A systems biology view of cancer

In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new -omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B-cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.     

Detection, annotation and visualization of alternative splicing from RNA-Seq data with SplicingViewer

Alternative splicing is a crucial mechanism by which diverse gene products can be generated from a limited number of genes, and is thought to be involved in complex orchestration of eukaryotic gene expression. Next-generation sequencing technologies, with reduced time and cost, provide unprecedented opportunities for deep interrogation of alternative splicing at the genome-wide scale. In this study, an integrated software SplicingViewer has been developed for unambiguous detection, annotation and visualization of splice junctions and alternative splicing events from RNA-Seq data. Specifically, it allows easy identification and characterization of splice junctions, and holds a versatile computational pipeline for in-depth annotation and classification of alternative splicing with different patterns. Moreover, it provides a user-friendly environment in which an alternative splicing landscape can be displayed in a straightforward and flexible manner. In conclusion, SplicingViewer can be widely used for studying alternative splicing easily and efficiently. SplicingViewer can be freely accessed at http://bioinformatics.zj.cn/splicingviewer.     

Computational cell biology in the post-genomic era

Rapid accumulation of biological data from novel high throughput technologies characteristic of genomic and proteomic research as well as advances in more traditional biological disciplines are leading to wider use of detailed and complex computational models of cell behavior. These models address a variety of dynamic intracellular processes ranging from interactions within a gene regulation network to intracellular and intercellular signal transduction. This review focuses on the current trends in computation cell biology, particularly emphasizing the role of experimental validation. The recent successes and future challenges facing computational cell biology are also discussed.     

整合分析多组学数据筛选疾病靶点的精准医学策略

随着高通量测序技术的不断发展与完善,对于不同层次和类型的生物组学数据的获取及分析方法也日趋成熟与完善。基于单组学数据的疾病研究已经发现了诸多新的疾病相关因子,而整合多组学数据研究疾病靶点的工作方兴未艾。生命体是一个复杂的调控系统,疾病的发生与发展涉及基因变异、表观遗传改变、基因表达异常以及信号通路紊乱等诸多层次的复杂调控机制,利用单一组学数据分析致病因子的局限性愈发显著。通过对多种层次和来源的高通量组学数据的整合分析,系统地研究临床发病机理、确定最佳疾病靶点已经成为精准医学研究的重要发展方向,将为疾病研究提供新的思路,并对疾病的早期诊断、个体化治疗和指导用药等提供新的理论依据。本文详细介绍了基因组、转录组和表观组等系统组学研究在疾病靶点筛选方面出现的新技术手段和研究进展,并对它们之间的整合分析新策略和优势进行了讨论。     

Application of a systems approach to study developmental gene regulation

All cells in a multicellular organism contain the same genome, yet different cell types express different sets of genes. Recent advances in high throughput genomic technologies have opened up new opportunities to understand the gene regulatory network in diverse cell types in a genome-wide manner. Here, I discuss recent advances in experimental and computational approaches for the study of gene regulation in embryonic development from a systems perspective. This review is written for computational biologists who have an interest in studying developmental gene regulation through integrative analysis of gene expression, chromatin landscape, and signaling pathways. I highlight the utility of publicly available data and tools, as well as some common analysis approaches.     

Perturbations to uncover gene networks

After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide technologies. Here, we summarise how systems identification algorithms, originating from physics and control theory, have been adapted for use in biology. We also explain how experimental perturbations combined with genome-wide measurements are being used to uncover gene networks. Perturbation techniques could pave the way for identifying gene networks in more complex settings such as multifactorial diseases and for improving the efficacy of drug evaluation.     

复杂疾病基因鉴定的计算生物学方法

过去20多年复杂疾病易感基因鉴定的主要方法是连锁分析和关联研究。因为连锁分析确定的数量性状位点通常很宽, 加之对区域内大部分基因的功能以及基因功能和疾病之间联系的认识十分有限, 所以从数量性状位点到基因的识别是一个挑战。近年来发展了一些利用公共数据库的信息预测疾病易感基因的计算生物学方法。文章简要介绍了DGP、GeneSeeker、Prioritizer、PROSPECTR and SUSPECTS及Endeavor 5种计算生物学方法的基本原理, 以2型糖尿病/肥胖和骨质疏松症易感基因的预测为例说明它们的应用方法, 并讨论了这些方法的局限及应用前景。     

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.     

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.     

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network(interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy(IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.