A perspective of synthetic biology: assembling building blocks for novel functions

Synthetic biology is a recently emerging field that applies engineering formalisms to design and construct new biological parts, devices, and systems for novel functions or life forms that do not exist in nature. Synthetic biology relies on and shares tools from genetic engineering, bioengineering, systems biology and many other engineering disciplines. It is also different from these subjects, in both insights and approach. Applications of synthetic biology have great potential for novel contributions to established fields and for offering opportunities to answer fundamentally new biological questions. This article does not aim at a thorough survey of the literature and detailing progress in all different directions. Instead, it is intended to communicate a way of thinking for synthetic biology in which basic functional elements are defined and assembled into living systems or biomaterials with new properties and behaviors. Four major application areas with a common theme are discussed and a procedure (or "protocol") for a standard synthetic biology work is suggested.     

Development of nerve connections under the control of neurotrophic factors: parallels with consumer-resource systems in population biology

The development of connections between neurons and their target cells involves competition between axons for target-derived neurotrophic factors. Although the notion of competition is commonly used in neurobiology, the process is not well understood, and only a few formal models exist. In population biology, in contrast, the concept of competition is well developed and has been studied by means of many formal models of consumer-resource systems. Here we show that a recently formulated model of axonal competition can be rewritten as a general consumer-resource system. This allows neurobiological phenomena to be interpreted in population biological terms and, conversely, results from population biology to be applied to neurobiology. Using findings from population biology, we have studied two extensions of our axonal competition model. In the first extension, the spatial dimension of the target is explicitly taken into account. We show that distance between axons on their target mitigates competition and permits the coexistence of axons. The model can account for the fact that in many types of neurons a positive correlation exists between the size of the dendritic tree and the number of innervating axons surviving into adulthood. In the second extension, axons are allowed to respond to more than one neurotrophic factor. We show that this permits competitive exclusion among axons of one type, while at the same time there is coexistence with axons of another type innervating the same target. The model offers an explanation for the innervation pattern found on cerebellar Purkinje cells, where climbing fibres compete with each other until only a single one remains, which coexists with parallel fibre input to the same Purkinje cell.     

Ingénierie génétique et spermatogenèse

The knowledge of mammal spermatogenesis takes great advantage of the powerful method of functional analysis by genetic engineering. This method allows to study the factors implicated in meiosis depending on the sex and their possible relationships with tumorigenesis and apoptosis. It can be distinguished the factors possibly involved in sterilities and those that can be compensated by genetic redundancy. The mechanisms of spermiogenesis can be dissected, as can be those of the heat shock response and the hemato-testicular barrier. Data extent to the biology of reproduction at various levels in both sexes and to the question of sterility-associated diseases. Knock-out mice also show the importance of the male germ cell genotype/phenotype dissociation in hemizygous. As an experimental approach, the method knows some limitations such as the differences between species in the function and expression of homologous genes. The unexpected failures of spermatogenesis found in many cases supports the notion that spermatogenesis is very sensitive to genetic damages and show that many genes have to be investigated.     

Systems biology strategy to study lipotoxicity and the metabolic syndrome

Systems biology views and studies the biological systems in the context of complex interactions between their building blocks and processes. Given its multi-level complexity, metabolic syndrome (MetS) makes a strong case for adopting the systems biology approach. Despite many MetS traits being highly heritable, it is becoming evident that the genetic contribution to these traits is mediated via gene–gene and gene–environment interactions across several spatial and temporal scales, and that some of these traits such as lipotoxicity may even be a product of long-term dynamic changes of the underlying genetic and molecular networks. This presents several conceptual as well as methodological challenges and may demand a paradigm shift in how we study the undeniably strong genetic component of complex diseases such as MetS. The argument is made here that for adopting systems biology approaches to MetS an integrative framework is needed which glues the biological processes of MetS with specific physiological mechanisms and principles and that lipotoxicity is one such framework. The metabolic phenotypes, molecular and genetic networks can be modeled within the context of such integrative framework and the underlying physiology.     

Epithelial topology

It is universally accepted that genetic control over basic aspects of cell and molecular biology is the primary organizing principle in development and homeostasis of living systems. However, instances do exist where important aspects of biological order arise without explicit genetic instruction, emerging instead from simple physical principles, stochastic processes, or the complex self-organizing interaction between random and seemingly unrelated parts. Being mostly resistant to direct genetic dissection, the analysis of such emergent processes falls into a grey area between mathematics, physics and molecular cell biology and therefore remains very poorly understood. We recently proposed a mathematical model predicting the emergence of a specific non-Gaussian distribution of polygonal cell shapes from the stochastic cell division process in epithelial cell sheets; this cell shape distribution appears to be conserved across a diverse set of animals and plants.1 The use of such topological models to study the process of cellular morphogenesis has a long history, starting almost a century ago, and many insights from those original works influence current experimental studies. Here we review current and past literature on this topic while exploring some new ideas on the origins and implications of topological order in proliferating epithelia.     

Complex systems in pulmonary medicine: a systems biology approach to lung disease

The lung is a highly complex organ that can only be understood by integrating the many aspects of its structure and function into a comprehensive view. Such a view is provided by a systems biology approach, whereby the many layers of complexity, from the molecular genetic, to the cellular, to the tissue, to the whole organ, and finally to the whole body, are synthesized into a working model of understanding. The systems biology approach therefore relies on the expertise of many disciplines, including genomics, proteomics, metabolomics, physiomics, and, ultimately, clinical medicine. The overall structure and functioning of the lung cannot be predicted from studying any one of these systems in isolation, and so this approach highlights the importance of emergence as the fundamental feature of systems biology. In this paper, we will provide an overview of a systems biology approach to lung disease by briefly reviewing the advances made at many of these levels, with special emphasis on recent work done in the realm of pulmonary physiology and the analysis of clinical phenotypes.     

Global view of bionetwork dynamics: adaptive landscape

Based on recent work, I will give a nontechnical brief review of a powerful quantitative concept in biology, adaptive landscape, ini- tially proposed by S. Wright over 70 years ago, reintroduced by one of the founders of molecular biology and by others in different bio- logical contexts, but apparently forgotten by modem biologists for many years. Nevertheless, this concept finds an increasingly important role in the development of systems biology and bionetwork dynamics modeling, from phage lambda genetic switch to endogenous net- work for cancer genesis and progression. It is an ideal quantification to describe the robustness and stability of bionetworks. Here, I will first introduce five landmark proposals in biology on this concept, to demonstrate an important common thread in theoretical biology. Then I will discuss a few recent results, focusing on the studies showing theoretical consistency of adaptive landscape. From the perspec- tive of a working scientist and of what is needed logically for a dynamical theory when confronting empirical data, the adaptive landscape is useful both metaphorically and quantitatively, and has captured an essential aspect of biological dynamical processes. Though at the theoretical level the adaptive landscape must exist and it can be used across hierarchical boundaries in biology, many associated issues are indeed vague in their initial formulations and their quantitative realizations are not easy, and are good research topics for quantitative biologists. I will discuss three types of open problems associated with the adaptive landscape in a broader perspective.     

Microbial interventions are an easier alternative to engineer higher organisms

Advances in synthetic biology have made microbes easier to engineer than ever before. However, synthetic biology in animals and plants has lagged behind. Since it is now known that the phenotype of higher organisms depends largely on their microbiota, we propose that this is an easier route to achieving synthetic biology applications in these organisms.

A transition from reading to writing biology has blurred the lines between basic science and engineering creating the field of synthetic biology. With an ever‐expanding genetic toolbox, we now manipulate natural biological systems to optimize our anthropocentric activities. From the synthesis of complex aromatic compounds, to the production of safer vaccines, a problem identified may find its solution lying in the metabolism of a single cell. Initially, synthetic biology was largely focused on the production of such commodities at the industrial scale, not only to maximize profitability, but also to minimize energy and resource consumption. Consequently, this paradigm shift has come to alter the notion of a factory by many orders of magnitude and to create a new bridge between the built and natural world, as we employ nature’s evolutionary machinery to address our modern endeavours.Growth of the genetic toolbox and maturation of synthetic biology as a field has led to speculation about increasingly ambitious applications of writing biology with implications beyond biosynthesis. To date, most applications have been developed using microbes because they are less complex, more well understood and easier to manipulate. Single‐celled organisms can be optimized for production of complicated organic molecules; however, other exploits of genetic engineering will target more ambitious feats and thus require engineering of more than a large monoculture of microbes. Applications of synthetic biology outside of the bioreactor can address such issues as health and longevity, challenges in industrial agriculture and farming, the degradation of natural habitats and the reclamation of limited natural resources.Scope and scale of these applications provide obvious obstacles to the development of effective biotechnologies, but a more immediate limitation to realizing these technologies is the relative lack of genetic tools and insights which would allow the tinkering and rewiring of more complex organisms such as animals and plants. However, because of the natural intimate interactions between higher eukaryotes and microbes and the effect of these on phenotype, it is our vision that a faster, more tractable route to the engineering animal and plant phenotypes is via engineering their microbiomes.     

Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology.     

Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly

Synthetic Biology is a rapidly growing interdisciplinary field that is primarily built upon foundational advances in molecular biology combined with engineering design principles such as modularity and interoperability. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies and methodological advances. A key concept driving the field is the Design-Build-Test-Learn cycle which provides a systematic framework for building new biological systems. One major application area for synthetic biology is biosynthetic pathway engineering that requires the modular assembly of different genetic regulatory elements and biosynthetic enzymes. In this review we provide an overview of modular DNA assembly and describe and compare the plethora of in vitro and in vivo assembly methods for combinatorial pathway engineering. Considerations for part design and methods for enzyme balancing are also presented, and we briefly discuss alternatives to intracellular pathway assembly including microbial consortia and cell-free systems for biosynthesis. Finally, we describe computational tools and automation for pathway design and assembly and argue that a deeper understanding of the many different variables of genetic design, pathway regulation and cellular metabolism will allow more predictive pathway design and engineering.     

Synthetic modular systems--reverse engineering of signal transduction

During the last decades, biology has decomposed cellular systems into genetic, functional and molecular networks. It has become evident that these networks consist of components with specific functions (e.g., proteins and genes). This has generated a considerable amount of knowledge and hypotheses concerning cellular organization. The idea discussed here is to test the extent of this knowledge by reconstructing, or reverse engineering, new synthetic biological systems from known components. We will discuss how integration of computational methods with proteomics and engineering concepts might lead us to a deeper and more abstract understanding of signal transduction systems. Designing and successfully introducing synthetic proteins into cellular pathways would provide us with a powerful research tool with many applications, such as development of biosensors, protein drugs and rewiring of biological pathways.     

The emerging age of cell-free synthetic biology

The engineering of and mastery over biological parts has catalyzed the emergence of synthetic biology. This field has grown exponentially in the past decade. As increasingly more applications of synthetic biology are pursued, more challenges are encountered, such as delivering genetic material into cells and optimizing genetic circuits in vivo. An in vitro or cell-free approach to synthetic biology simplifies and avoids many of the pitfalls of in vivo synthetic biology. In this review, we describe some of the innate features that make cell-free systems compelling platforms for synthetic biology and discuss emerging improvements of cell-free technologies. We also select and highlight recent and emerging applications of cell-free synthetic biology.     

Bias in random forest variable importance measures: Illustrations,sources and a solution

Background  

Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories.     

Gene transfer into chicken embryos by retrovirus vectors

While chickens have many properties that are advantageous for embryological studies, their genetic analysis has been restricted. However, by using retrovirus vector systems in combination with classical techniques of experimental developmental biology, it has recently become possible to analyze the function of genes involved in the development of this organism. Avian retrovirus vectors are unique in that they can be divided into two categories: replication-competent and replication-defective (replication-incompetent). By choosing the vectors correctly, there are many experimental applications of these vectors such as induction of constitutive (or regulated) gene expression in a restricted region of tissues, organs and embryos; cell lineage analysis; and formation of concentration gradients of morphogens in micromass cultures. In this paper, several retrovirus vectors available for the chicken will be introduced and their applications in developmental biology will be reviewed.     

Proteomics technology in systems biology

It has now become apparent that a full understanding of a biological process (e.g. a disease state) is only possible if all biomolecular interactions are taken into account. Systems biology works towards understanding the intricacies of cellular life through the collaborative efforts of biologists, chemists, mathematicians and computer scientists and recently, a number of laboratories around the world have embarked upon such research agendas. The fields of genomics and proteomics are foundational in systems biology studies and a great deal of research is currently being conducted in each worldwide. Moreover, many technological advances (particularly in mass spectrometry) have led to a dramatic rise in the number of proteomic studies over the past two decades. This short review summarizes a selection of technological innovations in proteomics that contribute to systems biology studies.     

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.     

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.