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.     

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.     

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

ABSTRACT: BACKGROUND: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. RESULTS: In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having rm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. CONCLUSION: By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efcient and can be used to infer gene networkshaving multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach.     

Understanding systems-level properties: timely stories from the study of clocks

After several decades dominated by reductionist approaches in biology, researchers are returning to the study of complex biology with a litany of new and old techniques--this paradigm has been termed systems biology. Here we detail how systems biology is being used to uncover complex systems-level properties of the circadian clock. These properties include robustness, periodicity and temperature compensation. We describe how clock researchers are using systems-biology techniques, such as genetic perturbations, kinetic luminescence imaging, synthetic biology and mathematical modelling, to untangle these complex properties in mammals, fungi and bacteria. The strategies developed in the context of circadian clocks may prove useful for tackling similar problems in other systems.     

Synthetic biology: a foundation for multi-scale molecular biology

The field of synthetic biology has made rapid progress in a number of areas including method development, novel applications and community building. In seeking to make biology "engineerable," synthetic biology is increasing the accessibility of biological research to researchers of all experience levels and backgrounds. One of the underlying strengths of synthetic biology is that it may establish the framework for a rigorous bottom-up approach to studying biology starting at the DNA level. Building upon the existing framework established largely by the Registry of Standard Biological Parts, careful consideration of future goals may lead to integrated multi- scale approaches to biology. Here we describe some of the current challenges that need to be addressed or considered in detail to continue the development of synthetic biology. Specifically, discussion on the areas of elucidating biological principles, computational methods and experimental construction methodologies are presented.     

Synthetic biology: a new approach to study biological pattern formation

The principles and molecular mechanisms underlying biological pattern formation are difficult to elucidate in most cases due to the overwhelming physiologic complexity associated with the natural context. The understanding of a particular mechanism, not to speak of underlying universal principles, is difficult due to the diversity and uncertainty of the biological systems. Although current genetic and biochemical approaches have greatly advanced our understanding of pattern formation, the progress mainly relies on experimental phenotypes obtained from time-consuming studies of gain or loss of function mutants. It is prevailingly considered that synthetic biology will come to the application age, but more importantly synthetic biology can be used to understand the life. Using periodic stripe pattern formation as a paradigm, we discuss how to apply synthetic biology in understanding biological pattern formation and hereafter foster the applications like tissue engineering.     

Basic science through engineering? Synthetic modeling and the idea of biology-inspired engineering

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more “biology inspired.”     

Modeling synthetic gene oscillators

Genetic oscillators have long held the fascination of experimental and theoretical synthetic biologists alike. From an experimental standpoint, the creation of synthetic gene oscillators represents a yardstick by which our ability to engineer synthetic gene circuits can be measured. For theorists, synthetic gene oscillators are a playground in which to test mathematical models for the dynamics of gene regulation. Historically, mathematical models of synthetic gene circuits have varied greatly. Often, the differences are determined by the level of biological detail included within each model, or which approximation scheme is used. In this review, we examine, in detail, how mathematical models of synthetic gene oscillators are derived and the biological processes that affect the dynamics of gene regulation.     

基因组工程在医学合成生物学中的应用

合成生物学旨在建立一套完整的工程理论和方法,通过设计和组装基本生物学元件,更为有效地实现复杂生物系统的设计,并使其完成可编程的生物学功能。近年来随着可编程基因组元件的出现,特别是CRISPR和CRISPRi技术平台的建立和完善,使得合成生物学进入了一个全新发展的时期。本文重点综述CRISPR等基因组编辑和调控技术,其在构建可编程生物学元件和复杂基因线路的应用以及合成生物学在医学中(称为医学合成生物学)的发展前景。     

Creating parts that allow for rational design: Synthetic biology and the problem of context-sensitivity

The parts-based engineering approach in synthetic biology aims to create pre-characterised biological parts that can be used for the rational design of novel functional systems. Given the context-sensitivity of biological entities, a key question synthetic biologists have to address is what properties these parts should have so that they give a predictable output even when they are used in different contexts. In the first part of this paper I will analyse some of the answers that synthetic biologists have given to this question and claim that the focus of these answers on parts and their properties does not allow us to tackle the problem of context-sensitivity. In the second part of the paper, I will argue that we might have to abandon the notions of parts and their properties in order to understand how independence in biology could be achieved. Using Robert Cummins’ account of functional analysis, I will then develop the notion of a capacity and its condition space and show how these notions can help to tackle the problem of context-sensitivity in biology.     

Synthetic biology: new strategies for directing design

The advancement of synthetic biology is thanks, in large part, to continuing improvements in DNA synthesis. The expansion of synthetic biology into the realm of metabolic engineering has shifted the focus from simply making novel synthetic biological parts to answering the question of how we employ these biological parts to construct genomes that ultimately give rise to useful phenotypes. Much like protein engineering, the answer to this will be arrived at following the combination of rational design and evolutionary approaches. This review will highlight some of the new DNA synthesis-enabled search methods and discuss the application of such methods to the creation of synthetic gene networks and genomes.     

Technological biology? Things and kinds in synthetic biology

Social scientific and humanistic research on synthetic biology has focused quite narrowly on questions of epistemology and ELSI. I suggest that to understand this discipline in its full scope, researchers must turn to the objects of the field—synthetic biological artifacts—and study them as the objects in the making of a science yet to be made. I consider one fundamentally important question: how should we understand the material products of synthetic biology? Practitioners in the field, employing a consistent technological optic in the study and construction of biological systems, routinely employ the mantra ‘biology is technology’. I explore this categorization. By employing an established definition of technological artifects drawn from the philosophy of technology, I explore the appropriateness of attributing to synthetic biological artifacts the four criteria of materiality, intentional design, functionality, and normativity. I then explore a variety of accounts of natural kinds. I demonstrate that synthetic biological artifacts fit each kind imperfectly, and display a concomitant ontological ‘messiness’. I argue that this classificatory ambivalence is a product of the field’s own nascence, and posit that further work on kinds might help synthetic biology evaluate its existing commitments and practices.     

Synthetic tissue biology: tissue engineering meets synthetic biology

We propose the term "synthetic tissue biology" to describe the use of engineered tissues to form biological systems with metazoan-like complexity. The increasing maturity of tissue engineering is beginning to render this goal attainable. As in other synthetic biology approaches, the perspective is bottom-up; here, the premise is that complex functional phenotypes (on par with those in whole metazoan organisms) can be effected by engineering biology at the tissue level. To be successful, current efforts to understand and engineer multicellular systems must continue, and new efforts to integrate different tissues into a coherent structure will need to emerge. The fruits of this research may include improved understanding of how tissue systems can be integrated, as well as useful biomedical technologies not traditionally considered in tissue engineering, such as autonomous devices, sensors, and manufacturing.     

Engineering microbial systems to explore ecological and evolutionary dynamics

A major goal of biological research is to provide a mechanistic understanding of diverse biological processes. To this end, synthetic biology offers a powerful approach, whereby biological questions can be addressed in a well-defined framework. By constructing simple gene circuits, such studies have generated new insights into the design principles of gene regulatory networks. Recently, this strategy has been applied to analyze ecological and evolutionary questions, where population-level interactions are critical. Here, we highlight recent development of such systems and discuss how they were used to address problems in ecology and evolutionary biology. As illustrated by these examples, synthetic ecosystems provide a unique platform to study ecological and evolutionary phenomena that are challenging to study in their natural contexts.     

BIOCOMPUTATION: Some history and prospects

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science.     

Synthetic biology - the state of play

Just over two years ago there was an article in Nature entitled "Five Hard Truths for Synthetic Biology". Since then, the field has moved on considerably. A number of economic commentators have shown that synthetic biology very significant industrial potential. This paper addresses key issues in relation to the state of play regarding synthetic biology. It first considers the current background to synthetic biology, whether it is a legitimate field and how it relates to foundational biological sciences. The fact that synthetic biology is a translational field is discussed and placed in the context of the industrial translation process. An important aspect of synthetic biology is platform technology, this topic is also discussed in some detail. Finally, examples of application areas are described.     

Systems biology,emergence and antireductionism

This study explores the conceptual history of systems biology and its impact on philosophical and scientific conceptions of reductionism, antireductionism and emergence. Development of systems biology at the beginning of 21st century transformed biological science. Systems biology is a new holistic approach or strategy how to research biological organisms, developed through three phases. The first phase was completed when molecular biology transformed into systems molecular biology. Prior to the second phase, convergence between applied general systems theory and nonlinear dynamics took place, hence allowing the formation of systems mathematical biology. The second phase happened when systems molecular biology and systems mathematical biology, together, were applied for analysis of biological data. Finally, after successful application in science, medicine and biotechnology, the process of the formation of modern systems biology was completed.Systems and molecular reductionist views on organisms were completely opposed to each other. Implications of systems and molecular biology on reductionist–antireductionist debate were quite different. The analysis of reductionism, antireductionism and emergence issues, in the era of systems biology, revealed the hierarchy between methodological, epistemological and ontological antireductionism. Primarily, methodological antireductionism followed from the systems biology. Only after, epistemological and ontological antireductionism could be supported.     

Perspectives on the automatic design of regulatory systems for synthetic biology

Automatic design is based on computational modeling and optimization methods to provide prototype designs to targeted problems in an unsupervised manner. For biological circuits, we need to produce quantitative predictions of cell behavior for a given genotype as consequence of the different molecular interactions. Automatic design techniques aim at solving the inverse problem of finding the sequences of nucleotides that better fit a targeted behavior. In the post-genomic era, our molecular knowledge and modeling capabilities have allowed to start using such methodologies with success. Herein, we describe how the emergence of this new type of tools could enable novel synthetic biology applications. We highlight the essential elements to develop automatic design procedures for synthetic biology pointing out their advantages and bottlenecks. We discuss in detail the experimental difficulties to overcome in the in vivo implementation of designed networks. The use of automatic design to engineer biological networks is starting to emerge as a new technique to perform synthetic biology, which should not be neglected in the future.