Computational analysis of the roles of ER-Golgi network in the cell cycle

An Integrative Analysis of Time-varying Regulatory Networks From High-dimensional Data

Directed networks have been widely used to describe many biological processes and functions. Understanding the structure of biological networks, especially regulatory networks, could help discover the mechanisms underlying important biological processes and pathogenesis of diseases. Most network inference methods assume the network structure is time-invariant or stationary. However, in some processes, the network structure is non-stationary or time-varying. The stationary network inference methods might not be able to directly used to reconstruct time-varying networks. Some non-stationary network learning methods have been proposed to infer the networks, but, the inferred networks are not regulatory networks which require activation and inhibition information. This work proposes an integrative approach, which combines the changepoint estimation, weighted network learning and searching, and model checking technique, to reconstruct time varying regulatory networks from high-dimensional time series data. We illustrate this approach to study the structure changes of Drosophila's regulatory networks in its life cycle.

GOLGA2 loss causes fibrosis with autophagy in the mouse lung and liver

Autophagy is a biological recycling process via the self-digestion of organelles, proteins, and lipids for energy-consuming differentiation and homeostasis. The Golgi serves as a donor of the double-membraned phagophore for autophagosome assembly. In addition, recent studies have demonstrated that pulmonary and hepatic fibrosis is accompanied by autophagy. However, the relationships among Golgi function, autophagy, and fibrosis are unclear. Here, we show that the deletion of GOLGA2, encoding a cis-Golgi protein, induces autophagy with Golgi disruption. The induction of autophagy leads to fibrosis along with the reduction of subcellular lipid storage (lipid droplets and lamellar bodies) by autophagy in the lung and liver. GOLGA2 knockout mice clearly demonstrated fibrosis features such as autophagy-activated cells, densely packed hepatocytes, increase of alveolar macrophages, and decrease of alveolar surfactant lipids (dipalmitoylphosphatidylcholine). Therefore, we confirmed the associations among Golgi function, fibrosis, and autophagy. Moreover, GOLGA2 knockout mice may be a potentially valuable animal model for studying autophagy-induced fibrosis.

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Nuclear Expression of GS28 Protein: A Novel Biomarker that Predicts Prognosis in Colorectal Cancers

Aims: GS28 (Golgi SNARE protein, 28 kDa), a member of the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) protein family, plays a critical role in mammalian endoplasmic reticulum (ER)-Golgi or intra-Golgi vesicle transport. To date, few researches on the GS28 protein in human cancer tissues have been reported. In this study, we assessed the prognostic value of GS28 in patients with colorectal cancer (CRC). Methods and results: We screened for GS28 expression using immunohistochemistry in 230 surgical CRC specimens. The CRCs were right-sided and left-sided in 28.3% (65/230) and 71.3% (164/230) of patients, respectively. GS28 staining results were available in 214 cases. Among these, there were 26 nuclear predominant cases and 188 non-nuclear predominant cases. Stromal GS28 expression was noted in 152 cases of CRC. GS28 nuclear predominant immunoreactivity was significantly associated with advanced tumour stage (p = 0.045) and marginally associated with perineural invasion (p = 0.064). Decreased GS28 expression in the stromal cells was significantly associated with lymph node metastasis (N stage; p = 0.036). GS28 expression was not associated with epidermal growth factor receptor (EGFR) immunohistochemical positivity or KRAS mutation status. Investigation of the prognostic value of GS28 with Kaplan-Meier analysis revealed a correlation with overall survival (p = 0.004). Cases with GS28 nuclear predominant expression had significantly poorer overall survival than those with a non-nuclear predominant pattern. Conclusions: Taken together, these results indicate that GS28 nuclear predominant expression could serve as a prognostic marker for CRC and may help in identifying aggressive forms of CRC.

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.

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.

InCoB2014: Systems Biology update from the Asia-Pacific. Introduction

Selected papers from the 13th International Conference on Bioinformatics (InCoB2014), July 31-2 August, 2014 in Sydney, Australia have been compiled in this supplement. These range from network analysis and gene regulatory networks to systems level biological analysis, providing the 2014 update to InCoB's computational systems biology research.