5:00 PM - SM03.02.09
Will Synthetic Acorns Grow into Biobuildings—Comparing the Coding Complexity of Natural Materials with the Software of Man-Made Systems
Joseph Riolo1,Andrew Steckl1
University of Cincinnati1
While the field of biology seeks to understand the functions and mechanisms of natural organisms, synthetic biology aims to use biological functions to engineer materials and systems. This engineering view is at the core of synthetic biology and seeks to define fundamental building blocks in nature that can be manipulated in a variety of combinations to design and produce complex systems. To illustrate this approach Ball compared2 the components and the level of complexity that goes into the design and manufacturing of an automobile to the process of biological cell formation – building materials (glass, metal vs nucleotides, amino acids); components (pistons, wheels vs proteins); modules (microcontroller vs genetic circuits), etc.
Barnatt1 asked a thought provoking question related to synthetic biology: “will synthetic acorns grow into biobuildings?” There are many aspects that need to be considered to begin to answer the question: 1. what materials that are needed for this quest; 2. what is the level of complexity involved; 3. what are associated building costs, are they competitive. While this rhetorical question draws attention to the possibilities of synthetic biology, a rapidly growing community is exploring nearer-term applications (synthetic biochemical production, biocomputing, biosensors, etc).
To partially answer this question we have initiated a study to characterize the complexity required to create engineered organic systems. While complexity is a relative term, computer software development can be a suitable benchmark due to similarities between instruction storing function of DNA and binary code. Further similarities between organic and software systems are the use of limited serial instruction sets (quaternary or binary) to produce arbitrarily complex outcomes when executed on deterministic systems.
The starting point is the comparison of the genome of different organisms to landmark software programming projects. The number of base pairs in the genome of organisms3 ranges from ~ 1 to 10 million for bacteria to ~ 1 billion for birds to a few billions for humans and other mammals. Major software projects for which some details (number of lines of code) are available4 have been selected for comparison, including the Hubble Space Telescope, the Mars Curiosity Rover, the Android smart phone operating system. The extent of compiled object code ranges from several hundred million to a few billion characters. The similarity between numbers of software characters in complex computer programs and base pairs in genomes, suggests that programming complex synthetic biosystems, while quite challenging and requiring a major effort, is likely to be feasible.
Biological coding provides additional functionality beyond storing of genetic information. Molecular structure and shape provide the means for reproduction of the genetic code, its transformation into amino acids and proteins, etc. This represents the storage of additional (2nd, 3rd order) valuable information that needs to be accounted in future analysis. It is important to note that genome size that does not necessarily correlate with organism complexity. Interestingly, a similar case may be made for software programs, in that their size is not only related to the complexity of the system function but also on the computer language used, the period when the programming was carried out, other needs of the system, etc. Nonetheless, our preliminary evaluation indicates that the level of complexity involved in the genome of even the most sophisticated organism is comparable to large-scale man-made computer programs.
1. C. Barnatt, Synthetic Biology, Jan. 2018.
2. P. Ball, Synthetic Biology-Engineering nature to make materials, MRS Bulletin, 43 (7), 477-484, July 2018.
3. R. Milo and R. Phillips, Cell Biology by the Numbers, 2015, Garland Science, Taylor & Francis.
4. T. Wendt, et al., Consolidation in Vehicle Electronic Architectures, 2015, Roland Berger Strategy Consultants.