Periodic Reporting for period 4 - Bio4Comp (Parallel network-based biocomputation: technological baseline, scale-up and innovation ecosystem)
Periodo di rendicontazione: 2021-01-01 al 2022-06-30
Main objectives
The overall aim for Bio4Comp was to establish the technological and mathematical baseline and the interdisciplinary innovation eco-system needed to systematically scale up NBC technology and thus to create the pre-conditions for applications. Specifically, we targeted the following breakthroughs:
1) A network-based biocomputer that solves a more complex problem than any other alternative parallel computing approach. This was enabled through targeted mathemati-cal, biological and nano-technological advances and their integration, to enable fully scalable, ef-ficient, parallel problem solving.
2) New networks that efficiently encode combinatorial problems of practical im-portance. To solve a specific type of problem, NBC requires an “algorithm” in the form of a network designed to encode the problem. The focus of our efforts was to efficiently and in a scalable manner solve the 3-SAT problem that is at the core of electronic computer design and mathematics.
3) A structured, interdisciplinary community with critical mass and an innovation eco-system for NBC development. Through a set of coordinated, targeted actions we aimed to create a structured community from basic research to end-users.
Regarding Aim (2), to efficiently encode combinatorial problems of practical importance, we significantly optimized the network encoding for Exact Cover and developed two 3-SAT network encodings: (i) the space-encoded SAT network algorithm that is conceptually similar to the existing Subset Sum and Exact Cover network algorithms, and (ii) the agent-encoded SAT network algorithm that enables very compact networks but requires the ability to store information on the agents themselves. This is a key step towards broad general applicability of NBC because it enables solving NP-complete problem for which a polynomial conversion to SAT ex-ists. Furthermore, this result provides a novel NBC approach that is significantly more scalable because the networks are much more compact. In particular the agent-encoded approach – once demonstrated experimentally – holds promise for future applications of NBC, because it allows compact networks that scale linearly with the number of variables and are independent of the number of clauses in the 3-SAT instance. Furthermore, such networks can be used to solve many different SAT instances.
Regarding Aim (3), to create a structured community for NBC development, our most valuable outcome is our Roadmap for network-based bio-computation, which will help to “identify pre-competitive research domains, enabling cooperation between industries, institutes and universities for sharing R&D efforts and reduction of development cost and time”. Our ability to perform the originally planned stakeholder workshops was substantially limited by the pandemic that started in early 2020.
We delivered a solid baseline for the future development of NBC by publishing a roadmap (Falco C M J M van Delft et al 2022 Nano Futures 6 032002
https://doi.org/10.1088/2399-1984/ac7d81 ), that provides benchmarks and technology targets to guide structured pre-competitive research as well as scaling models to enable prediction of how far the foreseeable and fundamentally possible improvements might take the technology.