Anomalous Microwave Surface Resistance of CeCu6

We present surface resistance measurements of the archetypical heavy-fermion compound CeCu6 for frequencies between 3.7 and 18 GHz and temperatures from 1.2 to 6 K. The measurements were performed with superconducting stripline resonators that allow simultaneous measurements at multiple frequencies. The surface resistance of CeCu6 exhibits a pronounced decrease below 3 K, in consistence with dc resistivity. The low-temperature frequency dependence of the surface resistance follows a power law with exponent 2/3. While for conventional metals this would be consistent with the anomalous skin effect, we discuss the present situation of a heavy-fermion metal, where this frequency dependence might instead stem from the influence of electronic correlations.Comment: 6 pages, 3 figures, proceedings of SCES 201

Mapping behavioral specifications to model parameters in synthetic biology

With recent improvements of protocols for the assembly of transcriptional parts, synthetic biological devices can now more reliably be assembled according to a given design. The standardization of parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal design specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified design constraints are robustly satisfied. In this work we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction. The method is general and can hence be incorporated in a pipeline for the rational forward design of arbitrary devices in synthetic biology

Quantitative Analysis of Robustness in Systems Biology:Combining Global and Local Approaches

To characterize the behavior and robustness of cellular circuits is a major challenge for Systems Biology. Many of the published methods that address this question quantify the local robustness of the models. In this thesis, I tried to underpin the inappropriateness of such local measures and proposed an alternative solution: a glocal measure for robustness that combines both global and local aspects. It comprises a broad exploration of the parameter space and a further refinement based on different local measures. The method is general and such glocal analysis could be applied to many problems. Along with the theoretical and formal aspects of this new analysis method, I developed sampling algorithms that efficiently investigate the generally high-dimensional parameter space of models. To show the usefulness of my method, I applied it on different models of cyclic systems such as the circadian clock and the mitotic cycle. I first considered two models of the cyanobacterial circadian clock and compared their robustness properties. Also in the context of circadian rhythms, I studied the effect of additional feedback loops on the robustness properties in relation with entrainment. Models of the mitotic cycle are also used to assess the effect of an additional positive feedback loop on circuit robustness to parameter changes and molecular noise. Finally, I established some principles for the design of a synthetic circuit based on robustness. The thesis carries on with a discussion that emphasizes the advantages of the glocal method for robustness analysis: in all works, correlations between parameter values and local robustness can be found. Such results facilitate our understanding of the biochemical systems and can be a guide for new experiments to discriminate models or give directions for altering the robustness of the systems. I conclude by discussing potential applications for my method and possible improvements

Компьютерная психодиагностика

Substantiated the thesis that computer psychodiagnosis formed in independent field of research, with the aim of creating a psychodiagnostic tools for the development of methods to work with experimental psychological information

Microwave spectroscopy on heavy-fermion systems: probing the dynamics of charges and magnetic moments

Investigating solids with light gives direct access to charge dynamics, electronic and magnetic excitations. For heavy fermions, one has to adjust the frequency of the probing light to the small characteristic energy scales, leading to spectroscopy with microwaves. We review general concepts of the frequency-dependent conductivity of heavy fermions, including the slow Drude relaxation and the transition to a superconducting state, which we also demonstrate with experimental data taken on UPd2Al3. We discuss the optical response of a Fermi liquid and how it might be observed in heavy fermions. Microwave studies with focus on quantum criticality in heavy fermions concern the charge response, but also the magnetic moments can be addressed via electron spin resonance (ESR). We discuss the case of YbRh2Si2, the open questions concerning ESR of heavy fermions, and how these might be addressed in the future. This includes an overview of the presently available experimental techniques for microwave studies on heavy fermions, with a focus on broadband studies using the Corbino approach and on planar superconducting resonators.Comment: 11 pages, 6 figures, proceedings of QCnP 201

Efficient characterization of high-dimensional parameter spaces for systems biology

BACKGROUND: A biological system's robustness to mutations and its evolution are influenced by the structure of its viable space, the region of its space of biochemical parameters where it can exert its function. In systems with a large number of biochemical parameters, viable regions with potentially complex geometries fill a tiny fraction of the whole parameter space. This hampers explorations of the viable space based on "brute force" or Gaussian sampling. RESULTS: We here propose a novel algorithm to characterize viable spaces efficiently. The algorithm combines global and local explorations of a parameter space. The global exploration involves an out-of-equilibrium adaptive Metropolis Monte Carlo method aimed at identifying poorly connected viable regions. The local exploration then samples these regions in detail by a method we call multiple ellipsoid-based sampling. Our algorithm explores efficiently nonconvex and poorly connected viable regions of different test-problems. Most importantly, its computational effort scales linearly with the number of dimensions, in contrast to "brute force" sampling that shows an exponential dependence on the number of dimensions. We also apply this algorithm to a simplified model of a biochemical oscillator with positive and negative feedback loops. A detailed characterization of the model's viable space captures well known structural properties of circadian oscillators. Concretely, we find that model topologies with an essential negative feedback loop and a nonessential positive feedback loop provide the most robust fixed period oscillations. Moreover, the connectedness of the model's viable space suggests that biochemical oscillators with varying topologies can evolve from one another. CONCLUSIONS: Our algorithm permits an efficient analysis of high-dimensional, nonconvex, and poorly connected viable spaces characteristic of complex biological circuitry. It allows a systematic use of robustness as a tool for model discrimination

Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.National Institutes of Health (U.S.) (NIH NIGMS grant R01GM086881)National Science Foundation (U.S.) (NSF Award #1001092)National Science Foundation (U.S.) (NSF Graduate Research Fellowship Program)Swiss National Science Foundation (SystemsX.ch grant

Разработка микроконтроллерной системы удаленного контроля переключающего устройства без возбуждения высоковольтного трансформатора

Представлена разработка микроконтроллерной системы удаленного контроля переключающего устройства без возбуждения высоковольтного трансформатора. Система обеспечивает мониторинг состояний устройства переключения в реальном времени, с защитой передаваемого сигнала от помех.The development of a microcontroller system for remote control of a switching device without excitation of a high-voltage transformer is presented. The system monitors the state of the switching device in real time, with protection of the transmitted signal from interference

Analysis of growth factor signaling in genetically diverse breast cancer lines

Background: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. Results: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. Conclusions: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/