(141f) Reverse Engineering Validation Using a Benchmark Synthetic Gene Circuit in Human Cells Conference: AIChE Annual MeetingYear: 2013Proceeding: 2013 AIChE Annual MeetingGroup: Topical Conference: Systems BiologySession: Paradigms in Systems Biology - Session II Time: Monday, November 4, 2013 - 2:00pm-2:18pm Authors: Kang, T., The University of Texas at Dallas White, J., University of Texas at Dallas Xie, Z., Tsinghua University Benenson, Y., Swiss Federal Institute of Technology (ETH Zürich) Sontag, E., Northeastern University Bleris, L., The University of Texas at Dallas Multi-component biological networks are often understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. As a consequence, developing automated and rigorously validated methodologies for unraveling the complexity of biomolecular networks in human cells remains a central challenge to life scientists and engineers. Today, when it comes to experimental and analytical requirements, there exists a great deal of diversity in reverse engineering methods, which renders the independent validation and comparison of their predictive capabilities difficult1, 2, 3, 4. While these methods have demonstrated successful network reconstructions on its own, the lack of unifying standards and procedures for validation, along with the high degree of expertise required for computational algorithms, can be regarded as one of the major obstacles which prevents the widespread use of network inference methods. To address this issue, a community-wide effort, DREAM (Dialogue for Reverse Engineering Assessments and Methods), has been initiated to facilitate discussion and refine existing methodologies, resulting in valuable insights about relationships between algorithm performance and experimental parameters5, 6, 7, 8, 9. In this study10 we describe an experimental and theoretical platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. Specifically, we stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for an in vitro platform for reverse engineering verification. The network, which resembles a natural network topology, is orthogonal to endogenous cellular signaling pathway. It combines a set of regulatory interactions consisting of transcriptional and post-transcriptional regulatory elements, where the output fluorescent proteins are subject to three different modes of control: no activation, single source of activation, and combination of activation and repression. As our baseline reverse engineering method we use an approach based on Modular Response Analysis (MRA), where we take experimentally measured steady-state responses following near-linear perturbation of each modular component of the benchmark system11, 12, 13. After performing successive perturbations to each modular components of the network, the pre- and post-perturbation steady states were then used to predict the network structure. To capture the system’s response after each perturbation, we chose to utilize protein and mRNA as the two representative species. We compare the results of the algorithm against the intended network structure, and use these results to determine the appropriate statistical procedure for each measurement in order to increase the prediction confidence. 1. Bansal, M., Belcastro, V., Ambesi-Impiombato, A., and di Bernardo, D. (2007) How to infer gene networks from expression profiles Mol. Syst. Biol. 3, 78. 2. Sprinzak, D., and Elowitz, M. B. (2005) Reconstruction of genetic circuits Nature. 438, 443-448. 3. Albert, R., Dasgupta, B., and Sontag, E. (2010) Inference of signal transduction networks from double causal evidence. Methods Mol. Biol. 673, 239-251. 4. Hendrickx, D. M., Hendriks, M. M., Eilers, P. H., Smilde, A. K., and Hoefsloot, H. C. (2011) Reverse engineering of metabolic networks, a critical assessment Mol. Biosyst. 7, 511-520. 5. Stolovitzky, G., Monroe, D., and Califano, A. (2007) Dialogue on reverse-engineering assessment and methods: The DREAM of high-throughput pathway inference Ann. N. Y. Acad. Sci. 1115, 1-22. 6. Stolovitzky, G., Prill, R. J., and Califano, A. (2009) Lessons from the DREAM2 challenges. Ann. N. Y. Acad. Sci. 1158, 159-195. 7. Marbach, D., Prill, R. J., Schaffter, T., Mattiussi, C., Floreano, D., and Stolovitzky, G. (2010) Revealing strengths and weaknesses of methods for gene network inference Proc. Natl. Acad. Sci. U. S. A. 107, 6286-6291. 8. Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., and Stolovitzky, G. (2012) Wisdom of crowds for robust gene network inference. Nat Meth. 9, 796-804. 9. Prill, R. J., Saez-Rodriguez, J., Alexopoulos, L. G., Sorger, P. K., and Stolovitzky, G. (2011) Crowdsourcing network inference: The DREAM predictive signaling network challenge Sci. Signal. 4, mr7. 10. Kang, T., White, J. T., Xie, Z., Benenson, Y., Sontag, E., and Bleris, L. (2013) Reverse engineering validation using a benchmark synthetic gene circuit in human cells. ACS Synth Biol. http://pubs.acs.org/doi/abs/10.1021/sb300093y 11. Kholodenko, B., Yaffe, M. B., and Kolch, W. (2012) Computational approaches for analyzing information flow in biological networks. Science signaling. 5, re1. 12. Kholodenko, B. N. (2007) Untangling the signalling wires Nat. Cell Biol. 9, 247 <last_page> 249. 13. Sontag, E. D. (2008) Network reconstruction based on steady-state data. 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