Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A \sqrt N Dynamic Load Distribution Algorithm Using Anti-Tasks and Load State Vectors

One of the fundamental issues to ensure maximal performance improvement in a cluster computing environment is load distribution, which is commonly achieved by using polling-based load distribution algorithms. Such algorithms suffer from two weaknesses: (1) Load information exchanged during a polling session is confined to the two negotiating nodes only. (2) Such algorithms are not scalable in that growth of the distributed system is accompanied with increasing amount of polling sessions.In this paper, we proposed a LD algorithm which is based on anti-tasks and load state vectors. Anti-tasks travel around the distributed system for pairing up task senders and receivers. As an anti-task travels, timed load information is collected and disseminated over the entire system via the load state vector bundled with the anti-task. Guided by load state vectors, anti-tasks are spontaneously directed towards processing nodes having high transient workload, thus allowing their surplus workload to be relocated soonest possible. No peer-to-peer negotiations between senders and receivers are needed.To reduce the network bandwidth consumption caused by the anti-task algorithm, the number of hosts that an anti-task needs to travel to must be carefully limited. The algorithm achieves this by employing the mathematical notion of Finite Projective Plane (FPP). By employing FPP, the number of nodes that each anti-task has to travel is at most , where N is the number of nodes in the system, without sacrifying the spread of load information.     

A P system and a constructive membrane-inspired DNA algorithm for solving the Maximum Clique Problem

We present a P system with replicated rewriting to solve the Maximum Clique Problem for a graph. Strings representing cliques are built gradually. This involves the use of inhibitors that control the space of all generated solutions to the problem. Calculating the maximum clique for a graph is a highly relevant issue not only on purely computational grounds, but also because of its relationship to fundamental problems in genomics. We propose to implement the designed P system by means of a DNA algorithm. This algorithm is then compared with two standard papers that addressed the same problem and its DNA implementation in the past. This comparison is carried out on the basis of a series of computational and physical parameters. Our solution features a significantly lower cost in terms of time, the number and size of strands, as well as the simplicity of the biological implementation.     

基于质粒DNA匹配问题的分子算法

给定无向图,图的最小极大匹配问题是寻找每条边都不相邻的最大集中的最小者,这个问题是著名的NP-完全问题.1994年Adleman博士首次提出用DNA计算解决NP-完全问题,以编码的DNA序列为运算对象,通过分子生物学的运算操作解决复杂的数学难题,使得NP-完全问题的求解可能得到解决.提出了基于质粒DNA的无向图的最大匹配问题的DNA分子生物算法,通过限制性内切酶的酶切和凝胶电泳完成解的产生和最终接的分离,依据分子生物学的实验手段,算法是有效并且可行的.     

BDCGrid:基于Globus的生物信息高性能计算网格系统

随着分子生物信息数据量高速增长,生物信息学面临着大规模、高通量、密集型计算的巨大挑战。为有效利用计算机资源,缩短高通量生物信息计算程序执行时间,我们基于Globus Toolkit网格中间件,实现了一个支持高通量生物数据计算的网格系统(Biological Data Computing Grid,简称BDCGrid)。BDCGrid计算网格系统模型可以有效整合中小型生物信息学实验室计算机资源,大大缩短高通量生物信息计算程序执行时间,为相关研究人员利用现有计算机资源处理大规模、高通量生物信息计算任务提供一种新的途径。     

Changes of enzyme activity in lipid signaling pathways related to substrate reordering

The static fluid mosaic model of biological membranes has been progressively complemented by a dynamic membrane model that includes phospholipid reordering in domains that are proposed to extend from nanometers to microns. Kinetic models for lipolytic enzymes have only been developed for homogeneous lipid phases. In this work, we develop a generalization of the well-known surface dilution kinetic theory to cases where, in a same lipid phase, both domain and nondomain phases coexist. Our model also allows understanding the changes in enzymatic activity due to a decrease of free substrate concentration when domains are induced by peptides. This lipid reordering and domain dynamics can affect the activity of lipolytic enzymes, and can provide a simple explanation for how basic peptides, with a strong direct interaction with acidic phospholipids (such as beta-amyloid peptide), may cause a complex modulation of the activities of many important enzymes in lipid signaling pathways.     

Culturally Situated Design Tools: Ethnocomputing from Field Site to Classroom

Ethnomathematics is the study of mathematical ideas and practices situated in their cultural context. Culturally Situated Design Tools (CSDTs) are web-based software applications that allow students to create simulations of cultural arts—Native American beadwork, African American cornrow hairstyles, urban graffiti, and so forth—using these underlying mathematical principles. This article is a review of the anthropological issues raised in the CSDT project: negotiating the representations of cultural knowledge during the design process with community members, negotiating pedagogical features with math teachers and their students, and reflecting on the software development itself as a cultural construction. The move from ethnomathematics to ethnocomputing results in an expressive computational medium that affords new opportunities to explore the relationships between youth identity and culture, the cultural construction of mathematics and computing, and the formation of cultural and technological hybridity.     

Converting differential-equation models of biological systems to membrane computing

This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand–receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.