Browsing by Author "Liu, Zhen"

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    DBS: a fast and informative segmentation algorithm for DNA copy number analysis
    Ruan, Jun; Liu, Zhen; Sun, Ming; Wang, Yue; Yue, Junqiu; Yu, Guoqiang (2019-01-03)
    Background Genome-wide DNA copy number changes are the hallmark events in the initiation and progression of cancers. Quantitative analysis of somatic copy number alterations (CNAs) has broad applications in cancer research. With the increasing capacity of high-throughput sequencing technologies, fast and efficient segmentation algorithms are required when characterizing high density CNAs data. Results A fast and informative segmentation algorithm, DBS (Deviation Binary Segmentation), is developed and discussed. The DBS method is based on the least absolute error principles and is inspired by the segmentation method rooted in the circular binary segmentation procedure. DBS uses point-by-point model calculation to ensure the accuracy of segmentation and combines a binary search algorithm with heuristics derived from the Central Limit Theorem. The DBS algorithm is very efficient requiring a computational complexity of O(n*log n), and is faster than its predecessors. Moreover, DBS measures the change-point amplitude of mean values of two adjacent segments at a breakpoint, where the significant degree of change-point amplitude is determined by the weighted average deviation at breakpoints. Accordingly, using the constructed binary tree of significant degree, DBS informs whether the results of segmentation are over- or under-segmented. Conclusion DBS is implemented in a platform-independent and open-source Java application (ToolSeg), including a graphical user interface and simulation data generation, as well as various segmentation methods in the native Java language.
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    Identifying disease associations via genome-wide association studies
    Huang, Wenhui; Wang, Pengyuan; Liu, Zhen; Zhang, Liqing (2009-01-30)
    Background Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels - the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level. Results Our results show that Crohn's disease, rheumatoid arthritis, and type 1 diabetes share evidence of genetic associations at all levels of analysis, offering strong molecular support for the current grouping of the diseases. On the other hand, coronary artery disease, hypertension, and type 2 diabetes, despite being considered as a natural group with potential aetiological overlap, do not show any evidence of shared genetic basis at all levels. Conclusion Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach.
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    Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features
    Liu, Zhen (Virginia Tech, 2012-11-13)
    In this thesis we study stochastic modeling and simulation methods for biochemical systems. The thesis is focused on systems with multi-state and multi-scale features and divided into two parts. In the first part, we propose new algorithms that improve existing multi-state simulation methods. We first compare the well known Gillespie\\\'s stochastic simulation algorithm (SSA) with the StochSim, an agent-based simulation method. Based on the analysis, we propose a hybrid method that possesses the advantages of both methods. Then we propose two new methods that extend the Network-Free Algorithm (NFA) for rule-based models. Numerical results are provided to show the performance improvement by our new methods. In the second part, we investigate two simulation schemes for the multi-scale feature: Haseltine and Rawlings\\\' hybrid method and the quasi-steady-state stochastic simulation method. We first propose an efficient partitioning strategy for the hybrid method and an efficient way of building stochastic cell cycle models with this new partitioning strategy. Then, to understand conditions where the two simulation methods can be applied, we develop a way to estimate the relaxation time of the fast sub-network, and compare it with the firing interval of the slow sub-network. Our analysis are verified by numerical experiments on different realistic biochemical models.
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    Stochastic Simulation Methods for Solving Systems with Multi-State Species
    Liu, Zhen (Virginia Tech, 2009-05-06)
    Gillespie's stochastic simulation algorithm (SSA) has been a conventional method for stochastic modeling and simulation of biochemical systems. However, its population-based scheme faces the challenge from multi-state situations in many biochemical models. To tackle this problem, Morton-Firth and Bray's stochastic simulator (StochSim) was proposed with a particle-based scheme. The thesis first provides a detailed comparison between these two methods, and then proposes improvements on StochSim and a hybrid method to combine the advantages of the two methods. Analysis and numerical experiment results demonstrate that the hybrid method exhibits extraordinary performance for systems with both the multi-state feature and a high total population. In order to deal with the combinatorial complexity caused by the multi-state situation, the rules-based modeling was proposed by Hlavacek's group and the particle-based Network-Free Algorithm (NFA) has been used for its simulation. In this thesis, we improve the NFA so that it has both the population-based and particle-based features. We also propose a population-based method for simulation of the rule-based models. The bacterial chemotaxis model has served as a good biological example involving multi-state species. We implemented different simulation methods on this model. Then we constructed a graphical interface and compared the behaviors of the bacterium under different mechanisms, including simplified mathematical models and chemically reacting networks which are simulated stochastically.