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Cells  2013 

Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

DOI: 10.3390/cells2040635

Keywords: systems biology, bioinformatics, systems synthetic biology, systems metabolic engineering

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Abstract:

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

References

[1]  Joyce, A.R.; Palsson, B.O. The model organism as a system: integrating 'omics' data sets. Nat. Rev. Mol. Cell. Bio. 2006, 7, 198–210, doi:10.1038/nrm1857.
[2]  Ideker, T.; Bafna, V.; Lemberger, T. Integrating scientific cultures. Mol. Syst. Biol. 2007, 3, 105–106.
[3]  Chuang, H.Y.; Lee, E.; Liu, Y.T.; Lee, D.; Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 2007, 3, 140–157.
[4]  Oti, M.; Snel, B.; Huynen, M.A.; Brunner, H.G. Predicting disease genes using protein-protein interactions. J. Med. Genet. 2006, 43, 691–698, doi:10.1136/jmg.2006.041376.
[5]  Peri, S.; Navarro, J.D.; Amanchy, R.; Kristiansen, T.Z.; Jonnalagadda, C.K.; Surendranath, V.; Niranjan, V.; Muthusamy, B.; Gandhi, T.K.B.; Gronborg, M.; et al. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 2003, 13, 2363–2371, doi:10.1101/gr.1680803.
[6]  Zhang, S.H.; Jin, G.X.; Zhang, X.S.; Chen, L.N. Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics 2007, 7, 2856–2869, doi:10.1002/pmic.200700095.
[7]  Voit, E.O. Computational Analysis of Biochemical Systems: a Practical Guide for Biochemists and Molecular Biologists; Cambridge University Press: New York, NY, USA,, 2000.
[8]  Chen, B.S.; Wu, C.C. On the calculation of signal transduction ability of signaling transduction pathways in intracellular communication: systematic approach. Bioinformatics 2012, 28, 1604–1611, doi:10.1093/bioinformatics/bts159.
[9]  Kitano, H. Opinon—Cancer as a robust system: Implications for anticancer therapy. Nat. Rev. Cancer 2004, 4, 227–235, doi:10.1038/nrc1300.
[10]  Stelling, J.; Sauer, U.; Szallasi, Z.; Doyle, F.J.; Doyle, J. Robustness of cellular functions. Cell 2004, 118, 675–685, doi:10.1016/j.cell.2004.09.008.
[11]  Storey, J.D.; Xiao, W.Z.; Leek, J.T.; Tompkins, R.G.; Davis, R.W. Significance analysis of time course microarray experiments. Proc. Natl. Acad. Sci. USA 2005, 102, 12837–12842.
[12]  Cherry, J.M.; Adler, C.; Ball, C.; Chervitz, S.A.; Dwight, S.S.; Hester, E.T.; Jia, Y.K.; Juvik, G.; Roe, T.; Schroeder, M.; et al. SGD, Saccharomyces genome database. Nucleic Acids Res. 1998, 26, 73–79, doi:10.1093/nar/26.1.73.
[13]  Gasch, A.P.; Spellman, P.T.; Kao, C.M.; Carmel-Harel, O.; Eisen, M.B.; Storz, G.; Botstein, D.; Brown, P.O. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell. 2000, 11, 4241–4257, doi:10.1091/mbc.11.12.4241.
[14]  Harbison, C.T.; Gordon, D.B.; Lee, T.I.; Rinaldi, N.J.; Macisaac, K.D.; Danford, T.W.; Hannett, N.M.; Tagne, J.B.; Reynolds, D.B.; Yoo, J.; et al. Transcriptional regulatory code of a eukaryotic genome. Nature 2004, 431, 99–104, doi:10.1038/nature02800.
[15]  Teixeira, M.C.; Monteiro, P.; Jain, P.; Tenreiro, S.; Fernandes, A.R.; Mira, N.P.; Alenquer, M.; Freitas, A.T.; Oliveira, A.L.; Sa-Correia, I. The YEASTRACT database, a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res. 2006, 34, D446–D451, doi:10.1093/nar/gkj013.
[16]  Stark, C.; Breitkreutz, B.J.; Reguly, T.; Boucher, L.; Breitkreutz, A.; Tyers, M. BioGRID, a general repository for interaction datasets. Nucleic Acids Res. 2006, 34, D535–D539, doi:10.1093/nar/gkj109.
[17]  Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene ontology, Tool for the unification of biology. The gene ontology consortium. Nat. Genet. 2000, 25, 25–29, doi:10.1038/75556.
[18]  Chen, H.C.; Lee, H.C.; Lin, T.Y.; Li, W.H.; Chen, B.S. Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics 2004, 20, 1914–1927, doi:10.1093/bioinformatics/bth178.
[19]  Chen, B.S.; Wang, Y.C.; Wu, W.S.; Li, W.H. A new measure of the robustness of biochemical networks. Bioinformatics 2005, 21, 2698–2705, doi:10.1093/bioinformatics/bti348.
[20]  Chang, W.C.; Li, C.W.; Chen, B.S. Quantitative inference of dynamic regulatory pathways via microarray data. BMC Bioinf. 2005, 6, 44–62, doi:10.1186/1471-2105-6-44.
[21]  Lin, L.H.; Lee, H.C.; Li, W.H.; Chen, B.S. Dynamic modeling of cis-regulatory circuits and gene expression prediction via cross-gene identification. BMC Bioinf. 2005, 6, 258–274, doi:10.1186/1471-2105-6-258.
[22]  Chang, Y.H.; Wang, Y.C.; Chen, B.S. Identification of transcription factor cooperativity via stochastic system model. Bioinformatics 2006, 22, 2276–2282, doi:10.1093/bioinformatics/btl380.
[23]  Wu, W.S.; Li, W.H.; Chen, B.S. Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle. BMC Bioinf. 2006, 7, 421–435, doi:10.1186/1471-2105-7-421.
[24]  Wu, W.S.; Li, W.H.; Chen, B.S. Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data. BMC Bioinf. 2007, 8, 188–205, doi:10.1186/1471-2105-8-188.
[25]  Chen, B.S.; Wang, Y.C. On the attenuation and amplification of molecular noise in genetic regulatory networks. BMC Bioinf. 2006, 7, 52–65, doi:10.1186/1471-2105-7-52.
[26]  Chen, B.-S.; Li, C.-W. Analysing microarray data in drug discovery using systems biology. Expert Opin. Drug Discovery 2007, 2, 755–768, doi:10.1517/17460441.2.5.755.
[27]  Chen, B.S.; Wu, W.S.; Wang, Y.C.; Li, W.H. On the Robust Circuit Design Schemes of Biochemical Networks, Steady-State Approach. IEEE T Biomed. Circ. S 2007, 1, 91–104, doi:10.1109/TBCAS.2007.907060.
[28]  Chen, B.S.; Chen, P.W. Robust Engineered Circuit Design Principles for Stochastic Biochemical Networks With Parameter Uncertainties and Disturbances. IEEE T Biomed. Circ. S 2008, 2, 114–132, doi:10.1109/TBCAS.2008.926728.
[29]  Chen, B.S.; Lin, Y.P. A unifying mathematical framework for genetic robustness, environmental robustness, network robustness and their trade-off on phenotype robustness in biological networks Part I, Gene regulatory networks in systems and evolutionary biology. Evol. Bioinform 2013, 9, 43–68, doi:10.4137/EBO.S10080.
[30]  Chen, B.S.; Lin, Y.P. A Unifying mathematical framework for genetic robustness, environmental robustness, Network robustness and their tradeoff on phenotype robustness in biological networks Part II, Ecological networks. Evol. Bioinform 2013, 9, 69–85, doi:10.4137/EBO.S10685.
[31]  Wang, Y.C.; Huang, S.H.; Lan, C.Y.; Chen, B.S. Prediction of phenotype-associated genes via a cellular network approach, a candida albicans infection case study. PloS One 2012, 7, e35339.
[32]  Kuo, Z.Y.; Chuang, Y.J.; Chao, C.C.; Liu, F.C.; Lan, C.Y.; Chen, B.S. Identification of infection- and defense-related genes via a dynamic host-pathogen interaction network using a candida albicans -zebrafish infection model. J. Innate Immun. 2013, 5, 137–152, doi:10.1159/000347104.
[33]  Tu, C.T.; Chen, B.S. New measurement methods of network robustness and response ability via microarray data. PloS One 2013, 8, e55230.
[34]  Tu, C.T.; Chen, B.S. On the Increase in Network Robustness and Decrease in Network Response Ability During the Aging Process, A Systems Biology Approach via Microarray Data. IEEE/ACM Trans. Comput. Biol. Bioinf. 2013, 10, 468–480, doi:10.1109/TCBB.2013.23.
[35]  Csete, M.E.; Doyle, J.C. Reverse engineering of biological complexity. Science 2002, 295, 1664–1669, doi:10.1126/science.1069981.
[36]  Lee, K.H.; Park, J.H.; Kim, T.Y.; Kim, H.U.; Lee, S.Y. Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol. 2007, 3, 149.
[37]  Park, J.H.; Lee, S.Y.; Kim, T.Y.; Kim, H.U. Application of systems biology for bioprocess development. Trends Biotechnol 2008, 26, 404–412, doi:10.1016/j.tibtech.2008.05.001.
[38]  Chen, B.S.; Chang, Y.T.; Wang, Y.C. Robust H infinity-stabilization design in gene networks under stochastic molecular noises, fuzzy-interpolation approach. IEEE Trans. Syst. Man Cybern B Cybern 2008, 38, 25–42, doi:10.1109/TSMCB.2007.906975.
[39]  Chu, L.H.; Chen, B.S. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets. BMC Syst. Biol. 2008, 2, 56, doi:10.1186/1752-0509-2-56.
[40]  Chen, B.S.; Chang, C.H.; Chuang, Y.J. Robust model matching control of immune systems under environmental disturbances, Dynamic game approach. J. Theor. Biol. 2008, 253, 824–837, doi:10.1016/j.jtbi.2008.04.024.
[41]  Chen, B.S.; Yang, S.K.; Lan, C.Y.; Chuang, Y.J. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining. BMC Med. Genomics 2008, 1, 46, doi:10.1186/1755-8794-1-46.
[42]  Chen, B.S.; Chen, P.W. On the estimation of robustness and filtering ability of dynamic biochemical networks under process delays, internal parametric perturbations and external disturbances. Math. Biosci. 2009, 222, 92–108, doi:10.1016/j.mbs.2009.09.004.
[43]  Wang, Y.C.; Lan, C.Y.; Hsieh, W.P.; Murillo, L.A.; Agabian, N.; Chen, B.S. Global screening of potential Candida albicans biofilm-related transcription factors via network comparison. BMC Bioinf. 2010, 11, 53–73, doi:10.1186/1471-2105-11-53.
[44]  Li, C.W.; Chen, B.S. Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses. Comp. Funct. Genom. 2010, 2010, 408705.
[45]  Gardner, T.S.; Cantor, C.R.; Collins, J.J. Construction of a genetic toggle switch in Escherichia coli. Nature 2000, 403, 339–342, doi:10.1038/35002131.
[46]  Elowitz, M.B.; Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 2000, 403, 335–338, doi:10.1038/35002125.
[47]  Hasty, J.; McMillen, D.; Collins, J.J. Engineered gene circuits. Nature 2002, 420, 224–230, doi:10.1038/nature01257.
[48]  McDaniel, R.; Weiss, R. Advances in synthetic biology, on the path from prototypes to applications. Curr. Opin. Biotechnol. 2005, 16, 476–483, doi:10.1016/j.copbio.2005.07.002.
[49]  Endy, D. Foundations for engineering biology. Nature 2005, 438, 449–453, doi:10.1038/nature04342.
[50]  McAdams, H.H.; Arkin, A. It’s a noisy business! Genetic regulation at the nanomolar scale. Trends Genet. 1999, 15, 65–69, doi:10.1016/S0168-9525(98)01659-X.
[51]  Batt, G.; Yordanov, B.; Weiss, R.; Belta, C. Robustness analysis and tuning of synthetic gene networks. Bioinformatics 2007, 23, 2415–2422, doi:10.1093/bioinformatics/btm362.
[52]  Chen, B.S.; Chang, C.H.; Lee, H.C. Robust synthetic biology design, stochastic game theory approach. Bioinformatics 2009, 25, 1822–1830, doi:10.1093/bioinformatics/btp310.
[53]  Chen, B.S.; Wu, C.H. A systematic design method for robust synthetic biology to satisfy design specifications. BMC Syst. Biol. 2009, 3, 66–83, doi:10.1186/1752-0509-3-66.
[54]  Chen, B.S.; Chen, P.W. GA-based Design Algorithms for the Robust Synthetic Genetic Oscillators with Prescribed Amplitude, Period and Phase. Gene Regul. Syst. Bio. 2010, 4, 35–52, doi:10.4137/GRSB.S4818.
[55]  Chen, B.S.; Wu, C.H. Robust optimal reference-tracking design method for stochastic synthetic biology systems, T-S fuzzy approach. IEEE T. Fuzzy Syst. 2010, 18, 1144–1159, doi:10.1109/TFUZZ.2010.2070842.
[56]  Wu, C.H.; Zhang, W.H.; Chen, B.S. Multiobjective H-2/H-infinity synthetic gene network design based on promoter libraries. Math. Biosci. 2011, 233, 111–125, doi:10.1016/j.mbs.2011.07.001.
[57]  Wu, C.H.; Lee, H.C.; Chen, B.S. Robust synthetic gene network design via library-based search method. Bioinformatics 2011, 27, 2700–2706, doi:10.1093/bioinformatics/btr465.
[58]  Chen, B.S.; Hsu, C.Y.; Liou, J.J. Robust design of biological circuits, Evolutionary systems biology approach. J. Biomed. Biotechnol. 2011, 2011, 30423.
[59]  Chen, B.S.; Lin, Y.P. A unifying mathematical framework for genetic robustness, Environmental robustness, Network robustness and their trade-offs on phenotype robustness in biological networks. Part III, Synthetic gene networks in synthetic biology. Evol. Bioinform 2013, 9, 87–109, doi:10.4137/EBO.S10686.
[60]  Chen, B.S.; Hsu, C.Y. Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing. BMC Systems Biology 2012, 6, 136–150, doi:10.1186/1752-0509-6-136.
[61]  Kim, P.J.; Lee, D.Y.; Kim, T.Y.; Lee, K.H.; Jeong, H.; Lee, S.Y.; Park, S. Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc. Natl. Acad. Sci. USA 2007, 104, 13638–13642.
[62]  Segre, D.; Vitkup, D.; Church, G.M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 2002, 99, 15112–15117, doi:10.1073/pnas.232349399.
[63]  Chandran, D.; Copeland, W.B.; Sleight, S.C.; Sauro, H.M. Mathematical modeling and synthetic biology. Drug Discovery Today 2008, 5, 299–309, doi:10.1016/j.ddmec.2008.09.006.
[64]  Zhou, S.; Iverson, A.G.; Grayburn, W.S. Engineering a native homoethanol pathway in Escherichia coli B for ethanol production. Biotechnol. Lett. 2008, 30, 335–342.
[65]  Shlomi, T.; Eisenberg, Y.; Sharan, R.; Ruppin, E. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol. 2007, 3, 101–107.
[66]  Jamshidi, N.; Palsson, B.O. Formulating genome-scale kinetic models in the post-genome era. Mol. Syst. Biol. 2008, 4, 171–186.
[67]  Bonneau, R.; Facciotti, M.T.; Reiss, D.J.; Schmid, A.K.; Pan, M.; Kaur, A.; Thorsson, V.; Shannon, P.; Johnson, M.H.; Bare, J.C.; et al. A predictive model for transcriptional control of physiology in a free living cell. Cell 2007, 131, 1354–1365, doi:10.1016/j.cell.2007.10.053.
[68]  Kuepfer, L.; Sauer, U.; Parrilo, P.A. Efficient classification of complete parameter regions based on semidefinite programming. BMC Bioinf. 2007, 8, 12–22, doi:10.1186/1471-2105-8-12.
[69]  Yao, C.W.; Hsu, B.D.; Chen, B.S. Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana. BMC Bioinf. 2011, 12, 335, doi:10.1186/1471-2105-12-335.
[70]  Wang, Y.C.; Chen, B.S. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer. BMC Med. Genomics 2011, 4, 2–16, doi:10.1186/1755-8794-4-2.
[71]  Wang, Y.C.; Chen, B.S. Integrated cellular network of transcription regulations and protein-protein interactions. BMC Syst. Biol. 2010, 4, 20–36, doi:10.1186/1752-0509-4-20.
[72]  Shiue, E.; Prather, K.L.J. Synthetic biology devices as tools for metabolic engineering. Biochem. Eng. J. 2012, 65, 82–89, doi:10.1016/j.bej.2012.04.006.
[73]  Chen, B.S.; Zhang, W.H. Stochastic H(2)/H(infinity) control with state-dependent noise. IEEE T Automat. Contr. 2004, 49, 45–57, doi:10.1109/TAC.2003.821400.
[74]  Zhang, W.H.; Chen, B.S.; Tseng, C.S. Robust H-infinity filtering for nonlinear stochastic systems. IEEE T Signal. Proces. 2005, 53, 589–598, doi:10.1109/TSP.2004.840724.
[75]  Zhang, W.H.; Chen, B.S. State feedback H(infinity) control for a class of nonlinear stochastic systems. Siam J. Control. Optim 2006, 44, 1973–1991, doi:10.1137/S0363012903423727.
[76]  Hughes, T.R.; Marton, M.J.; Jones, A.R.; Roberts, C.J.; Stoughton, R.; Armour, C.D.; Bennett, H.A.; Coffey, E.; Dai, H.Y.; He, Y.D.D.; et al. Functional discovery via a compendium of expression profiles. Cell 2000, 102, 109–126, doi:10.1016/S0092-8674(00)00015-5.
[77]  Johansson, R. System Modeling & Identification; Prentice-Hall, Inc.: London, UK, 1993.
[78]  Li, C.W.; Chu, Y.H.; Chen, B.S. Construction and clarification of dynamic gene regulatory network of cancer cell cycle via microarray data. Cancer Informatics 2006, 2, 223–241.
[79]  Boyd, S.P. Linear Matrix Inequalities in System and Control Theory; Society for Industrial and Applied Mathematics: Philadelphia, PA, USA, 1994.
[80]  Kitano, H. Biological robustness. Nat. Rev. Genet. 2004, 5, 826–837, doi:10.1038/nrg1471.
[81]  Chen, B.S.; Chen, W.H.; Wu, H.L. Robust H-2/H-infinity global linearization filter design for nonlinear stochastic systems. IEEE T Circuits-I 2009, 56, 1441–1454, doi:10.1109/TCSI.2008.2007059.
[82]  Chen, B.S.; Lee, T.S.; Feng, J.H. A nonlinear H-Infinity control design in robotic systems under parameter perturbation and external disturbance. Int J. Control. 1994, 59, 439–461, doi:10.1080/00207179408923085.
[83]  Chen, B.S.; Tseng, C.S.; Uang, H.J. Mixed H-2/H-infinity fuzzy output feedback control design for nonlinear dynamic systems, An LMI approach. IEEE T. Fuzzy Syst. 2000, 8, 249–265, doi:10.1109/91.855915.
[84]  Stricker, J.; Cookson, S.; Bennett, M.R.; Mather, W.H.; Tsimring, L.S.; Hasty, J. A fast, robust and tunable synthetic gene oscillator. Nature 2008, 456, 516–519, doi:10.1038/nature07389.
[85]  Tigges, M.; Marquez-Lago, T.T.; Stelling, J.; Fussenegger, M. A tunable synthetic mammalian oscillator. Nature 2009, 457, 309–312, doi:10.1038/nature07616.
[86]  Basu, S.; Gerchman, Y.; Collins, C.H.; Arnold, F.H.; Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 2005, 434, 1130–1134, doi:10.1038/nature03461.
[87]  Tsai, T.Y.; Choi, Y.S.; Ma, W.; Pomerening, J.R.; Tang, C.; Ferrell, J.E., Jr. Robust, Tunable biological oscillations from interlinked positive and negative feedback loops. Science 2008, 321, 126–129, doi:10.1126/science.1156951.
[88]  Alon, U. An Introduction to Systems Biology, Design Principles of Biological Circuits; Chapman & Hall/CRC: Boca Raton, FL, USA, 2007.
[89]  Ellis, T.; Wang, X.; Collins, J.J. Diversity-based, Model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol 2009, 27, 465–471, doi:10.1038/nbt.1536.
[90]  Mondragon-Palomino, O.; Danino, T.; Selimkhanov, J.; Tsimring, L.; Hasty, J. Entrainment of a Population of Synthetic Genetic Oscillators. Science 2011, 333, 1315–1319, doi:10.1126/science.1205369.
[91]  Ghosh, S.; Matsuoka, Y.; Asai, Y.; Hsin, K.Y.; Kitano, H. Software for systems biology, from tools to integrated platforms. Nat. Rev. Genet. 2011, 12, 821–832.
[92]  Tsai, K.Y.; Wang, F.S. Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics 2005, 21, 1180–1188, doi:10.1093/bioinformatics/bti099.
[93]  Chen, B.S.; Tseng, C.S.; Uang, H.J. Robustness design of nonlinear dynamic systems via fuzzy linear control. IEEE T. Fuzzy Syst. 1999, 7, 571–585, doi:10.1109/91.797980.
[94]  Kitano, H. Computational systems biology. Nature 2002, 420, 206–210, doi:10.1038/nature01254.

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