this theory, showing explicitly the cross-linking, motor and boundary conditions required for geometric collapse or tension generation in a net- work comprised of those elements. In this theory, we move away from the sarcomeric contractility mechanism typically reconciled in disordered non-muscle structures. It is our hope that this study adds theoretical knowledge as well as computational tools to study the diverse contractile assemblies found in non-muscle actomyosin networks.
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    LARGE SYSTEMS OF MANY INTERCONNECTED DYNAMICAL UNITS: GENE NETWORK INFERENCE, EPIGENETIC HERITABILITY, AND EMERGENT BEHAVIOR IN OSCILLATOR SYSTEMS
    (2014) Ku, Wai Lim; Ott, Edward; Girvan, Michelle; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this thesis, which consists of three parts, we investigate problems related to systems biology and collective behavior in complex systems. The first part studies genetic networks that are inferred using gene expression data. Here we use established transcriptional regulatory interactions (TRIs) in combination with microarray expression data from both Escherichia coli (a prokaryote) and Saccharomyces cerevisiae (a eukaryote) to assess the accuracy of predictions of coregulated gene pairs and TRIs from observations of coexpressed gene pairs. We find that highly coexpressed gene pairs are more likely to be coregulated than to share a TRI for Saccharomyces cerevisiae, while the incidence of TRIs in highly coexpressed gene pairs is higher for Escherichia coli. The data processing inequality (DPI) of information theory has previously been applied for the inference of TRIs. We consider the case where a transcription factor gene is known to regulate two genes (one of which is a transcription factor gene) that are known not to regulate one another. According to the DPI if certain conditions hold, the non-interacting gene pairs should have the smallest mutual information among all pairs in the triplets. While we observe that this is sometimes the case for Escherichia coli, we find that it is almost always not the case for Saccharomyces cerevisiae, thus indicating that the assumed conditions under which the DPI was derived do not hold. The second part of this dissertation is related to the dynamical process of epigentic heritability. Epigenetic modifications to histones may promote either activation or repression of the transcription of nearby genes. Recent experimental studies show that the promoters of many lineage-control genes in stem cells have "bivalent domains" in which the nucleosomes contain both active (H3K4me3) and repressive (H3K27me3) marks. Here we formulate a mathematical model to investigate the dynamic properties of bivalent histone modification patterns, and we predict some interesting and potentially experimental observable features. The third part of this dissertation studies dynamical systems in which a large number $N$ of identical Landau-Stuart oscillators are globally coupled via a mean-field. Previously, it has been observed that this type of system can exhibit a variety of different dynamical behaviors including clumped states (in which each oscillator is in one of a small number of groups for which all oscillators in each group have the same state which is different from group to group), as well as extensive chaos (a situation in which all oscillators have different states and the macroscopic dynamics of the mean field is chaotic). One of our foci is the transition between clumped states and extensive chaos as the system is subjected to slow adiabatic parameter change. We observe and analyze explosive discontinuous transitions between the clumped states and the extensively chaotic states. Also, we study the fractal structures of the extensively chaotic attractors.
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    Phase Transitions in Complex Network Dynamics
    (2014) Squires, Shane Anthony; Girvan, Michelle; Ott, Edward; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Two phase transitions in complex networks are analyzed. The first of these is a percolation transition, in which the network develops a macroscopic connected component as edges are added to it. Recent work has shown that if edges are added "competitively" to an undirected network, the onset of percolation is abrupt or "explosive." A new variant of explosive percolation is introduced here for directed networks, whose critical behavior is explored using numerical simulations and finite-size scaling theory. This process is also characterized by a very rapid percolation transition, but it is not as sudden as in undirected networks. The second phase transition considered here is the emergence of instability in Boolean networks, a class of dynamical systems that are widely used to model gene regulation. The dynamics, which are determined by the network topology and a set of update rules, may be either stable or unstable, meaning that small perturbations to the state of the network either die out or grow to become macroscopic. Here, this transition is analytically mapped onto a well-studied percolation problem, which can be used to predict the average steady-state distance between perturbed and unperturbed trajectories. This map applies to specific Boolean networks with few restrictions on network topology, but can only be applied to two commonly used types of update rules. Finally, a method is introduced for predicting the stability of Boolean networks with a much broader range of update rules. The network is assumed to have a given complex topology, subject only to a locally tree-like condition, and the update rules may be correlated with topological features of the network. While past work has addressed the separate effects of topology and update rules on stability, the present results are the first widely applicable approach to studying how these effects interact. Numerical simulations agree with the theory and show that such correlations between topology and update rules can have profound effects on the qualitative behavior of these systems.