Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks
- PDF / 479,778 Bytes
- 13 Pages / 595.28 x 793.7 pts Page_size
- 97 Downloads / 141 Views
RESEARCH
Open Access
Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks Elena S Dimitrova1*, Indranil Mitra2 and Abdul Salam Jarrah3,4
Abstract Elucidating the structure and/or dynamics of gene regulatory networks from experimental data is a major goal of systems biology. Stochastic models have the potential to absorb noise, account for un-certainty, and help avoid data overfitting. Within the frame work of probabilistic polynomial dynamical systems, we present an algorithm for the reverse engineering of any gene regulatory network as a discrete, probabilistic polynomial dynamical system. The resulting stochastic model is assembled from all minimal models in the model space and the probability assignment is based on partitioning the model space according to the likeliness with which a minimal model explains the observed data. We used this method to identify stochastic models for two published synthetic network models. In both cases, the generated model retains the key features of the original model and compares favorably to the resulting models from other algorithms. Keywords: Stochastic modeling, polynomial dynamical systems, reverse engineering, discrete modeling
Introduction The enormous accumulation of experimental data on the activities of the living cell has triggered an increasing interest in uncovering the biological networks behind the observed data. This interest could be in identifying either the static network, which is usually a labeled directed graph describing how the different components of the network are wired together, or the dynamic network, which describes how the different components of the network influence each other. Identifying dynamic models for gene regulatory networks from transcriptome data is the topic of numerous published articles, and methods have been proposed within different computational frameworks, such as continuous models using differential equations [1,2], discrete models using Boolean networks [3], Petri nets [4-6], or Logical models [7,8], and statistical models using dynamic Baysein networks [9,10], among many other methods. For an up-to-date review of the state-of-the-art of the field, see, for example [11,12]. Most of these methods identify a particular model of the network which could be deterministic or stochastic. Due to the fact that the experimental data are typically noisy * Correspondence: [email protected] 1 Department of Mathematical Sciences, Clemson University, Clemson, SC 29634-0975, USA Full list of author information is available at the end of the article
and of limited amount and that gene regulatory networks are believed to be stochastic, regardless of the used framework, stochastic models seem a natural choice [9,13,14]. Furthermore, discrete models where a gene could be in one of a finite number of states are more intuitive, phenomenological descriptions of gene regulatory networks and, at the same time, do not require much data to build. These models could actually be more suitable, especially for lar
Data Loading...
