ASU Scholarship Showcase

Recently, the phenomenon of quantum-classical correspondence breakdown was uncovered in optomechanics, where in the classical regime the system exhibits chaos but in the corresponding quantum regime the motion is regular - there appears to be no signature of classical chaos whatsoever in the corresponding quantum system, generating a paradox. We find that transient chaos, besides being a physically meaningful phenomenon by itself, provides a resolution. Using the method of quantum state diffusion to simulate the system dynamics subject to continuous homodyne detection, we uncover transient chaos associated with quantum trajectories. The transient behavior is consistent with chaos in the classical limit, while the long term evolution of the quantum system is regular. Transient chaos thus serves as a bridge for the quantum-classical transition (QCT). Strikingly, as the system transitions from the quantum to the classical regime, the average chaotic transient lifetime increases dramatically (faster than the Ehrenfest time characterizing the QCT for isolated quantum systems). We develop a physical theory to explain the scaling law.

A remarkable phenomenon in spatiotemporal dynamical systems is chimera state, where the structurally and dynamically identical oscillators in a coupled networked system spontaneously break into two groups, one exhibiting coherent motion and another incoherent. This phenomenon was typically studied in the setting of non-local coupling configurations. We ask what can happen to chimera states under systematic changes to the network structure when links are removed from the network in an orderly fashion but the local coupling topology remains invariant with respect to an index shift. We find the emergence of multicluster chimera states. Remarkably, as a parameter characterizing the amount of link removal is increased, chimera states of distinct numbers of clusters emerge and persist in different parameter regions. We develop a phenomenological theory, based on enhanced or reduced interactions among oscillators in different spatial groups, to explain why chimera states of certain numbers of clusters occur in certain parameter regions. The theoretical prediction agrees well with numerics.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

Background: Immunomodulatory drugs (IMiDs), such as lenalidomide, are therapeutically active compounds that bind and modulate the E3 ubiquitin ligase substrate recruiter cereblon, thereby affect steady-state levels of cereblon and cereblon binding partners, such as ikaros and aiolos, and induce many cellular responses, including cytotoxicity to multiple myeloma (MM) cells. Nevertheless, it takes many days for MM cells to die after IMiD induced depletion of ikaros and aiolos and thus we searched for other cereblon binding partners that participate in IMiD cytotoxicity.

Methods: Cereblon binding partners were identified from a MM cell line expressing histidine-tagged cereblon by pulling down cereblon and its binding partners and verified by co-immunoprecipitation. IMiD effects were determined by western blot analysis, cell viability assay, microRNA array and apoptosis analysis.

Results: We identified argonaute 2 (AGO2) as a cereblon binding partner and found that the steady-state levels of AGO2 were regulated by cereblon. Upon treatment of IMiD-sensitive MM cells with lenalidomide, the steady-state levels of cereblon were significantly increased, whereas levels of AGO2 were significantly decreased. It has been reported that AGO2 plays a pivotal role in microRNA maturation and function. Interestingly, upon treatment of MM cells with lenalidomide, the steady-state levels of microRNAs were significantly altered. In addition, silencing of AGO2 in MM cells, regardless of sensitivity to IMiDs, significantly decreased the levels of AGO2 and microRNAs and massively induced cell death.

Conclusion: These results support the notion that the cereblon binding partner AGO2 plays an important role in regulating MM cell growth and survival and AGO2 could be considered as a novel drug target for overcoming IMiD resistance in MM cells.

Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.