Modeling and simulation of genetic regulatory systems: a literature review
H De Jong - Journal of computational biology, 2002 - liebertpub.com
In order to understand the functioning of organisms on the molecular level, we need to know
which genes are expressed, when and where in the organism, and to which extent. The …
which genes are expressed, when and where in the organism, and to which extent. The …
Gene regulatory network inference: data integration in dynamic models—a review
M Hecker, S Lambeck, S Toepfer, E Van Someren… - Biosystems, 2009 - Elsevier
Systems biology aims to develop mathematical models of biological systems by integrating
experimental and theoretical techniques. During the last decade, many systems biological …
experimental and theoretical techniques. During the last decade, many systems biological …
Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
Motivation: Our goal is to construct a model for genetic regulatory networks such that the
model class:(i) incorporates rule-based dependencies between genes;(ii) allows the …
model class:(i) incorporates rule-based dependencies between genes;(ii) allows the …
[图书][B] Bioinformatics: the machine learning approach
A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …
data. An unprecedented wealth of data is being generated by genome sequencing projects …
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory
interactions, and apply the method to predict a large portion of the regulatory network of the …
interactions, and apply the method to predict a large portion of the regulatory network of the …
Controllability of probabilistic Boolean control networks based on transition probability matrices
In this paper, we propose a new approach to investigate the controllability and reachability
of probabilistic Boolean control networks (PBCNs) with forbidden states. We first give a …
of probabilistic Boolean control networks (PBCNs) with forbidden states. We first give a …
Reverse-engineering transcription control networks
TS Gardner, JJ Faith - Physics of life reviews, 2005 - Elsevier
Microarray technologies, which enable the simultaneous measurement of all RNA
transcripts in a cell, have spawned the development of algorithms for reverse-engineering …
transcripts in a cell, have spawned the development of algorithms for reverse-engineering …
[图书][B] Handbook of computational molecular biology
S Aluru - 2005 - taylorfrancis.com
The enormous complexity of biological systems at the molecular level must be answered
with powerful computational methods. Computational biology is a young field, but has seen …
with powerful computational methods. Computational biology is a young field, but has seen …
Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data
S Kim, S Imoto, S Miyano - Biosystems, 2004 - Elsevier
We propose a dynamic Bayesian network and nonparametric regression model for
constructing a gene network from time series microarray gene expression data. The …
constructing a gene network from time series microarray gene expression data. The …
Modeling T-cell activation using gene expression profiling and state-space models
Motivation: We have used state-space models to reverse engineer transcriptional networks
from highly replicated gene expression profiling time series data obtained from a well …
from highly replicated gene expression profiling time series data obtained from a well …