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國家理論科學研究中心學術演講 with Biological Applications |
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Speaker: (1) Mr. Wei-Sheng Wu, 吳謂勝 (National Tsing Hua University) (2) Prof. Bor-Sen Chen, 陳博現 (National Tsing Hua University) (3) Prof. Bor-Sen Chen, 陳博現 (National Tsing Hua University)
Time: (1) May 5 (Thu.), 2005 PM 1:30~3:00 (2) May 12 (Thu.), 2005 PM 1:30~3:00 (3) May 19 (Thu.), 2005 PM 1:30~3:00
Topic: (1) Computational reconstruction of transcriptional regulatory modules of the yeast (2) A new measure of biochemical network robustness (3) On the Attenuation and Exploitation of Molecular Noise in
Genetic Regulatory Networks Abstract: (1) Motivation: Identifying the organization of transcriptional regulatory modules is crucial for understanding cellular responses to internal and externalsignals. The advances of high-throughput genomic tools such as DNA microarrays and chromatin immunoprecipitation DNA chips (ChIP-chips) have made computational reconstruction of transcriptional regulatory modules of a eukaryotic cell possible. A transcriptional regulatory module consists of a set of genes, which are regulated by a set of transcription factors (TFs). Results: By integrating the ChIP-chips data and gene expression data, the MOdule Finding (MOF) algorithm identified many biologically relevant transcriptional regulatory modules of the yeast cell cycle. Many findings derived from analyzing these modules confirm the results of previous studies, hence validating our approach. Many known or novel combinations of TFs are found to regulate genes in the yeast cell cycle. This analysis shows that different combinations of a small number of TFs are responsible for regulating a large number of genes in different cell cycle stages and there may exist cross-talking between cell cycle process and the other cellular processes. The relationship between TFs, which regulate the same module, can be classified into three categories. Besides, the MOF algorithm is more powerful than previous approaches. Using the MOF algorithm can lower the rate of false negatives without substantially increasing the rate of false positives in determining TF-gene binding events. Hence, the MOF algorithm can find many extra regulator-gene relationships that cannot be identified by previous studies. In addition, the regulatory roles of 11 regulators can be determined by the MOF algorithm, four of which are identified to be capable of being activators and repressors in regulating differentmodules. Finally, the MOF algorithm refined modules from Spellman et al. and provided a better understanding of how the cell regulates the complex expression program in the cell cycle process.
(2) Motivation: The robustness of a biochemical network is defined as the tolerance of variations in kinetic parameters with respect to the maintenance of steady state. (phenotype) Robustness also plays an important role in the fail-safe mechanism in the evolutionary process of biochemical networks. The purposes of this paper are to use the synergism and saturation system (S-system) representation to describe a biochemical network and to develop a robustness measure of a biochemical network subject to variations in kinetic parameters. Since most biochemical networks in nature operate close to the steady state, only the robustness measurement of a biochemical network at the steady state is considered. Results We show that the upper bound of the tolerated parameter variations is related to the system matrix of a biochemical network at the steady state. Using this upper bound, we can calculate the tolerance of a biochemical network without testing all possible parametric perturbations and gain much insight into the robustness of a biochemical network. We find that a biochemical network with a large tolerance can also better at tenuate the effects of variations in rate parameters and environments. Compensatory parameter variations and network redundancy are found to play important roles in the robustness of biochemical networks. Finally, four biochemical networks, i.e., a cascaded biochemical network, the glycolytic-glycogenolytic pathway in a perfused rat liver, the tricarboxylic acid (TCA) cycle in Dictyostellium discoideum and the cAMP oscillation network in bacterial chemotaxis, are used to illustrate the usefulness of the proposed robustness measure.
(3) Noise has many important roles in biological functions of cells. At present, no good theory exists for identifying all possible mechanisms of genetic regulatory networks to attenuate or exploit the molecular noise. Based on stochastic dynamic regulation equation, the intrinsic fluctuation is modeled as state-dependent stochastic process and the robust stability of genetic regulatory network is discussed under intrinsic noise. Then the mechanisms of genetic regulatory network to attenuate or exploit extrinsic fluctuation are revealed from nonlinear stochastic filtering point of view. Furthermore, a simple measure of attenuation level or exploitation level of extrinsic noise by genetic regulatory networks is also introduced by nonlinear H∞ filtering technique via solving Hamilton-Jacobi inequality (HJI). In the linear stochastic regulatory network, the attenuation level of extrinsic noise can be calculated by solving a corresponding linear matrix inequality (LMI).
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Place: |
Room 213, Science One Building, National Chiao Tung University |