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第三届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Yang Cong Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong

Probabilistic Boolean Networks(PBNs) provide a convenient tool for studying the interactions among different genes while allowing *** paper deals with the issue of finite-horizon control with multiple hard-constraints in a *** precisely,under the constraint of the number of times that each control method can be applied,we develop a control strategy by which the state of a given genetic network falls into a desired state set with a prescribed minimum *** propose an efficient algorithm to find the feasible *** upper bound for the computational cost is also *** numerical experiment is then conducted to demonstrate the efficiency of our proposed method.

关键词： Probability Boolean Networks Finite-Horizon Multiple Hard-Constraints Intervention Markov Chain Optimal Control

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A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction

A Study of Network-based Kernel Methods on Protein-Protein I...

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第三届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Limin Li Yat-Ming Chan Hiroshi Mamitsuka Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong

Predicting protein functions is an important issue in the post-genomic *** this paper, we studied several network-based kernels including Local Linear Embedding(LLE) kernel method, Diffusion kernel and Laplacian Kernel to uncover the relationship between proteins functions and Protein-Protein Interactions(PPI).We first construct kernels based on PPI networks,we then apply Support Vector Machine(SVM) techniques to classify proteins into different functional groups.5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chisquare *** we made predictions of functions of some unknown genes and verified the preciseness of our prediction in part by the information of other data source.

关键词： Protein Function Prediction Kernel Method Local Linear Embedding(LLE) Kernel Laplacian Kernel Diffusion Kernel Support Vector Machine

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Efficient computation of impact degrees for multiple reactions in metabolic networks with cycles

Efficient computation of impact degrees for multiple reactio...

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3rd ACM International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009

作者： Cong, Yang Tamura, Takeyuki Akutsu, Tatsuya Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Bioinformatics Center Institute for Chemical Research Kyoto University Gokasho Uji Kyoto 611-0011 Japan

ISBN: (纸本)9781605588032

Analysis of the robustness of a metabolic network against of single or multiple reaction(s) is useful for mining important enzymes/genes. For that purpose, the impact degree was proposed by Jiang et al. In this short paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm for its computation. Furthermore, we propose an improved algorithm for computing impact degrees for deletions of multiple reactions. The results of preliminary computational experiments suggest that the improved algorithm is several tens of times faster than a simple algorithm. Copyright 2009 ACM.

关键词： Metabolism

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A Heuristic Method for Generating Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix

A Heuristic Method for Generating Probabilistic Boolean Netw...

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第二届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Xi Chen Nam-Kiu Tsing Ho-Yin Leung Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong

Probabilistic Boolean Networks (PBNs) have received much attention for modeling genetic regulatory networks. In this paper, we propose efficient algorithms for constructing a probabilistic Boolean network when its transition probability matrix is given. This is an important inverse problem in network inference from steady-state data, as most microarray data sets are assumed to be obtained from sampling the steady-state.

关键词： Boolean Networks Probabilistic Boolean Networks Inverse Problem Transition Probability Matrix.

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A Genetic Algorithm for Optimal Control of Probabilistic Boolean Networks

A Genetic Algorithm for Optimal Control of Probabilistic Boo...

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第二届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Ho-Yin Leung Nam-Kiu Tsing Shu-Qin Zhang Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Faculty of Mathematical Sciences Fudan University

We study the problem of finding optimal control policies for Probabilistic Boolean Networks (PBNs). Boolean Networks (BNs) and PBNs are effective tools for modeling genetic regulatory networks. A PBN is a collection of BNs driven by a Markov chain process. It is well-known that the control/intervention of a genetic regulatory network is useful for avoiding undesirable states associated with diseases like cancer. The optimal control problem can be formulated as a probabilistic dynamic programming problem. However, due to the curse of dimensionality, the complexity of the problem is huge. The main objective of this paper is to introduce a Genetic Algorithm (GA) approach for the optimal control problem. Numerical results are given to demonstrate the efficiency of our proposed GA method.

关键词： Boolean Networks Dynamic Programming Genetic Algorithm Intervention Opti- mal Control Policy Probabilistic Boolean Networks.

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Some Infection Models for the Development of AIDS

Some Infection Models for the Development of AIDS

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第二届最优化与系统生物学国际研讨会

作者： Wai-Ki Ching Yang Cong Tuen-Wai Ng Zheng-Jian Bai Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Department of Information and Computational Mathematics Xiamen University

The co-infection of HIV viruses can affect the viral evolution in vivo. Wodarz and Levy 2007 [1] study the effect of HIV co-infection by investigating the values of the virus cytopathicity when the basic reproductive ratio of the virus and the total number of the target cells reach their extreme point respectively. Here based on their ideas, we further extended the discussion to a more general model.

关键词： HIV Dynamics Mathematical Models

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A simplified multivariate Markov chain model for the construction and control of genetic regulatory networks

A simplified multivariate Markov chain model for the constru...

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2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

作者： Zhang, Shu-Qin Ching, Wai-Ki Jiao, Yue Wu, Ling-Yun Chan, Raymond H. School of Mathematical Sciences Fudan University Shanghai200433 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Institute of Applied Mathematics Academy of Mathematics and System Sciences Chinese Academy of Sciences Department of Mathematics Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781424417483

The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN. The new model can preserve the strength of PBNs and at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are then given to demonstrate the effectiveness of our proposed model and control policy. © 2008 IEEE.

关键词： Gene expression

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A Multiple Regression Approach for Building Genetic Networks

A Multiple Regression Approach for Building Genetic Networks

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Shu-Qin Zhang Wai-Ki Ching Nam-Kiu Tsing Ho-Yin Leung Diane D. Guo School of Mathematical Sciences Fudan University Shanghai China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong China Department of Biology Chinese University of Hong Kong Hong Kong China

The construction of genetic regulatory networks from time series gene expression data is an important research topic in bioinformatics as large amounts of quantitative gene expression data can be routinely generated nowadays. One of the main difficulties in building such genetic networks is that the data set has huge number of genes but small number of time points. In this paper, we propose a linear regression model for uncovering the relations among the genes by using multiple regression method with filtering. The model takes into account of the fact that real biological networks have the scale-free property. Based on this property and the statistical tests, a filter can be constructed and the interactions among the genes can be inferred by minimizing the distance between the observed data and the predicted data. Numerical examples based on yeast gene expression data are given to demonstrate our method.

关键词： Genetics Gene expression Buildings Biological system modeling Bioinformatics Linear regression Nonlinear filters Filtering Testing Fungi

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Construction and Control of Genetic Regulatory Networks:A Multivariate Markov Chain Approach

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Journal of Biomedical Science and Engineering 2008年第1期1卷 15-21页

作者： Shu-Qin Zhang Ling-Yun Wu Wai-Ki Ching Yue Jiao Raymond, H. Chan Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Pokfulam Road Hong Kong Department of Mathematics The Chinese University of Hong Kong Shatin N.T. Hong Kong Institute of Applied Mathematics Academy of Mathematics and System Sciences Chinese Academy of Sciences School of Mathematical Sciences Fudan University Shanghai200433 China

In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN The new model can preserve the strength of PBNs, the ability to capture the inter-dependence of the genes in the network, qnd at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are given to demonstrate the effectiveness of our proposed model and control policy.

关键词： Gene Expression Sequences Multivariate Markov Chain Optimal Control Policy Probabilistic Boolean Networks.

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A Simplified Multivariate Markov Chain Model for the Construction and Control of Genetic Regulatory Networks

A Simplified Multivariate Markov Chain Model for the Constru...

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International Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Shu-Qin Zhang Wai-Ki Ching Yue Jiao Ling-Yun Wu Raymond H. Chan School of Mathematical Sciences Fudan University Shanghai China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong China Institute of Applied Mathematics Academy of Mathematics and System Sciences Chinese Academy and Sciences China Department of Mathematics Chinese University of Hong Kong New Territories Hong Kong China

关键词： Genetics Mathematical model Mathematics Gene expression Optimal control Electronic mail Biological system modeling Computational efficiency Fungi Diseases

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