- freely available
Life 2014, 4(4), 1092-1116; doi:10.3390/life4041092
Abstract: Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications.
In 1665, Hooke observed cellular structure from cork materials and coined the word “cell”. Later, Schleiden and Schwann described cells as the basic unit of life [1,2]. Thus, “cell biology” emerged from that time and has gone through more than one hundred years of study. Despite the advancement of knowledge in cell biology, the integral functioning of cells is still not fully understood, which is likely due to the inherent complexity of natural cells. To this end, the complexity of natural cells could be overcome by building artificial cells to simplify and mimic natural cells. The concept of the artificial cell was first posed by Dr. Thomas Ming Swi Chang in 1957 , and it is now a pioneering and widely-known research field. Over the past few decades, extensive achievements have been made in the biotechnological and industrial applications of artificial cells [4,5,6,7,8], including co-translational insertion of membrane proteins into liposomes [9,10,11,12,13,14,15,16,17,18,19], directed evolution of cellular components [20,21], studies of primordial cells [4,22,23,24,25], delivery of drugs [26,27,28,29] and synthesis of proteins using nano-factories [30,31].
The definition of artificial cells is broad and includes various types of synthetic cells: protocells for addressing questions about the origin of life [22,23,24,25,32,33]; minimal natural cells that possess only the necessary genes for their basic maintenance [34,35,36,37,38,39]; and artificial cells that are constructed using synthetic membranes and cellular components [27,29,40,41]. These artificial cells consist of membranes that are made from lipids, fatty acids, polymers and combined lipid-polymer complexes. The artificial cells encapsulate various contents, ranging from transcription/translation machinery to multi-enzyme systems. This review focuses on artificial cells that are composed of a lipid bilayer, transcriptional/translational machinery and genetic information, with special emphasis on the application of synthetic biology in the construction of artificial cells.
The development of artificial cells indeed shares many fundamental characteristics of synthetic biology, which focuses on the minimality [22,42], modularity [43,44,45] and controllability [46,47,48,49,50] of synthetic biological systems. Taking advantage of synthetic biology, scientists could endow artificial cells with cellular functions inspired by natural systems. For artificial cells, DNA can be designed to carry information and to form genetic circuits [51,52,53,54,55]; transcription/translation machinery can be controlled under different conditions [56,57,58,59,60,61,62]; and cell membranes can be reconstituted with functional membrane proteins [9,10,11,12,13,14,15,16,18,19].
To further push the field of artificial cells, they could be engineered with the more complex characteristics of natural cells, including metabolism and autonomous replication. Artificial cells could encode for metabolic pathways using genetic circuits and cellular components [54,61]. Artificial cells could also exchange materials and information between the inside and outside of cells . In addition, artificial cells could be created to replicate both the informational genome and the three-dimensional structure [4,63].
In this review, we first depict the basic concept of artificial cellular systems. Next, we describe the gaps between artificial cells and natural cells. We then highlight the current limitations of artificial cells, as well as new challenges and opportunities in the field. Finally, we discuss the bridging of these gaps using synthetic biology approaches, focusing on genetic circuits, non-genetic factors, cell communication and self-reproduction. The filling of these gaps will eventually enable robust and efficient artificial cells (Figure 1).
2. Construction of Artificial Cells
Artificial cells are well-defined in vitro (or cell-free) systems that mimic certain phenotypes and functions of natural cells [4,5,23,31,32]. In general, artificial cells are made of three parts: cellular compartments (the shell), transcription and translation machinery (the engine) and genetic components (the information) . Artificial cells can be constructed in three basic steps, which correspond to the three parts (Figure 2). The first step is to generate and characterize genetic circuits (the information) in vivo, including logic gates (e.g., AND, NOR, OR), promoters (e.g., lac, T7, Escherichia coli endogenous promoters), as well as different transcription factors [52,54,64]. The aim of this step is to design and test genetic circuits that give rise to the desired functions. While in vivo systems allow large-scale synthesis of molecular components, the genetic parts may not function in vitro due to differences in the operating environment, such as DNA structure  and molecular crowding [66,67]. Therefore, the synthesis and testing of parts are often conducted in cycles between in vivo and in vitro systems.
The second step is to test constructed circuits in cell-free systems (the engine), because the functions of the parts might be affected by artificial chemical environments that are different from the intracellular environments of natural cells. There are two major types of cell-free systems: whole cell extracts  and protein synthesis using recombinant elements (PURE) systems . The details of these systems can be found in other review papers [62,69]. Briefly, cell extracts are directly derived from prokaryotic or eukaryotic cytosols by removing natural cell walls, in which the exact composition of the extracts is not known. The PURE system is constructed based on purified components from E. coli, and the concentration of each component is tightly controlled. The aim of this step is to make sure that the synthetic machinery and circuits can function inside artificial cells.
The third step is to encapsulate the cell-free systems inside membranes (the shell), which are composed of either fatty acids or phospholipids. The shell can be constructed using the extrusion method, water-in-oil method, lyophilization method and microfluidic devices. The extrusion methods can generate artificial cells with a uniform size; however, the cellular diameter is usually limited within 1 µm, and membranes could be multi-lamellar . Water-in-oil methods can produce large, unilamellar artificial cells with a heterogeneous size [71,72]. Lyophilization methods can produce large artificial cells with a heterogeneous size and lamellarity [66,73,74]. Microfluidic devices can generate artificial cells with a controllable size by adjusting the diameter of the channels and the flow speed . Other factors may also affect encapsulation processes, such as the component and viscosity of cell-free systems, pH and lipid composition. These issues suggest significant room for advancing the construction of artificial cells.
3. Gaps between Artificial and Natural Cells
There are huge gaps between natural cells and artificial cells in the complexity of genetic materials, membrane composition and structural organization. For instance, the prokaryotic organism, E. coli, contains 4.6 million base pairs of DNA. The DNA encodes for 4288 annotated genes, which belong to 2584 operons . These genes are translated to proteins, which yield multiple interacting partners of 2667 proteins . The membranes of E. coli consist of three main types of phospholipids , which support the activity of approximately 1050 different membrane proteins . The chromosome of E. coli is organized in specific structural domains that regulate gene expression . Cell division of E. coli is controlled tightly by the Z ring  and MinCDE pathways .
In contrast to bacteria, state-of-the-art artificial cells are much simpler and composed of fewer components. In the latest work, artificial cells contain 1.77 kilo-base pair DNA (coding sequence for functional proteins), which encode for two genes . The highest number of functional proteins included inside artificial cells is three  (excluding machinery that support transcription and translation). Crowded environments inside artificial cells are created by adding crowding agents, including PEG, ficoll and dextran . For each artificial cell, its membrane is typically reconstituted using one to two types of phospholipids [15,73] and a maximum of one type of pore-forming protein . Significant progress has been made in the construction of artificial cellular systems, such as encapsulation of different genetic circuits, incorporation of natural and non-natural components and assembly of natural and synthetic membranes [4,5,32,61,75]. On the one hand, these achievements have seemingly closed the gaps between prokaryotic cells and artificial cells by establishing the basic structure of cells, which include membranes, transcription-translation machinery and genetic pathways. On the other hand, the gaps between natural cells and artificial cells are constantly increasing due to the rapid discovery of new mechanisms in simple prokaryotic cells, including RNA localization [85,86] and CRISPR-based defense against phages . The gaps between prokaryotic and artificial cells bring tremendous opportunity to improve artificial cells by exploiting new concepts and tools in synthetic biology.
Synergy between Synthetic Biology and Studies of Artificial Cells
Synthetic biology is a field that focuses on using well-defined genetic parts to build new synthetic systems. One of the goals is to have the capacity to design and build synthetic cells with predictable functions and applications, including the production of biocommodities, therapeutic treatment and biosensors [88,89]. Indeed, recent reviews of synthetic biology have highlighted artificial cells as promising synthetic systems [88,89]. The field approaches biological engineering from several unique angles. First, synthetic biology highlights design principles that can guide the control of biological systems [90,91]. Rational design is a mainstay of synthetic biology that relies on the idea that biological systems are fundamentally modular [54,92]. During the design process, genes are defined as the basic biological units. The aim of rational design is to generate an optimized outcome through logical assembly of these basic units. For example, the synthesis of the precursor of the anti-malaria drug, artemisinic acid was achieved by coupling an engineered mevalonate pathway with two enzymes (amorphadiene synthase and cytochrome P450 monooxygenase) in yeast .
Second, synthetic biologists use well-characterized and interoperable modules, such as promoters, operators, transcriptional factors and ribosome binding sites, as building blocks to create higher-order circuits [53,94,95,96,97]. For example, a promoter library was used to ascertain rules that describe the responsiveness of a promoter to transcriptional factors . Specifically, promoters were sub-divided into the core, proximal and distal regions. For prokaryotes, the strength of transcriptional repression was shown to be the greatest when a repressor site was located in the core region of a promoter. The repression strength was less strong when located in the proximal region and was the weakest when located in the distal region. Conversely, activators worked only in the distal region and had no effect in the core and proximal regions . These basic principles could be exploited to assemble promoter modules in a bottom-up approach.
Third, synthetic biology uses a bottom-up approach to understand biological circuits. One can design and construct simple genetic circuits from well-characterized genes and proteins, followed by the analysis of their behavior in living cells. Through this approach, tremendous insights are gained into noise propagation [100,101,102,103], network motifs  and the dynamics of nonlinear genetic circuits [105,106,107,108,109,110]. In addition, DNA synthesis is a powerful tool for large-scale synthesis of genetic circuits. A state-of-the-art DNA synthesis method that employs error correction reaction can achieve a low error rate of one in 8700 base pairs . Such DNA synthesis has been exploited for metabolic engineering  and genome construction [113,114].
Fourth, synthetic biology provides a computational toolbox to model synthetic systems. Modeling is useful to ensure that the assembled systems operate as desired. Modeling is also useful for suggesting the specific manipulation of system components. For example, a thermodynamic model was developed to predict protein expression by designing various ribosome binding sites (RBS). To test the model, designed RBS sequences were connected to the PBAD promoter and an AND-gate circuit to optimize green fluorescent protein (GFP) expression . In silico modeling based on libraries of diversified components was used to design a synthetic gene network that functioned as a timer .
The engineering of artificial cells shares many characteristics of synthetic biology approaches. Both biological and non-natural building blocks can be used to create genetic circuits in artificial cells [116,117,118,119,120,121]. For example, incorporation of non-natural nucleic acids (e.g., XNA, PNA) can increase the resistance of nucleic acids to nucleases and improve binding specificity between two nucleic-acid strands [122,123]. Non-natural amino-acid incorporation will expand the structural and functional diversity of proteins [124,125]. Nanotubes/pores can be used as membrane channels for the transport of biological and inorganic molecules [126,127,128]. In the following sections, we will highlight four areas to apply concepts from synthetic biology toward the construction of artificial cells, including genetic circuits, non-genetic factors, communication and replication.
4. Using Synthetic Biology to Shrink the Gaps between Artificial and Natural Cells
4.1. The Design of Genetic Circuits to Control Functions of Artificial Cells
Synthetic biologists can now rewire natural cellular networks by either constructing orthogonal genetic circuits or modifying endogenous circuits of the host cells [52,54,129,130]. For example, anti-malaria drug was synthesized by increasing farnesyl pyrophosphate production, introducing an amorphadiene synthase gene and incorporating a novel cytochrome P450 and its redox partner . These methods of designing and constructing synthetic gene circuits could be exploited for the control of artificial cells.
Two main requirements are required to endow artificial cells with more complex genetic circuits. The first requirement is the ability to synthesize long DNA, which carries more information than a short fragment of DNA. The continuous improvement in de novo DNA synthesis and gene assembly technologies has enabled the synthesis of DNA at mega-base pair scale with high accuracy [131,132]. The J. Craig Venter Institute synthetic 1.0 genome is 1.08 million base pairs in length and contains about 430 genes . Furthermore, a functional eukaryotic chromosome (272,871 base pairs) has been synthesized in a stepwise manner . Therefore, DNA synthesis is a powerful tool to design long DNA for implementation in artificial cells.
The second requirement involves the design of genetic circuits for controlled gene expression. A classical engineering procedure could be implemented to fulfill this requirement: understanding, design, and analysis . First, engineers set clear objectives for the intended design of genetic circuits. Next, to accomplish the objectives, computational algorithms are used for genetic design  and network wiring . To this end, diverse datasets have been integrated to build a database of essential genes and to design metabolic pathways and signal transductions [137,138,139,140]. Computational tools can also be used to predict expression levels of selected genes for the better construction of cellular networks in artificial cells . Third, system analysis is performed according to the desired outcomes. A new round of the engineering procedure is initiated until the desired outcomes are achieved. When compared to natural cells, the simplicity of artificial cells allows precise control of desired cellular phenotype and behavior. In addition, it is now plausible to synthesize genomic DNA for the construction of artificial cells. One of the future challenges will be the true design and engineering of a synthetic genome without any reference templates .
To date, DNA , RNA  and peptides  have been synthesized in liposomes using synthetic gene circuits. The first example of DNA amplification was implemented through polymerase chain reaction (PCR) inside liposomes. The liposomes were stable at high temperature conditions used for the PCR . Template-independent RNA polymerase was encapsulated inside dimyristoyl phosphatidylcholine vesicles. Long chain RNA was synthesized when adenosine diphosphate (ADP) was externally provided . The synthesis of functional GFP was the first successful attempt at protein expression inside liposomes . Based on this foundation, continuous synthesis of enhanced GFP (eGFP) inside artificial cells was achieved by incorporating α-hemolysin in the membranes. These artificial cells could sustain protein production for up to four days (Figure 3a,b) . A two-stage genetic network was constructed in liposomes using two different plasmids. In this study, SP6 promoter regulated the expression of T7 RNA polymerase (T7 RNAP) in one plasmid, and the T7 RNAP regulated GFP expression through a T7 promoter in another plasmid . A positive feedback loop was introduced into artificial cells and was shown to increase the signal-to-noise ratio to 800 when compared to circuits without the positive feedback loop (Figure 3c,d) .
However, gene circuits constructed in artificial cells are still limited to a few genes. How much information do we need to encode into an artificial cell to mimic a minimal cell? The minimal genome refers to a set of genes that are required to maintain life [34,35,36,37,38,39]. To date, the smallest genome is predicted to be 113 kilo-base pairs long and contains 151 genes. These genes include 38 RNAs and 113 proteins that form the core cellular replication machinery . We envision that such a minimal genome could be incorporated into artificial cells to establish the foundation of free-living artificial cells. The incorporation is non-trivial, as it requires understanding all genetic and non-genetic factors that modulate critical cellular functions.
4.2. Non-Genetic Factors That Modulate Gene Expression in Artificial Cellular Systems
In addition to the genome, natural cells are regulated by various non-genetic factors, which could be harnessed to improve the control of artificial cells. DNA structure is one of the non-genetic factors that modulates gene expression . For example, a poly (dG)-poly (dC) sequence was used to form different lengths of a DNA triplex. This non-B-form DNA structure modulated gene expression when placed at the 5’ end of a promoter. The activity was length dependent: the sequence affected the expression of reporter genes when placed 27–30 base pairs upstream of the promoter, but exhibited no effects when placed further than 35 base pairs upstream of the promoter . Indeed, DNA structures have been exploited to control synthetic biological systems. For example, single-strand DNA was engineered as scaffolds to form extracellular matrix with proteins. In this work, the persistence and stiffness of the DNA scaffold were controlled by adding single-stranded domains. This kind of extracellular matrix could affect cytoskeletal arrangement and cellular shape, as well as signal transduction . Therefore, the DNA structure is a potential tool for controlling the dynamics of artificial cells.
Non-DNA binding factors represent another class of non-genetic factors that regulate gene expression. Non-DNA binding factors refer to factors that affect gene expression, but do not directly bind to DNA molecules. For example, osmZ (also known as hnsA) can increase the DNA supercoil in bacteria . Mutation of this gene affects the expression of ompF, ompC, fimA and bgl operons . A histone-binding protein, nucleoplasmin, binds to histone and reduces its affinity to DNA, which, in turn, increases the binding probability of transcription factors (GAL4-AH, USF, Sp1) to DNA . Histone acetyltransferase and deacetylase are responsible for histone acetylation and de-acetylation that play causative roles in gene transcription . These non-DNA binding factors could be exploited to improve the control of gene expression inside artificial cells.
In addition, molecular crowding is another non-genetic factor that modulates gene expression [65,66,67,84,152,153,154,155,156,157,158,159,160]. The cytosol of natural cells consists of highly-packed macromolecules, including proteins, nucleic acids, carbohydrates and ribosomes. The typical concentration of these macromolecules is 300–400 mg/mL . This molecular crowding has been shown to limit the diffusion of macromolecules and to enhance their interactions in vitro. Furthermore, molecular crowding enhances the stability of DNA  and proteins [162,163], affects the diffusion of transcription proteins [153,164,165,166] and promotes self-assembly of macromolecules . At present, whole cell extracts and PURE systems are commonly used to provide the machinery for transcription and translation in artificial cells. The most common crude lysates are derived from three sources, including E. coli extract , wheat germ extract (WGE)  and rabbit reticulocyte lysate (RRL) . Protein concentration in bacterial extracts is approximately 10 mg/mL, which is 30-fold lower than the protein concentration in living cells .
To mimic molecular crowding, crowding agents that are inert macromolecules, such as PEG, dextran and ficoll, can be supplemented in cell-free systems [66,170]. A recent work mimicked crowded environment by inducing coacervation in E.coli lysate using osmosis pressure. The study showed that transcription rates were five- to six-times higher in a crowded environment than non-crowded conditions . Tan et al.  introduced molecular crowding to artificial cellular systems and found that the binding of RNA polymerase to the promoter was increased by a large crowding agent. They also found that molecular crowding enhanced the robustness of gene expression under chemical perturbations (Figure 4). Recently, an additive-free cell extract (AFCE) was used to construct life-mimicking artificial cells (L-MACs). Specifically, a semi-permeable membrane was used to condense the extract to 260 mg/mL of macromolecules, which was close to the protein concentration in living cells. However, protein expression was low in L-MACs, suggesting that simple condensation of the bacterial extract gave rise to sub-optimal conditions for gene expression . Importantly, this work suggests that active, unknown cellular pathways may be necessary for the modulation of crowding conditions inside natural and artificial cells for optimal gene expression.
4.3. Communication between Artificial Cells and Their Environment
Living cells communicate with their surroundings to adapt to changing environments. The communication is achieved through cell membranes that control signal transduction, energy production and trans-membrane channels for molecular transport. Along this line, components of the natural cell membrane could be incorporated into artificial cells, which would transform them from passive entities into active systems that can interact with environments.
The simplest way for artificial cells to sense the environment is through small molecules that directly permeate membranes. For example, theophylline was used as a signal that diffused through artificial cell membranes and directly bound to mRNA to turn on yellow fluorescent protein (YPet) expression . Based on this communication mechanism, Lentini and colleagues implemented artificial cells that translated chemical signals for E.coli. In this work, theophylline could not be recognized by E. coli, but would permeate into artificial cells and turn on α-hemolysin expression through a theophylline riboswitch. The α-hemolysin formed an unspecific pore that allowed the transition of isopropyl β-D-1-thiogalactopyranoside (IPTG). The entrapped IPTG was released from artificial cells to trigger gene expression in E. coli. This work represents the first example of cross-species communication between artificial and natural cells (Figure 5) . Despite the successful implementation, the authors encountered an issue with leaky expression of α-hemolysin, which caused gene expression in E. coli to eventually converge for systems with and without the input signal, theophylline. This issue suggests significant room to improve the control of synthetic modules in vitro.
Artificial cell membranes can also be modified using membrane proteins to perform certain functions. For example, α-hemolysin was expressed and inserted into artificial cell membrane. It formed unspecific pores on the cell membrane, which allowed molecules smaller than 3 kDa to diffuse through the membranes . Cytochrome b5 and its fusion proteins were synthesized and directly localized on liposome membranes . Potassium channel KcsA was introduced into the lipid bilayers of artificial cells with a controllable orientation . Functional Sec translocon machinery were localized in the membranes of artificial cells . BmOR1 and BmOrco that formed an olfactory receptor complex were incorporated into liposomes to detect the ligand, bombykol (Figure 6) . These studies provide examples to reconstitute membrane proteins in artificial cell membranes, which could be used as modules to construct communicating artificial cells.
4.4. Replication and Division of Artificial Cells
Recent work has demonstrated that artificial cells could grow by incorporating micelles, which provided additional phospholipids for the growth of cell membranes. Division of artificial cells was then achieved by agitation [25,174,175]. To date, artificial cells cannot self-replicate autonomously without external intervention . The challenge of autonomous replication arises due to the difficulty of reconstituting both metabolism and division machinery inside artificial cells. Attempts have been made toward supporting metabolism inside artificial cells. To enable the consistent supplement of nutrients, α-hemolysin was incorporated into artificial cell membranes. Based on the approach, gene expression was sustained inside artificial cells for up to four days (Figure 3a,b) .
Other findings have provided preliminary results for reconstituting cell division machinery inside artificial cells. To approach autonomous division, the phosphatidic acid (PA) synthesis pathway was reconstituted inside liposomes, which generated functional sn-glycerol-3-phosphate acyltransferase (GPAT) and lysophosphatidic acid acyltransferase (LPAAT) for membrane growth (Figure 7a) . In addition, bacterial cytoskeleton MreB was reconstituted into lipid membrane and shown to exhibit filament structures (Figure 7b) . FtsZ was demonstrated to form Z rings in liposomes and to generate a force for fission (Figure 7c) . To this end, elementary steps of natural cell division have been reconstituted inside artificial cells. However, significant work is required to connect these steps for integral functioning of autonomous, artificial cell replication.
Instead of reconstituting the replication machinery of complex cells, the machinery of simpler organisms, such as phages and viruses, could be exploited for the construction of artificial cells. Indeed, T7 and encephalomyocarditis virus (EMCV) were shown to replicate and assemble themselves in whole cell extracts of prokaryotes  and eukaryotes . The 40 kilo-base pair T7 genome DNA was incubated in E. coli whole cell extract that contained nutrients for transcription and translation at 29 °C for 12 h. Phage replication in the reaction mix was measured by counting plaque forming units. More than a billion infectious T7 phage per milliliter were generated. Similarly, the 7.1 kilo-base pair EMCV genome DNA was assembled in a T7 promoter/terminator unit and incubated in a HeLa cell extract containing T7 RNAP. The plaque forming unit was determined by using BHK-21 cells and reached eight billion particles after 8 h of incubation at 34 °C. These studies suggest a potential direction for constructing artificial cells following phage replication pathways.
5. Conclusions and Future Outlook
In 1925, Gorter and Grendel presented the first evidence that cellular membranes are composed of lipid bilayers . Today, we have reached a critical barrier in the research of artificial cells that consist of bilayer membranes and protein synthesis machinery. Can we implement the minimal set of components required to create free-living artificial cells? Answers to the question will challenge our basic understanding of natural cells and emergent properties of complex systems. Based on studies of free-living natural cells, Mycobacterium genitalium has the smallest genome size of 580,076 base pairs that contain only 475 coding sequences . The coding sequences include genes required for DNA replication, transcription and translation, DNA repair, cellular transport and energy metabolism. Indeed, due to the minimality of the genome, it was the first genome to be synthesized chemically and used to create the first synthetic Mycobacterium mycoides . In this work, a chemically-synthesized genome was inserted into natural Mycobacterium capricolum that was stripped of its original genome. Essentially, the synthetic genome was used to reboot the bacteria by using existing cellular proteins and structures. Can a similar strategy be used to insert synthetic genomes into artificial cells?
To this end, synthetic biology has established a rich library of tools and cellular parts, which could be exploited to achieve free-living artificial cells. Pathway databases and automated design tools could be used to design and predict cellular pathways inside artificial cells [182,183,184,185]. High-throughput cloning, gene synthesis and assembling of pathways could be used to rapidly investigate a large set of candidate pathways [186,187,188,189,190]. In addition, random DNA mutation and microfluidics could be used to evolve a large library of DNA for implementation in artificial cells [75,191,192,193]. Despite the availability of these tools, significant research is required to identify the minimal set of cellular parts required for efficient metabolism and replication [37,39,42]. Furthermore, the understanding of how intracellular non-genetic factors, including crowding, chemical species and cellular structures, regulate efficient functioning of cellular pathways is lacking [4,5,6,22]. The minimal cellular parts and non-genetic factors together constitute the minimum physical genome that is necessary to construct free-living artificial cells. While the realization of free-living artificial cells is still far from current technology, the progress toward this ultimate goal will likely reveal tremendous insights into fundamental principles that govern robust and efficient functioning of natural cells.
Artificial cellular systems are emerging as potential biotechnological systems due to their capability of mimicking certain cellular functions in vitro. We envision that further development of genetic circuits, non-genetic modules, cell-cell communication and self-replication will enhance the control and implementation of artificial cells. Artificial cells could be built module-by-module using nucleic acids, lipids, proteins and other molecules essential for life . One day, it may be possible to create modular toolkits for computer-guided design of artificial cells, which will represent a new class of synthetic cells.
This work is supported by the Society-in-Science: Branco-Weiss Fellowship (Cheemeng Tan).
Conflicts of Interest
The authors declare no conflict of interest.
References and Notes
- Hooke, R. Micrographia: Or Some Physiological Descriptions of Minute Bodies Made by Magnifying Glasses With Observations and Inquiries Thereupon; John Martyn, printer to the Royal Society, and are to be sold at his shop at the Bell a little without Temple Barr. Martin and Allestry, London, UK, 1665.
- Schwann, T. Mikroskopische untersuchungen über die übereinstimmung in der struktur und dem wachstum der tiere und pflanzen. In Klassische Schriften zur Zellenlehre, 2nd ed.; Jahn, I., Ed.; Wissenschaftlicher Verlag Harri Deutsch GmbH: Frankfurt, Germany, 2003. [Google Scholar]
- Chang, T.M. 1957 Report on “method for preparing artificial hemoglobin corpuscles”. Available online: http://www.worldscientific.com/doi/pdf/10.1142/9789812770370_bmatter (accessed on 11 December 2014).
- Noireaux, V.; Maeda, Y.T.; Libchaber, A. Development of an artificial cell, from self-organization to computation and self-reproduction. Proc. Natl. Acad. Sci. USA 2011, 108, 3473–3480. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Ruder, W.C.; LeDuc, P.R. Artificial cells: Building bioinspired systems using small-scale biology. Trends Biotechnol. 2008, 26, 14–20. [Google Scholar] [CrossRef] [PubMed]
- Wu, F.; Tan, C. The engineering of artificial cellular nanosystems using synthetic biology approaches. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 2014, 6, 369–383. [Google Scholar] [CrossRef] [PubMed]
- Chang, T.M. 50th anniversary of artificial cells: Their role in biotechnology, nanomedicine, regenerative medicine, blood substitutes, bioencapsulation, cell/stem cell therapy and nanorobotics. Artif. Cells Blood Substit. Biotechnol. 2007, 35, 545–554. [Google Scholar] [CrossRef]
- Chang, T.M. From artificial red blood cells, oxygen carriers, and oxygen therapeutics to artificial cells, nanomedicine, and beyond. Artif. Cells Blood Substit. Biotechnol. 2012, 40, 197–199. [Google Scholar] [CrossRef]
- Nomura, S.M.; Kondoh, S.; Asayama, W.; Asada, A.; Nishikawa, S.; Akiyoshi, K. Direct preparation of giant proteo-liposomes by in vitro membrane protein synthesis. J. Biotechnol. 2008, 133, 190–195. [Google Scholar] [CrossRef] [PubMed]
- Kaneda, M.; Nomura, S.M.; Ichinose, S.; Kondo, S.; Nakahama, K.; Akiyoshi, K.; Morita, I. Direct formation of proteo-liposomes by in vitro synthesis and cellular cytosolic delivery with connexin-expressing liposomes. Biomaterials 2009, 30, 3971–3977. [Google Scholar] [CrossRef] [PubMed]
- Moritani, Y.; Nomura, S.M.; Morita, I.; Akiyoshi, K. Direct integration of cell-free-synthesized connexin-43 into liposomes and hemichannel formation. FEBS J. 2010, 277, 3343–3352. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.J.; Hansen, G.P.; Venancio-Marques, A.; Baigl, D. Cell-free preparation of functional and triggerable giant proteoliposomes. Chembiochem 2013, 14, 2243–2247. [Google Scholar] [CrossRef] [PubMed]
- Yanagisawa, M.; Iwamoto, M.; Kato, A.; Yoshikawa, K.; Oiki, S. Oriented reconstitution of a membrane protein in a giant unilamellar vesicle: Experimental verification with the potassium channel kcsa. J. Am. Chem. Soc. 2011, 133, 11774–11779. [Google Scholar] [CrossRef] [PubMed]
- Long, A.R.; O’Brien, C.C.; Alder, N.N. The cell-free integration of a polytopic mitochondrial membrane protein into liposomes occurs cotranslationally and in a lipid-dependent manner. PLOS ONE 2012, 7. [Google Scholar] [CrossRef] [PubMed]
- Tanaka-Takiguchi, Y.; Itoh, T.; Tsujita, K.; Yamada, S.; Yanagisawa, M.; Fujiwara, K.; Yamamoto, A.; Ichikawa, M.; Takiguchi, K. Physicochemical analysis from real-time imaging of liposome tubulation reveals the characteristics of individual f-bar domain proteins. Langmuir 2013, 29, 328–336. [Google Scholar] [CrossRef] [PubMed]
- Matsubayashi, H.; Kuruma, Y.; Ueda, T. In vitro synthesis of the E. Coli sec translocon from DNA. Angew. Chem. 2014, 53, 7535–7538. [Google Scholar] [CrossRef]
- Hovijitra, N.T.; Wuu, J.J.; Peaker, B.; Swartz, J.R. Cell-free synthesis of functional aquaporin Z in synthetic liposomes. Biotechnol. Bioeng. 2009, 104, 40–49. [Google Scholar] [CrossRef] [PubMed]
- Ritz, S.; Hulko, M.; Zerfass, C.; May, S.; Hospach, I.; Krasteva, N.; Nelles, G.; Sinner, E.K. Cell-free expression of a mammalian olfactory receptor and unidirectional insertion into small unilamellar vesicles (suvs). Biochimie 2013, 95, 1909–1916. [Google Scholar] [CrossRef] [PubMed]
- Kuruma, Y.; Stano, P.; Ueda, T.; Luisi, P.L. A synthetic biology approach to the construction of membrane proteins in semi-synthetic minimal cells. Biochim. Biophys. Acta 2009, 1788, 567–574. [Google Scholar] [CrossRef] [PubMed]
- Tawfik, D.S.; Griffiths, A.D. Man-made cell-like compartments for molecular evolution. Nat. Biotechnol. 1998, 16, 652–656. [Google Scholar] [CrossRef] [PubMed]
- Miller, O.J.; Bernath, K.; Agresti, J.J.; Amitai, G.; Kelly, B.T.; Mastrobattista, E.; Taly, V.; Magdassi, S.; Tawfik, D.S.; Griffiths, A.D. Directed evolution by in vitro compartmentalization. Nat. Methods 2006, 3, 561–570. [Google Scholar] [CrossRef] [PubMed]
- Blain, J.C.; Szostak, J.W. Progress toward synthetic cells. Annu. Rev. Biochem. 2014, 83, 615–640. [Google Scholar] [CrossRef] [PubMed]
- Dzieciol, A.J.; Mann, S. Designs for life: Protocell models in the laboratory. Chem. Soc. Rev. 2012, 41, 79–85. [Google Scholar] [CrossRef] [PubMed]
- Mansy, S.S.; Schrum, J.P.; Krishnamurthy, M.; Tobe, S.; Treco, D.A.; Szostak, J.W. Template-directed synthesis of a genetic polymer in a model protocell. Nature 2008, 454, 122–125. [Google Scholar] [CrossRef] [PubMed]
- Zhu, T.F.; Szostak, J.W. Coupled growth and division of model protocell membranes. J. Am. Chem. Soc. 2009, 131, 5705–5713. [Google Scholar] [CrossRef] [PubMed]
- Schroeder, A.; Goldberg, M.S.; Kastrup, C.; Wang, Y.; Jiang, S.; Joseph, B.J.; Levins, C.G.; Kannan, S.T.; Langer, R.; Anderson, D.G. Remotely activated protein-producing nanoparticles. Nano Lett. 2012, 12, 2685–2689. [Google Scholar] [CrossRef] [PubMed]
- Samad, A.; Sultana, Y.; Aqil, M. Liposomal drug delivery systems: An update review. Curr. Drug Deliv. 2007, 4, 297–305. [Google Scholar] [CrossRef] [PubMed]
- Ganta, S.; Devalapally, H.; Shahiwala, A.; Amiji, M. A review of stimuli-responsive nanocarriers for drug and gene delivery. J. Control. Release 2008, 126, 187–204. [Google Scholar] [CrossRef] [PubMed]
- Agarwal, R.; Iezhitsa, I.; Agarwal, P.; Abdul Nasir, N.A.; Razali, N.; Alyautdin, R.; Ismail, N.M. Liposomes in topical ophthalmic drug delivery: An update. Drug Deliv. 2014. [Google Scholar] [CrossRef]
- Jewett, M.C.; Calhoun, K.A.; Voloshin, A.; Wuu, J.J.; Swartz, J.R. An integrated cell-free metabolic platform for protein production and synthetic biology. Mol. Syst. Biol. 2008, 4. [Google Scholar] [CrossRef]
- Monnard, P.A.; DeClue, M.S.; Ziock, H.J. Organic nano-compartments as biomimetic reactors and protocells. Curr. Nanosci. 2008, 4, 71–87. [Google Scholar] [CrossRef]
- Hammer, D.A.; Kamat, N.P. Towards an artificial cell. FEBS Lett. 2012, 586, 2882–2890. [Google Scholar] [CrossRef] [PubMed]
- Szostak, J.W.; Bartel, D.P.; Luisi, P.L. Synthesizing life. Nature 2001, 409, 387–390. [Google Scholar] [CrossRef] [PubMed]
- Koonin, E.V.; Mushegian, A.R. Complete genome sequences of cellular life forms: Glimpses of theoretical evolutionary genomics. Curr. Opin. Genet. Dev. 1996, 6, 757–762. [Google Scholar] [CrossRef] [PubMed]
- Kolisnychenko, V.; Plunkett, G.; Herring, C.D.; Feher, T.; Posfai, J.; Blattner, F.R.; Posfai, G. Engineering a reduced escherichia coli genome. Genome Res. 2002, 12, 640–647. [Google Scholar] [CrossRef] [PubMed]
- Gil, R.; Sabater-Munoz, B.; Latorre, A.; Silva, F.J.; Moya, A. Extreme genome reduction in buchnera spp.: Toward the minimal genome needed for symbiotic life. Proc. Natl. Acad. Sci. USA 2002, 99, 4454–4458. [Google Scholar] [CrossRef] [PubMed]
- Gil, R.; Silva, F.J.; Pereto, J.; Moya, A. Determination of the core of a minimal bacterial gene set. Microbiol. Mol. Biol. Rev. 2004, 68, 518–537. [Google Scholar] [CrossRef] [PubMed]
- Glass, J.I.; Assad-Garcia, N.; Alperovich, N.; Yooseph, S.; Lewis, M.R.; Maruf, M.; Hutchison, C.A., III; Smith, H.O.; Venter, J.C. Essential genes of a minimal bacterium. Proc. Natl. Acad. Sci. USA 2006, 103, 425–430. [Google Scholar] [CrossRef]
- Luisi, P.L. Chemical aspects of synthetic biology. Chem. Biodivers. 2007, 4, 603–621. [Google Scholar] [CrossRef] [PubMed]
- Vemuri, S.; Rhodes, C.T. Preparation and characterization of liposomes as therapeutic delivery systems: A review. Pharm. Acta Helv. 1995, 70, 95–111. [Google Scholar] [CrossRef] [PubMed]
- Szoka, F., Jr.; Papahadjopoulos, D. Comparative properties and methods of preparation of lipid vesicles (liposomes). Annu. Rev. Biophys. Bioeng. 1980, 9, 467–508. [Google Scholar] [CrossRef] [PubMed]
- Forster, A.C.; Church, G.M. Towards synthesis of a minimal cell. Mol. Syst. Biol. 2006, 2. [Google Scholar] [CrossRef]
- Guido, N.J.; Wang, X.; Adalsteinsson, D.; McMillen, D.; Hasty, J.; Cantor, C.R.; Elston, T.C.; Collins, J.J. A bottom-up approach to gene regulation. Nature 2006, 439, 856–860. [Google Scholar] [CrossRef] [PubMed]
- Chin, J.W. Modular approaches to expanding the functions of living matter. Nat. Chem. Biol. 2006, 2, 304–311. [Google Scholar] [CrossRef] [PubMed]
- Buchler, N.E.; Gerland, U.; Hwa, T. On schemes of combinatorial transcription logic. Proc. Natl. Acad. Sci. USA 2003, 100, 5136–5141. [Google Scholar] [CrossRef] [PubMed]
- Karzbrun, E.; Tayar, A.M.; Noireaux, V.; Bar-Ziv, R.H. Synthetic biology. Programmable on-chip DNA compartments as artificial cells. Science 2014, 345, 829–832. [Google Scholar] [CrossRef] [PubMed]
- Hao, N.; Budnik, B.A.; Gunawardena, J.; O’Shea, E.K. Tunable signal processing through modular control of transcription factor translocation. Science 2013, 339, 460–464. [Google Scholar] [CrossRef] [PubMed]
- Moon, T.S.; Lou, C.; Tamsir, A.; Stanton, B.C.; Voigt, C.A. Genetic programs constructed from layered logic gates in single cells. Nature 2012, 491, 249–253. [Google Scholar] [CrossRef] [PubMed]
- Gibson, D.G.; Glass, J.I.; Lartigue, C.; Noskov, V.N.; Chuang, R.Y.; Algire, M.A.; Benders, G.A.; Montague, M.G.; Ma, L.; Moodie, M.M.; et al. Creation of a bacterial cell controlled by a chemically synthesized genome. Science 2010, 329, 52–56. [Google Scholar] [CrossRef] [PubMed]
- Levskaya, A.; Weiner, O.D.; Lim, W.A.; Voigt, C.A. Spatiotemporal control of cell signalling using a light-switchable protein interaction. Nature 2009, 461, 997–1001. [Google Scholar] [CrossRef] [PubMed]
- Ellis, T.; Wang, X.; Collins, J.J. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol. 2009, 27, 465–471. [Google Scholar] [CrossRef] [PubMed]
- Nandagopal, N.; Elowitz, M.B. Synthetic biology: Integrated gene circuits. Science 2011, 333, 1244–1248. [Google Scholar] [CrossRef] [PubMed]
- Slusarczyk, A.L.; Lin, A.; Weiss, R. Foundations for the design and implementation of synthetic genetic circuits. Nat. Rev. Genet. 2012, 13, 406–420. [Google Scholar] [CrossRef] [PubMed]
- Brophy, J.A.; Voigt, C.A. Principles of genetic circuit design. Nat. Methods 2014, 11, 508–520. [Google Scholar] [CrossRef] [PubMed]
- Canton, B.; Labno, A.; Endy, D. Refinement and standardization of synthetic biological parts and devices. Nat. Biotechnol. 2008, 26, 787–793. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.M.; Swartz, J.R. Prolonging cell-free protein synthesis with a novel atp regeneration system. Biotechnol. Bioeng. 1999, 66, 180–188. [Google Scholar] [CrossRef] [PubMed]
- Shimizu, Y.; Inoue, A.; Tomari, Y.; Suzuki, T.; Yokogawa, T.; Nishikawa, K.; Ueda, T. Cell-free translation reconstituted with purified components. Nat. Biotechnol. 2001, 19, 751–755. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.M.; Swartz, J.R. Efficient production of a bioactive, multiple disulfide-bonded protein using modified extracts of escherichia coli. Biotechnol. Bioeng. 2004, 85, 122–129. [Google Scholar] [CrossRef] [PubMed]
- Jewett, M.C.; Swartz, J.R. Mimicking the escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnol. Bioeng. 2004, 86, 19–26. [Google Scholar] [CrossRef] [PubMed]
- Jewett, M.C.; Swartz, J.R. Substrate replenishment extends protein synthesis with an in vitro translation system designed to mimic the cytoplasm. Biotechnol. Bioeng. 2004, 87, 465–472. [Google Scholar] [CrossRef] [PubMed]
- Noireaux, V.; Bar-Ziv, R.; Godefroy, J.; Salman, H.; Libchaber, A. Toward an artificial cell based on gene expression in vesicles. Phys. Biol. 2005, 2, 1–8. [Google Scholar] [CrossRef] [PubMed]
- Pedersen, A.; Hellberg, K.; Enberg, J.; Karlsson, B.G. Rational improvement of cell-free protein synthesis. New Biotechnol. 2011, 28, 218–224. [Google Scholar] [CrossRef]
- Ichihashi, N.; Matsuura, T.; Kita, H.; Sunami, T.; Suzuki, H.; Yomo, T. Constructing partial models of cells. Cold Spring Harb Perspect Biol. 2010, 2. [Google Scholar] [CrossRef]
- Komili, S.; Silver, P.A. Coupling and coordination in gene expression processes: A systems biology view. Nat. Rev. Genet. 2008, 9, 38–48. [Google Scholar] [CrossRef] [PubMed]
- Li, G.-W.; Berg, O.G.; Elf, J. Effects of macromolecular crowding and DNA looping on gene regulation kinetics. Nat. Phys. 2009, 5, 294–297. [Google Scholar] [CrossRef]
- Tan, C.; Saurabh, S.; Bruchez, M.P.; Schwartz, R.; Leduc, P. Molecular crowding shapes gene expression in synthetic cellular nanosystems. Nat. Nanotechnol. 2013, 8, 602–608. [Google Scholar] [CrossRef] [PubMed]
- Matsuda, H.; Putzel, G.G.; Backman, V.; Szleifer, I. Macromolecular crowding as a regulator of gene transcription. Biophys. J. 2014, 106, 1801–1810. [Google Scholar] [CrossRef] [PubMed]
- Zubay, G. In vitro synthesis of protein in microbial systems. Annu. Rev. Genet. 1973, 7, 267–287. [Google Scholar] [CrossRef] [PubMed]
- Harris, D.C.; Jewett, M.C. Cell-free biology: Exploiting the interface between synthetic biology and synthetic chemistry. Curr. Opin. Biotechnol. 2012, 23, 672–678. [Google Scholar] [CrossRef] [PubMed]
- Oberholzer, T.; Nierhaus, K.H.; Luisi, P.L. Protein expression in liposomes. Biochem. Biophys. Res. Commun. 1999, 261, 238–241. [Google Scholar] [CrossRef] [PubMed]
- Noireaux, V.; Libchaber, A. A vesicle bioreactor as a step toward an artificial cell assembly. Proc. Natl. Acad. Sci. USA 2004, 101, 17669–17674. [Google Scholar] [CrossRef] [PubMed]
- Hamada, S.; Tabuchi, M.; Toyota, T.; Sakurai, T.; Hosoi, T.; Nomoto, T.; Nakatani, K.; Fujinami, M.; Kanzaki, R. Giant vesicles functionally expressing membrane receptors for an insect pheromone. Chem. Commun. 2014, 50, 2958–2961. [Google Scholar] [CrossRef]
- Lentini, R.; Santero, S.P.; Chizzolini, F.; Cecchi, D.; Fontana, J.; Marchioretto, M.; Del Bianco, C.; Terrell, J.L.; Spencer, A.C.; Martini, L.; et al. Integrating artificial with natural cells to translate chemical messages that direct E. Coli behaviour. Nat. Commun. 2014, 5. [Google Scholar] [CrossRef] [PubMed]
- Ishikawa, K.; Sato, K.; Shima, Y.; Urabe, I.; Yomo, T. Expression of a cascading genetic network within liposomes. FEBS Lett. 2004, 576, 387–390. [Google Scholar] [CrossRef] [PubMed]
- Matosevic, S.; Paegel, B.M. Layer-by-layer cell membrane assembly. Nat. Chem. 2013, 5, 958–963. [Google Scholar] [CrossRef] [PubMed]
- Blattner, F.R.; Plunkett, G., III; Bloch, C.A.; Perna, N.T.; Burland, V.; Riley, M.; Collado-Vides, J.; Glasner, J.D.; Rode, C.K.; Mayhew, G.F.; et al. The complete genome sequence of escherichia coli k-12. Science 1997, 277, 1453–1462. [Google Scholar] [CrossRef] [PubMed]
- Arifuzzaman, M.; Maeda, M.; Itoh, A.; Nishikata, K.; Takita, C.; Saito, R.; Ara, T.; Nakahigashi, K.; Huang, H.C.; Hirai, A.; et al. Large-scale identification of protein-protein interaction of escherichia coli k-12. Genome Res. 2006, 16, 686–691. [Google Scholar] [CrossRef] [PubMed]
- Pramanik, J.; Keasling, J.D. Stoichiometric model of escherichia coli metabolism: Incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. Biotechnol. Bioeng. 1997, 56, 398–421. [Google Scholar] [CrossRef] [PubMed]
- Wallin, E.; von Heijne, G. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci. 1998, 7, 1029–1038. [Google Scholar] [CrossRef] [PubMed]
- Travers, A.; Muskhelishvili, G. Bacterial chromatin. Curr. Opin. Genet. Dev. 2005, 15, 507–514. [Google Scholar] [CrossRef] [PubMed]
- Lutkenhaus, J.; Pichoff, S.; Du, S. Bacterial cytokinesis: From z ring to divisome. Cytoskeleton 2012, 69, 778–790. [Google Scholar] [CrossRef] [PubMed]
- Bisicchia, P.; Arumugam, S.; Schwille, P.; Sherratt, D. Minc, mind, and mine drive counter-oscillation of early-cell-division proteins prior to escherichia coli septum formation. mBio 2013, 4. [Google Scholar] [CrossRef]
- Kobori, S.; Ichihashi, N.; Kazuta, Y.; Yomo, T. A controllable gene expression system in liposomes that includes a positive feedback loop. Mol. Biosyst. 2013, 9, 1282–1285. [Google Scholar] [CrossRef] [PubMed]
- Elcock, A.H. Models of macromolecular crowding effects and the need for quantitative comparisons with experiment. Curr. Opin. Struct. Biol. 2010, 20, 196–206. [Google Scholar] [CrossRef] [PubMed]
- Valencia-Burton, M.; Shah, A.; Sutin, J.; Borogovac, A.; McCullough, R.M.; Cantor, C.R.; Meller, A.; Broude, N.E. Spatiotemporal patterns and transcription kinetics of induced rna in single bacterial cells. Proc. Natl. Acad. Sci. USA 2009, 106, 16399–16404. [Google Scholar] [CrossRef] [PubMed]
- Russell, J.H.; Keiler, K.C. Subcellular localization of a bacterial regulatory RNA. Proc. Natl. Acad. Sci. USA 2009, 106, 16405–16409. [Google Scholar] [CrossRef] [PubMed]
- Barrangou, R.; Fremaux, C.; Deveau, H.; Richards, M.; Boyaval, P.; Moineau, S.; Romero, D.A.; Horvath, P. Crispr provides acquired resistance against viruses in prokaryotes. Science 2007, 315, 1709–1712. [Google Scholar] [CrossRef] [PubMed]
- Ruder, W.C.; Lu, T.; Collins, J.J. Synthetic biology moving into the clinic. Science 2011, 333, 1248–1252. [Google Scholar] [CrossRef] [PubMed]
- Attwater, J.; Holliger, P. A synthetic approach to abiogenesis. Nat. Methods 2014, 11, 495–498. [Google Scholar] [CrossRef] [PubMed]
- Lu, T.K.; Khalil, A.S.; Collins, J.J. Next-generation synthetic gene networks. Nat. Biotechnol. 2009, 27, 1139–1150. [Google Scholar] [CrossRef] [PubMed]
- Mukherji, S.; van Oudenaarden, A. Synthetic biology: Understanding biological design from synthetic circuits. Nat. Rev. Genet. 2009, 10, 859–871. [Google Scholar] [PubMed]
- Haseltine, E.L.; Arnold, F.H. Synthetic gene circuits: Design with directed evolution. Annu. Rev. Biophys. Biomol. Struct. 2007, 36, 1–19. [Google Scholar] [CrossRef] [PubMed]
- Ro, D.K.; Paradise, E.M.; Ouellet, M.; Fisher, K.J.; Newman, K.L.; Ndungu, J.M.; Ho, K.A.; Eachus, R.A.; Ham, T.S.; Kirby, J.; et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 2006, 440, 940–943. [Google Scholar] [CrossRef] [PubMed]
- Gardner, T.S.; Cantor, C.R.; Collins, J.J. Construction of a genetic toggle switch in escherichia coli. Nature 2000, 403, 339–342. [Google Scholar] [CrossRef] [PubMed]
- Elowitz, M.B.; Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 2000, 403, 335–338. [Google Scholar] [CrossRef] [PubMed]
- Endy, D. Foundations for engineering biology. Nature 2005, 438, 449–453. [Google Scholar] [CrossRef] [PubMed]
- Schwille, P. Bottom-up synthetic biology: Engineering in a tinkerer’s world. Science 2011, 333, 1252–1254. [Google Scholar] [CrossRef] [PubMed]
- Hammer, K.; Mijakovic, I.; Jensen, P.R. Synthetic promoter libraries—Tuning of gene expression. Trends Biotechnol. 2006, 24, 53–55. [Google Scholar] [CrossRef] [PubMed]
- Cox, R.S., III; Surette, M.G.; Elowitz, M.B. Programming gene expression with combinatorial promoters. Mol. Syst. Biol. 2007, 3. [Google Scholar] [CrossRef]
- Thattai, M.; van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. USA 2001, 98, 8614–8619. [Google Scholar] [CrossRef] [PubMed]
- Elowitz, M.B.; Levine, A.J.; Siggia, E.D.; Swain, P.S. Stochastic gene expression in a single cell. Science 2002, 297, 1183–1186. [Google Scholar] [CrossRef] [PubMed]
- Swain, P.S.; Elowitz, M.B.; Siggia, E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA 2002, 99, 12795–12800. [Google Scholar] [CrossRef] [PubMed]
- Blake, W.J.; Kaern, M.; Cantor, C.R.; Collins, J.J. Noise in eukaryotic gene expression. Nature 2003, 422, 633–637. [Google Scholar] [CrossRef] [PubMed]
- Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U. Network motifs: Simple building blocks of complex networks. Science 2002, 298, 824–827. [Google Scholar] [CrossRef] [PubMed]
- Buchler, N.E.; Gerland, U.; Hwa, T. Nonlinear protein degradation and the function of genetic circuits. Proc. Natl. Acad. Sci. USA 2005, 102, 9559–9564. [Google Scholar] [CrossRef] [PubMed]
- Stricker, J.; Cookson, S.; Bennett, M.R.; Mather, W.H.; Tsimring, L.S.; Hasty, J. A fast, robust and tunable synthetic gene oscillator. Nature 2008, 456, 516–519. [Google Scholar] [CrossRef] [PubMed]
- Smith, R.; Tan, C.; Srimani, J.K.; Pai, A.; Riccione, K.A.; Song, H.; You, L. Programmed allee effect in bacteria causes a tradeoff between population spread and survival. Proc. Natl. Acad. Sci. USA 2014, 111, 1969–1974. [Google Scholar] [CrossRef] [PubMed]
- Hooshangi, S.; Thiberge, S.; Weiss, R. Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. Proc. Natl. Acad. Sci. USA 2005, 102, 3581–3586. [Google Scholar] [CrossRef] [PubMed]
- Tamsir, A.; Tabor, J.J.; Voigt, C.A. Robust multicellular computing using genetically encoded nor gates and chemical “wires”. Nature 2011, 469, 212–215. [Google Scholar] [CrossRef] [PubMed]
- Purcell, O.; Lu, T.K. Synthetic analog and digital circuits for cellular computation and memory. Curr. Opin. Biotechnol. 2014, 29, 146–155. [Google Scholar] [CrossRef] [PubMed]
- Saaem, I.; Ma, S.; Quan, J.; Tian, J. Error correction of microchip synthesized genes using surveyor nuclease. Nucleic Acids Res. 2012, 40. [Google Scholar] [CrossRef] [PubMed]
- Martin, V.J.; Pitera, D.J.; Withers, S.T.; Newman, J.D.; Keasling, J.D. Engineering a mevalonate pathway in escherichia coli for production of terpenoids. Nat. Biotechnol. 2003, 21, 796–802. [Google Scholar] [CrossRef] [PubMed]
- Cello, J.; Paul, A.V.; Wimmer, E. Chemical synthesis of poliovirus cdna: Generation of infectious virus in the absence of natural template. Science 2002, 297, 1016–1018. [Google Scholar] [CrossRef] [PubMed]
- Gibson, D.G.; Benders, G.A.; Andrews-Pfannkoch, C.; Denisova, E.A.; Baden-Tillson, H.; Zaveri, J.; Stockwell, T.B.; Brownley, A.; Thomas, D.W.; Algire, M.A.; et al. Complete chemical synthesis, assembly, and cloning of a mycoplasma genitalium genome. Science 2008, 319, 1215–1220. [Google Scholar] [CrossRef] [PubMed]
- Salis, H.M.; Mirsky, E.A.; Voigt, C.A. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 2009, 27, 946–950. [Google Scholar] [CrossRef] [PubMed]
- Moffet, D.A.; Hecht, M.H. De novo proteins from combinatorial libraries. Chem. Rev. 2001, 101, 3191–3203. [Google Scholar] [CrossRef] [PubMed]
- Hecht, M.H.; Das, A.; Go, A.; Bradley, L.H.; Wei, Y. De novo proteins from designed combinatorial libraries. Protein Sci. 2004, 13, 1711–1723. [Google Scholar] [CrossRef] [PubMed]
- Hilvert, D. Design of protein catalysts. Annu. Rev. Biochem. 2013, 82, 447–470. [Google Scholar] [CrossRef] [PubMed]
- King, N.P.; Bale, J.B.; Sheffler, W.; McNamara, D.E.; Gonen, S.; Gonen, T.; Yeates, T.O.; Baker, D. Accurate design of co-assembling multi-component protein nanomaterials. Nature 2014, 510, 103–108. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.J.; Dalchau, N.; Srinivas, N.; Phillips, A.; Cardelli, L.; Soloveichik, D.; Seelig, G. Programmable chemical controllers made from DNA. Nat. Nanotechnol. 2013, 8, 755–762. [Google Scholar] [CrossRef] [PubMed]
- Grigoryan, G.; Kim, Y.H.; Acharya, R.; Axelrod, K.; Jain, R.M.; Willis, L.; Drndic, M.; Kikkawa, J.M.; DeGrado, W.F. Computational design of virus-like protein assemblies on carbon nanotube surfaces. Science 2011, 332, 1071–1076. [Google Scholar] [CrossRef] [PubMed]
- Appella, D.H. Non-natural nucleic acids for synthetic biology. Curr. Opin. Chem. Biol. 2009, 13, 687–696. [Google Scholar] [CrossRef] [PubMed]
- Hyrup, B.; Nielsen, P.E. Peptide nucleic acids (PNA): Synthesis, properties and potential applications. Bioorg. Med. Chem. 1996, 4, 5–23. [Google Scholar] [CrossRef] [PubMed]
- Hohsaka, T.; Sisido, M. Incorporation of non-natural amino acids into proteins. Curr. Opin. Chem. Biol. 2002, 6, 809–815. [Google Scholar] [CrossRef] [PubMed]
- Link, A.J.; Mock, M.L.; Tirrell, D.A. Non-canonical amino acids in protein engineering. Curr. Opin. Biotechnol. 2003, 14, 603–609. [Google Scholar] [CrossRef] [PubMed]
- Siwy, Z.; Trofin, L.; Kohli, P.; Baker, L.A.; Trautmann, C.; Martin, C.R. Protein biosensors based on biofunctionalized conical gold nanotubes. J. Am. Chem. Soc. 2005, 127, 5000–5001. [Google Scholar] [CrossRef] [PubMed]
- Fan, R.; Karnik, R.; Yue, M.; Li, D.; Majumdar, A.; Yang, P. DNA translocation in inorganic nanotubes. Nano Lett. 2005, 5, 1633–1637. [Google Scholar] [CrossRef] [PubMed]
- Kowalczyk, S.W.; Blosser, T.R.; Dekker, C. Biomimetic nanopores: Learning from and about nature. Trends Biotechnol. 2011, 29, 607–614. [Google Scholar] [CrossRef] [PubMed]
- Keasling, J.D. Synthetic biology and the development of tools for metabolic engineering. Metab. Eng. 2012, 14, 189–195. [Google Scholar] [CrossRef] [PubMed]
- Cameron, D.E.; Bashor, C.J.; Collins, J.J. A brief history of synthetic biology. Nat. Rev. Microbiol. 2014, 12, 381–390. [Google Scholar] [CrossRef] [PubMed]
- Ma, S.; Tang, N.; Tian, J. DNA synthesis, assembly and applications in synthetic biology. Curr. Opin. Chem. Biol. 2012, 16, 260–267. [Google Scholar] [CrossRef] [PubMed]
- Kosuri, S.; Church, G.M. Large-scale de novo DNA synthesis: Technologies and applications. Nat. Methods 2014, 11, 499–507. [Google Scholar] [CrossRef] [PubMed]
- Annaluru, N.; Muller, H.; Mitchell, L.A.; Ramalingam, S.; Stracquadanio, G.; Richardson, S.M.; Dymond, J.S.; Kuang, Z.; Scheifele, L.Z.; Cooper, E.M.; et al. Total synthesis of a functional designer eukaryotic chromosome. Science 2014, 344, 55–58. [Google Scholar] [CrossRef] [PubMed]
- Smolke, C.D.; Silver, P.A. Informing biological design by integration of systems and synthetic biology. Cell 2011, 144, 855–859. [Google Scholar] [CrossRef] [PubMed]
- Hillson, N.J.; Rosengarten, R.D.; Keasling, J.D. J5 DNA assembly design automation software. ACS Synth. Biol. 2012, 1, 14–21. [Google Scholar] [CrossRef] [PubMed]
- Rodrigo, G.; Jaramillo, A. Autobiocad: Full biodesign automation of genetic circuits. ACS Synth. Biol. 2013, 2, 230–236. [Google Scholar] [CrossRef] [PubMed]
- Schomburg, I.; Chang, A.; Placzek, S.; Sohngen, C.; Rother, M.; Lang, M.; Munaretto, C.; Ulas, S.; Stelzer, M.; Grote, A.; et al. Brenda in 2013: Integrated reactions, kinetic data, enzyme function data, improved disease classification: New options and contents in brenda. Nucleic Acids Res. 2013, 41, D764–D772. [Google Scholar] [CrossRef] [PubMed]
- McClymont, K.; Soyer, O.S. Metabolic tinker: An online tool for guiding the design of synthetic metabolic pathways. Nucleic Acids Res. 2013, 41. [Google Scholar] [CrossRef] [PubMed]
- Carbonell, P.; Parutto, P.; Herisson, J.; Pandit, S.B.; Faulon, J.L. Xtms: Pathway design in an extended metabolic space. Nucleic Acids Res. 2014, 42, W389–W394. [Google Scholar] [CrossRef] [PubMed]
- Huynh, L.; Tsoukalas, A.; Koppe, M.; Tagkopoulos, I. Sbrome: A scalable optimization and module matching framework for automated biosystems design. ACS Synth. Biol. 2013, 2, 263–273. [Google Scholar] [CrossRef] [PubMed]
- Alves, R.; Antunes, F.; Salvador, A. Tools for kinetic modeling of biochemical networks. Nat. Biotechnol. 2006, 24, 667–672. [Google Scholar] [CrossRef] [PubMed]
- Oberholzer, T.; Albrizio, M.; Luisi, P.L. Polymerase chain reaction in liposomes. Chem. Biol. 1995, 2, 677–682. [Google Scholar] [CrossRef] [PubMed]
- Chakrabarti, A.C.; Breaker, R.R.; Joyce, G.F.; Deamer, D.W. Production of RNA by a polymerase protein encapsulated within phospholipid vesicles. J. Mol. Evol. 1994, 39, 555–559. [Google Scholar] [CrossRef] [PubMed]
- Yu, W.; Sato, K.; Wakabayashi, M.; Nakaishi, T.; Ko-Mitamura, E.P.; Shima, Y.; Urabe, I.; Yomo, T. Synthesis of functional protein in liposome. J. Biosci. Bioeng. 2001, 92, 590–593. [Google Scholar] [CrossRef] [PubMed]
- Bode, J.; Goetze, S.; Heng, H.; Krawetz, S.A.; Benham, C. From DNA structure to gene expression: Mediators of nuclear compartmentalization and dynamics. Chromosome Res. 2003, 11, 435–445. [Google Scholar] [CrossRef] [PubMed]
- Kohwi, Y.; Kohwi-Shigematsu, T. Altered gene expression correlates with DNA structure. Genes Dev. 1991, 5, 2547–2554. [Google Scholar] [CrossRef] [PubMed]
- Aldaye, F.A.; Senapedis, W.T.; Silver, P.A.; Way, J.C. A structurally tunable DNA-based extracellular matrix. J. Am. Chem. Soc. 2010, 132, 14727–14729. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Li, G.W.; Chen, C.; Xie, X.S.; Zhuang, X. Chromosome organization by a nucleoid-associated protein in live bacteria. Science 2011, 333, 1445–1449. [Google Scholar] [CrossRef] [PubMed]
- Higgins, C.F.; Dorman, C.J.; Stirling, D.A.; Waddell, L.; Booth, I.R.; May, G.; Bremer, E. A physiological role for DNA supercoiling in the osmotic regulation of gene expression in s. Typhimurium and E. Coli. Cell 1988, 52, 569–584. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Li, B.; Workman, J.L. A histone-binding protein, nucleoplasmin, stimulates transcription factor binding to nucleosomes and factor-induced nucleosome disassembly. EMBO J. 1994, 13, 380–390. [Google Scholar] [PubMed]
- Kuo, M.H.; Allis, C.D. Roles of histone acetyltransferases and deacetylases in gene regulation. BioEssays 1998, 20, 615–626. [Google Scholar] [CrossRef] [PubMed]
- Zimmerman, S.B.; Trach, S.O. Estimation of macromolecule concentrations and excluded volume effects for the cytoplasm of escherichia coli. J. Mol. Biol. 1991, 222, 599–620. [Google Scholar] [CrossRef] [PubMed]
- Dix, J.A.; Verkman, A.S. Crowding effects on diffusion in solutions and cells. Annu. Rev. Biophys. 2008, 37, 247–263. [Google Scholar] [CrossRef] [PubMed]
- Spitzer, J.; Poolman, B. The role of biomacromolecular crowding, ionic strength, and physicochemical gradients in the complexities of life's emergence. Microbiol. Mol. Biol. Rev. 2009, 73, 371–388. [Google Scholar] [CrossRef] [PubMed]
- McGuffee, S.R.; Elcock, A.H. Diffusion, crowding & protein stability in a dynamic molecular model of the bacterial cytoplasm. PLOS Comput. Biol. 2010, 6. [Google Scholar] [CrossRef]
- Mika, J.T.; Poolman, B. Macromolecule diffusion and confinement in prokaryotic cells. Curr. Opin. Biotechnol. 2011, 22, 117–126. [Google Scholar] [CrossRef] [PubMed]
- Morelli, M.J.; Allen, R.J.; Wolde, P.R. Effects of macromolecular crowding on genetic networks. Biophys. J. 2011, 101, 2882–2891. [Google Scholar] [CrossRef] [PubMed]
- Klumpp, S.; Scott, M.; Pedersen, S.; Hwa, T. Molecular crowding limits translation and cell growth. Proc. Natl. Acad. Sci. USA 2013, 110, 16754–16759. [Google Scholar] [CrossRef] [PubMed]
- Minton, A.P. Implications of macromolecular crowding for protein assembly. Curr. Opin. Struct. Biol. 2000, 10, 34–39. [Google Scholar] [CrossRef] [PubMed]
- Zhou, H.X.; Rivas, G.; Minton, A.P. Macromolecular crowding and confinement: Biochemical, biophysical, and potential physiological consequences. Annu. Rev. Biophys. 2008, 37, 375–397. [Google Scholar] [CrossRef] [PubMed]
- Miyoshi, D.; Sugimoto, N. Molecular crowding effects on structure and stability of DNA. Biochimie 2008, 90, 1040–1051. [Google Scholar] [CrossRef] [PubMed]
- Cheung, M.S.; Klimov, D.; Thirumalai, D. Molecular crowding enhances native state stability and refolding rates of globular proteins. Proc. Natl. Acad. Sci. USA 2005, 102, 4753–4758. [Google Scholar] [CrossRef] [PubMed]
- Stagg, L.; Zhang, S.Q.; Cheung, M.S.; Wittung-Stafshede, P. Molecular crowding enhances native structure and stability of alpha/beta protein flavodoxin. Proc. Natl. Acad. Sci. USA 2007, 104, 18976–18981. [Google Scholar] [CrossRef] [PubMed]
- Guthold, M.; Zhu, X.; Rivetti, C.; Yang, G.; Thomson, N.H.; Kasas, S.; Hansma, H.G.; Smith, B.; Hansma, P.K.; Bustamante, C. Direct observation of one-dimensional diffusion and transcription by escherichia coli RNA polymerase. Biophys. J. 1999, 77, 2284–2294. [Google Scholar] [CrossRef] [PubMed]
- Richter, K.; Nessling, M.; Lichter, P. Macromolecular crowding and its potential impact on nuclear function. Biochim. Biophys. Acta 2008, 1783, 2100–2107. [Google Scholar] [CrossRef] [PubMed]
- Bancaud, A.; Huet, S.; Daigle, N.; Mozziconacci, J.; Beaudouin, J.; Ellenberg, J. Molecular crowding affects diffusion and binding of nuclear proteins in heterochromatin and reveals the fractal organization of chromatin. EMBO J. 2009, 28, 3785–3798. [Google Scholar] [CrossRef] [PubMed]
- Macnab, R.M. Microbiology. Action at a distance—Bacterial flagellar assembly. Science 2000, 290, 2086–2087. [Google Scholar] [CrossRef] [PubMed]
- Takai, K.; Sawasaki, T.; Endo, Y. Practical cell-free protein synthesis system using purified wheat embryos. Nat. Protoc. 2010, 5, 227–238. [Google Scholar] [CrossRef] [PubMed]
- Pelham, H.R.; Jackson, R.J. An efficient mRNA-dependent translation system from reticulocyte lysates. Eur. J. Biochem. 1976, 67, 247–256. [Google Scholar] [CrossRef] [PubMed]
- Ge, X.; Luo, D.; Xu, J. Cell-free protein expression under macromolecular crowding conditions. PLOS ONE 2011, 6. [Google Scholar] [CrossRef] [PubMed]
- Sokolova, E.; Spruijt, E.; Hansen, M.M.; Dubuc, E.; Groen, J.; Chokkalingam, V.; Piruska, A.; Heus, H.A.; Huck, W.T. Enhanced transcription rates in membrane-free protocells formed by coacervation of cell lysate. Proc. Natl. Acad. Sci. USA 2013, 110, 11692–11697. [Google Scholar] [CrossRef] [PubMed]
- Fujiwara, K.; Nomura, S.M. Condensation of an additive-free cell extract to mimic the conditions of live cells. PLOS ONE 2013, 8. [Google Scholar] [CrossRef] [PubMed]
- Martini, L.; Mansy, S.S. Cell-like systems with riboswitch controlled gene expression. Chem. Commun. 2011, 47, 10734–10736. [Google Scholar] [CrossRef]
- Berclaz, N.; Müller, M.; Walde, P.; Luisi, P.L. Growth and transformation of vesicles studied by ferritin labeling and cryotransmission electron microscopy. J. Phys. Chem. B 2000, 105, 1056–1064. [Google Scholar] [CrossRef]
- Budin, I.; Szostak, J.W. Physical effects underlying the transition from primitive to modern cell membranes. Proc. Natl. Acad. Sci. USA 2011, 108, 5249–5254. [Google Scholar] [CrossRef] [PubMed]
- Maeda, Y.T.; Nakadai, T.; Shin, J.; Uryu, K.; Noireaux, V.; Libchaber, A. Assembly of mreb filaments on liposome membranes: A synthetic biology approach. ACS Synth. Biol. 2012, 1, 53–59. [Google Scholar] [CrossRef] [PubMed]
- Osawa, M.; Anderson, D.E.; Erickson, H.P. Reconstitution of contractile ftsz rings in liposomes. Science 2008, 320, 792–794. [Google Scholar] [CrossRef] [PubMed]
- Shin, J.; Jardine, P.; Noireaux, V. Genome replication, synthesis, and assembly of the bacteriophage t7 in a single cell-free reaction. ACS Synth. Biol. 2012, 1, 408–413. [Google Scholar] [CrossRef] [PubMed]
- Kobayashi, T.; Nakamura, Y.; Mikami, S.; Masutani, M.; Machida, K.; Imataka, H. Synthesis of encephalomyocarditis virus in a cell-free system: From DNA to RNA virus in one tube. Biotechnol. Lett. 2012, 34, 67–73. [Google Scholar] [CrossRef] [PubMed]
- Gorter, E.; Grendel, F. On bimolecular layers of lipoids on the chromocytes of the blood. J. Exp. Med. 1925, 41, 439–443. [Google Scholar] [CrossRef] [PubMed]
- Fraser, C.M.; Gocayne, J.D.; White, O.; Adams, M.D.; Clayton, R.A.; Fleischmann, R.D.; Bult, C.J.; Kerlavage, A.R.; Sutton, G.; Kelley, J.M.; et al. The minimal gene complement of mycoplasma genitalium. Science 1995, 270, 397–403. [Google Scholar] [CrossRef] [PubMed]
- Demir, E.; Babur, O.; Dogrusoz, U.; Gursoy, A.; Nisanci, G.; Cetin-Atalay, R.; Ozturk, M. Patika: An integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 2002, 18, 996–1003. [Google Scholar] [CrossRef] [PubMed]
- Krishnamurthy, L.; Nadeau, J.; Ozsoyoglu, G.; Ozsoyoglu, M.; Schaeffer, G.; Tasan, M.; Xu, W. Pathways database system: An integrated system for biological pathways. Bioinformatics 2003, 19, 930–937. [Google Scholar] [CrossRef] [PubMed]
- Demir, E.; Babur, O.; Dogrusoz, U.; Gursoy, A.; Ayaz, A.; Gulesir, G.; Nisanci, G.; Cetin-Atalay, R. An ontology for collaborative construction and analysis of cellular pathways. Bioinformatics 2004, 20, 349–356. [Google Scholar] [CrossRef] [PubMed]
- Holford, M.; Li, N.; Nadkarni, P.; Zhao, H. Vitapad: Visualization tools for the analysis of pathway data. Bioinformatics 2005, 21, 1596–1602. [Google Scholar] [CrossRef] [PubMed]
- Busso, D.; Delagoutte-Busso, B.; Moras, D. Construction of a set gateway-based destination vectors for high-throughput cloning and expression screening in escherichia coli. Anal. Biochem. 2005, 343, 313–321. [Google Scholar] [CrossRef] [PubMed]
- Richmond, K.E.; Li, M.H.; Rodesch, M.J.; Patel, M.; Lowe, A.M.; Kim, C.; Chu, L.L.; Venkataramaian, N.; Flickinger, S.F.; Kaysen, J.; et al. Amplification and assembly of chip-eluted DNA (aaced): A method for high-throughput gene synthesis. Nucleic Acids Res. 2004, 32, 5011–5018. [Google Scholar] [CrossRef] [PubMed]
- Tian, J.; Ma, K.; Saaem, I. Advancing high-throughput gene synthesis technology. Mol. Biosyst. 2009, 5, 714–722. [Google Scholar] [CrossRef] [PubMed]
- Gibson, D.G.; Young, L.; Chuang, R.Y.; Venter, J.C.; Hutchison, C.A., III; Smith, H.O. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 2009, 6, 343–345. [Google Scholar] [CrossRef] [PubMed]
- Trinh, C.T.; Wlaschin, A.; Srienc, F. Elementary mode analysis: A useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl. Microbiol. Biotechnol. 2009, 81, 813–826. [Google Scholar] [CrossRef] [PubMed]
- Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: In vitro recombination for molecular evolution. Proc. Natl. Acad. Sci. USA 1994, 91, 10747–10751. [Google Scholar] [CrossRef] [PubMed]
- Neylon, C. Chemical and biochemical strategies for the randomization of protein encoding DNA sequences: Library construction methods for directed evolution. Nucleic Acids Res. 2004, 32, 1448–1459. [Google Scholar] [CrossRef] [PubMed]
- Matosevic, S.; Paegel, B.M. Stepwise synthesis of giant unilamellar vesicles on a microfluidic assembly line. J. Am. Chem. Soc. 2011, 133, 2798–2800. [Google Scholar] [CrossRef] [PubMed]
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