Synthetic Biology: Molecular Tools for Engineering Organisms


Synthetic biology is a biological engineering discipline based on abstracting living systems through the lens of physical engineering concepts. In particular, synthetic biology places an emphasis on the characterisation of simple parts that can be modularly assembled into configurations that give rise to complex, higher‐order behaviours. Within the past two decades, this approach has enabled the development number of new molecular biology tools for modifying living systems in order to investigate fundamental processes or imbuing functions into cells that do not exist in nature. While specific synthetic biology applications span a huge range of seemingly unrelated disciplines (from biofuel producing microbes, to malaria‐resistant mosquitoes, to living medical therapies), these distinct examples derive from reuse and rearrangement of relatively limited set of cell engineering technologies. The continuing development of these core molecular biology tools for controlling gene expression, protein activity and signalling networks can promote evermore ambitious biological engineering projects.

Key Concepts

  • Synthetic biology is a discipline for biological engineering using principles of physical engineering.
  • Complex biological systems can be abstracted as a set of core ‘parts’ with separable functions.
  • A biological part is modular when it confers a discrete function, which can be repurposed and rearranged in many different contexts while maintaining fidelity.
  • Biological systems naturally display many modular features that are evident at many different scales.
  • Synthetic biology attempts to identify, characterise and repurpose biological modules to build devices with novel functions.
  • The field has created several new molecular biology tools for controlling living systems through exploiting the recombination of modular parts.
  • The tools of synthetic biology are frequently used to imbue organisms with technologically useful traits.
  • As synthetic biology expands beyond model microbes, its potential to contribute to our fundamental understanding of biology continues to increase.

Keywords: synthetic biology; modularity; standardisation; genetic circuit; gene expression; metabolic engineering; predictive engineering

Figure 1. Abstraction of biology as composed of modular subunits. A core tenant of synthetic biology is the conceptualisation of biological systems as composed of many, relatively simple interconnected parts that can be recombined in a modular manner. (a) A gene coding region is readily recognised as containing modular features, including the promoter, ribosome binding site (RBS), coding sequence and terminator. If these ‘parts’ are to be used in a truly modular manner, it should be possible to repurpose them in a different context, yet retain their core function. Promoter ‘i’ (dark green – bottom left) represents a highly modular promoter element because it drives transcription of the downstream sequence in a highly predictable manner, no matter what the sequence is. Promoter ‘ii’ (light green) displays variable properties depending on the context, and therefore possesses poor modularity. (b) Modularity in the design of living systems can be found across many scales. This includes protein domains, which often contain homologous sequence to domains in other proteins. The SH2 (Src‐homology domain 2) is a domain with the self‐contained property of binding amino acid sequences P–X–X–P, and is a domain naturally found widely across many proteins in eukaryotes. Whole proteins are often modular and can be exported from one organism to another while retaining their function, or even repurposed in different contexts in the same cell for different functions. Here, the MAPKKK Ste11 is at the top of the kinase cascade for signalling responses to both mating factor and osmotic shock in yeast. The function of Ste11 (grey box) remains the same while the context (i.e. which scaffold protein it is associated with; either Ste5 or Pbs2) has important implications for its output. At larger scales, examples of modularity of tissue types or whole organs can be found (e.g. the capacity to transplant hearts across a relatively large evolutionary space). Yet, at each biological scale, examples of poor modularity also exist (e.g. cannot transplant brain tissue from even closely related species). This highlights the necessity of utilising a process (c) to characterise biological parts in a standardisable way for their functionality, thereby identifying valuable parts and design principles that facilitate biological engineering using a modular approach.
Figure 2. Appropriate design principles can improve the modularity of component parts. An example of a lack of modularity within biology can be found in the activity of RBS elements, which (a) often exhibit variability in the degree of translation they promote, depending on context. For example, the same RBS element may drive a high expression of GFP (green fluorescent protein; top) but low expression of RFP (red fluorescent protein) because of unexpected nucleotide interactions within the mRNA that cause formation of secondary structure that inhibits ribosome binding (bottom). More predictive expression of a broad range of genes can be achieved through design principles that mitigate these problems. (b) Creating bi‐cistronic elements that consist of a leading RBS (RBS1) and a standardised leader sequence allows for upstream binding of ribosomes. The helicase activity of ribosomes translocating through the leader sequence disrupts secondary structure, revealing the internal RBS (RBS2). This design has been successfully implemented to greatly reduce the variability in gene expression that is achieved when using a given promoter/RBS combination, regardless of the target gene to be expressed (Mutalik et al., ).
Figure 3. Examples of modular molecular biology tools utilised in synthetic biology. (a) TALE (transcription activator‐like effector) effectors are characterised by a modular DNA (deoxyribonucleic acid)‐targeting region that is composed of multiple repeats of a protein domain. Each domain is nearly identical to the others, except that they can vary in two key amino acid residues (see blow‐up insert) and these two residues confer specificity for binding to a target nucleotide. When multiple domains are connected in series, they can bind to a target DNA sequence by arraying next to one another within the major groove of the DNA (right). (b) Cas9 protein recognises small noncoding RNAs (ribonucleic acids) that have a characteristic hairpin sequence. When bound to a guide RNA (red), the Cas9 protein is able to use standard base pairing interactions to bind to the complimentary sequence within a target genome. Both Cas9 and TALE proteins can be readily modified with a functional domain (FD) to confer a desired function that will preferentially affect the target sequence: capacity to induce double‐ or single‐stranded DNA breaks, or domains that enhance/repress recruitment of transcriptional machinery. (c) Concentration of proteins to a subcellular location (top) is a recurring theme to improve fidelity and efficiency within signalling and metabolic pathways. Artificial scaffolds have been constructed by encoding a string of binding domains (e.g. SH2 domains) on a single polypeptide that correspond to ligand domains that are appended to target proteins. When the artificial scaffold is expressed, it recruits the target proteins through receptor–ligand interactions, effectively concentrating the enzymes relative to one another. Early designs consisted only of single, isolated scaffolds, while more recent examples have favoured scaffolding proteins that can self‐assemble into defined, macromolecular arrays (depicted as tiled hexagons). (d) Protein degradation can be experimentally controlled by modifying target proteins so they encode C‐terminal ‘degron’ tags (ssrA tag). These peptides are typically recognised by endogenous proteasome machinery (ClpX) and targeted for degradation. By introducing point mutations to the ssrA tag sequence, the marked protein can only be recognised when the adaptor protein (SspB) is present to recruit it to the proteasome, allowing for inducible downregulation of the target by controlling the expression of sspB.
Figure 4. Assembly of simple modules to create increasingly complex circuits and systems. (a) Two‐component logic gates process two input signals and activate an output response (1) when the appropriate conditions are met. A lookup table (grey inset) illustrates the output of four basic logic gates under each condition of for commonly used gates. (b) Connection of component logic gates together in series can allow for higher‐order complexity in genetic circuit design. For instance, connecting three AND gates will create a coincidence detector that activates a target output (e.g. gene expression; red line) only when all four input criteria are met. Other complex output patterns, such as ‘memory’ or oscillatory outputs, can be generated by connecting simple circuits. Complex behaviours can arise when feedback loops are present in otherwise simple networks – for example, a device that activates only when exposed to two signals and retains memory of this activation (bottom). (c) Individual cells or species can also be abstracted as modules within a larger community. Here, an autotrophic module (the cyanobacterium S. elongatus) has been engineered to utilise photosynthesis to fix carbon and export a simple sugar (sucrose). In synthetic communities, this can be regarded as an ‘autotrophic’ module that provides organic carbon to power other desirable metabolic reactions in heterotrophic modules. Combining species modules can confer desired properties into synthetic consortia without having to engineer complex processes (e.g. light‐harvesting or nitrogen‐fixation) into a single chassis.


Ambrosio R, Ortiz‐Marquez JCF and Curatti L (2017) Metabolic engineering of a diazotrophic bacterium improves ammonium release and biofertilization of plants and microalgae. Metabolic Engineering 40: 59–68. DOI: 10.1016/j.ymben.2017.01.002.

Baker TA and Sauer RT (2012) ClpXP, an ATP‐powered unfolding and protein‐degradation machine. Biochimica et Biophysica Acta. DOI: 10.1016/j.bbamcr.2011.06.007.

Bashor CJ, Horwitz AA, Peisajovich SG and Lim WA (2010) Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems. Annual Review of Biophysics 39: 515–537. DOI: 10.1146/annurev.biophys.050708.133652.

Bhattacharyya RP, Reményi A, Yeh BJ and Lim WA (2006) Domains, motifs, and scaffolds: the role of modular interactions in the evolution and wiring of cell signaling circuits. Annual Review of Biochemistry 75: 655–680. DOI: 10.1146/annurev.biochem.75.103004.142710.

Bonde MT, Pedersen M, Klausen MS, et al. (2016) Predictable tuning of protein expression in bacteria. Nature Methods 13: 233–236. DOI: 10.1038/nmeth.3727.

Boyden ES, Zhang F, Bamberg E, Nagel G and Deisseroth K (2005) Millisecond‐timescale, genetically targeted optical control of neural activity. Nature Neuroscience 8: 1263–1268. DOI: 10.1038/nn1525.

Brockman IM and Prather KLJ (2015) Dynamic metabolic engineering: new strategies for developing responsive cell factories. Biotechnology Journal 10: 1360–1369. DOI: 10.1002/biot.201400422.

Brophy JAN and Voigt CA (2014) Principles of genetic circuit design. Nature Methods 11: 508–520. DOI: 10.1038/nmeth.2926.

Cambray G, Guimaraes JC, Mutalik VK, et al. (2013) Measurement and modeling of intrinsic transcription terminators. Nucleic Acids Research 41: 5139–5148. DOI: 10.1093/nar/gkt163.

Cameron DE, Bashor CJ and Collins JJ (2014) A brief history of synthetic biology. Nature Reviews. Microbiology 12: 381–390. DOI: 10.1038/nrmicro3239.

Cameron DE and Collins JJ (2014) Tunable protein degradation in bacteria. Nature Biotechnology 32: 1276–1281. DOI: 10.1038/nbt.3053.

Collins JJ, Gardner TS and Cantor CR (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403: 339–342. DOI: 10.1038/35002131.

Compaoré J and Stal LJ (2010) Oxygen and the light‐dark cycle of nitrogenase activity in two unicellular cyanobacteria. Environmental Microbiology 12: 54–62. DOI: 10.1111/j.1462-2920.2009.02034.x.

Daeffler KN, Galley JD, Sheth RU, et al. (2017) Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Molecular Systems Biology 13: 923. DOI: 10.15252/msb.20167416.

Danino T, Mondragón‐Palomino O, Tsimring L and Hasty J (2010) A synchronized quorum of genetic clocks. Nature 463: 326–330. DOI: 10.1038/nature08753.

Dueber JE, Wu GC, Malmirchegini GR, et al. (2009) Synthetic protein scaffolds provide modular control over metabolic flux. Nature Biotechnology 27: 753–759. DOI: 10.1038/nbt.1557.

Ellis T, Adie T and Baldwin GS (2011) DNA assembly for synthetic biology: from parts to pathways and beyond. Integrative Biology 3: 109. DOI: 10.1039/c0ib00070a.

Elowitz MB and Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403: 335–338. DOI: 10.1038/35002125.

Espah Borujeni A, Channarasappa AS and Salis HM (2014) Translation rate is controlled by coupled trade‐offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites. Nucleic Acids Research 42: 2646–2659. DOI: 10.1093/nar/gkt1139.

Flores E and Herrero A (2010) Compartmentalized function through cell differentiation in filamentous cyanobacteria. Nature Reviews. Microbiology 8: 39–50. DOI: 10.1038/nrmicro2242.

Gaj T, Gersbach CA and Barbas CF (2013) ZFN, TALEN, and CRISPR/Cas‐based methods for genome engineering. Trends in Biotechnology. DOI: 10.1016/j.tibtech.2013.04.004.

Gordley RM, Bugaj LJ and Lim WA (2016) Modular engineering of cellular signaling proteins and networks. Current Opinion in Structural Biology. DOI: 10.1016/

Hammer K, Mijakovic I and Jensen PR (2006) Synthetic promoter libraries– tuning of gene expression. Trends in Biotechnology 24: 53–55. DOI: 10.1016/j.tibtech.2005.12.003.

Hays SG, Yan LLW, Silver PA and Ducat DC (2017) Synthetic photosynthetic consortia define interactions leading to robustness and photoproduction. Journal of Biological Engineering 11: 4. DOI: 10.1186/s13036-017-0048-5.

Kashtan N and Alon U (2005) Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences 102: 13773–13778. DOI: 10.1073/pnas.0503610102.

Kim S, Lee MJ, Kim H, Kang M and Kim J‐S (2011) Preassembled zinc‐finger arrays for rapid construction of ZFNs. Nature Methods 8: 7. DOI: 10.1038/nmeth0111-7a.

Kosuri S and Church GM (2014) Large‐scale de novo DNA synthesis: technologies and applications. Nature Methods 11: 499–507. DOI: 10.1038/nmeth.2918.

Kotula JW, Kerns SJ, Shaket LA, et al. (2014) Programmable bacteria detect and record an environmental signal in the mammalian gut. Proceedings of the National Academy of Sciences 111: 4838–4843. DOI: 10.1073/pnas.1321321111.

Li T, Li C‐T, Butler K, et al. (2017) Mimicking lichens: incorporation of yeast strains together with sucrose‐secreting cyanobacteria improves survival, growth, ROS removal, and lipid production in a stable mutualistic co‐culture production platform. Biotechnology for Biofuels 10: 55. DOI: 10.1186/s13068-017-0736-x.

Liu BA, Jablonowski K, Raina M, et al. (2006) The human and mouse complement of SH2 domain proteins‐establishing the boundaries of phosphotyrosine signaling. Molecular Cell 22: 851–868. DOI: 10.1016/j.molcel.2006.06.001.

Löwe H, Hobmeier K, Moos M, Kremling A and Pflüger‐Grau K (2017) Photoautotrophic production of polyhydroxyalkanoates in a synthetic mixed culture of Synechococcus elongatus cscB and Pseudomonas putida cscAB. Biotechnology for Biofuels 10: 190. DOI: 10.1186/s13068-017-0875-0.

Mee MT and Wang HH (2012) Engineering ecosystems and synthetic ecologies. Molecular Biosystems 8: 2470. DOI: 10.1039/c2mb25133g.

Minty JJ, Singer ME, Scholz SA, et al. (2013) Design and characterization of synthetic fungal‐bacterial consortia for direct production of isobutanol from cellulosic biomass. Proceedings of the National Academy of Sciences 110: 14592–14597. DOI: 10.1073/pnas.1218447110.

Moon TS, Lou C, Tamsir A, Stanton BC and Voigt CA (2012) Genetic programs constructed from layered logic gates in single cells. Nature 491: 249–253. DOI: 10.1038/nature11516.

Mutalik VK, Guimaraes JC, Cambray G, et al. (2013) Precise and reliable gene expression via standard transcription and translation initiation elements. Nature Methods 10: 354–360. DOI: 10.1038/nmeth.2404.

Olson EJ and Tabor JJ (2014) Optogenetic characterization methods overcome key challenges in synthetic and systems biology. Nature Chemical Biology 10: 502–511. DOI: 10.1038/nchembio.1559.

Ortiz‐Marquez JCF, Do Nascimento M, Zehr JP and Curatti L (2013) Genetic engineering of multispecies microbial cell factories as an alternative for bioenergy production. Trends in Biotechnology 31: 521–529. DOI: 10.1016/j.tibtech.2013.05.006.

Pastrana E (2011) Optogenetics: controlling cell function with light. Nature Methods 8: 24–25. DOI: 10.1038/nmeth.f.323.

Pawson T and Nash P (2003) Assembly of cell regulatory systems through protein interaction domains. Science 300: 445–452. DOI: 10.1126/science.1083653.

Ravasz E (2002) Hierarchical organization of modularity in metabolic networks. Science 297: 1551–1555. DOI: 10.1126/science.1073374.

Regot S, Macia J, Conde N, et al. (2011) Distributed biological computation with multicellular engineered networks. Nature 469: 207–211. DOI: 10.1038/nature09679.

Ricci DP, Melfi MD, Lasker K, et al. (2016) Cell cycle progression in Caulobacter requires a nucleoid‐associated protein with high AT sequence recognition. Proceedings of the National Academy of Sciences of the United States of America 113: E5952–E5961. DOI: 10.1073/pnas.1612579113.

Shcherbakova DM, Shemetov AA, Kaberniuk AA and Verkhusha VV (2015) Natural photoreceptors as a source of fluorescent proteins, biosensors, and optogenetic tools. Annual Review of Biochemistry 84: 519–550. DOI: 10.1146/annurev-biochem-060614-034411.

Siu KH, Chen RP, Sun Q, et al. (2015) Synthetic scaffolds for pathway enhancement. Current Opinion in Biotechnology. DOI: 10.1016/j.copbio.2015.08.009.

Siuti P, Yazbek J and Lu TK (2013) Synthetic circuits integrating logic and memory in living cells. Nature Biotechnology 31: 448–452. DOI: 10.1038/nbt.2510.

Smith MJ and Francis MB (2016) A designed A. vinelandii‐S. elongatus coculture for chemical photoproduction from air, water, phosphate, and trace metals. ACS Synthetic Biology 5: 955–961. DOI: 10.1021/acssynbio.6b00107.

Wagner GP, Pavlicev M and Cheverud JM (2007) The road to modularity. Nature Reviews. Genetics 8: 921–931. DOI: 10.1038/nrg2267.

Waters CM and Bassler BL (2005) Quorum sensing: communication in bacteria. Annual Review of Cell and Developmental Biology 21: 319–346. DOI: 10.1146/annurev.cellbio.21.012704.131001.

Weiss TL and Ducat DC (2017) Designing stable, synthetic, light‐driven cyanobacteria‐heterotroph consortia for bioproduction. Metabolic Engineering. DOI: 10.1016/j.ymben.2017.10.009.

Young EJ, Burton R, Mahalik JP, et al. (2017) Engineering the bacterial microcompartment domain for molecular scaffolding applications. Frontiers in Microbiology 8. DOI: 10.3389/fmicb.2017.01441.

Further Reading

Church GM and Regis E (2014) Regenesis: How Synthetic Biology will Reinvent Nature and Ourselves. New York, NY: Basic Books.

Keasling JD (2012) Synthetic biology and the development of tools for metabolic engineering. Metabolic Engineering 14 (3): 189–195.

Kirschner MW and Gerhart JC (2006) The Plausibility of Life: Resolving Darwin's Dilemma. New Haven, CT: Yale University Press.

Lazebnik Y (2002) Can a biologist fix a radio? – or, what I learned while studying apoptosis. Cancer Cell 2 (3): 179–182.

Lim W, Mayer B and Pawson T (2014) Cell Signaling: Principles and Mechanisms. New York, NY: Taylor & Francis.

Purnick PE and Weiss R (2009) The second wave of synthetic biology: from modules to systems. Nature Reviews. Molecular Cell Biology 10 (6): 410–422.

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Ducat, Daniel C(Feb 2018) Synthetic Biology: Molecular Tools for Engineering Organisms. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0020883]