Reverse engineering of genetic networks with time delayed recurrent neural networks and clustering techniques
Reads0
Chats0
TLDR
This dissertation proposes a novel model to simulate gene regulatory networks using a specific type of time delayed recurrent neural networks and introduces a parameter clustering method to select groups of parameter sets from the simulations representing biologically reasonable networks.Abstract:
In the iterative process of experimentally probing biological networks and computationally inferring models for the networks, fast, accurate and flexible computational frameworks are needed for modeling and reverse engineering biological networks. In this dissertation, I propose a novel model to simulate gene regulatory networks using a specific type of time delayed recurrent neural networks. Also, I introduce a parameter clustering method to select groups of parameter sets from the simulations representing biologically reasonable networks. Additionally, a general purpose adaptive function is used here to decrease and study the connectivity of small gene regulatory networks modules. In this dissertation, the performance of this novel model is shown to simulate the dynamics and to infer the topology of gene regulatory networks derived from synthetic and experimental time series gene expression data. Here, I assess the quality of the inferred networks by the use of graph edit distance measurements in comparison to the synthetic and experimental benchmarks. Additionally, I compare between edition costs of the inferred networks obtained with the time delay recurrent networks and other previously described reverse engineering methods based on continuous time recurrent neural and dynamic Bayesian networks. Furthermore, I address questions of network connectivity and correlation between data fitting and inference power by simulating common experimental limitations of the reverse engineering process as incomplete and highly noisy data. The novel specific type of time delay recurrent neural networks model in combination with parameter clustering substantially improves the inference power of reverse engineered networks. Additionally, some suggestions for future improvements are discussed, particularly under the data driven perspective as the solution for modeling complex biological systems.read more
References
More filters
Journal ArticleDOI
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Journal ArticleDOI
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon,Andrew Markiel,Owen Ozier,Nitin S. Baliga,Jonathan T. Wang,Daniel Ramage,Nada Amin,Benno Schwikowski,Trey Ideker +8 more
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI
Exact Stochastic Simulation of Coupled Chemical Reactions
TL;DR: In this article, a simulation algorithm for the stochastic formulation of chemical kinetics is proposed, which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of a given chemical system.