合成生物学技术在材料科学中的应用

材料是人类赖以生存与发展的物质基础,科技和社会的进步都离不开材料技术的发展,未来先进材料的合成和制备必然朝着绿色可持续、低耗高产出、精细可调控、高效多功能的方向发展。以"基因调控·工程设计"为核心的合成生物学技术从分子、细胞层面极大地推动了生命科学的发展,也已经并继续为材料科学的发展注入新的思路和活力。本文将围绕合成生物学技术在材料科学中的应用,以基因回路设计为核心,概念应用为线索,重点介绍合成生物学技术在高分子生物材料和无机纳米材料领域的开发和生产,细胞展示和蛋白定向进化战略对分子材料的筛选和优化,"活体"功能材料、工程菌调节的人工光合系统功能材料体系以及基因回路在材料科学中的应用。     

Co-evolution of physical and social sciences in synthetic biology

Emerging technologies research often covers various perspectives in disciplines and research areas ranging from hard sciences, engineering, policymaking, and sociology. However, the interrelationship between these different disciplinary domains, particularly the physical and social sciences, often occurs many years after a technology has matured and moved towards commercialization. Synthetic biology may serve an exception to this idea, where, since 2000, the physical and the social sciences communities have increasingly framed their research in response to various perspectives in biological engineering, risk assessment needs, governance challenges, and the social implications that the technology may incur. This paper reviews a broad collection of synthetic biology literature from 2000–2016, and demonstrates how the co-development of physical and social science communities has grown throughout synthetic biology’s earliest stages of development. Further, this paper indicates that future co-development of synthetic biology scholarship will assist with significant challenges of the technology’s risk assessment, governance, and public engagement needs, where an interdisciplinary approach is necessary to foster sustainable, risk-informed, and societally beneficial technological advances moving forward.     

Colour spaces in ecology and evolutionary biology

The recognition that animals sense the world in a different way than we do has unlocked important lines of research in ecology and evolutionary biology. In practice, the subjective study of natural stimuli has been permitted by perceptual spaces, which are graphical models of how stimuli are perceived by a given animal. Because colour vision is arguably the best‐known sensory modality in most animals, a diversity of colour spaces are now available to visual ecologists, ranging from generalist and basic models allowing rough but robust predictions on colour perception, to species‐specific, more complex models giving accurate but context‐dependent predictions. Selecting among these models is most often influenced by historical contingencies that have associated models to specific questions and organisms; however, these associations are not always optimal. The aim of this review is to provide visual ecologists with a critical perspective on how models of colour space are built, how well they perform and where their main limitations are with regard to their most frequent uses in ecology and evolutionary biology. We propose a classification of models based on their complexity, defined as whether and how they model the mechanisms of chromatic adaptation and receptor opponency, the nonlinear association between the stimulus and its perception, and whether or not models have been fitted to experimental data. Then, we review the effect of modelling these mechanisms on predictions of colour detection and discrimination, colour conspicuousness, colour diversity and diversification, and for comparing the perception of colour traits between distinct perceivers. While a few rules emerge (e.g. opponent log–linear models should be preferred when analysing very distinct colours), in general model parameters still have poorly known effects. Colour spaces have nonetheless permitted significant advances in ecology and evolutionary biology, and more progress is expected if ecologists compare results between models and perform behavioural experiments more routinely. Such an approach would further contribute to a better understanding of colour vision and its links to the behavioural ecology of animals. While visual ecology is essentially a transfer of knowledge from visual sciences to evolutionary ecology, we hope that the discipline will benefit both fields more evenly in the future.     

Synthetic biology of minimal systems

“Synthetic biology” is a concept that has developed together with, or slightly after, “systems biology”. But while systems biology aims at the full understanding of large systems by integrating more and more details into their models, synthetic biology phrases different questions, namely: what particular biological function could be obtained with a certain known subsystem of reduced complexity; can this function be manipulated or engineered in artificial environments or genetically modified organisms; and if so, how? The most prominent representation of synthetic biology has so far been microbial engineering by recombinant DNA technology, employing modular concepts known from information technology. However, there are an increasing number of biophysical groups who follow similar strategies of dissecting cellular processes and networks, trying to identify functional minimal modules that could then be combined in a bottom-up approach towards biology. These modules are so far not as particularly defined by their impact on DNA processing, but rather influenced by core fields of biophysics, such as cell mechanics and membrane dynamics. This review will give an overview of some classical and some quite new biophysical strategies for constructing minimal systems of certain cellular modules. We will show that with recent advances in understanding of cytoskeletal and membrane elements, the time might have come to experimentally challenge the concept of a minimal cell.     

Executable cell biology

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.     

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.     

Nanotechnology: convergence with modern biology and medicine

The worldwide emergence of nanoscale science and engineering was marked by the announcement of the National Nanotechnology Initiative (NNI) in January 2000. Recent research on biosystems at the nanoscale has created one of the most dynamic science and technology domains at the confluence of physical sciences, molecular engineering, biology, biotechnology and medicine. This domain includes better understanding of living and thinking systems, revolutionary biotechnology processes, the synthesis of new drugs and their targeted delivery, regenerative medicine, neuromorphic engineering and developing a sustainable environment. Nanobiosystems research is a priority in many countries and its relevance within nanotechnology is expected to increase in the future.     

Bringing the physical sciences into your cell biology research

Historically, much of biology was studied by physicists and mathematicians. With the advent of modern molecular biology, a wave of researchers became trained in a new scientific discipline filled with the language of genes, mutants, and the central dogma. These new molecular approaches have provided volumes of information on biomolecules and molecular pathways from the cellular to the organismal level. The challenge now is to determine how this seemingly endless list of components works together to promote the healthy function of complex living systems. This effort requires an interdisciplinary approach by investigators from both the biological and the physical sciences.     

Intervertebral disc biology, degeneration and novel tissue engineering and regenerative medicine therapies

Degeneration of the intervertebral disc (IVD) is a major cause of low back pain affecting a large percentage of the population at some point in their lives. Consequently IVD degeneration and its associated low back pain has a huge socio-economic impact and places a burden on health services world-wide. Current treatments remove the symptoms without treating the underlying problem and can result in reoccurrence in the same or adjacent discs. Tissue engineering offers hope that new therapies can be developed which can regenerate the IVD. Combined with this, development of novel biomaterials and an increased understanding of mesenchymal stem cell and IVD cell biology mean that tissue engineering of the IVD may soon become a reality. However for any regenerative medicine approach to be successful there must first be an understanding of the biology of the tissue and the pathophysiology of the disease process. This review covers these key areas and gives an overview of the recent developments in the fields of biomaterials, cell biology and tissue engineering of the IVD.     

系统生物学和合成生物学研究在生物燃料生产菌株改造中的应用

基于生物质资源生产环境友好的生物燃料,对经济和社会的可持续发展具有重要意义,但其生产成本高的问题十分突出,而高效生产菌株的获得是解决这一问题的根本出路。以下综述了利用系统生物学研究所获得的信息进行菌种改造的过程,重点论述了生产菌株胁迫耐受性方面的研究进展,并讨论了系统生物学、合成生物学和代谢工程技术在改造生物燃料生产菌株中的应用,展望了合成生物学在构建高效生物能源生产菌株方面应用的前景。     

Integrating Interactive Computational Modeling in Biology Curricula

While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by “building and breaking it” via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the “Vision and Change” call to action in undergraduate biology education by providing a hands-on approach to biology.     

Kits and their unique role in molecular biology: a brief retrospective

Since their initial development nearly 20 years ago, molecular biology kits have evolved from simple protocols and reagents for cloning of DNA to the more recent complex reagent sets that enable whole genomic sequencing. Initially met with resistance by some who felt that using them deprived researchers of the basic knowledge of how to create reagents, molecular biology kits have taken on an important role in the biological sciences. In this article we describe kit development, why kits have succeeded in molecular biology, and how they have paved the way for the more recent widespread use of core facilities.     

New tools and new biology: Recent miniaturized systems for molecular and cellular biology

Recent advances in applied physics and chemistry have led to the development of novel microfluidic systems. Microfluidic systems allow minute amounts of reagents to be processed using μm-scale channels and offer several advantages over conventional analytical devices for use in biological sciences: faster, more accurate and more reproducible analytical performance, reduced cell and reagent consumption, portability, and integration of functional components in a single chip. In this review, we introduce how microfluidics has been applied to biological sciences. We first present an overview of the fabrication of microfluidic systems and describe the distinct technologies available for biological research. We then present examples of microsystems used in biological sciences, focusing on applications in molecular and cellular biology.     

Translational synthetic biology

Synthetic biology is a recent scientific approach towards engineering biological systems from both pre-existing and novel parts. The aim is to introduce computational aided design approach in biology leading to rapid delivery of useful applications. Though the term reprogramming has been frequently used in the synthetic biology community, currently the technological sophistication only allows for a probabilistic approach instead of a precise engineering approach. Recently, several human health applications have emerged that suggest increased usage of synthetic biology approach in developing novel drugs. This mini review discusses recent translational developments in the field and tries to identify some of the upcoming future developments.     

Synthetic biology and metabolic engineering of actinomycetes for natural product discovery

Actinomycetes are one of the most valuable sources of natural products with industrial and medicinal importance. After more than half a century of exploitation, it has become increasingly challenging to find novel natural products with useful properties as the same known compounds are often repeatedly re-discovered when using traditional approaches. Modern genome mining approaches have led to the discovery of new biosynthetic gene clusters, thus indicating that actinomycetes still harbor a huge unexploited potential to produce novel natural products. In recent years, innovative synthetic biology and metabolic engineering tools have greatly accelerated the discovery of new natural products and the engineering of actinomycetes. In the first part of this review, we outline the successful application of metabolic engineering to optimize natural product production, focusing on the use of multi-omics data, genome-scale metabolic models, rational approaches to balance precursor pools, and the engineering of regulatory genes and regulatory elements. In the second part, we summarize the recent advances of synthetic biology for actinomycetal metabolic engineering including cluster assembly, cloning and expression, CRISPR/Cas9 technologies, and chassis strain development for natural product overproduction and discovery. Finally, we describe new advances in reprogramming biosynthetic pathways through polyketide synthase and non-ribosomal peptide synthetase engineering. These new developments are expected to revitalize discovery and development of new natural products with medicinal and other industrial applications.     

Models in biology: form and function

The relationships between physical and biological sciences are important in science education. This is shown in the links between the structure of biological science and the use of models. Although the physical sciences contain many principles of wide application, much of biology consists of very distinct examples. When these examples are used as models of organisms or processes, misunderstanding can occur if the characteristics of the model are used to make inaccurate generalizations. In biological education, stress on the importance of unique features must continually accompany the demonstration of similarities.

Theoretical models are constructed and reconstructed by students learning science, particularly in relation to broadly applicable principles. In biology a student may build a theoretical model of a subject which is itself a model used as an example. Distinct features of biological science may influence a variety of learning situations including problem solving.     

Biomimetic modelling

Biomimetics is seen as a path from biology to engineering. The only path from engineering to biology in current use is the application of engineering concepts and models to biological systems. However, there is another pathway: the verification of biological mechanisms by manufacture, leading to an iterative process between biology and engineering in which the new understanding that the engineering implementation of a biological system can bring is fed back into biology, allowing a more complete and certain understanding and the possibility of further revelations for application in engineering. This is a pathway as yet unformalized, and one that offers the possibility that engineers can also be scientists.     

合成生物学生物安全风险评价与管理

合成生物学(synthetic biology)已迅速发展为生命科学最具发展潜力的分支学科之一,但它同时也会给生态环境和人类健康带来潜在的风险。结合国内外合成生物学发展现状,本文综述了基因回路(DNA-based biocircuits)、最小基因组(minimal genome)、原型细胞(protocells)、化学合成生物学(chemical synthetic biology)等涉及的风险评价、合成生物学与生物安全工程(biosafety engineering)、合成生物学对社会伦理道德法律的影响以及当前热点议题,如生物朋(黑)客(biopunk(or biohackery))、家置生物学(garage biology)、DIY生物学(do-it-yourselfbiology)、生物恐怖主义(bioterrorism)等方面的新进展。分析讨论了世界各国合成生物学以自律监管或技术为主的安全管理原则和基于5个不同政策干预点的5P管理策略的合理性与潜在不足。同时结合我国合成生物学当前研究进展以及现有的安全管理规范,提出了建立以安全评价为核心的法规体系、生物学生物安全规范以及加强研发单位内部管理和生物安全科普宣传等我国合成生物学安全管理制度与措施等建议。     

Quantitative biology: where modern biology meets physical sciences

Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines.What does a mathematician looking at bacterial division under a microscope have in common with a biologist programming a stochastic simulation of microtubule growth? For one, both can be found at the Physiology Course at the Marine Biological Laboratory (MBL) in Woods Hole, MA, which brings together graduate students and young postdocs who have a passion for quantitative biology. Students enter the course with a wide array of scientific backgrounds, including chemistry, molecular biology, mathematics, and theoretical physics. Although at first hesitant to step outside their comfort zones, students leave the course confident and courageous in their abilities to work across traditional academic boundaries. Having experienced this transformation ourselves as participants in the 2014 Physiology Course, we wanted to share some of our insights and how they have influenced our perspectives on the present challenges and exciting future of quantitative cell biology.“When you cannot express [what you are speaking about] in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science.” The need for quantification in the life sciences could not have been better worded than it is in this quote from Lord Kelvin. One of the key take-home messages from the course has been the crucial need for advancement of quantitative cell biology, which uses accurate measurements to refine a hypothesis, with the aim of comparing experimental data with predictions generated by theoretical models. We strongly believe that quantitative approaches not only aid in better addressing existing biological questions but also enable the formulation of new ones.The present time is particularly ripe for implementing quantitative approaches in cell biology, due to the wealth of data available and the depth of control we now have over many experimental systems. In the past 20 years, we have sequenced the human genome, broken the diffraction limit in microscopy, and begun to explore the possibilities of the micron-scaled experiments with microfluidics. With these tools in hand, the means to obtain quantitative data are not limited to a select few model systems; this level of experimental detail allows us to craft theoretical models that not only fit the data but have real predictive power. We can then return to our respective experimental systems with new hypotheses and interrogate them anew, reaping the benefits of an approach that has benefited physicists for decades.Building quantitative models in biology has been a powerful approach that has often revealed counterintuitive phenomena and insights while at the same time leading to novel research directions. This is of particular importance today, as experiments are becoming increasingly expensive and are rapidly accumulating vast amounts of data. It is now possible to perform “virtual” preliminary experiments in silico using quantitative models and pre-existing data and only then move to “real” laboratory experiments to test the developed hypotheses. Researchers trained this way can perform more focused experiments instead of adopting the traditional exploratory mode in the lab, saving both time and resources. However, we recognize that a majority of biology graduates have not been rigorously trained in the mathematical and physical sciences. Similarly, many physics graduates often remember their introductory biology classes simply for the rote memorization of protein names and signaling pathways, leading to the wrong assumption that biology is all about remembering three-letter abbreviations such as WNT, MYC, and so on. This can often create a misleading picture of biology.These challenges could be overcome by finding a common language between biologists, physicists, and mathematicians. A simple example of this is the word “model.” The same word can mean very different things to scientists depending upon their training: to a physicist it refers to quantitative visualization of a process via certain well-defined mathematical parameters; a biologist, on the other hand, might use the word to refer to a schematic depiction (also called a cartoon) of a biochemical reaction. We aim to reduce this gap between biological and physical sciences and bring these two communities together.One way to train young scientists in such an approach is to provide opportunities for hands-on engagement with the methods and thought processes necessary to partake in both fields. The MBL Physiology Course is an excellent case study for a training program that gives young scientists the building blocks and community necessary for success in bridging quantitative/physical sciences and biology. The course starts with a weeklong boot camp designed to bring students of different backgrounds up to speed on basic tools in quantitative biology. Students purify proteins, program in MATLAB, and build microscopes. The most important skill that biologists acquire is not simply learning how to write lines of MATLAB code, but rather phrasing biological phenomena in mathematical terms through equations and simulations. Building confocal microscopes and optical tweezers on a bare optical table creates trust in the tools we depend on to acquire quantitative data. Physicists, on the other hand, learn to purify motor proteins like kinesins and dyneins from native sources (squid), in the process coming face-to-face with the natural context of the biological questions that they are addressing.This interdisciplinary approach helps students from diverse backgrounds develop a common language. After the boot camp, students work together on three 2-week-long research projects under the guidance of leading scientists. Projects range from studying the spatial organization of the human oral microbiome and observing the development of the Caenorhabditis elegans embryo all the way to performing computational simulations of cytoskeletal polymers. By working together in a highly informal and stimulating environment, physicists learn to appreciate biological problems and biologists begin to see biological phenomena in a new light as a result of the novel physical tools and methodologies they learn from their peers. As an example, course participants Rikki Garner and Daniel Feliciano successfully collaborated to study how competition between two highly processive microtubule motors that work in opposition controls microtubule length. While Rikki (mentored by Jané Kondev) tackled the question theoretically using a random walk model, Daniel, under the mentorship of Joe Howard, carried out the experimental measurements via an in vitro assay to test Rikki''s predictions. Other examples of quantitative and biological expertise coming together to address biological questions include studying the displacement and transport of proteins at the interface between cells and synthetic supported lipid bilayers (Figure 1), observing and quantifying the cytoplasmic streaming as well as the filter-feeding flow vortices in the giant single-celled organism Stentor coeruleus (Figure 2), and imaging the spatial organization of complex oral microbial communities (Figure 3).Open in a separate windowFIGURE 1:Proteins are organized based on size at the membrane interface. The membrane interface between a cell and a supported lipid bilayer (SLB) was formed by the interaction of synthetic adhesion molecules, one protein (bound to the membrane via a His-tag) and another protein that interacts and binds with the membrane-bound protein (expressed in the S2 cells). Note that the long noninteracting protein (21 nm, magenta colored) but not the shorter noninteracting protein (8 nm, magenta colored), which is bigger than the synthetic interacting dimer (16 nm, red–green colored) is excluded from the cell–SLB interface. Both of these noninteracting proteins are bound to the SLB and do not interact with the cell. Scale bar: 10 μm. (Prepared by L.Z. and Nan Hyung Hong under the guidance of Matt Bakalar, Eva Schmid, and Dan Fletcher.)Open in a separate windowFIGURE 2:Stentor coeruleus is a giant single-celled organism that feeds by creating flow vortices in water and directing prey into its oral opening using this flow. (A) Maximum intensity projection of time-lapse images showing flow fields in the feeding flow generated by S. coeruleus. The flow is generated by the coordinated ciliary beating of the mouth cilia. (B) Flow velocity and flow directions were quantified by the particle image velocimetry method. The circularity of flow has also been indicated—the blue cloud around the oral cilia indicates clockwise flow, and red indicates anticlockwise flow. Scale bar: 100 μm. (Prepared by S.S. under the guidance of Mark Slabodnick, Tatyana Makushok, and Wallace Marshall in collaboration with Jack Costello, Providence College.)Open in a separate windowFIGURE 3:Spatial organization of complex microbial communities in an oral plaque sample taken from a volunteer as seen by combinatorial labeling and spectral imaging–fluorescent in situ hybridization (CLASI-FISH). Microbes seen here are Corynebacterium (pink), Neisseriaceae (blue), Fusobacterium (green), Pasteurellaceae (yellow), Streptococcus (cyan), and Actinomyces (red). (Prepared by Bryan Weinstein, Lishibanya Mohapatra, and Matti Gralka under the guidance of Blair Rossetti, Jessica Mark Welch, and Gary Borisy.)An invaluable aspect of the course is the informal nature of the interaction. There are a wide variety of morning seminar speakers, and in the question-and-answer sessions following the talks, the speakers discuss not only science but also the successes and failures they experienced while moving across the boundaries of biological and physical sciences. The interactive and collaborative nature of the course encourages students to not just learn from one another but to actually teach one another. “Chalk talks” and interactions happen spontaneously and are the strongest indication of the richness of the intellectual exchange among members of the community. Prime examples in the 2014 course are the chalk talks on Python (Bryan Weinstein), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) cloning (Dan Dickinson), and the basics of microfluidics (Sindy Tang).Although we have benefited immensely from this interdisciplinary course, we understand that it might not be feasible for all graduate students to participate in such courses. Nevertheless, we believe that the scientific community should work together to replicate elsewhere, at least partially, the strengths of this course to allow students to benefit from this approach. Students should be encouraged to host and attend seminars from speakers with diverse backgrounds, which will expose them to research areas different from their own. Biology students should also be exposed to mathematical and statistical instruction early in their research careers, preferably at the undergraduate level, to enable them to build strong foundations. Students should also be encouraged to participate in short-term, low-pressure interdisciplinary collaborations to broaden their understanding and initiate interactions with other fields. Short summer/winter schools—for example, the physical biology of the cell courses at Cold Spring Harbor and the International Centre for Theoretical Physics (Italy)–International Centre for Theoretical Sciences (India) Winter School on Quantitative Systems Biology, 2013, in Bangalore, India—can serve as the perfect stage for this.In conclusion, we expect that quantitative approaches will be indispensable for better addressing biological questions in the future. Our experience is that combining traditional experimental cell biology with quantitative thinking leads to hitherto unknown scientifically rich domains, and we ourselves have found this exploratory journey to be both achievable and rewarding. Although bridging the gap may appear to be difficult at times, it is extremely satisfying when accomplished, and doing it within a highly motivated and supportive community is what makes the connection possible and extremely useful.