Boolean network inference from time series data incorporating prior biological knowledge

Saad Haider,Ranadip Pal

doi:10.1186/1471-2164-13-s6-s9

Abstract

BackgroundNumerous approaches exist for modeling of genetic regulatory networks (GRNs) but the low sampling rates often employed in biological studies prevents the inference of detailed models from experimental data. In this paper, we analyze the issues involved in estimating a model of a GRN from single cell line time series data with limited time points.ResultsWe present an inference approach for a Boolean Network (BN) model of a GRN from limited transcriptomic or proteomic time series data based on prior biological knowledge of connectivity, constraints on attractor structure and robust design. We applied our inference approach to 6 time point transcriptomic data on Human Mammary Epithelial Cell line (HMEC) after application of Epidermal Growth Factor (EGF) and generated a BN with a plausible biological structure satisfying the data. We further defined and applied a similarity measure to compare synthetic BNs and BNs generated through the proposed approach constructed from transitions of various paths of the synthetic BNs. We have also compared the performance of our algorithm with two existing BN inference algorithms.ConclusionsThrough theoretical analysis and simulations, we showed the rarity of arriving at a BN from limited time series data with plausible biological structure using random connectivity and absence of structure in data. The framework when applied to experimental data and data generated from synthetic BNs were able to estimate BNs with high similarity scores. Comparison with existing BN inference algorithms showed the better performance of our proposed algorithm for limited time series data. The proposed framework can also be applied to optimize the connectivity of a GRN from experimental data when the prior biological knowledge on regulators is limited or not unique.

Highlights

Technological advances in the last two decades have provided numerous approaches to measure various aspects of the regulome in a cell
The proposed framework can be applied to optimize the connectivity of a genetic regulatory networks (GRNs) from experimental data when the prior biological knowledge on regulators is limited or not unique
In systems biology, we are often faced with the issue of reverse engineering a GRN model from limited time series data

Summary

Introduction

Technological advances in the last two decades have provided numerous approaches to measure various aspects of the regulome in a cell. Models of genetic regulatory network (GRN) are regularly being inferred from limited time series data on average tissue expression as measured by technologies such as microarray. The inferred DE models were unable to capture any rhythmic behavior present in the experimental data and produced models with significantly different transient and steady-state behaviors for different runs of the parameter optimization procedure. To capture the rhythmic behavior, specific DE models such as Goodwin Models [2] were employed but optimization procedures were unable to infer stable rhythmic models from the 6 data points. Numerous approaches exist for modeling of genetic regulatory networks (GRNs) but the low sampling rates often employed in biological studies prevents the inference of detailed models from experimental data. We analyze the issues involved in estimating a model of a GRN from single cell line time series data with limited time points

Methods

Results

Conclusion