Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# Comparative analysis of genome code complexity and manufacturability with engineering benchmarks

## Abstract

When knowledge has advanced to a state that includes a predictive understanding of the relationship between genome sequence and organism phenotype it will be possible for future engineers to design and produce synthetic organisms. However, the possibility of synthetic biology does not necessarily guarantee its feasibility, in much the same way that the possibility of a brute force attack fails to ensure the timely breaking of robust encryption. The size and range of natural genomes, from a few million base pairs for bacteria to over 100 billion base pairs for some plants, suggests it is necessary to evaluate the practical limits of designing genomes of similar complexity. This analysis characterizes the complexity of natural genomes, compares them to existing engineering benchmarks, and shows that existing large software programs are on similar scale with the genome of complex natural organisms.

## Introduction

It took a hundred years from the advent of the term “synthetic biology”1,2 to the modern confirmation of its essential technologies when, in 2010, researchers at the J. Craig Venter Institute (JCVI) created a synthetic Mycoplasma mycoides genome and transplanted it into a recipient Mycoplasma capricolum cell3. Mycoplasma genitalium was originally selected because, at the time, it had the fewest genes of any known organism capable of independent growth but Mycoplasma mycoides and Mycoplasma capricolum were later selected for their faster growth rate. The creation of a viable bacteria cell controlled by a synthetic genome (JCVI-syn1.0) ushered in an era where scientists and engineers intend to produce artificial life forms for their own purposes and represented a major break from genetic engineering that was initially focused on modifying existing organisms. Synthetic biology is a discipline that “uses engineering principles to design and assemble biological components”4. Simply stated, synthetic biology aims to use the knowledge that sequencing of the genome of species has provided (the biological code) to manufacture biological components. Synthetic biology has the potential to be the next epochal technological human advancement following microelectronics and the internet. The potential of synthetic biology has been recognized by many organizations, from being highlighted as a top technology by the World Economic Forum5 to being included on the list of "Big Ideas for Future Investments" of the US National Science Foundation (NSF)6. This high level of emphasis, including major research stimulus by NSF in 2019 to understand the “Rules of Life”7, has resulted in significant practical and academic advancements, such as genome rewriting for streamlined synthesis8.

Based on his experiences Venter observed that the “genome design's greatest limitation” is the lack of fundamental knowledge9. It is unclear at this time if a predictive understanding of organism phenotype will ever be realized but, if the necessary knowledge is gained, it seems likely that practical applications of synthetic biology would be enabled by technologies such as abstraction, standardization, and decoupling10. The presence of fundamental knowledge and the availability of appropriate tools does not necessarily guarantee the creation of useful synthetic organisms since it has not yet been demonstrated that complexity itself is not a limitation. Is it practical to design large scale synthetic organisms or is comprehensive genome design too complex for economic usefulness? The objective of this analysis is to begin a discussion on the feasibility of potential synthetic biology undertakings by establishing metrics against which genome complexity and manufacturability can be measured. Software (the current human coding champion) in its compiled form and semiconductor manufacturing have been selected as the yardsticks for comparison. Large scale software programs are increasingly being developed for all aspects of life from automotive entertainment to aerospace controls and financial systems to weather prediction. Indeed, with the advent of the “internet-of-things”, digital programming is destined to become the ubiquitous communication and control sinew of mankind. While the study presented here is not quantitative in the sense of providing an expected timeline for synthetic biology breakthroughs it may provide a qualitative assessment of feasibility in a manner similar to how a brute force encryption vulnerability assessment would determine if a key could be broken in a reasonable time11. As illustrated in Fig. 1, our analysis indicates a rough equivalence in complexity between various major engineering systems and the human genome.

## Results

### Selecting suitable benchmarks for measuring genome complexity

The sequence of nucleotides in a genome is commonly referred to as its genetic code which, for electrical and software engineers, is immediately reminiscent of computer object code. Object code is the output of a compiler that translates source code in a high-level programming language into machine useable binary instructions. The characterization of genetic and binary digital information as ‘code’ may make comparisons seem superficial12. However, there are meaningful similarities that make software a suitable benchmark and, at the least, more promising than alternatives13. For example, both types of code store data in limited serial instruction sets (quaternary or binary) and can produce arbitrarily complex outcomes when executed on deterministic systems. These similarities were not wasted on Venter who observed that “DNA is the software of the cell”9. Indeed, the programming and self-assembling functions of DNA have generated great interest for high density information storage14, biological computing15,16, and nanostructure formation17,18. Further, the unusual electronic and optical properties of DNA thin films have been utilized in a variety of device applications19,20.

It is important to point out at the outset two key differences between DNA coding and digital coding. First, DNA coding regions represent a small fraction of the overall polymer. However, this coding fraction varies among species, and it also changes with time as new roles for sections of the supposedly non-coding regions are being discovered. In this initial assessment of biological vs digital coding including the entire DNA sequence in the comparison has the advantage of working with “hard” numbers. Furthermore, this approach establishes the upper bound of biological complexity, with the possibility of reducing that level as DNA portions are confirmed to have no coding significance. Second, the functionality of bioorganisms can be described as bottom-up, distributed, self-replicating, and non-deterministic, whereas computer system design and functionality is top-down, concentrated, not self-replicating (yet), and deterministic. For example, the entire machinery for self-replication and functioning of an E. coli bacterium is contained in a volume of one femtoliter. Research is being pursued to create self-replicating machines, which could be a more complete analog to biological systems. However, at this time they are far from the point where a comparison with biological systems would be meaningful. It will be interesting to see if advances in synthetic biology will inform our design of artificial systems in the future to be more encapsulated.

Figure 2 explores the parallels between genetic code and object code. Each language is based on a limited alphabet (nucleobases and bits) from which words are constructed (codons and bytes) leading to higher level organizations (genes and instruction sets). Additional similarities include context dependence, non-obvious regions of code, and resiliency to errors. Software is generally considered context independent due to the abstract nature of high-level languages and the arbitrary order that functional segments may be provided to compilers, but the resulting object code is highly context and platform dependent. Without complete understanding of the object code, making changes to individual bits, or even encapsulated functions, would present challenges like those experienced by current DNA designers attempting to manipulate base pairs or sequences. Instructions would be identified first, similar to our understanding of coding DNA, but compiled object code would also have non-obvious regions like graphical textures, audio files, or other large data sets that would be as difficult to reverse engineer as noncoding DNA. Programs even have an analog to the C-Value enigma, the observation that genome size varies largely between species and has no relationship to the organism’s presumed complexity, with recent program memory requirements exploding despite having similar functional complexity. For example, one game might even vary in installed size by nearly 200% (17–49 GB) depending on the platform due to the difference in audio file compression21. DNA and binary code are also susceptible to errors, which occur at an average rate of 1.28 × 10–8 mutations per nucleotide per generation for humans22 and 1 × 10–14 nonrecoverable read errors per bits read for hard drives23,24. These rates are not directly comparable, but they do necessitate appropriate error detection, correction, and redundancy to be built into both biological and digital systems.

While this equivalence between the components of DNA molecules and computer instructions may be an oversimplification, it at least provides a starting point for further thought and future more detailed comparisons. Even those that object to the metaphor for semantic reasons find the associated tools to be “remarkably useful in practice”25. It is interesting to point out that nature has developed the genetic code for each organism and has provided the built-in mechanisms for its replication and conversion into function. On the other hand, digital software designed for each specific application requires a separate hardware design and fabrication for conversion to a desired set of functions. However, here too we can see some similarities in that one can identify the alphabet of electronics (resistors, capacitors, transistors) leading to words (memory cells, amplifiers, etc.) and sentences (shift registers, etc.) that are joined into complex functioning systems (computers, phones, etc.). The evolution of the integrated circuit (Moore’s law) provides another useful yardstick for comparison between the development of electronics technology and the technology for genome sequencing and synthesis.

In addition to the similarities between the genome and the object code (binary) produced by a compiler, there are likely similarities to future processes for use in the design of synthetic organisms. Specifically, it seems likely that a high level abstract organism description language will be necessary along with a biological “compiler” to facilitate designs and translate the designers’ intent into a synthetic genome. This organization, shown schematically in Fig. 3, suggests a framework by which observable complexities in natural organisms and large scale software projects might be used to infer the feasibility of potential synthetic organism design efforts. Figure 3 also contains a simple example of high level software code being compiled into object code and explores how future tools might be used to design synthetic genomes based on desired phenotype. Synthetic biologists in training may start by coding simple algae similarly to novice software designers starting with the message “Hello world!”.

### Comparing genome complexity to benchmarks

The combinatorial complexity of a string of binary values, Cbinary, is calculated using the number of bits in the string, Nbits (Eq. 1). Using digital combinatorial complexity as a model, genome complexity, Cgenome, is quantified by calculating an equivalent byte value with the number of base pairs, Nbp. Since there are four natural nucleobases, each pair of bases (cytosine [C], guanine [G], adenine [A], and thymine [T]) has a combinatorial complexity equivalent to two bits (Eq. 2). This can then be converted to bytes (and higher order units like gigabytes) to easily understand the magnitude of a genome’s complexity. Equations (1) and (2) can be combined to derive the relationship between binary and genome complexity (Eq. 3) as well as the associated ratio of bits and base pairs (Eq. 4). Additionally, one byte is composed of eight bits (Eq. 5).

$$C_{binary} = 2^{{N_{bits} }}$$
(1)
$$C_{genome} = 4^{{N_{bp} }}$$
(2)
$$C_{genome} \;(N_{bp} ) = C_{binary} \;(2N_{bits} )$$
(3)
$$N_{bp} = 2N_{bits}$$
(4)
$$4N_{bp} = N_{bytes}$$
(5)

This approach makes genome complexity easily quantified in terms of stored information, or equivalent bytes, based on the number of base pairs. Paradoxically, the corresponding software object code complexity is more difficult to calculate because most historical engineering projects do not have documentation available for file size or physical memory requirements. Typically, the information available is mainly on the number of Lines of Code (LOC). One study, using the C programming language, reported an average of 17 ± 3.5 bytes per line of code26. While this is only one study with one language that is not necessarily inclusive of all benchmarks, C is a very common programming language and will provide a qualitative comparison. A wide range of software benchmarks were selected from available sources across industries, scope, production volume, and time to yield the best opportunity for finding equivalent complexity with naturally occurring genomes.

A wide range is necessary since natural genomes vary from as few as thousands of base pairs for some viruses, to millions of base pairs for bacteria, and even as high as 100 billion base pairs for some plants. The resulting combinatorial complexity of representative examples of natural genomes and selected digital benchmarks is shown in Fig. 4. The Porcine circovirus27 has one of the smallest genomes at ~ 1800 bases (not base pairs in this case since it consists of a circular single-stranded DNA molecule in contrast to the other genome examples which are double stranded DNA molecules). The genome of the Pandoravirus salinus28 is an example of one the larger virus genomes at ~ 2.5 Mbp. By comparison, the genome of the new coronavirus responsible for the COVID 19 pandemic contains 29 kbp29. Nasuia deltocephalinicola, a bacterium that helps certain insects synthesize essential amino acids has the smallest genome of any cellular organism sequenced to date30, has just ~ 100 kbp, whereas Sorangium cellulosum, a cellulose degrading bacterium, has a circular DNA genome31 of ~ 13 Mbp. In the plant category, Genlisea tuberosa has the smallest genome reported32 at 61 Mbp, while Paris japonica, a plant native to the mountainous regions of Japan, contains possibly the largest known genome33 of all living species at ~ 150 Gbp. In the animal kingdom, the nematode Pratylenchus coffeae has one of the smaller genomes34 of 20 Mbp, while Protopterus aethiopicus, the marbled lungfish, comes in a close second to P. japonica for the largest genome35 found to date of ~ 130 Gbp. Finally, the genome of our own species36, Homo sapiens, weighs in at ~ 3.3 Gbp. While this is a very large genome, it is not the largest of either animal or plant species. As indicated above, the genome of P. japonica is ~ 50 × larger.

The relative complexity of several synthetic organisms is also shown for comparison. JCVI-syn1.0, the first reported cell with a chemically synthesized genome, contains approximately 1 Mbp3 and is a modified version of the Mycoplasma mycoides bacterium with synthetic ‘watermark’ sequences to differentiate from the natural genome. The synthetic M. mycoides cells were reported to have the phenotypic properties of natural cells and to be able self-replicate. Researchers at JCVI minimized the Mycoplasma mycoides genome further and tested the effects of reorganization on JCVI-syn2.0 with 580 Kbp and JCVI-syn3.0 with 530 Kbp. While all of the JCVI species were viable the simpler forms did demonstrate reduced growth rate with a doubling time of 60, 92, and 180 min for syn1.0, syn2.0 and syn3.0, respectively37. Caulobacter ethensis-2.0 (C. eth-2.0) is the product of recent research at the Institute of Molecular Systems Biology in Zurich, Switzerland that demonstrated synthesis optimized sequence rewriting on Caulobacter crescentus, a widely distributed fresh water bacteria and common cell cycle model8. Interestingly, even JCVI-syn1.0, the largest synthetic genome to date, is only about 20% the complexity of the pacemaker (4 × 104 LOC)38 which is the simplest benchmark at just over 1 MB equivalent.

In addition to the pacemaker, several engineering projects with significant software content (for their time) were selected to act as benchmarks for the complexity of natural and synthetic organisms. The next three more complex projects in terms of lines of code being the Space Shuttle flight control (4 × 105 LOC)39, the Hubble Telescope (2 × 106 LOC)40, and the Mars Curiosity Rover (5 × 106 LOC)40, corresponding to 6.8, 34, and 85 MB equivalent, respectively. These space applications range in time but their software complexity is somewhat limited based on the harsh radiation environment and high reliability requirements. The F-35 environment, which is perhaps not as harsh are space, still results in severe requirements limiting software size. However, more recent technology and emphasis on the ‘digital plane’ have resulted in a flight control software complexity of 25 million lines of code40 or 425 MB equivalent. Lastly, consumer electronics including cars and phones have experienced an explosion in functionality and interconnectivity that have driven their software complexity up to the point where your smart phone with its Android OS (1.2 × 107 LOC)40 can connect to your car (1 × 108 LOC)40 with equivalent complexities of 204 MB and 1.7 GB, respectively. This level of complexity is enabled by the mass production and ubiquitousness of these products and will continue to grow with the desire for more integration.

The similarity in complexity between software projects and natural genomes suggests that designing complex synthetic biosystems, while quite challenging and requiring major effort, will be feasible once the relationship between genotype and phenotype is well understood. Unfortunately, the ad hoc process that has been utilized for most synthetic biology research is still the standard mode for current efforts10. As important as the creation of the 531 kbp JCVI-syn3.0 minimal bacterial genome was in 201637, the method of arriving at the genetic sequence was empirical and a key factor in the selection of the target organism (M. mycoides) was previous experience in synthesizing its complete genome (JCVI-syn1.0)3. The design process is in its infancy but the work on JCVI-syn3.0 showed that reorganizing modularized genome segments did not observably affect the resulting cells, which suggests that the development of standard parts by organizations like iGEM41 could find wide applicability.

This first order analysis attempts to provide a qualitative evaluation of genome complexity but does not consider all known complexities and cannot consider future breakthroughs on the path to complete understanding of the relationship between phenotype and genotype. Therefore, the complexity of natural organisms may be somewhat understated because of the absence of epigenetic factors like DNA methylation and histone modification. As advanced sequencing technologies, for example single molecule real time (SMRT), provide additional information in parallel with the primary DNA sequence42 these nuances should be weighed against potential efficiencies, such as the significant reduction in base pairs observed between JCVI-syn1.0 to JCVI-syn3.0. It will be interesting to see if human ingenuity can expand the accessibility of synthetic biology through simplification or if mitigating efforts will be overcome by our increasing understanding of the complexities of fundamental biological processes.

### Evaluating manufacturability of synthetic biology products

Since the 1st order comparison study indicates that designing synthetic organisms that approach the complexity of natural examples would be of a similar order of magnitude to existing engineering projects the remaining considerations would be engineering issues, such as manufacturing methods and cost. After the non-recurring engineering costs necessary to design a synthetic organism, the next most significant will be gene sequencing and synthesis. Gene sequencing is the process of determining the order of nucleotides in DNA and gene synthesis is the process of creating short oligonucleotide fragments and assembling them into a complete genome. Gene synthesis also requires many intermediate error-checking and correcting steps that involve multiple sequencing operations. Costs for both processes must be considered since a prohibitively high estimate for a complex genome could negatively impact the practicality of synthetic biology.

In addition to the functional similarities between software and genetic code, the sequencing and synthesis of genes also appear to be benefiting from significant reductions in cost as a function of time similar to the advancement of semiconductor circuit (“chip”) fabrication that is described as Moore’s Law, namely the doubling of transistors per chip every 1.5–2 years while the overall chip cost per unit area has remained more or less constant43. This empirical relationship is consistently reported as about to expire, but to date it has achieved a nearly 50-year validity. A corollary of Moore’s Law indicates a roughly halving of cost per transistor every 2 years43. As shown in Fig. 5a, DNA sequencing price and cost have closely followed this trend since 2000 except during 2008 when sequencing cost dropped precipitously as researchers worked toward the goal of a $1000 human genome to facilitate cost-effective personal diagnostics44. Price and cost are often used interchangeably in everyday language, but the distinction is critical when considering production strategies. Cost is the expense incurred by a company to produce and sell a product or service and price is the amount a customer is willing to pay for that product or service. Figure 5b shows the sequencing price/cost ratio for the two decades from 2000 to 2020. Also shown in Fig. 5b is an extracted simplified time-dependent linear relationship that was used in conjunction with synthesis price data to estimate a synthesis cost in order to obtain a complete picture of the expenses related to sequencing and synthesis. Synthesis price and an estimate of cost are shown in Fig. 5c. The synthesis cost is important for consideration, but its classification as an estimate is important since Fig. 5a,b show that there may be a discrepancy between the data sets causing sequencing price to fall below sequencing cost in the 2006–2009 timeframe or the data may faithfully show temporary market conditions driven by liquidation, oversupply, or other factors. While price and cost can vary depending on method and number of nucleobases it will be important to consider both depending on application just like current microchip designers much choose to fabricate their devices or purchase wafers from an independent manufacturer. It will also be interesting to see if the DNA sequencing price and cost continue to converge, with the ratio of the two falling from 47 in 2001 to ~ 10 in 2018, as technology advances and commoditizes the industry. ## Discussion With the costs of gene sequencing and synthesis dropping precipitously the main manufacturing challenge is the manual gene assembly, isolation, and transplantation processes used for current projects. Automation of these processes may be complex, but overall, the manufacturing needs for synthetic biology appear to be similar to those used for the fabrication of consumer electronics. The similarities to micro fabrication suggest processes where a large non-recurring engineering investment up front leads to high throughput, inexpensive manufacturing. Synthetic biology also has the benefit of being self-replicating, which could provide additional benefit over microfabrication if it can be adapted to contemporary lean manufacturing principles like takt, or cycle, time47,48. These parallels and a potential process overview of a synthetic biology factory of the future are shown in Fig. 6. Alternatively, if long gestation and/or adolescent periods seen in complex natural organisms cannot be overcome and production steps remain long like additive manufacturing, then there may be opportunities for new manufacturing philosophies that better utilize batch production49. The comparison of complexity between natural species and major software endeavors makes it clear that designing synthetic organisms will be feasible when the fundamental knowledge becomes available. If synthesis costs continue to shrink at a rate similar to transistor cost as described by Moore’s law, it is also likely that the cost to synthesize genomes of equivalent complexity to even the largest genomes observed in natural organisms will be within the reach of government, commercial, and perhaps research organizations by the time this genome/phenotype linkage is fully mapped out. Advancements in the genome design may even spur additional genome synthesis cost reductions similar to those seen in sequencing during the 2008–2010 period. Extrapolation of the Moore’s Law gene synthesis estimation shown in Fig. 5c would predict a cost of$0.0003 per base pair in the 2021–2022 time period. This would mean that synthesizing an artificial human genome (~ 3.3 Bbp) would cost approximately one million dollars and simpler applications like a custom bacterium (~ 13 Mbp) could be synthesized for as little as $4000. This combination of surmountable complexity and moderate cost justifies the academic enthusiasm for synthetic biology and will continue to inspire interest in the rules of life. ## References 1. Leduc, S. Theorie Physico-Chimique de la Vie et Generations Spontanees [Physicochemical Theory of Life and Spontaneous Generations] (A. Poinat, 1910). 2. Peretó, J. Erasing borders: A brief chronicle of early synthetic biology. J. Mol. Evol. 83, 176–183 (2016). 3. Gibson, D. G. et al. Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329, 52–56 (2010). 4. Wellhausen, R. & Oye, K. A. in Conference on Science, Technology and Innovation Policy (IEEE, 2007). 5. Cann, O. These are the Top 10 Emerging Technologies of 2016. https://www.weforum.org/agenda/2016/06/top-10-emerging-technologies-2016/ (2016). 6. Crim, F. F. NSF Ideas for Future Investment. https://www.nsf.gov/attachments/136042/public/12_Big_Ideas_MPSAC_November_2016.pdf (2016). 7. NSF. NSF commits$36 million to uncovering Rules of Life that will drive next-generation research. https://www.nsf.gov/news/special_reports/announcements/090419.jsp (2019).

8. Venetz, J. E. et al. Chemical synthesis rewriting of a bacterial genome to achieve design flexibility and biological functionality. Proc. Natl. Acad. Sci. 116, 8070–8079 (2019).

9. Venter, J. C. Synthesizing life. Scientist 25, 60 (2011).

10. Endy, D. Foundations for engineering biology. Nature 438, 449–453 (2005).

11. Ciesla, R. Encryption for Organizations and Individuals: Basics of Contemporary and Quantum Cryptography 297 (APress, 2020).

12. Kay, L. E. A book of life?: How the genome became an information system and DNA a language. Perspect. Biol. Med. 41, 504–528 (1998).

13. Keller, E. F. The century beyond the gene. J. Biosci. 301, 3–10 (2005).

14. Church, G. M., Gao, Y. & Kosuri, S. Next-generation digital information storage in DNA. Science 337, 1628. https://doi.org/10.1126/science.1226355 (2012).

15. Jones, M. R., Seeman, N. C. & Mirkin, C. A. Programmable materials and the nature of the DNA bond. Science 347, 840–851. https://doi.org/10.1126/science.1260901 (2015).

16. Braich, R. S., Chelyapov, N., Johnson, C., Rothemund, P. W. K. & Adleman, L. Solution of a 20-variable 3-SAT problem on a DNA computer. Science 296, 499–502. https://doi.org/10.1126/science.1069528 (2002).

17. Zhang, F., Nangreave, J., Liu, Y. & Yan, H. Structural DNA nanotechnology: State of the art and future perspective. J. Am. Chem. Soc. 136, 11198–11211. https://doi.org/10.1021/ja505101a (2014).

18. Seeman, N. C. An overview of structural DNA nanotechnology. Mol. Biotechnol. 37, 246–257 (2007).

19. Steckl, A. J. DNA—A new material for photonics?. Nat. Photonics 1, 3–5. https://doi.org/10.1038/nphoton.2006.56 (2007).

20. Gomez, E. F. & Steckl, A. J. In Green Materials for Electronics (eds Glowacki, E. D. et al.) 191–233 (Wiley-VCH, 2018).

21. Yin-Poole, W. Why the PC version of Titanfall is a 48GB install. https://www.eurogamer.net/articles/2014-03-11-why-the-pc-version-of-titanfall-is-a-48gb-install (2014).

22. Rahbari, R. et al. Timing, rates and spectra of human germline mutation. Nat. Genet. 48, 126–136 (2015).

23. Gray, J. & Ingen, C. V. Empirical Measurements of Disk Failure Rates and Error Rates. Microsoft Research Technical Report (Microsoft, 2005).

24. Desktop SATA HDD Data Sheet. Seagate, Ed. (2016).

25. Szymanski, E. & Scher, E. Models for DNA design tools: The trouble with metaphors is that they don’t go away. ACS Synth. Biol. 39, 385–403 (2019).

26. Hatton, L. Estimating source lines of code from object code: Windows and Embedded Control Systems. https://www.leshatton.org/LOC2005.html (2005).

27. Mankertz, A. In Animal Viruses: Molecular Biology (eds Mettenleiter, T. C. & Sobrino, F.) (Caister Academic, 2008).

28. Philippe, N. et al. Pandoraviruses: Amoeba viruses with genomes up to 2.5 Mb reaching that of parasitic eukaryotes. Science 341, 281–286 (2013).

29. Sah, R. et al. Complete genome sequence of a 2019 novel coronavirus (SARS-CoV-2) strain isolated in Nepal. Am. Soc. Microbiol. 9, 1–3 (2019).

30. Bennett, G. M. & Moran, N. A. Small, smaller, smallest: The origins and evolution of ancient dual symbioses in a phloem-feeding insect. Genome Biol. Evol. 5, 1675–1688 (2013).

31. Schneiker, S. et al. Complete genome sequence of the Myxobacterium sorangium cellulosum. Nat. Biotechnol. 25, 1281–1289 (2007).

32. Fleischmann, A. et al. Evolution of genome size and chromosome number in the carnivorous plant genus Genlisea (Lentibulariaceae), with a new estimate of the minimum genome size in angiosperms. Ann. Bot. (Oxford, U.K.) 114, 1651–1663 (2014).

33. Pellicer, J., Fay, M. & Leitch, I. The largest eukaryotic genome of them all?. Bot. J. Linn. Soc. 164, 10–15 (2010).

34. Leroy, S., Bouamer, S., Morand, S. & Fargette, M. Genome size of plant-parasitic nematodes. Nematology 9, 449–450 (2007).

35. Gregory, T. R. Animal Genome Size Database. http://www.genomesize.com

36. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

37. Hutchison, C. A. III. et al. Design and synthesis of a minimal bacterial genome. Science 351, 1414–1425 (2016).

38. Jiang, Z. & Mangharam, R. University of Pennsylvania Develops Electrophysiological Heart Model for Real-Time Closed-Loop Testing of Pacemakers. https://www.mathworks.com/company/newsletters/articles/university-of-pennsylvania-develops-electrophysiological-heart-model-for-real-time-closed-loop-testing-of-pacemakers.html (2013).

39. Siceloff, S. Shuttle Computers Navigate Record of Reliability. https://www.nasa.gov/mission_pages/shuttle/flyout/flyfeature_shuttlecomputers.html

40. Wendt, T. F., Bernhart, W., Behl, J., Mishoulam, D. & Goldsmith, E. Consolidation in Vehicle Electronic Architectures (Roland Berger Strategy Consultants, 2015).

41. iGEM. http://igem.org/

42. Davis, B. M., Chao, M. C. & Waldor, M. K. Entering the era of bacterial epigenomics with single molecule real time DNA sequencing. Curr. Opin. Microbiol. 16, 192–198 (2013).

43. Hutcheson, G. D. in Proceedings of SPIE—The International Society for Optical Engineering (SPIE).

44. Mardis, E. R. Anticipating the \$1000 genome. Genome Biol. 7, 5 (2006).

45. Wetterstrand, K. DNA Sequencing Costs: Data. http://www.genome.gov/sequencingcostsdata (2018).

46. Carlson, R. Bioeconomy Dashboard: Economic Metrics. http://www.bioeconomycapital.com/bioeconomy-dashboard/ (2018).

47. Ōno, T. & Bodek, N. Toyota Production System: Beyond Large-Scale Production (CRC Press, 1988).

48. Shang, G. & Low, S. P. Lean Construction Management: The Toyota Way (Springer, 2014).

49. Hoogerhuis, P. in Industrial 3D Printing Conference (Additive World).

## Acknowledgements

The authors acknowledge the contribution of E. F. Gomez for stimulating this project. The authors thank the reviewers for helping to improve the manuscript with critical comments.

## Author information

Authors

### Contributions

J.R. and A.J.S. designed the project. J.R. carried out the analysis. Both authors contributed to the writing of the manuscript.

### Corresponding author

Correspondence to Andrew J. Steckl.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

### Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Rights and permissions

Reprints and Permissions

Riolo, J., Steckl, A.J. Comparative analysis of genome code complexity and manufacturability with engineering benchmarks. Sci Rep 12, 2808 (2022). https://doi.org/10.1038/s41598-022-06723-5

• Accepted:

• Published:

• DOI: https://doi.org/10.1038/s41598-022-06723-5