(141f) Reverse Engineering Validation Using a Benchmark Synthetic Gene Circuit in Human Cells

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.

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