Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models.     

Implementing behaviour in individual-based models using neural networks and genetic algorithms

Even though individual-based models (IBMs) have become very popular in ecology during the last decade, there have been few attempts to implement behavioural aspects in IBMs. This is partly due to lack of appropriate techniques. Behavioural and life history aspects can be implemented in IBMs through adaptive models based on genetic algorithms and neural networks (individual-based-neural network-genetic algorithm, ING). To investigate the precision of the adaptation process, we present three cases where solutions can be found by optimisation. These cases include a state-dependent patch selection problem, a simple game between predators and prey, and a more complex vertical migration scenario for a planktivorous fish. In all cases, the optimal solution is calculated and compared with the solution achieved using ING. The results show that the ING method finds optimal or close to optimal solutions for the problems presented. In addition it has a wider range of potential application areas than conventional techniques in behavioural modelling. Especially the method is well suited for complex problems where other methods fail to provide answers. This revised version was published online in July 2006 with corrections to the Cover Date.     

生物分子计算机

生物分子计算机是一种用生物分子元件组装成的具有并行数据处理、三维存储器和神经网络等特征的智能化计算机。它不但运行速度快、能处理复杂性的非线性问题、具有一定的逻辑推理等认知能力 ,而且具备生物分子形成的细胞样的自行修复或自体复制能力。也许从分子计算机到蛋白质计算机、DNA计算机再到生物分子计算机是一种研究和制造生物分子计算机的路径。     

Atrial local Ca signaling and inositol 1,4,5-trisphosphate receptors

In atrial myocytes lacking t-tubules, action potential triggers junctional Ca²⁺ releases in the cell periphery, which propagates into the cell interior. The present article describes growing evidence on atrial local Ca²⁺ signaling and on the functions of inositol 1,4,5-trisphosphate receptors (IP₃Rs) in atrial myocytes, and show our new findings on the role of IP₃R subtype in the regulation of spontaneous focal Ca²⁺ releases in the compartmentalized areas of atrial myocytes. The Ca²⁺ sparks, representing focal Ca²⁺ releases from the sarcoplasmic reticulum (SR) through the ryanodine receptor (RyR) clusters, occur most frequently at the peripheral junctions in isolated resting atrial cells. The Ca²⁺ sparks that were darker and longer lasting than peripheral and non-junctional (central) sparks, were found at peri-nuclear sites in rat atrial myocytes. Peri-nuclear sparks occurred more frequently than central sparks. Atrial cells express larger amounts of IP₃Rs compared with ventricular cells and possess significant levels of type 1 IP₃R (IP₃R1) and type 2 IP₃R (IP₃R2). Over the last decade the roles of atrial IP₃R on the enhancement of Ca²⁺-induced Ca²⁺ release and arrhythmic Ca²⁺ releases under hormonal stimulations have been well documented. Using protein knock-down method and confocal Ca²⁺ imaging in conjunction with immunocytochemistry in the adult atrial cell line HL-1, we could demonstrate a role of IP₃R1 in the maintenance of peri-nuclear and non-junctional Ca²⁺ sparks via stimulating a posttranslational organization of RyR clusters.     

Not so different after all: a comparison of methods for detecting amino acid sites under selection

We consider three approaches for estimating the rates of nonsynonymous and synonymous changes at each site in a sequence alignment in order to identify sites under positive or negative selection: (1) a suite of fast likelihood-based counting methods that employ either a single most likely ancestral reconstruction, weighting across all possible ancestral reconstructions, or sampling from ancestral reconstructions; (2) a random effects likelihood (REL) approach, which models variation in nonsynonymous and synonymous rates across sites according to a predefined distribution, with the selection pressure at an individual site inferred using an empirical Bayes approach; and (3) a fixed effects likelihood (FEL) method that directly estimates nonsynonymous and synonymous substitution rates at each site. All three methods incorporate flexible models of nucleotide substitution bias and variation in both nonsynonymous and synonymous substitution rates across sites, facilitating the comparison between the methods. We demonstrate that the results obtained using these approaches show broad agreement in levels of Type I and Type II error and in estimates of substitution rates. Counting methods are well suited for large alignments, for which there is high power to detect positive and negative selection, but appear to underestimate the substitution rate. A REL approach, which is more computationally intensive than counting methods, has higher power than counting methods to detect selection in data sets of intermediate size but may suffer from higher rates of false positives for small data sets. A FEL approach appears to capture the pattern of rate variation better than counting methods or random effects models, does not suffer from as many false positives as random effects models for data sets comprising few sequences, and can be efficiently parallelized. Our results suggest that previously reported differences between results obtained by counting methods and random effects models arise due to a combination of the conservative nature of counting-based methods, the failure of current random effects models to allow for variation in synonymous substitution rates, and the naive application of random effects models to extremely sparse data sets. We demonstrate our methods on sequence data from the human immunodeficiency virus type 1 env and pol genes and simulated alignments.     

High-throughput screening of monoclonal antibodies against plant cell wall glycans by hierarchical clustering of their carbohydrate microarray binding profiles

Antibody-producing hybridoma cell lines were created following immunisation with a crude extract of cell wall polymers from the plant Arabidopsis thaliana. In order to rapidly screen the specificities of individual monoclonal antibodies (mAbs), their binding to microarrays containing 50 cell wall glycans immobilized on nitrocellulose was assessed. Hierarchical clustering of microarray binding profiles from newly produced mAbs, together with the profiles for mAbs with previously defined specificities allowed the rapid assignments of mAb binding to antigen classes. mAb specificities were further investigated using subsequent immunochemical and biochemical analyses and two novel mAbs are described in detail. mAb LM13 binds to an arabinanase-sensitive pectic epitope and mAb LM14, binds to an epitope occurring on arabinogalactan-proteins. Both mAbs display novel patterns of recognition of cell walls in plant materials.     

Computational identification of protein-protein interactions in rice based on the predicted rice interactome network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

昆虫种群的一类时空动态模型研究

昆虫种群的时空动态包括种群的数量变化和空间分布变化.根据密度制约性原理, F推导出描述昆虫种群时空动态的非线性偏微分方程模型.该模型由扩散、迁移、出生及死亡等成分组成.建立了模型的差分解法,也给出模型参数的拟合方法.模型的初始分布确定为二项分布,Poisson分布,以及负二项分布.给出产生3种空间分布的计算方法.给定初始分布类型及参数,由各算法组装的计算机模型可得到初始分布,田间各点各时刻的昆虫数量 ,以及该时刻的空间分布类型和聚集性.