Mathematical models for biological networks and machine learning with applications

Systems biology studies complex systems which involve a large number of interacting entities such that their dynamics follow systematical regulations for transition. To develop computational models becomes an urgent need for studying and manipulating biologically relevant systems. The properties and behaviors of complex biological systems can be analyzed and studied by using computational biological network models. In this thesis, construction and computation methods are proposed for studying biological networks. Modeling Genetic Regulatory Networks (GRNs) is an important topic in genomic research. A number of promising formalisms have been developed in capturing the behavior of gene regulations in biological systems. Boolean Network (BN) has received sustainable attentions. Furthermore, it is possible to control one or more genes in a network so as to avoid the network entering into undesired states. Many works have been done on the control policy for a single randomly generated BN, little light has been shed on the analysis of attractor control problem for multiple BNs. An efficient algorithm was developed to study the attractor control problem for multiple BNs. However, one should note that a BN is a deterministic model, a stochastic model is more preferable in practice. Probabilistic Boolean Network (PBN), was proposed to better describe the behavior of genetic process. A PBN can be considered as a Markov chain process and the construction of a PBN is an inverse problem which is computationally challenging. Given a positive stationary distribution, the problem of constructing a sparse PBN was discussed. For the related inverse problems, an efficient algorithm was developed based on entropy approach to estimate the model parameters. The metabolite biomarker discovery problem is a hot topic in bioinformatics. Biomarker identification plays a vital role in the study of biochemical reactions and signalling networks. The lack of essential metabolites may result in triggering human diseases. An effective computational approach is proposed to identify metabolic biomarkers by integrating available biomedical data and disease-specific gene expression data. Pancreatic cancer prediction problem is another hot topic. Pancreatic cancer is known to be difficult to diagnose in the early stage, and early research mainly focused on predicting the survival rate of pancreatic cancer patients. The correct prediction of various disease states can greatly benefit patients and also assist in design of effective and personalized therapeutics. The issue of how to integrating the available laboratory data with classification techniques is an important and challenging issue. An effective approach was suggested to construct a feature space which serves as a significant predictor for classification. Furthermore, a novel method for identifying the outliers was proposed for improving the classification performance. Using our preoperative clinical laboratory data and histologically confirmed pancreatic cancer samples, computational experiments are conducted successfully with the use of Support Vector Machine (SVM) to predict the status of patients.published_or_final_versionMathematicsDoctoralDoctor of Philosoph

Exact Identification of the Structure of a Probabilistic Boolean Network from Samples

We study the number of samples required to uniquely determine the structure of a probabilistic Boolean network (PBN), where PBNs are probabilistic extensions of Boolean networks. We show via theoretical analysis and computational analysis that the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for interesting classes of PBNs of bounded indegree. On the other hand, we also show that there exist classes of PBNs for which it is impossible to uniquely determine the structure of a PBN from samples

ON CLASSIFICATION OF BIOLOGICAL DATA USING OUTLIER DETECTION

Abstract With the rapid development of information technology, the number of datasets, as well as their complexity and dimension, have been growing dramatically. This dramatic growth of biology data and non-biological commercial databases becomes a challenging issue in data mining. Classification technique is one of the major tools in the captured research area. However, the performance of classification may be degraded when there exists noise in the captured databases. Therefore, outlier detection becomes an urgent need and the issue of how to integrate outlier detection method and classification techniques is an important and challenging issue. In this paper, we proposed a novel and effective approach based on k-means clustering to identify outliers in the databases. In particular, we employed one of famous classification techniques, Support Vector Machine (SVM), owing to its ability to handle highdimensional data set. We also compare the classification results with the multivariate outlier detection method. Numerical results on two different data sets indicate that the classification results after removing the outliers by our proposed method are much better than the multivariate outlier detection method

Identification of a comprehensive alternative splicing function during epithelial-mesenchymal transition

Summary: Epithelial-to-mesenchymal transition (EMT) is the underlying mechanism for tumor metastasis and shows the metastatic potential of tumor cells. Although the transcriptional regulation of EMT has been well studied, the role of alternative splicing (AS) regulation in EMT remains largely uncharacterized. The rapid accumulation of RNA-seq datasets has provided the opportunities for developing computational methods to associate mRNA isoform variations with EMT. In this study, we propose regularization models to identify significant AS events during EMT. Our experimental results confirm that the predicted AS events are closely related to apoptosis, focal adhesion-invadopodium shift and tight junction formation that are essential during EMT. Therefore, our study highlights the broad role of posttranscriptional regulation during EMT and identifies key subsets of AS events serving as distinct regulatory nodes

PCB: A pseudotemporal causality-based Bayesian approach to identify EMT-associated regulatory relationships of AS events and RBPs during breast cancer progression.

During breast cancer metastasis, the developmental process epithelial-mesenchymal (EM) transition is abnormally activated. Transcriptional regulatory networks controlling EM transition are well-studied; however, alternative RNA splicing also plays a critical regulatory role during this process. Alternative splicing was proved to control the EM transition process, and RNA-binding proteins were determined to regulate alternative splicing. A comprehensive understanding of alternative splicing and the RNA-binding proteins that regulate it during EM transition and their dynamic impact on breast cancer remains largely unknown. To accurately study the dynamic regulatory relationships, time-series data of the EM transition process are essential. However, only cross-sectional data of epithelial and mesenchymal specimens are available. Therefore, we developed a pseudotemporal causality-based Bayesian (PCB) approach to infer the dynamic regulatory relationships between alternative splicing events and RNA-binding proteins. Our study sheds light on facilitating the regulatory network-based approach to identify key RNA-binding proteins or target alternative splicing events for the diagnosis or treatment of cancers. The data and code for PCB are available at: http://hkumath.hku.hk/~wkc/PCB(data+code).zip

EFHD1 expression is correlated with tumor-infiltrating neutrophils and predicts prognosis in gastric cancer

Background: Gastric cancer (GC) ranks third in terms of mortality worldwide. The tumor microenvironment is critical for the progression of gastric cancer. This study investigated the association between EF-hand domain containing 1 (EFHD1) expression and its clinical significance in the tumor microenvironment (TME) of gastric cancer. Methods: We used bioinformatic analyses to assess the relevance of EFHD1 mRNA in the TME of gastric carcinoma tissues and its relationship with clinical features. Therefore, we performed multiplex immunohistochemistry analyses to determine the potential role of the EFHD1 protein in the TME of gastric cancer. Results: EFHD1 expression increased dramatically in gastric cancer tissues compared to levels in non-cancerous tissue samples (t = 6.246, P < 0.001). The EFHD1 protein presentation was associated with invasion depth (χ2 = 19.120, P < 0.001) and TNM stages (χ2 = 14.468, P = 0.002). Notably, EFHD1 protein expression was significantly related to CD66b + neutrophil infiltration of the intratumoral (r = 0.420, P < 0.001) and stromal (r = 0.367, P < 0.001) TME in gastric cancer. Additionally, Cox regression analysis revealed that EFHD1 was an independent prognostic predictor (hazard ratio [HR] = 2.262, P < 0.001) in patients with gastric cancer. Conclusions: Our study revealed the pattern of EFHD1 overexpression in the TME of patients with gastric cancer and demonstrated its utility as a biomarker for unfavorable clinical outcomes, thereby providing a potential immunotherapy target

Postsynthetic Functionalization of Three-Dimensional Covalent Organic Frameworks for Selective Extraction of Lanthanide Ions

International audienceChemical functionalization of covalent organic frameworks (COFs) is critical for tuning their properties and broadening their potential applications. However, the introduction of functional groups, especially to three-dimensional (3D) COFs, still remains largely unexplored. Reported here is a general strategy for generating a 3D carboxy-functionalized COF through postsynthetic modification of a hydroxy-functionalized COF, and for the first time exploration of the 3D carboxy-functionalized COF in the selective extraction of lanthanide ions. The obtained COF shows high crystallinity, good chemical stability, and large specific surface area. Furthermore, the carboxy-functionalized COF displays high metal loading capacities together with excellent adsorption selectivity for Nd3+ over Sr2+ and Fe3+ as confirmed by the Langmuir adsorption isotherms and ideal adsorbed solution theory (IAST) calculations. This study not only provides a strategy for versatile functionalization of 3D COFs, but also opens a way to their use in environmentally related applications

Effects of Modified Sanzi Yangqin Decoction on Tyrosine Phosphorylation of IRS-1 in Skeletal Muscle of Type 2 Diabetic Rats

This study aimed to investigate the effect of Modified Sanzi Yangqin Decoction on tyrosine phosphorylation of insulin receptor substrate 1 (IRS-1) in skeletal muscle of type 2 diabetic rats. The rat model of type 2 diabetes was induced by high-fat diet and multiple low-dose streptozotocin injections. Diabetic model rats were randomly divided into 5 groups: the model control group, the metformin group, and Modified Sanzi Yangqin Decoction groups of low, medium, and high doses. OGTT was conducted every two weeks during treatment period. At the end of the treatment, the fasting blood glucose (FBG) level and the fasting C-peptide level were measured to calculate insulin resistance index. The levels of IRS-1, p-IRS-1Tyr895, and protein tyrosine phosphates 1B (PTP1B) in skeletal muscle were also measured. Modified Sanzi Yangqin Decoction significantly reduced the FBG level, increased the fasting C-peptide level, and lowered the insulin resistance index in type 2 diabetic rats. It also significantly increased the protein level of p-IRS-1Tyr895 and reduced the PTP1B protein level in skeletal muscle of type 2 diabetic rats. Modified Sanzi Yangqin Decoction increases tyrosine phosphorylation of IRS-1 in skeletal muscle of type 2 diabetic rats, which results from the increase of p-IRS-1Tyr895 protein and is related to the suppression of PTP1B protein