Oscillatory and anti-oscillatory motifs in genetic regulatory networks

Recently,self-sustained oscillatory genetic regulatory networks(GRNs) have attracted significant attention in the biological field.Given a GRN,it is important to anticipate whether the network could generate oscillation with proper parameters,and what the key ingredients for the oscillation are.In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered.In particular,some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed.Moreover,there are some anti-oscillatory motifs which have a strong oscillation repressing effect.Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.     

Effect of alkyl chain length in protic ionic liquids: an AIMD perspective

In this study we have explored, by means of ab initio molecular dynamics, a subset of three different protic ionic liquids (ILs). We present both structural and dynamical information of the liquid state of these compounds as revealed by accurate ab initio computations of the interactions. Our analysis figures out the presence of a strong hydrogen bond network in the bulk state, that is more stable in those ILs characterised by a longer alkyl side chain. Indeed it becomes more long-lasting passing from ethyl ammonium to butyl ammonium, owing to the hydrophobic effects stemming from alkyl chain contacts. Furthermore, the relative free energy landscape of the cation–anion interaction exhibits a progressively deeper well as the side chain of the cation gets longer. The hydrogen bond interaction, as already mentioned in previous works, leads to loss of degeneracy of the asymmetric stretching vibrations of the nitrate anions. The resulting frequency splitting between the two normal modes is about 90 cm^?1.     

量子信令交换机模型设计及性能分析

本文提出了量子信令交换机的模型, 该交换机由经典信息控制模块、交换控制模块和量子交换模块三部分组成. 经典控制模块负责将纠缠初态信息传送给纠缠测量及交换单元并更新路由信息. 交换控制模块实现通路选择, 为纠缠对的分发提供通路. 量子交换模块制备纠缠对, 进行Bell态的测量, 完成纠缠交换. 量子信令交换机可以实现多用户间的信令传输及局域网通信. 通过对交换机的性能分析与仿真, 结果表明该交换机结构简单、安全保密、便于扩展、时延小, 对于构建量子通信网有很好的支撑作用. 关键词： 量子通信 量子信令网 量子信令交换机 纠缠交换     

Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent “motifs”, that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.     

Coarse-grain reconstruction of genetic networks from expression levels

In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at a coarse-grain level from high throughput gene expression data. The method provides the a posteriori probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to choose those genes that will be perturbed in subsequent experiments in order to refine our knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by in numero experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as recovering a small subnetwork of interest by performing perturbation over selected genes.     

Physical limits on computation by assemblies of allosteric proteins

Assemblies of allosteric proteins are the principle information processing devices in biology. Using the Ca2+-sensitive cardiac regulatory assembly as a paradigm for Brownian computation, I examine how system complexity and system resetting impose physical limits on computation. Nearest-neighbor-limited interactions among assembly components constrain the topology of the system's macrostate free energy landscape and produce degenerate transition probabilities. As a result, signaling fidelity and deactivation kinetics cannot be simultaneously optimized. This imposes an upper limit on the rate of information processing by assemblies of allosteric proteins that couple to a single ligand type.     

State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates

This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions. The main purpose of the problem addressed is to design a linear estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, the desired state estimator, that can ensure the estimation error dynamics to be globally robustly asymptotically stable in the mean square, exists. The obtained LMI conditions are dependent on both the lower and the upper bounds of the delays. An illustrative example is presented to demonstrate the feasibility of the proposed estimation schemes.     

光互连网络中四功能交换开关研究

由节点开关单元按照一定的连接规则所构成的光互连网络,在全光通信和光信息处理中具有重要的应用。基于节点开关一般实现光信号的直通和交换功能的特点,利用成熟的偏振光控制技术,通过位相型空间光调制器对信号光偏振态的控制,完成信号的路由和开关动作,从而设计出具有直通、交换、上播和下播功能的节点交换开关。与传统设计思想相比,该方法不仅能够充分利用信号光的能量,有效的减少光能量的损耗,而且还具有与偏振无关等特点。该四功能交换开关,对于优化和拓展光互连网络的结构和功能具有一定的意义。     

Interface free energy and critical line for the ising model with nearest and next-nearest-neighbor interactions

A method due to Müller-Hartmann and Zittartz is used to calculate the free energy of a diagonal interface in an Ising model on a square lattice with interactions between nearest and next-nearest neighbors. Instead of solving the full bulk problem, one only takes into account a simple subset of interface configurations. The subset, which is somewhat different from that considered by Müller-Hartmann and Zittartz, is chosen in such a way that exact results for the interface free energy are reproduced if the system has only nearest or only next-nearest-neighbor interactions. When the critical line of ferromagnetic transitions is obtained by demanding that the interface free energy as a function of both couplings vanish, a surprisingly accurate approximation is obtained.     

基于互联网+的高校能源管理信息系统设计与开发

为了实现高校能源管理的信息共享,提高对高校能源信息管理的智能水平,提出一种基于互联网+的高校能源管理信息系统开发设计方案。系统采用感知层、网络层和应用层的三层结构,采用RFID、 条形码、蓝牙、红外等数据信息感知技术进行高校能源信息的原始采集,在网络层通过ZigBee 和无线通信技术进行信息融合传输,在控制设备中导入原始数据,在互联网+环境下建立数据处理中心,根据能源管理系统的现实需求进行信息融合和数据存储管理,设计嵌入式控制器对串口、并口、USB端口、以太网口及GPIB接口进行集成控制,在Linux内核下实现高校能源管理信息系统应用程序开发。系统测试结果表明,采用该系统进行高校能源管理,具有较好的信息存储、信息调度和信息检索能力。     

Coevolution of information processing and topology in hierarchical adaptive random Boolean networks

Random Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) – subnetworks – connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.     

Visualization of distance distribution from pulsed double electron-electron resonance data

Double electron-electron resonance (DEER), also known as pulsed electron-electron double resonance (PELDOR), is a time-domain electron paramagnetic resonance method that can measure the weak dipole-dipole interactions between unpaired electrons. DEER has been applied to discrete pairs of free radicals in biological macromolecules and to clusters containing small numbers of free radicals in polymers and irradiated materials. The goal of such work is to determine the distance or distribution of distances between radicals, which is an underdetermined problem. That is, the spectrum of dipolar interactions can be readily calculated for any distribution of free radicals, but there are many, quite different distributions of radicals that could produce the same experimental dipolar spectrum. This paper describes two methods that are useful for approximating the distance distributions for the large subset of cases in which the mutual orientations of the free radicals are uncorrelated and the width of the distribution is more than a few percent of its mean. The first method relies on a coordinate transformation and is parameter-free, while the second is based on iterative least-squares with Tikhonov regularization. Both methods are useful in DEER studies of spin-labeled biomolecules containing more than two labels.     

基于光量子态避错及容错传输的量子通信

量子通信以量子态为信息载体在远距离的通信各方之间传递信息,因此量子态的传输和远距离共享是量子通信的首要步骤.信道噪声不仅会影响通信效率还可能被窃听者利用从而威胁通信安全,对抗信道噪声是实现安全高效量子通信亟需解决的问题.本文介绍基于光量子态的两类对抗信道噪声的实用方法——量子态的避错传输和容错的量子通信,包括对抗噪声的基本原理和两种方法的代表性方案,并从资源消耗和可操作性的角度分析了方案的实用价值.     

Self-sustained oscillations of complex genomic regulatory networks

Recently, self-sustained oscillations in complex networks consisting of non-oscillatory nodes have attracted great interest in diverse natural and social fields. Oscillatory genomic regulatory networks are one of the most typical examples of this kind. Given an oscillatory genomic network, it is important to reveal the central structure generating the oscillation. However, if the network consists of large numbers of genes and interactions, the oscillation generator is deeply hidden in the complicated interactions. We apply the dominant phase-advanced driving path method proposed in Qian et al. (2010) [1] to reduce complex genomic regulatory networks to one-dimensional and unidirectionally linked network graphs where negative regulatory loops are explored to play as the central generators of the oscillations, and oscillation propagation pathways in the complex networks are clearly shown by tree branches radiating from the loops. Based on the above understanding we can control oscillations of genomic networks with high efficiency.     

Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms

Most common pathologies in humans are not caused by the mutation of a single gene, rather they are complex diseases that arise due to the dynamic interaction of many genes and environmental factors. This plethora of interacting genes generates a complexity landscape that masks the real effects associated with the disease. To construct dynamic maps of gene interactions (also called genetic regulatory networks) we need to understand the interplay between thousands of genes. Several issues arise in the analysis of experimental data related to gene function: on the one hand, the nature of measurement processes generates highly noisy signals; on the other hand, there are far more variables involved (number of genes and interactions among them) than experimental samples. Another source of complexity is the highly nonlinear character of the underlying biochemical dynamics. To overcome some of these limitations, we generated an optimized method based on the implementation of a Maximum Entropy Formalism (MaxEnt) to deconvolute a genetic regulatory network based on the most probable meta-distribution of gene-gene interactions. We tested the methodology using experimental data for Papillary Thyroid Cancer (PTC) and Thyroid Goiter tissue samples. The optimal MaxEnt regulatory network was obtained from a pool of 25,593,993 different probability distributions. The group of observed interactions was validated by several (mostly in silico) means and sources. For the associated Papillary Thyroid Cancer Gene Regulatory Network (PTC-GRN) the majority of the nodes (genes) have very few links (interactions) whereas a small number of nodes are highly connected. PTC-GRN is also characterized by high clustering coefficients and network heterogeneity. These properties have been recognized as characteristic of topological robustness, and they have been largely described in relation to biological networks. A number of biological validity outcomes are discussed with regard to both the inferred model and the PTC.