A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches

Abstract

Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows "de novo" construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo "benchmarking" of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.

Discriminating direct and indirect connectivities in biological networks

“…Meanwhile, parallel developments in synthetic biology (23) have endowed researchers with new tools that allow precise emulation of naturally occurring topologies (21,22). Networks orthogonal to the cellular milieu can serve as a biomolecular topological "ground truth" (20,24). Data gathered from benchmark synthetic circuits can complement and inform algorithms, and offer a unique opportunity to correlate topological properties to system identification.…”

“…On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, synthetic networks are a versatile platform for investigating specific connectivities and topological properties and can ultimately guide us to deriving fundamental insights about biological systems and pathways (20)(21)(22)(23).…”

How to infer gene networks from expression profiles, revisited

“…While the relative merits of these competing approaches have previously been discussed in the context of in silico [11,13,15,16] and in vivo networks [8,12,13,15], there remains disagreement as to the best approach. Previous work by Bansal et al [11], published in 2007, suggests that while 'further improvements are needed', network inference algorithms have become 'practically useful'.…”

“…To address the various challenges associated with inferring GRNs from transcriptional time series, a number of theoretical approaches have been applied, including ordinary and stochastic differential equations (ODEs/SDEs) [10][11][12], Bayesian and dynamic Bayesian networks (BNs/DBNs) [7,8,[11][12][13] and information theoretic or correlation-based methods [11,14]. While the relative merits of these competing approaches have previously been discussed in the context of in silico [11,13,15,16] and in vivo networks [8,12,13,15], there remains disagreement as to the best approach.…”

Verification of systems biology research in the age of collaborative competition

“…Data simulated with mathematical models that are designed to be as biologically plausible as possible can be used, as simulated data assure a systematic, rigorous assessment 16 . But the use of simulated data does not ensure that the challenge is necessarily realistic 17 . Many different methods, including regression, mutual information, correlation, Bayesian networks and others 18 , have been used to address this challenge.…”