Regulatory network reconstruction using an integral additive model with flexible kernel functions

A novel procedure for statistical inference and verification of gene regulatory subnetwork

Background

The reconstruction of gene regulatory network from time course microarray data can help us comprehensively understand the biological system and discover the pathogenesis of cancer and other diseases. But how to correctly and efficiently decifer the gene regulatory network from high-throughput gene expression data is a big challenge due to the relatively small amount of observations and curse of dimensionality. Computational biologists have developed many statistical inference and machine learning algorithms to analyze the microarray data. In the previous studies, the correctness of an inferred regulatory network is manually checked through comparing with public database or an existing model.

Results

In this work, we present a novel procedure to automatically infer and verify gene regulatory networks from time series expression data. The dynamic Bayesian network, a statistical inference algorithm, is at first implemented to infer an optimal network from time series microarray data of S. cerevisiae, then, a weighted symbolic model checker is applied to automatically verify or falsify the inferred network through checking some desired temporal logic formulas abstracted from experiments or public database.

Conclusions

Our studies show that the marriage of statistical inference algorithm with model checking technique provides a more efficient way to automatically infer and verify the gene regulatory network from time series expression data than previous studies.

Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

Expression and network analysis of genes related to melanocyte development in the Silky Fowl and White Leghorn embryos

Silky Fowl is a natural mutant with hyperpigmentation of various internal tissues. Although the mechanism of hyperpigmentation remains unclear, recent studies have shown that the abnormal migration of melanoblast and the absence of environmental barrier molecules are responsible for the hyperpigmentation in Silky Fowl. In this study, 13 genes related to melanocyte development were selected to detect expression changes between Silky Fowl and White Leghorn [including SRY-box 10 (Sox10), paired box (Pax3), stem cell factor (Scf), v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (Kit), endothelin type-B receptor (Ednrb), endothelin 3 (Edn3), microphthalmia-associated transcription factor (Mitf), tyrosinase (Tyr), tyrosinase-related protein-1 (Trp1), tyrosinase-related protein-2 (Trp2), melanocortin-1 receptor (Mc1r), Agouti-related proteins (Agrp), and Proopiomelanocortin (Pomc)]. Transcript expression was detected in 11 stages from 2.5 to 15 days of incubation. In these embryonic periods, Mitf, Kit, Scf, and Agrp expressed earlier in Silky Fowl than in White Leghorn. Sox10, Ednrb, Kit, Mc1r, and Agrp, associating with the proliferation and differentiation of melanoblast, expressed higher (P < 0.05) in Silky Fowl than White Leghorn during 5-6 days of incubation. After day 8 of incubation, Mitf, Tyr, Trp1, Trp2, and Mc1r expressed higher (P < 0.05) in Silky Fowl than White Leghorn, while Agrp expressed higher (P < 0.05) in White Leghorn than Silky Fowl. Moreover, a regulatory network for melanocyte development was constructed based on the expression data. The network predicted novel regulatory relationships and confirmed relationships that have been reported. These results provide biological insight into the molecular mechanism of hyperpigmentation in the Silky Fowl. However, further investigation is needed to confirm these regulatory relationships.

Inference of gene regulatory networks using time-series data: a survey

The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.