Abstract
BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.
Highlights
Introduction to Evolutionary ComputationIn Evolutionary Computation 1: Basic Algorithms and Operators
We benchmarked our method with the broad range of inference methods proposed in [6,64,65] and [66]. They all used the IRMA network and its time series data to benchmark their methods with BANJO and TSNI [18,67], as reported by Cantone et al In Figure 4, we show first a comparison between the true IRMA network and the networks inferred by our algorithm for both the switch ON and OFF data
The development of algorithms for the inference of molecular networks from experimental data has received much attention in recent years, and new methods are published regularly. Most of these methods focus on the inference of the network topology and cannot use information about the temporal development of the network
Summary
Introduction to Evolutionary ComputationIn Evolutionary Computation 1: Basic Algorithms and Operators. The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. It has been argued that one can obtain a significant improvement in performance with inference methods that make use of data that capture the dynamics of a network in response to perturbations [3]. Since gene regulatory networks are known to be sparsely connected, many inference methods specify constraints to favor sparse networks in the inference process. Explicit knowledge of the network’s connectivity can be gathered from previous biological knowledge of the system in question [13,14,15,16] or from the contributed knowledge from different inference methods, when heterogenous data types are available (e.g. steady state data vs. time series)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
