Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein?protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a `tearing-and-zooming? approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between statevariables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits.     

Proximity of intracellular regulatory networks to monotone systems

Networks that contain only sign-consistent loops, such as positive feedforward and feedback loops, function as monotone systems. Simulated using differential equations, monotone systems display well-ordered behaviour that excludes the possibility for chaotic dynamics. Perturbations of such systems have unambiguous global effects and a predictability characteristic that confers robustness and adaptability. The authors assess whether the topology of biological regulatory networks is similar to the topology of monotone systems. For this, three intracellular regulatory networks are analysed where links are specified for the directionality and the effects of interactions. These networks were assembled from functional studies in the experimental literature. It is found that the three biological networks contain far more positive 'sign-consistent' feedback and feedforward loops than negative loops. Negative loops can be 'eliminated' from the real networks by the removal of fewer links as compared with the corresponding shuffled networks. The abundance of positive feedforward and feedback loops in real networks emerges from the presence of hubs that are enriched with either negative or positive links. These observations suggest that intracellular regulatory networks are 'close-to-monotone', a characteristic that could contribute to the dynamical stability observed in cellular behaviour.     

Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root‐locus method

Most of the biological systems including gene regulatory networks can be described well by ordinary differential equation models with rational non‐linearities. These models are derived either based on the reaction kinetics or by curve fitting to experimental data. This study demonstrates the applicability of the root‐locus‐based bifurcation analysis method for studying the complex dynamics of such models. The effectiveness of the bifurcation analysis in determining the exact parameter regions in each of which the system shows a certain dynamical behaviour, such as bistability, oscillation, and asymptotically equilibrium dynamics is shown by considering two mostly studied gene regulatory networks, namely Gardner''s genetic toggle switch and p53 gene network possessing two‐phase (mono‐stable/oscillation) dynamics.Inspec keywords: oscillations, curve fitting, differential equations, bifurcation, genetics, nonlinear dynamical systemsOther keywords: nonlinearities, reaction kinetics, root‐locus‐based bifurcation analysis method, complex dynamics, exact parameter regions, dynamical behaviour, equilibrium dynamics, studied gene regulatory networks, p53 gene network, bistable dynamics, oscillatory dynamics, biological networks, root‐locus method, biological systems, ordinary differential equation models     

SMILE: a novel procedure for subcellular module identification with localisation expansion

Computational clustering methods help identify functional modules in protein–protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global PPI networks in both the compartmentalised PPI and InWeb_InBioMap datasets. The authors’ results reveal that subcellular localisation is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.Inspec keywords: cellular biophysics, proteins, molecular biophysicsOther keywords: subcellular module identification, localisation expansion, computational clustering methods, protein‐protein interaction network, biological functions, protein interaction structure, protein subcellular localisation, subcellular modules, InWeb‐InBioMap datasets, subcellular localisation     

A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks With Wavelet Transform as a Preprocessor

We have developed a modular analog circuit fault- diagnostic system based on neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. Our proposed system has the ability to identify faulty components or modules in an analog circuit by analyzing its impulse response. In this approach, the circuit is divided into modules, which, in turn, are divided into smaller submodules successively. At each level, where a module is divided into submodules, a neural network is trained to identify the submodule that inherits the fault of interest from the parent module. This procedure finds the faulty component or module of any desirable size in an analog circuit by consecutive divisions of modules as many times as necessary. Our proposed approach has three advantages over the traditional neural-network-based diagnostic systems, which directly look for faulty components in the entire circuit. First, the performance of the modular systems is reliable and robust independent of the circuit size and can successfully classify similar fault classes with a significant overlap in the feature space where the traditional approach completely fails. Second, the modular approach requires significantly smaller neural network architectures, leading to much more efficient training. Third, for large real circuit boards, our diagnostic system proceeds to systematically reduce the size of the faulty modules until it is feasible to replace it.     

Measuring the evolutionary stability of software systems: case studies of Linux and FreeBSD

In software evolution, stability is defined as the ability of a module to remain largely unchanged when faced with newer requirements and/or changes in the environment. Although stability is an important long-term design characteristic for hardware systems, it has not been studied deeply for software systems. Stability is directly related to software evolvability and maintainability; and it affects the software evolution process. A model based on software version differences is presented to measure the evolutionary stability of software modules. This model represents and normalises two distances: the source code and the structure distances, between two versions of an evolving software module. As a case study based on this model, the evolutionary stability of Linux and FreeBSD modules is compared. The results of the study shows that the evolutionary stability of a software module in Linux and FreeBSD depends more on its function type and less on the system environment.     

Agent-based fractal architecture and modelling for developing distributed manufacturing systems

For their timely response to the rapidly changing manufacturing environment and markets, future manufacturing systems must be flexible, adaptable, and reusable. Recently, bionic (or biological), holonic, and fractal manufacturing systems (FrMS) have been discussed as potential candidates for the next generation of manufacturing systems. This study focuses on the FrMS, which is based on the concept of autonomous cooperating agents referred to as fractals. The major component of the FrMS is a basic fractal unit (BFU). It consists of five functional modules: observing module (observer), analysing module (analyser), resolving and executing module (resolver), organizing module (organizer), and reporting module (reporter). Although the FrMS has many conceptual advantages, the implementation of the system has been known to be difficult. This paper is a preliminary study of the basic components and the architecture with an eye toward the future implementation of FrMS. In order to describe the characteristics of a fractal, this paper presents several models including function models using IDEF0, working models using Petri-net, and static/dynamic models using the unified modelling language (UML).     

Large-scale molecular dynamics simulation of DNA: implementation and validation of the AMBER98 force field in LAMMPS

Molecular modelling played a central role in the discovery of the structure of DNA by Watson and Crick. Today, such modelling is done on computers: the more powerful these computers are, the more detailed and extensive can be the study of the dynamics of such biological macromolecules. To fully harness the power of modern massively parallel computers, however, we need to develop and deploy algorithms which can exploit the structure of such hardware. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a scalable molecular dynamics code including long-range Coulomb interactions, which has been specifically designed to function efficiently on parallel platforms. Here we describe the implementation of the AMBER98 force field in LAMMPS and its validation for molecular dynamics investigations of DNA structure and flexibility against the benchmark of results obtained with the long-established code AMBER6 (Assisted Model Building with Energy Refinement, version 6). Extended molecular dynamics simulations on the hydrated DNA dodecamer d(CTTTTGCAAAAG)(2), which has previously been the subject of extensive dynamical analysis using AMBER6, show that it is possible to obtain excellent agreement in terms of static, dynamic and thermodynamic parameters between AMBER6 and LAMMPS. In comparison with AMBER6, LAMMPS shows greatly improved scalability in massively parallel environments, opening up the possibility of efficient simulations of order-of-magnitude larger systems and/or for order-of-magnitude greater simulation times.     

高土石坝工程安全评价与预警信息管理系统

基于现场监测数据及先进的数值分析方法,建立土石坝全生命周期的工程安全评价及预警信息系统,是目前土石坝发展的必然趋势。以糯扎渡高心墙堆石坝为依托,开发了理论严密、方法先进且可靠实用的大坝工程安全评价与预警信息管理系统。该系统由系统管理模块、安全指标模块、监测数据与工程信息模块、数值计算模块、反演分析模块、安全预警与应急预案模块和数据库及管理模块共7个模块构成。其中,数值计算和反演分析模块可利用所取得的坝体监测数据,进行渗流、大坝应力变形、坝体裂缝、地震动力反应和坝坡稳定等土石坝关键计算分析,是本系统的核心部分。利用工程前期已经取得的坝体变形监测数据,对坝体进行了变形反演分析并对大坝关键时间结点的形态进行了预测分析。     

The diminishing role of hubs in dynamical processes on complex networks

It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here, we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics when compared with intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein–protein interaction network and a neural network, suggesting that it might indeed be a widespread property in nature.     

Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics

Linearized catalytic reaction equations (modelling, for example, the dynamics of genetic regulatory networks), under the constraint that expression levels, i.e. molecular concentrations of nucleic material, are positive, exhibit non-trivial dynamical properties, which depend on the average connectivity of the reaction network. In these systems, an inflation of the edge of chaos and multi-stability have been demonstrated to exist. The positivity constraint introduces a nonlinearity, which makes chaotic dynamics possible. Despite the simplicity of such minimally nonlinear systems, their basic properties allow us to understand the fundamental dynamical properties of complex biological reaction networks. We analyse the Lyapunov spectrum, determine the probability of finding stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network, and study how the frequency distributions of oscillatory modes of such a system depend on the average connectivity.     

基于FPGA和无线通信的冲击波超压采集系统设计

冲击波超压测试技术发展迅速.由于冲击波超压测试现场环境比较恶劣,为了提高测试系统的灵活性和可靠性,提出了一种基于FPGA和无线通信的冲击波超压采集系统的设计,从冲击波超压信号采集的发展现状出发,具体阐述了该系统总体方案的设计思路以及关键技术(电源管理优化设计、ZIGBEE模块和光纤模块等),同时分析了采用FPGA、无线...     

Integrated and concurrent approach for compound sheet metal cutting and punching

In sheet metal processing and manufacturing, there are a lot of small- and medium-sized job shops. These small- and medium-sized manufacturing companies have been facing keen competitive pressure in the market. This pressure has forced these companies to make every effort to shorten product development lead-time, improve production efficiency, approach high-quality standards, but at the same time cut down the costs. To meet the needs of these companies, this paper presents a compound cutting and punching production method supported by an integrated CAD/CAPP/CAM system in sheet metal manufacturing. Many existing commercial CAD/CAM systems are not suitable for this manufacturing method, especially under concurrent and global design and manufacturing environments. Some problems have to be solved before these CAD/CAM systems can be employed and integrated for this compound manufacturing method. This paper deals mainly with the solutions to solve some of these problems. The solutions include an integrated data integration platform based on Pro/INTRALINK and STEP, and a knowledge-based real time CAPP (RTCAPP) system for compound sheet metal cutting and punching. Within the presented CAD/CAPP/CAM system, some key modules have been developed. They are the automatic tool selection and manufacturing sequencing module, a shortest tool path optimization module, a cost estimation module and an automatic insertion of auxiliary path module based on knowledge bases. These modules will be addressed here.     

Analysis of external dynamic loads influence to photovoltaic module structural performance

Efficiency of modern photovoltaic (PV) systems decreases significantly when the crystalline structure of PV modules is damaged due to climatic factors, such as wind and mechanically similar dynamic effects. General certifications of PV modules consist of only static tests (according to Photovoltaic standard IEC 61215 and IEC 61646), however in reality, PV modules are operating in a dynamic environment. The purpose of the article is to show that for the certification of PV modules the dynamic characteristics of PV modules must be accounted also. Nowadays PV modules are used in different dynamic objects like cars, boats etc., where dominant loads are of dynamic environment. This paper presents theoretic and experimental studies. For the investigation of dynamic loads acting on PV modules, a testing stand has been designed. PV modules were loaded with cyclic dynamic loads. During the experiment, the PV modules were loaded with external excitation, the excitation amplitude is not exceeding more than 7 mm. During the experiment, the PV modules were excited, in the frequency range of 0 to 40 Hz and the sweep generating mode was used. The aim of this excitation to simulate different weather conditions. Experimental and theoretical results showed the reaction of PV modules in different weather conditions (which means that the effect of different wind speeds is evaluated). The proposed assessment methodology can be applied successfully when designing PV modules and accounting for mechanical dynamic effects.In conclusion, it is not accurate and appropriate to evaluate the safety and stability of PV modules just through the existing static analysis in IEC 61215. The dynamic effects of the loading on PV module also need to be paid attention to. The attention needs to be paid to the dynamic effects of the loading on PV module.     

柳钢中板双机架轧机Level Ⅱ控制系统开发

针对柳钢2 800 mm 双机架中厚板轧机,自主设计开发了完整的Level Ⅱ计算机控制系统。Level Ⅱ系统由数据通讯模块、模型设定模块、模型自学习模块、轧件跟踪模块、数据管理模块等多个模块组成。数据通讯模块负责Level Ⅱ控制系统和其他系统之间的数据通讯;数据管理模块包括数据采集与数据记录,为模型计算查询模型参数,记录每块轧件的实际工艺过程参数等;轧件跟踪模块负责轧件从出炉到轧制结束全过程位置和数据的跟踪,并在不同的触发点调用其他功能模块,进行自动轧钢的轧件调度控制和轧制节奏控制;模型设定模块利用数学模型,计算轧制规程;模型自学习模块包括长期自学习和短期自学习。通过各功能模块的协作,可以实现轧机生产过程的模型自动设定和全自动轧钢控制。该系统已经成功应用于生产现场,且应用效果良好。     

An information theoretical analysis of kinase activated phosphorylation dephosphorylation cycle

Signal transduction, the information processing mechanism in biological cells, is carried out by a network of biochemical reactions. The dynamics of driven biochemical reactions can be studied in terms of nonequilibrium statistical physics. Such systems may also be studied in terms of Shannon's information theory. We combine these two perspectives in this study of the basic units (modules) of cellular signaling: the phosphorylation dephosphorylation cycle (PdPC) and the guanosine triphosphatase (GTPase). We show that the channel capacity is zero if and only if the free energy expenditure of biochemical system is zero. In fact, a positive correlation between the channel capacity and free energy expenditure is observed. In terms of the information theory, a linear signaling cascade consisting of multiple steps of PdPC can function as a distributed "multistage code." With increasing number of steps in the cascade, the system trades channel capacity with the code complexity. Our analysis shows that while a static code can be molecular structure based, a biochemical communication channel has to have energy expenditure.     

Overlapping functional modules detection in PPI network with pair‐wise constrained non‐negative matrix tri‐factorisation

A large amount of available protein–protein interaction (PPI) data has been generated by high‐throughput experimental techniques. Uncovering functional modules from PPI networks will help us better understand the underlying mechanisms of cellular functions. Numerous computational algorithms have been designed to identify functional modules automatically in the past decades. However, most community detection methods (non‐overlapping or overlapping types) are unsupervised models, which cannot incorporate the well‐known protein complexes as a priori. The authors propose a novel semi‐supervised model named pairwise constrains nonnegative matrix tri‐factorisation (PCNMTF), which takes full advantage of the well‐known protein complexes to find overlapping functional modules based on protein module indicator matrix and module correlation matrix simultaneously from PPI networks. PCNMTF determinately models and learns the mixed module memberships of each protein by considering the correlation among modules simultaneously based on the non‐negative matrix tri‐factorisation. The experiment results on both synthetic and real‐world biological networks demonstrate that PCNMTF gains more precise functional modules than that of state‐of‐the‐art methods.Inspec keywords: proteins, molecular biophysics, cellular biophysics, matrix algebraOther keywords: overlapping functional module detection, PPI network, pair‐wise constrained nonnegative matrix trifactorisation, protein–protein interaction data, cellular functions, protein complexes, real‐world biological networks, synthetic biological networks     

Hybrid discrete-time neural networks

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.