Agrobacterium T-DNA integration: molecules and models

Genetic transformation mediated by Agrobacterium involves the transfer of a DNA molecule (T-DNA) from the bacterium to the eukaryotic host cell, and its integration into the host genome. Whereas extensive work has revealed the biological mechanisms governing the production, Agrobacterium-to-plant cell transport and nuclear import of the Agrobacterium T-DNA, the integration step remains largely unexplored, although several different T-DNA integration mechanisms have been suggested. Recent genetic and functional studies have revealed the importance of host proteins involved in DNA repair and maintenance for T-DNA integration. In this article, we review our understanding of the specific function of these proteins and propose a detailed model for integration.     

Gene expression microarrays and the integration of biological knowledge

Large-scale parallel measurement of whole-genome RNA expression is now possible with high-density arrays of cDNA or oligonucleotides. Using this technology efficiently will require the integration of other sources of biological information, such as gene identity, biomedical literature and biochemical pathway for a given gene. Such integration is essential to understand the cellular program of gene expression and the molecular physiology of an organism. Advances in microarray technology, and the expected rapid rise in microarray data will lead to new insight into fundamental biological problems such as the prediction of gene function from expression profiles and the identification of potential drug targets from biologically active compounds.     

Direct biologically based biosensing of dynamic physiological function

Dynamic regulation of biological systems requires real-time assessment of relevant physiological needs. Biosensors, which transduce biological actions or reactions into signals amenable to processing, are well suited for such monitoring. Typically, in vivo biosensors approximate physiological function via the measurement of surrogate signals. The alternative approach presented here would be to use biologically based biosensors for the direct measurement of physiological activity via functional integration of relevant governing inputs. We show that an implanted excitable-tissue biosensor (excitable cardiac tissue) can be used as a real-time, integrated bioprocessor to analyze the complex inputs regulating a dynamic physiological variable (heart rate). This approach offers the potential for long-term biologically tuned quantification of endogenous physiological function.     

Extraction of biological interaction networks from scientific literature

Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

Microscale and nanoscale compartments for biotechnology

Compartmentalization is essential in the organization of biological systems, playing a fundamental role in modulating biochemical activity. An appreciation of the impact that biological compartments have on chemical reactions and an understanding of the physical and chemical phenomena that affect their assembly and function have inspired the development of synthetic compartments. Organic compartments assembled from amphiphilic molecules or derived from biological materials, have formed the basis of initial work in the field. However, inorganic and hybrid organic-inorganic compartments that capitalize on the optical and catalytic properties of metal and semiconductor materials are emerging. Methods for arraying these microcompartment and nanocompartment materials in higher order systems promise to enable the scaling and integration of these technologies for industrial and commercial applications.     

An efficient strategy for extensive integration of diverse biological data for protein function prediction

MOTIVATION: With the increasing availability of diverse biological information, protein function prediction approaches have converged towards integration of heterogeneous data. Many adapted existing techniques, such as machine-learning and probabilistic methods, which have proven successful on specific data types. However, the impact of these approaches is hindered by a couple of factors. First, there is little comparison between existing approaches. This is in part due to a divergence in the focus adopted by different works, which makes comparison difficult or even fuzzy. Second, there seems to be over-emphasis on the use of computationally demanding machine-learning methods, which runs counter to the surge in biological data. Analogous to the success of BLAST for sequence homology search, we believe that the ability to tap escalating quantity, quality and diversity of biological data is crucial to the success of automated function prediction as a useful instrument for the advancement of proteomic research. We address these problems by: (1) providing useful comparison between some prominent methods; (2) proposing Integrated Weighted Averaging (IWA)--a scalable, efficient and flexible function prediction framework that integrates diverse information using simple weighting strategies and a local prediction method. The simplicity of the approach makes it possible to make predictions based on on-the-fly information fusion. RESULTS: In addition to its greater efficiency, IWA performs exceptionally well against existing approaches. In the presence of cross-genome information, which is overwhelming for existing approaches, IWA makes even better predictions. We also demonstrate the significance of appropriate weighting strategies in data integration.     

Re-analysis of data and its integration

To understand a biological process it is clear that a single approach will not be sufficient, just like a single measurement on a protein--such as its expression level--does not describe protein function. Using reference sets of proteins as benchmarks different approaches can be scaled and integrated. Here, we demonstrate the power of data re-analysis and integration by applying it in a case study to data from deletion phenotype screens and mRNA expression profiling.     

Expanding the Paradigm: Intrinsically Disordered Proteins and Allosteric Regulation

Allosteric regulatory processes are implicated at all levels of biological function. Recent advances in our understanding of the diverse and functionally significant class of intrinsically disordered proteins have identified a multitude of ways in which disordered proteins function within the confines of the allosteric paradigm. Allostery within or mediated by intrinsically disordered proteins ensures robust and efficient signal integration through mechanisms that would be extremely unfavorable or even impossible for globular protein interaction partners. Here, we highlight recent examples that indicate the breadth of biological outcomes that can be achieved through allosteric regulation by intrinsically disordered proteins. Ongoing and future work in this rapidly evolving area of research will expand our appreciation of the central role of intrinsically disordered proteins in ensuring the fidelity and efficiency of cellular regulation.     

Proteomic tools for biomedicine

Proteomic tools measure gene expression, protein activity and interactions of biological events at the protein level. Proteins are the major catalysts of biological functions and contain several dimensions of information that collectively indicate the actual rather than the potential functional state as indicated by mRNA analysis. Measurements can be made in terms of protein quantity, location, and time-point. For the future we see a further integration of existing and new technologies for proteomics from a wide range of areas of biochemistry, chemistry, physics, computing science and molecular biology. This will further advance our knowledge of how biological systems are built up and what mechanisms control these systems. However, the potential of proteomics to comprehensively answer all biological questions is limited as only protein activity is measured. A unification of genomics, proteomics, and other technologies is needed if we are to start to understand the complexity of biological function in the context of disease and health.     

合成生物学及其在生物技术中的应用进展

合成生物学是一门21世纪生物学的新兴学科,它着眼生物科学与工程科学的结合,把生物系统当作工程系统"从下往上"进行处理,由"单元"(unit)到"部件"(device)再到"系统"(system)来设计,修改和组装细胞构件及生物系统.合成生物学是分子和细胞生物学、进化系统学、生物化学、信息学、数学、计算机和工程等多学科交叉的产物.目前研究应用包括两个主要方面:一是通过对现有的、天然存在的生物系统进行重新设计和改造,修改已存在的生物系统,使该系统增添新的功能.二是通过设计和构建新的生物零件、组件和系统,创造自然界中尚不存在的人工生命系统.合成生物学作为一门建立在基因组方法之上的学科,主要强调对创造人工生命形态的计算生物学与实验生物学的协同整合.必须强调的是,用来构建生命系统新结构、产生新功能所使用的组件单元既可以是基因、核酸等生物组件,也可以是化学的、机械的和物理的元件.本文跟踪合成生物学研究及应用,对其在DNA水平编程、分子修饰、代谢途径、调控网络和工业生物技术等方面的进展进行综述.     

From information to understanding: the role of model organism databases in comparative and functional genomics

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.     

A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana

One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.     

Making better transgenic models

Over the last decade transgenic mouse models have become a common experimental tool for unraveling gene function. During this time there has been a growing expectation that transgenes resemble the in vivo state as much as possible. To this end, a preference away from heterologous promoters has emerged, and transgene constructs often utilize the endogenous promoter and gene sequences in BAC, PAC and YAC form without the addition of selectable markers, or at least their subsequent removal. There has been a trend toward controlled integration by homologous recombination, either at a characterized chromosomal localization or in some cases within the allele of interest. Markers such as green fluorescent protein (GFP), beta-galactosidase (LacZ), and alkaline phosphatase (AP) continue to be useful to trace transgenic cells, or transgene expression. The development of technologies such as RNA interference (RNAi), are introducing new ways of using transgenic models. Future developments in RNAi technology may revolutionize tissue specific inactivation of gene function, without the requirement of generating conditionally targeted mice and tissue specific recombinase mice. Transgenic models are biological tools that aid discovery. Overall, the main consideration in the generation of transgenic models is that they are bona fide biological models that best impart the disease model or biological function of the gene that they represent. The main consideration is to make the best model for the biological question at heart and this review aims to simplify that task somewhat. Here we take a historical perspective on the development of transgenic models, with many of the important considerations to be made in design and development along the way.