Biocomputing(The data in biology)
Synthetic Biology
Biocomputing takes molecular biology parts (composition, structure and interactions of cellular molecules) as the hardware for computational devices. This computational devices follow pre-defined rules which help devices process inputs and return outputs like normal computer processes.
Synthetic Biology has a variety of molecular tools that help generate circuits that mimic behavior of electronic functions such as logic gates. This field helps expand the molecular hardware beyond the realm of genetic parts by tapping into the host metabolism.
One of the fields that Biocomputing has been used is in medication. The goal is building an artificial intelligence platform for use in drug discovery and development that aims to identify promising targets as well as potential risks and side effects at an early stage. A central goal is to provide an AI-driven tool that not only helps developers identify potential risk factors early, but also offers insights into the underlying biology driving the efficacy of a particular treatment.
The question that sometimes this AI can help answer in such field is “Can we figure out what’s going on with the drug?” or “Why are patients responding the way they do?” which i s particularly relevant for researchers who see a particular treatment may produce a desired response, but they may not know why.
Biology, the new up and coming field in Data Science
Boolean logic is central to the field of computing. Therefore, the design and implementation of Boolean logic functions in cells — typically encoded into genetic material is key to the development of synthetic biology approaches rooted on biocomputing
To quote William Worhies form DataScienceCentral “From the biological researcher’s perspective CSB broadly refers to the design and fabrication of biological components and systems that don’t already exist in the natural world or to the redesign and fabrication of existing biological system.”
Inspired by computer science, distributed computations have also been designed and build in multicellular systems by modifying cell-cell communication programs. From solving relatively simple mathematical problems to compute intricate Boolean logic operations, biological systems have proved to be a powerful platform for tackling applications that are restricted to traditional “silicon-based” computer technologies, such as diagnosis, bioproduction, and bioremediation.
How it works.
Interfacing genetic and metabolic processes for high-performance biocomputations. (A) Biocomputing circuits are typically encoded into genetic material. Synthetic biology provides an extensive toolkit to build combinatorial logic circuits. (B) Biocomputation intersects synthetic biology. (C)Information processing flows in merged metabolic circuits. Both transcriptional and metabolic networks are able to sense external inputs and yield output responses; the feedback from one layer to the other can effectively communicate information.
In March 2013. a team of bioengineers from Stanford University, led by Drew Endy, announced that they had created the biological equivalent of a transistor, which they dubbed a “transcriptor”. The invention was the final of the three components necessary to build a fully functional computer: data storage, information transmission, and a basic system of logic.
In March 2018. A team from ETH Zurich and the University of Base Construct Biocomputer Made From Living Human Cells.Using nine different cell populations assembled into 3D cultures, the team of synthetic biologists has managed to get them to behave like a very simple electronic computational circuit. Take out the electrical wiring and signaling, and replace them with chemical inputs
In Conclusion…
From a broader perspective, evolution has shaped intricate cellular processes that merge both genetic and metabolic networks; yet, human-defined biocomputations rarely make use of both computing types. We are looking into high-performance biocomputing power to use on natural systems, empowering the design-build-test-learn cycle to entirely new directions.
