Allometric Scaling for Character Design

Geometric scaling transformations do not respect the biological processes which govern the size and shape of living creatures. In this paper, we describe an approach to scaling which can be related to biological function. We use known biological laws of allometry which are expressed as power laws to control the mesh deformation in the frequency domain. This approach is motivated by the relation between fractal biological systems and their underlying power‐law spectra. We demonstrate our approach to biology‐aware character scaling on triangle meshes representing quadrupedal mammals.     

Beta-binders for Biological Quantitative Experiments

The similarities between biological systems and distributed and mobile systems suggest that the theory of process calculi could be a useful starting point for understanding, if not predicting, the behaviour of complex biological systems.To formally model in vitro or in vivo experiments, appropriate quantitative extensions of process calculi have to be investigated. This paper focuses on Beta-binders, a language of processes with typed interaction sites which has been recently introduced to accurately represent biological entities.Here the syntax and semantics of Beta-binders are enriched to achieve a stochastic version of it, in order to obtain quantitative measures on biological phenomena. The quantitative parameters are derived from typed interaction sites introducing the concept of affinity. The relevance of quantitative reasoning is outlined running a biological example.     

On biological validity indices for soft clustering algorithms for gene expression data

Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed.     

A new index for mapping lichen-dominated biological soil crusts in desert areas

Tracking the presence, distribution and disappearance of biological soil crusts is important for ecosystem management of desert regions and provides highly valuable information on desertification and climate change studies in arid environments. Based on the analysis of the spectral features of biological soil crusts, we propose a new biological soil crust index (BSCI) for biological soil crusts mapping. Using both a radiative transfer simulation and an analysis of a Landsat Enhanced Thematic Mapper Plus (ETM+) image, we examined the ability of satellite remote sensing to detect and map biological soil crusts. Our simulation indicated that Landsat ETM+ data could be used, after atmospheric correction, to detect biological soil crusts if 33% or more of any pixel was accounted for by biological soil crusts. When the proposed BSCI was applied to the detection of biological soil crusts in the Gurbantonggut Desert, Xingjiang, China, a Kappa coefficient of 0.82 and an overall accuracy of 94.7% were achieved. These experimental results indicate that the new index is applicable to identification of lichen-dominated biological soil crusts, and therefore has good potential for biological soil crust detection and mapping in cool and cold deserts.     

生物化学动力学网络体系的计算机模拟

生物网络体系中的化学动力学模拟已成为生物体系研究过程中的一个重要环节。目前,已有数种针对于不同生物网络体系的模拟算法。在这些算法的基础上,开发出相应的一些模拟软件。本文对国际上常用的28个模拟软件进行了分类和总结。根据软件所支持模拟算法的原理及适用范围,将该28个软件大致分成4类:均匀体系确定性模拟软件、均匀体系随机性模拟软件、均匀体系混合性随机软件和扩散反应体系模拟软件。结合各类模拟算法的局限性,针对如何根据实际体系来正确选择生物网络体系的化学动力学模拟算法和模拟软件给出了必要的讨论。     

Petri net models for the semi-automatic construction of large scale biological networks

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. During the last 15 years, Petri nets have attracted more and more attention to help to solve this key problem. Regarding the published papers, it seems clear that hybrid functional Petri nets are the adequate method to model complex biological networks. Today, a Petri net model of biological networks is built manually by drawing places, transitions and arcs with mouse events. Therefore, based on relevant molecular database and information systems biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the application of Petri nets for modeling and simulation of biological networks. Furthermore, we will present a type of access to relevant metabolic databases such as KEGG, BRENDA, etc. Based on this integration process, the system supports semi-automatic generation of the correlated hybrid Petri net model. A case study of the cardio-disease related gene-regulated biological network is also presented. MoVisPP is available at .     

Computing reachable states for nonlinear biological models

In this paper, we describe reachability computation for continuous and hybrid systems and its potential contribution to the process of building and debugging biological models. We summarize the state-of-the-art for linear systems and then develop a novel algorithm for computing reachable states for nonlinear systems. We report experimental results obtained using a prototype implementation applied to several biological models. We believe these results constitute a promising contribution to the analysis of complex models of biological systems.     

Microdevices for Biological Analyses Recent Advances and Directions for the Future

Biological sampling is important for drug discovery applications and for the basic biochemical and neurochemical discovery process. Several issues arise when sampling from biological environments. First, important biology can occur in small volumes (nL-pL) and at low concentrations (μM-nM). In order to access this information, the important biochemicals must be detectable and the time of specific biochemical events must be recorded. This means that a sampling and analysis method should be selective for the analyte(s) of interest, without loss or dilution, for efficient analysis. Tremendous accomplishments in biological sampling and analysis have been made, both for sampling and microfluidic manipulation. This article focuses on advances in controlling small volumes of biological samples and the analysis of specific analytes in those samples.     

动态网络模式挖掘方法及其应用

静态复杂网络研究在揭示社会网络、信息网络和生物网络的形成和演化机制方面取得了重要成果,其方法和结果对系统生物学产生了重要影响.但现实世界中,很多网络是随时间发生变化的,即动态网络.以动态网络为对象,对动态网络的拓扑特性分析、动态网络相关的各种模式挖掘模型和方法进行了综述、比较和分析.特别地,将动态网络模式分析方法应用于生物网络和社会网络,分析了生物网络相关的动态功能模块和模式演化问题、科学家合作网络和社交网络的动态模式.最后指出了动态网络的模式挖掘方法及其在动态生物网络和社会网络研究中存在的问题和挑战,并对未来的研究方向进行了分析.     

基于SVG的生物学知识可视化表示框架及其应用

分析了当前生物学知识可视化表示方式存在的一些困难，提出了一种基于SVG的表示框架。这一框架在保证了交互性的同时，克服了上述困难，可以较好地用于生物学知识的表示和生物学数据的可视化浏览界面；并基于这个框架开发了一个SBML的可视化工具——SBML Viewer。     

Engineering microfluidic concentration gradient generators for biological applications

This paper reviews the latest developments in the design and fabrication of concentration gradient generators for microfluidics-based biological applications. New gradient generator designs and their underlying mass transport principles are discussed. The review provides a blueprint for design considerations of concentration gradients in different applications, specifically biological studies. The paper discusses the basic phenomena associated with microfluidic gradient generation and the different gradient generation modes used in static and dynamic biological assays. Finally, the paper summarizes all factors to consider when using concentration gradient generators and puts forward perspectives on the future development of these devices.     

Multilevel text mining for bone biology

Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results. Copyright © 2011 John Wiley & Sons, Ltd.     

BioDoser: Improved dose-estimation software for biological radiation dosimetry

This paper introduces a software program that was developed with the aims to improve the efficiency and veracity of calibration curve fitting and data processing in radiation biological dosimetry and other biological experiments, and which is termed BioDoser. BioDoser uses least squares and loop testing of monotonicity method and algorithm of non-uniformed confidence interval. In addition, this program enables integration of multiple different biomarkers typically used in biological dosimetry. These include partial body exposure, minimum number of cells to be analyzed, G function correction modules that are helpful in dose estimation when using chromosome aberration frequencies, micronucleus rate, comet assay and other biological methods. The software is freely available at http://bit.ly/kKBSNR.     

Software Implementation of Biological Repository for Human Genetic Material

To provide biological specimens for scientific studies, the Medical Automation Research Center (MARC) designed and constructed a large-scale device that emphasizes the use of robotics and automation to integrate many associated laboratory operations. These included analysis, dilution, archival storage, and retrieval of purified human-derived specimens. Designers of automated biological repositories are challenged by complex engineering problems. In this paper, we present an overview of the biological repository (biorepository) and give details of the software architecture.     

RE-OEM：一种半结构化生物数据的信息抽取模型*

在分析半结构化生物数据特点的基础上,提出了一种新的半结构化数据抽取模型RE-OEM。它将OEM数据模型和正则表达式有机地结合起来,不但能够灵活方便地表示各种数据结构,而且能够非常方便地进行模式匹配和数据的定位,为半结构化生物数据的抽取打下坚实基础。     

Linear Neural Circuitry Model for Visual Receptive Fields

The current state of art in the literature indicates that linear visual receptive fields are Gaussian or formed based on Gaussian kernels in biological visual systems. In this paper, by employing hypotheses based on the anatomy and physiology of vertebrate biological vision, we propose a neural circuitry possessing Gaussian-related visual receptive fields. Here, we present a plausible circuitry system matching the characteristic properties of an ideal visual front end of biological visual systems and then present a condition under which this circuit demonstrates a linear behaviour to model the linear receptive fields observed in the biological experimental data. The objective of this study is to understand the hardware circuitry from which various visual receptive fields in biological visual system can be deduced. In our model, a nonlinear neural network communicating with spikes is considered. The condition under which this neural network behaves linearly is discussed. The equivalent linear circuit proposed here employs some anatomical and physiological properties of the early biological visual pathway to derive the visual receptive field profiles for linear cells such as neurons with isotropic separable, non-isotropic separable and non-separable (velocity-adapted) Gaussian receptive fields in the LGN and striate cortex. In the model presented here, the theory of transmission lines for linear distributed electrical circuits is employed for two-dimensional transmission grids to model cell connectivities in a neural layer. The model presented here leads to a formulation similar to the Gaussian scale-space theory for the transmission of visual signals through various layers of neurons. Our model therefore presents a new insight on how the convolution process with Gaussian kernels can be implemented in vertebrate visual systems. The comparison of the numerical simulations of our model presented in this paper with the data analysis of receptive field profiles recorded in the biological literature demonstrates a complete agreement between our theoretical model and experimental data. Our model is also in good agreement with the numerical results of the Gaussian scale-space theory for the visual receptive fields.     

Lens-free imaging for biological applications

Lens-free (or lensless) imaging is emerging as a cost-effective, compact, and lightweight detection method that can serve numerous biological applications. Lens-free imaging can generate high-resolution images within a field-portable platform, which is ideal for affordable point-of-care devices aiming at resource-limited settings. In this mini-review, we first describe different modes of operation for lens-free imaging and then highlight several recent biological applications of this emerging platform technology.     

人工免疫算法在Flow-shop问题中的应用

文章针对Flow-shop问题提出了一种模拟生物免疫系统的人工免疫算法(AIA),并与模拟生物进化过程的遗传算法(GA)进行了比较,给出了AIA的具体步骤。仿真实验表明:AIA求解Flow-shop是有效的。     

前置反硝化污水生化处理过程优化控制

针对活性污泥前置反硝化污水生化处理工艺,提出一种以排水质量指标为约束条件、以能量消耗最小为目标的优化控制方案．由于污水生化过程机理复杂、参数众多,其最优问题求解困难．因此,在原有活性污泥1号模型和国际评价基准的基础上建立了能够有效模拟原有模型的简化模型,并进一步结合混合遗传算法进行寻优,显著提高了求解效率．仿真结果表明了该方案和方法的有效性．     

一种新的度量生物复杂网络模块性的方法

在生命科学领域,生物网络的概念也被大量引入,用以系统地表示复杂的生物过程。模块性是很多复杂网络都具有的特征,因此生物网络模块性的研究成为系统生物学一个重要的研究方向。为了更加深入地了解生物系统的组成和机理,本文提出了一种度量网络模块性的新方法,并将该序列指标应用于酵母转录因子调控网络和多个物种的代谢网络,结果发现酵母转录因子调控网络并不具有模块性而代谢网络具有显著的模块性。在应用中,代谢网络的功能模块度与平均聚类系数的相关性还说明我们的指标比平均聚类系数更适合刻画网络的功能模块性。另外,该指标的提出使得各种不同物种网络模块性的比较或者各种不同模块划分(或识别)方法的评价成为可能。