10,247 research outputs found
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated by the enormously large scale of the unknowns in a rather
small sample size. Furthermore, when the goal is to study causal relationships
within the network, tools capable of overcoming the limitations of correlation
networks are required. In this work, we make use of Bayesian Graphical Models
to attach this problem and, specifically, we perform a comparative study of
different state-of-the-art heuristics, analyzing their performance in inferring
the structure of the Bayesian Network from breast cancer data
Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes
<b>Method:</b> Dynamic Bayesian networks (DBNs) have been applied widely to reconstruct the structure of regulatory processes from time series data, and they have established themselves as a standard modelling tool in computational systems biology. The conventional approach is based on the assumption of a homogeneous Markov chain, and many recent research efforts have focused on relaxing this restriction. An approach that enjoys particular popularity is based on a combination of a DBN with a multiple changepoint process, and the application of a Bayesian inference scheme via reversible jump Markov chain Monte Carlo (RJMCMC). In the present article, we expand this approach in two ways. First, we show that a dynamic programming scheme allows the changepoints to be sampled from the correct conditional distribution, which results in improved convergence over RJMCMC. Second, we introduce a novel Bayesian clustering and information sharing scheme among nodes, which provides a mechanism for automatic model complexity tuning.
<b>Results:</b> We evaluate the dynamic programming scheme on expression time series for Arabidopsis thaliana genes involved in circadian regulation. In a simulation study we demonstrate that the regularization scheme improves the network reconstruction accuracy over that obtained with recently proposed inhomogeneous DBNs. For gene expression profiles from a synthetically designed Saccharomyces cerevisiae strain under switching carbon metabolism we show that the combination of both: dynamic programming and regularization yields an inference procedure that outperforms two alternative established network reconstruction methods from the biology literature
Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana
Regulation of gene expression is crucial for organism growth, and it is one of the challenges in Systems Biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyse two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants to infer their regulatory network. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale-free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation
Validating module network learning algorithms using simulated data
In recent years, several authors have used probabilistic graphical models to
learn expression modules and their regulatory programs from gene expression
data. Here, we demonstrate the use of the synthetic data generator SynTReN for
the purpose of testing and comparing module network learning algorithms. We
introduce a software package for learning module networks, called LeMoNe, which
incorporates a novel strategy for learning regulatory programs. Novelties
include the use of a bottom-up Bayesian hierarchical clustering to construct
the regulatory programs, and the use of a conditional entropy measure to assign
regulators to the regulation program nodes. Using SynTReN data, we test the
performance of LeMoNe in a completely controlled situation and assess the
effect of the methodological changes we made with respect to an existing
software package, namely Genomica. Additionally, we assess the effect of
various parameters, such as the size of the data set and the amount of noise,
on the inference performance. Overall, application of Genomica and LeMoNe to
simulated data sets gave comparable results. However, LeMoNe offers some
advantages, one of them being that the learning process is considerably faster
for larger data sets. Additionally, we show that the location of the regulators
in the LeMoNe regulation programs and their conditional entropy may be used to
prioritize regulators for functional validation, and that the combination of
the bottom-up clustering strategy with the conditional entropy-based assignment
of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio
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