Data integration in the era of omics: current and future challenges

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.     

生物组学在污染环境微生物修复研究中的应用

随着分子生物学、生物信息学和各种理化检测技术的发展,特别是人类基因组计划成功实施以来,基因组学研究取得了重大突破与进展。而包括转录组学、蛋白组学和代谢组学在内的后基因组学也相继出现,并被广泛应用在环境微生物学的各个研究领域。本文主要概述了当前基因组学、转录组学、蛋白质组学和代谢组学在污染环境生物修复研究中的最新研究进展,分析比较了各组学的优势与不足,同时结合本课题组的主要研究方向探讨了各生物组学在赤潮生消过程和有机污染物降解机理等研究中的应用。     

Viral diseases meet omics: Time for systems virology

正Emerging and re-emerging viral diseases pose a serious and continuous threat to the human society and place a heavy burden on the ecological system. Because of globalization,endemic viruses could be transmitted quickly across long distances, causing pandemic outbreaks. This has happened during the outbreak of the avian influenza viruses, Ebola virus, and Zika virus in recent years (Gao, 2018). Sudden viral attacks always evoke a large number of questions from the public. People ask what the pathogen is and where it is from, by what mechanism it infects the body, how they can     

Modeling biological systems using Dynetica--a simulator of dynamic networks

We present Dynetica, a user-friendly simulator of dynamic networks for constructing, visualizing, and analyzing kinetic models of biological systems. In addition to generic reaction networks, Dynetica facilitates construction of models of genetic networks, where many reactions are gene expression and interactions among gene products. Further, it integrates the capability of conducting both deterministic and stochastic simulations. AVAILABILITY AND SUPPLEMENTARY INFORMATION: Dynetica 1.0, example models, and the user's guide are available at http://www.its.caltech.edu/~you/Dynetica/Dynetica_page.htm     

Recent advances in the reconstruction of metabolic models and integration of omics data

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Technical note: Modeling primate occlusal topography using geographic information systems technology

Most functional analyses of primate tooth form have been limited to linear or area measurements. Such studies have offered but a limited glimpse at differences in occlusal relief among taxa. Such differences in dental topography may relate to tooth function and, so, have considerable implications for the inference of diet from fossil teeth. In this article, we describe a technique to model and compare primate molars in three dimensions using Geographic Resources Analysis Support System (GRASS) software. We examine unworn lower second molars of three extant hominoids with known differences in diet (Gorilla gorilla, Pan troglodytes, and Pongo pygmaeus), and two fossil forms, (Afropithecus turkanesis and Dryopithecus laietanus). First, we obtained approximately 400 landmarks on the occlusal surfaces of each tooth using an electromagnetic digitizer. Raster “terrain models” of occlusal surfaces were then created by interpolation of the coordinate data. We used GRASS terrain analysis automated techniques to quantify the volumes and slopes of individual cusps. We also used the GRASS watershed technique to identify the volume of liquid that would accumulate in each tooth's basin (a measure of basin area), and the directions and intensity of drainage over the occlusal surface. In sum, GRASS shows considerable potential for the characterization and comparison of tooth surfaces. Furthermore, techniques described here are not limited to the study of teeth, but may be broadly applicable to studies of skulls, joints, and other biological structures. Am J Phys Anthropol 107:137–142, 1998. © 1998 Wiley-Liss, Inc.     

Global dynamics of biological systems from time-resolved omics experiments

The emergent properties of biological systems, organized around complex networks of irregularly connected elements, limit the applications of the direct scientific method to their study. The current lack of knowledge opens new perspectives to the inverse scientific paradigm where observations are accumulated and analysed by advanced data-mining techniques to enable a better understanding and the formulation of testable hypotheses about the structure and functioning of these systems. The current technology allows for the wide application of omics analytical methods in the determination of time-resolved molecular profiles of biological samples. Here it is proposed that the theory of dynamical systems could be the natural framework for the proper analysis and interpretation of such experiments. A new method is described, based on the techniques of non-linear time series analysis, which is providing a global view on the dynamics of biological systems probed with time-resolved omics experiments.     

DynaMIT: the dynamic motif integration toolkit

De-novo motif search is a frequently applied bioinformatics procedure to identify and prioritize recurrent elements in sequences sets for biological investigation, such as the ones derived from high-throughput differential expression experiments. Several algorithms have been developed to perform motif search, employing widely different approaches and often giving divergent results. In order to maximize the power of these investigations and ultimately be able to draft solid biological hypotheses, there is the need for applying multiple tools on the same sequences and merge the obtained results. However, motif reporting formats and statistical evaluation methods currently make such an integration task difficult to perform and mostly restricted to specific scenarios. We thus introduce here the Dynamic Motif Integration Toolkit (DynaMIT), an extremely flexible platform allowing to identify motifs employing multiple algorithms, integrate them by means of a user-selected strategy and visualize results in several ways; furthermore, the platform is user-extendible in all its aspects. DynaMIT is freely available at http://cibioltg.bitbucket.org.     

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.     

Ultraslow information control systems in the integration of life activity processes in the brain and body

The published data and the results of studies of the Department of Human Neurophysiology, revealing the place and importance of ultraslow information-control systems in the study of mechanisms for the integration of interorgan and intersystemic interactions, with the leading role of CNS and the autonomic nervous system, are summarized. The existing notions of the universality and commensurability of amplitude-time parameters, ultraslow bioelectric potentials (USBPs) recorded in the brain and the brain-controlled systems and organs have been considered. Experimental justifications for including one of the USBP types, a stable potential in the millivolt range in the vertex-tenar derivation (the omegametry method) in the psychophysiological approach are provided. This approach consists of choosing complementary integral psychological and physiological parameters permitting the study of the contingency of the mechanisms that control the functional state (wakefulness level) with specific features of the organization of higher mental functions and adaptive behavior, as well as in differentiating diagnostic markers: (a) the balance level and disintegration of intersystemic interaction in the body; and (b) disturbances of compensatory-adaptive possibilities and the adaptation reserve of the body. The prospects of using this approach in theoretical and applied studies on developmental psychophysiology, child psychoneurology, and functional neurology of chronic diseases of the nervous system have been considered.     

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.     

Modeling solid-to-solid biocatalysis: integration of six consecutive steps

A quantitative model for the conversion of a solid-substrate salt to a solid-product salt in a batch bioreactor seeded with product crystals is presented. The overall process consists of six serial steps (with dissolution and crystallization each in themselves complex multistep processes): solid-salt dissolution, salt dissociation into an ionic substrate and a counter-ion, bioconversion accompanied by biocatalyst inactivation, complexation of the ionic product with the counter-ion, and salt crystal growth. In the model, the consecutive steps are integrated, including biocatalyst inactivation and assuming that salt dissociation and complexation of ions are at equilibrium. Model parameters were determined previously in separate independent experiments. To validate the model, either dissolved or solid Ca-maleate was converted to solid Ca-D-malate by permeabilized Pseudomonas pseudoalcaligenes in a batch bioreactor seeded with Ca-D-malate crystals. The model very well predicted the concentrations of all components in the liquid phase (Ca-maleate, Ca(2+), maleate(2-), D-malate(2-), and Ca-D-malate) and the amounts of the solid phases (Ca-maleate. H(2)O and Ca-D-malate. 3H(2)O), especially when high initial amounts of Ca-maleate. H(2)O and Ca-D-malate. 3H(2)O were present.