Information processing by biochemical networks: a dynamic approach

Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on informational grounds. In the context of signalling networks with multiple inputs, the approach identifies the routes and species involved in sequential information processing between input and output modules. An algorithm is developed which allows automated identification of decompositions for large networks and visualization using a tree that encodes the conditional independences. Only stoichiometric information is used and neither simulations nor knowledge of rate parameters are required. A bespoke version of the algorithm for signalling networks identifies the routes of sequential encoding between inputs and outputs, visualized as paths in the tree. Application to the toll-like receptor signalling network reveals that inputs can be informative in ways unanticipated by steady-state analyses, that the information processing structure is not well described as a bow tie, and that encoding for the interferon response is unusually sparse compared with other outputs of this innate immune system.     

Quasi-robust control of biochemical reaction networks via stochastic morphing

One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments, such as living or synthetic cells, controllers have to be calibrated to factor in the intrinsic noise. In this context, biochemical controllers put forward in the literature have focused on manipulating the mean (first moment) and reducing the variance (second moment) of the target molecular species. However, many critical biochemical processes are realized via higher-order moments, particularly the number and configuration of the probability distribution modes (maxima). To bridge the gap, we put forward the stochastic morpher controller that can, under suitable timescale separations, morph the probability distribution of the target molecular species into a predefined form. The morphing can be performed at a lower-resolution, allowing one to achieve desired multi-modality/multi-stability, and at a higher-resolution, allowing one to achieve arbitrary probability distributions. Properties of the controller, such as robustness and convergence, are rigorously established, and demonstrated on various examples. Also proposed is a blueprint for an experimental implementation of stochastic morpher.     

Influencing dynamics on social networks without knowledge of network microstructure

Social network-based information campaigns can be used for promoting beneficial health behaviours and mitigating polarization (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier to obtain information about individuals’ attributes (e.g. age, income), which are jointly informative of an individual’s opinions and their social network position. We investigate strategies for influencing the system state in a statistical mechanics based model of opinion formation. Using synthetic and data-based examples we illustrate the advantages of implementing coarse-grained influence strategies on Ising models with modular structure in the presence of external fields. Our work provides a scalable methodology for influencing Ising systems on large graphs and the first exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting the observation that strong ambient fields can simplify control of networked dynamics, our findings open the possibility of efficiently computing and implementing public information campaigns using insights from social network theory without costly or invasive levels of data collection.     

Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter

Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equations by substituting the higher-order moments with Monte Carlo-based estimates from a small number of simulations, and using an extended Kalman filter to counteract Monte Carlo noise. Our algorithm provides more accurate and robust results compared to traditional Monte Carlo and moment closure techniques, and we expect that it will be widely useful for the quantification of uncertainty in biochemical model predictions.     

Coarse-grained molecular dynamics simulations of ionic polymer networks

The stress-strain behavior of cross-linked polymeric networks was investigated using molecular dynamics simulations with a coarse-grained representation of the repeating units. The network structure was formed by dynamically cross-linking the reactants placed between two rigid layers comprised of particles of the same type. We studied two types of networks which differ only by one containing ionic pairs that amount to 7% of the total number of bonds present. The stress-strain curves were obtained after imposing deformation in tensile and shear modes to the networks and measuring their stress response. Under both forms of deformations there was improvement in the level of stress that the material could bear. Moreover, the time dependent behavior of the improvement in mechanical properties signified a self-healing mechanism.     

Autonomous Boolean modelling of developmental gene regulatory networks

During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development.     

Quantifying the reliability of dispersal paths in connectivity networks

Many biological systems, from fragmented landscapes to host populations, can be represented as networks of connected habitat patches. Links between patches in these connectivity networks can represent equally diverse processes, from individuals moving through the landscape to pathogen transmissions or successive colonization events in metapopulations. Any of these processes can be characterized as stochastic, with functional links among patches that exist with various levels of certainty. This stochasticity then needs to be reflected in the algorithms that aim to predict the dispersal routes in these networks. Here we adapt the concept of reliability to characterize the likelihood that a specific path will be used for dispersal in a probabilistic connectivity network. The most reliable of the paths that connect two patches will then identify the most likely sequence of intermediate steps between these patches. Path reliability will be sensitive to targeted disruptions of individual links that form the path, and this can then be used to plan the interventions aimed at either preserving or disrupting the dispersal along that path. The proposed approach is general, and can be used to identify the most likely dispersal routes in various contexts, such as predicting patterns of migrations, colonizations, invasions and epidemics.     

Steady-state invariant genetics: probing the role of morphogen gradient dynamics in developmental patterning

Morphogen-mediated patterning is the predominant mechanism by which positional information is established during animal development. In the classical view, the interpretation of positional signals depends on the equilibrium distribution of a morphogen, regardless of the dynamics of gradient formation. The problem of whether or not morphogen dynamics contribute to developmental patterning has not been explored in detail, partly because genetic experiments, which selectively affect signalling dynamics while maintaining unchanged the steady-state morphogen profile, are difficult to design and interpret. Here, I present a modelling-based approach to identify genetic mutations in developmental patterning that may affect the transient, but leave invariant the steady-state signalling gradient. As a case study, this approach is used to explore the dynamic properties of Hedgehog (Hh) signalling in the developing wing of the fruitfly, Drosophila melanogaster. This analysis provides insights into how different properties of the Hh gradient dynamics, such as the duration of exposure to the signal or the maximum width of the transient gradient, can be genetically perturbed without affecting the steady-state distribution of the Hh concentration profile. I propose that this method can be used as an experimental design tool to investigate the role of transient morphogen gradients in developmental patterning and discuss the generality of these ideas in other problems.     

Exploring Collective Dynamics in Communication Networks

A communication network, such as the Internet, comprises a complex system where cooperative phenomena may emerge from interactions among various traffic flows generated and forwarded by individual nodes. To identify and understand such phenomena, we model a network as a two-dimensional cellular automaton. We suspect such models can promote better understanding of the spatial-temporal evolution of network congestion, and other emergent phenomena in communication networks. To search the behavior space of the model, we study dynamic patterns arising from interactions among traffic flows routed across shared network nodes, as we employ various configurations of parameters and two different congestion-control algorithms. In this paper, we characterize correlation in congestion behavior within the model at different system sizes and time granularities. As expected, we find that long-range dependence (LRD) appears at some time granularities, and that for a given network size LRD decays as time granularity increases. As network size increases, we find that long-range dependence exists at larger time scales. To distinguish effects due to network size from effects due to collective phenomena, we compare congestion behavior within networks of selected sizes to congestion behavior within comparably sized sub-areas in a larger network. We find stronger long-range dependence for sub-areas within the larger network. This suggests the importance of modeling networks of sufficiently large size when studying the effects of collective dynamics.     

How the online social networks are used: dialogues-based structure of MySpace

Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as user''s psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipf''s distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting ‘weak-ties’ hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.     

On computing the reliability of opportunistic multihop networks with Mobile relays

Opportunistic multihop networks with mobile relays recently have drawn much attention from researchers across the globe due to their wide applications in various challenging environments. However, because of their peculiar intrinsic features like lack of continuous connectivity, network partitioning, highly dynamic behavior, and long delays, it is very arduous to model and effectively capture the temporal variations of such networks with the help of classical graph models. In this work, we utilize an evolving graph to model the dynamic network and propose a matrix‐based algorithm to generate all minimal path sets between every node pair of such network. We show that these time‐stamped‐minimal‐path sets (TS‐MPS) between each given source‐destination node pair can be used, by utilizing the well‐known Sum‐of‐Disjoint Products technique, to generate various reliability metrics of dynamic networks, ie, two‐terminal reliability of dynamic network and its related metrics, ie, two‐terminal reliabilities of the foremost, shortest, and fastest TS‐MPS, and Expected Hop Count. We also introduce and compute a new network performance metric?Expected Slot Count. We use two illustrative examples of dynamic networks, one of four nodes, and the other of five nodes, to show the salient features of our technique to generate TS‐MPS and reliability metrics.     

用OBDD算法评估无线传感网的可靠度和结点重要性

基于有序二叉判定图(OBDD),提出用结点扩张(NE)算法来评估无线传感网的可靠度和结点重要性.NE算法执行结点扩张操作来处理不可靠结点,从两方面增强了计算效率:利用OBDD结构表示网络状态,减少了大量冗余的等价状态;利用Hash表存储同构子网的OBDD,减少了同构子网的重复计算.另外,该算法对结点重要性进行了评估,为脆弱结点的保护提供参考.实验结果表明NE算法的计算开销比传统的factoring算法低,能有效评估无线传感网的可靠度.     

基于随机网络演算的无线网络TCP性能上界分析

为了准确地分析、判断无线网络传输控制协议(TCP)性能上界,以提高无线网络在数据传输上的服务质量(QoS),在综合分析无线网络特性的基础上,运用马尔科夫理论对无线网络TCP数据流的随机延迟上界、随机延迟抖动上界以及随机吞吐量理论上界进行了建模,并提出了一种基于随机网络演算的无线网络TCP性能上界的模型。该模型可以简化无线网络流量复杂性分析,而且能够给出网络数据流到达曲线、服务曲线、延迟上界、延迟抖动上界及吞吐量上界。研究表明,用这一模型,可以准确地预测无线网络TCP性能边界情况,这对进行无线网络TCP性能分析有重要意义。     

A numerical study of the nonlinear dynamics of multilayer spirally orthotropic cylinders

Based on the Wilkins two-dimensional algorithm, a method is developed for the numerical study of the geometrically and physically nonlinear axially symmetrical dynamic stress-strain state of multilayer thick-walled cylindrical elastoplastic shells with different spiral reinforcement structures. A numerical study of the specific features in the nonlinear dynamic behavior of single-and two-layer cylinders is carried out for different reinforcement configurations and loading amplitudes. Translated from Problemy Prochnosti, No. 6, pp. 110–121, November–December, 2008.     

材料摩擦磨损分子动力学模拟的研究进展

随着新技术的发展以及材料服役环境的日益复杂化,传统的试验研究已经不能满足人们对摩擦磨损的认识需求,因此必须借助数值模拟方法来研究材料的摩擦磨损行为.特别是随着近年来原子尺度理论模型的不断完善和计算机运算能力的不断提高,分子动力学模拟已经成为研究材料摩擦磨损行为和机制的重要方法.本文详细综述了材料摩擦磨损分子动力学模拟的国内外研究现状.首先阐述了分子动力学模拟中势能函数的建立;其次介绍了材料摩擦磨损分子动力学模拟常用的接触模型;然后概述了采用分子动力学模拟方法研究接触面积、载荷、温度、速度和晶体取向等因素对材料摩擦磨损的影响;最后指出了目前材料摩擦磨损分子动力学模拟中存在的一些问题,并对未来发展方向进行了展望.     

Methods of increasing the accuracy of measurements of the parameters of two-component two-terminal networks connected in a passive measuring circuit

Methods of measuring the parameters of two-component two-terminal networks, which enable the effect of the supply-voltage frequency on the results of measurements to be eliminated, are described. __________ Translated from Metrologiya, No. 9, pp. 16–24, September, 2007.     

A probabilistic framework for windows of opportunity: the role of temporal variability in critical transitions

The establishment of young organisms in harsh environments often requires a window of opportunity (WoO). That is, a short time window in which environmental conditions drop long enough below the hostile average level, giving the organism time to develop tolerance and transition into stable existence. It has been suggested that this kind of establishment dynamics is a noise-induced transition between two alternate states. Understanding how temporal variability (i.e. noise) in environmental conditions affects establishment of organisms is therefore key, yet not well understood or included explicitly in the WoO framework. In this paper, we develop a coherent theoretical framework for understanding when the WoO open or close based on simple dichotomous environmental variation. We reveal that understanding of the intrinsic timescales of both the developing organism and the environment is fundamental to predict if organisms can or cannot establish. These insights have allowed us to develop statistical laws for predicting establishment probabilities based on the period and variance of the fluctuations in naturally variable environments. Based on this framework, we now get a clear understanding of how changes in the timing and magnitude of climate variability or management can mediate establishment chances.     

A molecular dynamics study on the thickness and post-critical strength of carbon nanotubes

This paper presents a molecular dynamics (MDs) study on the linear, buckling and post-buckling behaviour of carbon nanotubes (CNTs) under pure shortening and pure twisting. Its objectives are (i) to clarify the issue about the most correct thickness value to adopt in the simulation of CNTs using shell models and (ii) to evaluate their post-critical strength. Three CNTs with similar length-to-diameter ratio but different atomic structures (zig-zag, armchair and chiral) are selected for this study. Then, MD simulations are performed to investigate the pre-critical, critical buckling and post-critical behaviour of CNTs under pure shortening and pure twisting. Using available analytical formulae derived from shell models, the influence of CNT thickness on their critical strain and critical angle of twist is investigated. Some conclusions are drawn regarding (i) the most appropriate choice of the thickness value to use in shell models and (ii) the effectiveness of post-critical stiffness and strength of CNTs.