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242 Reverse Engineering – Recent Advances and Applications Takahashi, Y., J. B. Rayman, et al. (2000). "Analysis of promoter binding by the E2F and pRB families in vivo: distinct E2F proteins mediate activation and repression." Genes Dev 14(7): 804-816. Tavazoie, S., J. D. Hughes, et al. (1999). "Systematic determination of genetic network architecture." Nat Genet 22(3): 281-285. Tegner, J., M. K. Yeung, et al. (2003). "Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling." Proc Natl Acad Sci U S A 100(10): 5944-5949. Troyanskaya, O., M. Cantor, et al. (2001). "Missing value estimation methods for DNA microarrays." Bioinformatics 17(6): 520-525. Tuncay, K., L. Ensman, et al. (2007). "Transcriptional regulatory networks via gene ontology and expression data." In Silico Biol 7(1): 21-34. Wang, E., A. Lenferink, et al. (2007). "Cancer systems biology: exploring cancer-associated genes on cellular networks." Cell Mol Life Sci 64(14): 1752-1762. Weigel, D. and H. Jackle (1990). "The fork head domain: a novel DNA binding motif of eukaryotic transcription factors?" Cell 63(3): 455-456. Wernicke, S. and F. Rasche (2006). "FANMOD: a tool for fast network motif detection." Bioinformatics 22(9): 1152-1153. Whitfield, M. L., G. Sherlock, et al. (2002). "Identification of genes periodically expressed in the human cell cycle and their expression in tumors." Mol Biol Cell 13(6): 1977-2000. Wingender, E., X. Chen, et al. (2001). "The TRANSFAC system on gene expression regulation." Nucleic Acids Res 29(1): 281-283. Wit, E. and J. McClure (2006). Statistics for Microarrays: Design, Analysis and Inference, John Wiley & Sons. Woolf, P. J. and Y. Wang (2000). "A fuzzy logic approach to analyzing gene expression data." Physiol Genomics 3(1): 9-15. Yeger-Lotem, E., S. Sattath, et al. (2004). "Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction." Proc Natl Acad Sci U S A 101(16): 5934-5939. Yeung, K. Y., M. Medvedovic, et al. (2004). "From co-expression to co-regulation: how many microarray experiments do we need?" Genome Biol 5(7): R48. Zhang, Y., J. Xuan, et al. (2008). "Network motif-based identification of transcription factortarget gene relationships by integrating multi-source biological data." BMC Bioinformatics 9: 203. Zhang, Y., J. Xuan, et al. (2009). "Reverse engineering module networks by PSO-RNN hybrid modeling." BMC Genomics: In Press. Zhang, Y., J. Xuan, et al. (2010). "Reconstruction of gene regulatory modules in cancer cell cycle by multi-source data integration." PLoS One 5(4): e10268. Zhu, J. and M. Q. Zhang (1999). "SCPD: a promoter database of the yeast Saccharomyces cerevisiae." Bioinformatics 15(7-8): 607-611. Zhu, J. W., S. J. Field, et al. (2001). "E2F1 and E2F2 determine thresholds for antigen-induced T-cell proliferation and suppress tumorigenesis." Mol Cell Biol 21(24): 8547-8564.
1. Introduction 11 Reverse-Engineering the Robustness of Mammalian Lungs Michael Mayo 1, Peter Pfeifer 2 and Chen Hou 3 1 Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 2 Department of Physics, University of Missouri, Columbia, MO 3 Department of Biological Sciences, Missouri University of Science and Technology, Rolla, MO, USA This chapter is devoted to reverse-engineering the cause of a dramatic increase in the total oxygen uptake rate by the lung, wherein oxygen is supplied to the blood to meet the increasing energetic demands between rest and exercise. This uptake rate increases despite a much smaller increase in the oxygen partial pressure difference across the lung’s exchange tissues (e.g. alveolar membranes), thought to mainly drive the oxygen-blood transfer in a similar way that electric currents are driven by voltage differences according to Ohm’s law. As we explain below, a full understanding of this special property has the potential to improve various engineering processes, such as stabilizing chemical yields in heterogeneous catalysis, improving the efficiency of heat-transporters, and improving energy generation in electrochemical reactors. To reverse-engineer the cause of this mostly pressure-independent increase in the oxygen uptake rate, we focus on the development of mathematical models based on the rate-limiting physical transport processes of i) diffusion through the airway spaces, and ii) “reaction” of the oxygen molecules across the surface of permeable membranes responsible for transferring oxygen from air to blood. Two of these mathematical models treat the terminal, or acinar, airways of mammalian lungs as hierarchical trees; another treats the entire permeable surface as fractal. By understanding how the parameters of these mathematical models restrict the overall oxygen uptake rate, we infer how the lung preserves its function when exposed to environmental hazards (e.g. smoking), damage (e.g. surgery), or disease (e.g. emphysema). The focus of our work here is to discover, or reverse engineer, the operational principles that allow mammalian lungs to match increased oxygen demands with supply without any significant alteration of its “hardware.” We first begin with a mathematical description of oxygen diffusion as the primary transport mechanism throughout the airways responsible for the oxygen uptake in the deep regions of the mammalian lungs studied here. We then discuss several different, but complementary analytical models that approach the oxygen-transport problem from different directions, while also developing a new one. Although these models are different from one another
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Contents Preface IX Part 1 Software
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Preface Introduction In the recent
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Preface XI and con’s related to t
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Preface XIII the step-by-step appli
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Part 1 Software Reverse Engineering
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1. Introduction 60 Surface Reconstr
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Surface Reconstruction from Unorgan
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1. Introduction A Systematic Approa
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A Systematic Approach for Geometric
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A Review on Shape Engineering and D
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Integrating Reverse Engineering and
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Integrating Reverse Engineering and
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