An Intuitive Modelling Interface for Systems Biology

We introduce a natural language interface for building stochastic \pi calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We give a formal semantics for this language and a translation algorithm into stochastic \pi calculus that delivers this semantics. We demonstrate the language on a model of Fcr receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic \pi calculus language, Microsoft Research''s SPiM, which can be used for simulation and analysis.     

Membrane automata with priorities

In this paper the one-way P automata with priorities are introduced. Such automata are P systems where the membranes are only allowed to consume objects from parent membranes, under the given conditions. The result of computation of these systems is the set of multiset sequences consumed by skin membrane into the system. The rules associated in some order with each membrane cannot modify any objects, they can only move them through membrane. We show that P automata with priorities and two membranes can accept every recursively enumerated language.     

Encoding Catalytic P Systems in π@

P systems are theoretical computing devices abstracted away from the biological architecture of the cell, introduced some years ago by Gheorghe Păun and now intensely studied. In the area of concurrent systems, process calculi have recently been applied and extended with similar aim, to simulate (and formalise) the behaviour of the cell. Although many common points can be found between the two approaches, no formal and exhaustive comparison has been carried out yet.π@ is a new calculus, strongly π-Calculus based, well-suited to easily encode biologically inspired process calculi. In this paper the encoding in π@ of one variant of P systems is proposed, thus allowing a better understanding of similarities between P systems and bio-inspired process calculi.     

Membrane Proteome of Invasive Retinoblastoma: Differential Proteins and Biomarkers

Purpose: Retinoblastoma (RB) is a pediatric ocular cancer which is caused due to the aberrations in the RB1 gene. The changes in the membrane proteomics would help in understanding the development of the retinoblastoma and could identify candidates for biomarkers and therapy. Experimental design: Quantitative proteomics is performed on the enriched membrane fractions from pooled normal retina (n = 5) and pooled retinoblastoma tissues (n = 5). The proteins are tryptic‐digested and tagged with iTRAQ labels. Orbitrap mass spectrometry is used to analyze and quantify the deregulated membrane proteins involved in the RB tumor progression. Immunohistochemistry (IHC) is used to further validate few of the differentially expressed proteins. Results: A total of 3122 proteins are identified of which, 663 proteins are found to be deregulated with ≥two fold change in the RB tumor compared to the retina. 282 proteins are upregulated and 381 are downregulated with ≥2 peptide identifications. Bioinformatic analysis revealed that, most of the proteins are involved in the transport, cellular communication, and growth. Overexpression of lamin B1 (LMNB1) and transferrin receptor (TFRC) are observed in RB tumors using IHC. Conclusion and clinical relevance: The present study, is the first comprehensive quantitative membrane proteomic atlas of the differentially regulated proteins in RB compared to the retina. LMNB1 and TFRC could be potential biomarkers for this childhood cancer.     

Stochastic Stability and Stabilization of Singular It ‐type Markovian Jump Systems with Uncertain Transition Rates: An LMI Approach

This paper investigates the stochastic stability and stabilization for a class of singular stochastic systems of Itô‐type with Markovian switching, the transition rates (TRs) in the jumping processes are uncertain. The aims are to establish sufficient conditions to ensure the considered system to be stochastically stable in the mean square sense, which is supported by a detailed proof of existence and uniqueness of the system solution, and to propose a controller such that the system can be stabilizable. The controller is first proposed and has advantage over traditional ones, the controller gain matrices are obtained by solving a strict linear matrix inequality (LMI). Finally, a numerical example is provided to illustrate the validity of the obtained methodology.     

A Simple Calculus for Proteins and Cells

The use of process calculi to represent biological systems has led to the design of different calculi such as brane calculi [Luca Cardelli. Brane calculi. In CMSB, pages 257–278, 2004] and κ-calculus [Vincent Danos and Cosimo Laneve. Formal molecular biology. Theoritical Computer Science, 325(1):69–110, 2004]. Both have proved to be useful to model different types of biological systems.As an attempt to unify the two directions, we introduce the bioκ-calculus, a simple calculus for describing proteins and cells, in which bonds are represented by means of shared names and interactions are modelled at the domain level. Protein-protein interactions have to be at most binary and cell interactions have to fit with sort constraints.We define the semantics of bioκ-calculus, analyse its properties, and discuss its expressiveness by modelling two significant examples: a signalling pathway and a virus infection.     

Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

Perfect Simulation of a Class of Stochastic Hybrid Systems with an Application to Peer to Peer Systems

In this paper we present a class of hybrid systems made of deterministic differential equations and random discrete jumps. We then show how to construct a simulation of such a stochastic hybrid system that provides perfect samples of its asymptotic behavior based on the extension to continuous state-space of coupling-from-the-past techniques introduced by Foss and Tweedie (Stoch Models 14:187–204, 1998) and using suitable envelope trajectories to tackle non-monotonicity. The applicability of the method is illustrated by showing how this framework can be used to model the Squirrel peer to peer system and by reporting a simulation study based on this approach. This paper provides both a framework on how to carry simulation based experimental studies of large and complex hybrid systems and its application in the Squirrel model demonstrating how versatile and powerful this approach can be over a typical example.

Finite Horizon Minimax Optimal Control of Nonlinear Continuous Time Systems with Stochastic Uncertainty

This paper develops a general continuous-time stochastic framework for robustness analysis and robust control synthesis. We consider a stochastic minimax optimization problem for general stochastic uncertain systems. A general method is presented for converting problems of performance analysis or controller synthesis into unconstrained optimization problems.     

计算系统生物学中并行随机仿真方法研究进展

随机仿真是计算系统生物学中对随机离散模型进行仿真研究的一类重要方法。本文对随机仿真方法的原理及并行化研究的进展进行了论述,指出了并行化是解决随机仿真性能开销问题的重要途径,并依照细粒度并行和粗粒度并行分类,阐述了当前并行随机仿真方法的研究现状,重点针对空间并行性,介绍了反应-扩散系统随机仿真的方法和相关工具。最后,对并行随机仿真方法研究未来发展进行了展望。     

关于约束方差控制器的评估问题的研究

本文研究当系统的性能指标表现为系统输入及输出的方差约束时一类随机控制器的评价问题，给出了系统反馈输入及输出的辨识算法，该算法可用来评价反馈控制器能否使反馈系统满足既定的方差约束，以便在对实际系统实施控制之前及时调整控制器，从而达到设计目的。     

生化网络的随机Petri网建模与分析

细胞的行为是随机性的,学习细胞中的随机性有助于理解细胞的组织,设计和进化。建立、确认和分析随机的生化网络模型是当前计算系统生物学领域的一个重要研究主题。当前,标准的Petri网模型已经成为生化网络模拟和定性分析的有力工具。尝试使用随机Petri网对生化网络进行建模与分析,简单描述了随机Petri网理论对标准Petri网的扩充,通过对二聚作用和肌动蛋白这两个典型例子的建模与演化模拟,介绍、论证了随机Petri网理论的新应用。     

输入随机的不可靠柔性制造系统的反馈控制

对于具有随机输入和随机需求的一类不可靠柔性制造系统,利用转移率一致化技术和随机动态规划方法,给出了输入率和服务率分配的最优反馈控制策略,指出系统的最优控制具有bang-bang形式的天关结构,数值例子验证了文中的结果。     

Deterministic and Stochastic Fixed-Time Stability of Discrete-time Autonomous Systems

This paper studies deterministic and stochastic fixedtime stability of autonomous nonlinear discrete-time(DT) systems.Lyapunov conditions are first presented under which the fixed-time stability of deterministic DT systems is certified.Extensions to systems under deterministic perturbations as well as stochastic noise are then considered.For the former,sensitivity to perturbations for fixed-time stable DT systems is analyzed,and it is shown that fixed-time attractiveness results from the present...     

A Suboptimal Dual Control Method for the Stochastic Systems with Parameters Drifting

This paper proposed a suboptimal dual control method for the stochastic systems with parameters drifting. Based on the consideration of the performance index control and the parameter estimation, the minimum variance of the system output and the estimated covariance matrix of the parameters estimation are both put into the performance index to evaluate the control quality. Furthermore, a dual control strategy is designed which is of the property of learning and control. Simulation results illustrate the effectiveness of the algorithm.     

随机系统的切换控制

基于线性矩阵不等式和控制理论,针对由一组由随机微分方程描述的子系统组成的切换系统,给出此类切换系统稳定的充分条件,并给出控制器的设计方法。仿真结果表明,所设计的两类控制器及相应的控制策略均能保证闭环系统在一定意义下的稳定。     

离散系统的鲁棒约束方差估计及应用--模型噪声强度不确定情形

考虑离散随机系统在模型噪声强度不确定及估计误差方差受约束情形下的一类鲁棒状态估计问题,即希望找到这样的滤波增益,使得当模型噪声强度在一定范围内变动时,每个状态分量的估计误差方差始终不大于预先指定值.文中给出了这种滤波增益的设计方法,并以一类机动目标跟踪问题为例,说明这种设计方法的直接性与有效性.     

具有非线性滞后关联的随机大系统的分散镇定

本文通过利用李雅普诺夫函数和李雅普诺夫矩阵方程的性质，对具有非线性滞后关联的一类随机大系统建立了分散鲁棒镇定的判据，所得闭环随机大系统的和稳定性不依赖于任意实数滞后，并对不确定系数矩阵和随机扰动强度具有鲁棒性。     

Stochastic Distribution Control of Singular Systems: Output PDF Shaping

This paper presents a new algorithm designed to control the shape of the output probability density function (PDF) of singular systems subjected to non-Gaussian input. The aim is to select a control input uk such that the output PDF is made as close as possible to a given PDF. Based on the B-spline neural network approximation of the output PDF, the control algorithm is formulated by extending the developed PDF control strategies of non-singular systems to singularsystems. It has been shown that under certain conditions the stability of the closed-loop system can be guaranteed. Simulation examples are given to show the effectiveness of the proposed controlalgorithm.