非常规酵母的分子遗传学及合成生物学研究进展

先进的合成生物学技术与传统的分子遗传学技术的结合更有助于实现酵母底盘细胞的快速改造和优化。酵母合成生物学研究最早开始于常规酵母——酿酒酵母(Saccharomyces cerevisiae),近些年来又迅速扩展至一些非常规酵母,包括巴斯德毕赤酵母(Pichiapastoris)、解脂耶氏酵母(Yarrowialipolytica)、乳酸克鲁维酵母(Kluyveromyces lactis)和多形汉逊酵母(Hansenula polymorpha)等。借助合成生物学技术与工具,目前科学家们已经成功开发出了能够高效生产生物材料、生物燃料、生物基化学品、蛋白质制剂、食品添加剂和药物等工业产品的重组非常规酵母工程菌株。本文系统总结了合成生物学工具(主要是基因组编辑工具)、合成生物学组件(主要是启动子和终止子)和相关分子遗传学方法在上述非常规酵母系统(底盘细胞)中的最新研究进展和应用情况,并讨论了其他合成生物学技术在这些非常规酵母表达系统中的潜在适用性和应用前景。这为研究人员利用合成生物学方法在这一新型非模式微生物底盘细胞中设计和构建各种高附加值工业产品的异源合成模块并最终实现目标化合物的高效生物合成提供了科学的理论指导。     

Towards a systems level analysis of health and nutrition

Although theoretical systems analysis has been available for over half a century, the recent advent of omic high-throughput analytical platforms along with the integration of individual tools and technologies has given rise to the field of modern systems biology. Coupled with information technology, bioinformatics, knowledge management and powerful mathematical models, systems biology has opened up new vistas in our understanding of complex biological systems. Currently there are two distinct approaches that include the inductively driven computational systems biology (bottom-up approach) and the deductive data-driven top-down analysis. Such approaches offer enormous potential in the elucidation of disease as well as defining key pathways and networks involved in optimal human health and nutrition. The tools and technologies now available in systems biology analyses offer exciting opportunities to develop the emerging areas of personalized medicine and individual nutritional profiling.     

Synthetic biology between technoscience and thing knowledge

Synthetic biology presents a challenge to traditional accounts of biology: Whereas traditional biology emphasizes the evolvability, variability, and heterogeneity of living organisms, synthetic biology envisions a future of homogeneous, humanly engineered biological systems that may be combined in modular fashion. The present paper approaches this challenge from the perspective of the epistemology of technoscience. In particular, it is argued that synthetic-biological artifacts lend themselves to an analysis in terms of what has been called ‘thing knowledge’. As such, they should neither be regarded as the simple outcome of applying theoretical knowledge and engineering principles to specific technological problems, nor should they be treated as mere sources of new evidence in the general pursuit of scientific understanding. Instead, synthetic-biological artifacts should be viewed as partly autonomous research objects which, qua their material-biological constitution, embody knowledge about the natural world—knowledge that, in turn, can be accessed via continuous experimental interrogation.     

A reductionist's systems biology: opinion

To tackle the complexity inherent in understanding large networks of interacting biomolecules, systems biology emphasizes cybernetic and systems theoretical approaches. The resulting focus on organization independent of physical manifestation threatens to throw away all that has been learned from molecular studies and ignores the reality that biologists are drawn together more by a shared interest in mechanism and structure than anything else. The field of reaction engineering suggests a reductionist approach to systems biology that fits easily within existing molecular paradigms but that can nonetheless be integrated into expansive physiological perspectives through the use of multi-scale modeling.     

Systems analysis of cellular networks under uncertainty

Besides the often-quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems-level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems. As a common denominator, the methods focus on structural, rather than more detailed, kinetic network properties. A deeper understanding, better scaling, and the ability to combine the approaches pose formidable challenges for future theory developments.     

Biomolecular strategies of bone augmentation in spinal surgery

Autologous bone grafts and allografts are the most accepted procedures for achieving spinal fusion. Recently, breakthroughs in understanding bone biology have led to the development of novel approaches to address the clinical problem of bone regeneration in an unfavorable environment, while bypassing the drawbacks of traditional treatments, including limited availability, donor site morbidity, risk of disease transmission and reduced osteogenicity. These approaches have also been studied for their effectiveness in reaching successful spinal fusion. This review focuses on the cellular and molecular mechanisms explaining the rationale behind these methods, including bone marrow aspirate and mesenchymal stem cells, platelet-rich plasma, bone morphogenetic proteins and gene therapy, which have opened a promising perspective in the field of bone formation in spinal surgery.     

Function prediction of uncharacterized proteins

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.     

Computational Modeling,Formal Analysis,and Tools for Systems Biology

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.     

The evolution of molecular biology into systems biology

Systems analysis has historically been performed in many areas of biology, including ecology, developmental biology and immunology. More recently, the genomics revolution has catapulted molecular biology into the realm of systems biology. In unicellular organisms and well-defined cell lines of higher organisms, systems approaches are making definitive strides toward scientific understanding and biotechnological applications. We argue here that two distinct lines of inquiry in molecular biology have converged to form contemporary systems biology.     

Circular causality in integrative multi-scale systems biology and its interaction with traditional medicine

This paper discusses the concept of circular causality in “biological relativity” (Noble, Interface Focus. 2, 56-64, 2012) in the context of integrative and multi-scale systems approaches to biology. It also discusses the relationship between systems biology and traditional medicine (sometimes called scholarly medical traditions) mainly from East Asia and India. Systems biology helps illuminate circular processes identified in traditional medicine, while the systems concept of attractors in complex systems will also be important in analysing dynamic balance in the body processes that traditional medicine is concerned with. Ways of nudging disordered processes towards good attractors through the use of traditional medicines can lead to the development of new ways not only of curing disease but also of its prevention. Examples are given of cost-effective multi-component remedies that use integrative ideas derived from traditional medicine.     

Integrating molecular and morphological approaches for characterizing parasite cryptic species: implications for parasitology

Herein we review theoretical and methodological considerations important for finding and delimiting cryptic species of parasites (species that are difficult to recognize using traditional systematic methods). Applications of molecular data in empirical investigations of cryptic species are discussed from an historical perspective, and we evaluate advantages and disadvantages of approaches that have been used to date. Developments concerning the theory and practice of species delimitation are emphasized because theory is critical to interpretation of data. The advantages and disadvantages of different molecular methodologies, including the number and kind of loci, are discussed relative to tree-based approaches for detecting and delimiting cryptic species. We conclude by discussing some implications that cryptic species have for research programmes in parasitology, emphasizing that careful attention to the theory and operational practices involved in finding, delimiting, and describing new species (including cryptic species) is essential, not only for fully characterizing parasite biodiversity and broader aspects of comparative biology such as systematics, evolution, ecology and biogeography, but to applied research efforts that strive to improve development and understanding of epidemiology, diagnostics, control and potential eradication of parasitic diseases.     

传统中国医药与现代系统生物学研究理论的殊途同归

系统生物学是研究一个生物系统中所有组成成分(基因、mRNA、蛋白质等)的构成与组分之间相互关系的学科,近年来,系统生物学作为后基因组学时代研究的一个重要内容,已广泛深入到生命科学和医药学的各个领域。而作为中国传统医学而言,似乎与之相去甚远,然而当我们对这两个新老学科基础理论进行比较时,我们发现:传统中国医药与现代系统生物学研究理论的殊途同归。有鉴于此,本文论述了系统生物学和中医学的思想起源、相互联系,基于系统生物学的发展、研究思路和方法,阐述了生物学由还原论的研究方法过渡到系统论的研究方法,强调对生命现象从系统和整体的层次进行研究和把握,对传统中医学研究方法的变革起到了推动作用,最后对系统生物学在中医药学未来发展进行了评价。     

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed 'omics' technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A 'system' approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with 'system approaches' in animal sciences, providing exciting opportunities to predict and modulate animal traits.     

Systems Biology,Systems Medicine,Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.     

药物重定位的计算分析方法

全新结构药物的研发存在周期长、耗资大、风险高的问题.通过各种技术预测已有药物的新适应症,即药物重定位,可以缩短药物研发时间、降低研发成本和风险.由于疾病种类和已知药物的数量繁多,完全通过实验筛选已知药物的新用途仍然具有很高的成本.随着组学和药物信息学数据的积累,药物重定位进入到了理性设计和实验筛选相结合的阶段,药物重定位的计算预测已经成为计算生物学和系统生物学的重要研究方向.本文将目前药物重定位计算分析的策略归纳为药物-靶标关系分析、药物-药物关系分析和药物-疾病关系分析,对已报道的技术方法及其成功应用实例进行了综述.     

Cell migration during gastrulation

As the gateway to shaping the body plan, gastrulation is an important problem in developmental biology, and recent advances in cell biology have overcome some of the limitations of past approaches to learning how genes control reshaping of embryonic tissues. The use of fluorescent tracer dyes and live cell imaging methods to evaluate at the cellular level the results of genetic and molecular manipulations has advanced our understanding of the cell motility and contact behavior underlying tissue remodeling during gastrulation.     

The dynamic systems approach to control and regulation of intracellular networks

Systems theory and cell biology have enjoyed a long relationship that has received renewed interest in recent years in the context of systems biology. The term 'systems' in systems biology comes from systems theory or dynamic systems theory: systems biology is defined through the application of systems- and signal-oriented approaches for an understanding of inter- and intra-cellular dynamic processes. The aim of the present text is to review the systems and control perspective of dynamic systems. The biologist's conceptual framework for representing the variables of a biochemical reaction network, and for describing their relationships, are pathway maps. A principal goal of systems biology is to turn these static maps into dynamic models, which can provide insight into the temporal evolution of biochemical reaction networks. Towards this end, we review the case for differential equation models as a 'natural' representation of causal entailment in pathways. Block-diagrams, commonly used in the engineering sciences, are introduced and compared to pathway maps. The stimulus-response representation of a molecular system is a necessary condition for an understanding of dynamic interactions among the components that make up a pathway. Using simple examples, we show how biochemical reactions are modelled in the dynamic systems framework and visualized using block-diagrams.