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H2020

Bio4Comp Report Summary

Project ID: 732482
Funded under: H2020-EU.1.2.2.

Periodic Reporting for period 1 - Bio4Comp (Parallel network-based biocomputation: technological baseline, scale-up and innovation ecosystem)

Reporting period: 2017-01-01 to 2017-12-31

Summary of the context and overall objectives of the project

Many technologically and societally important mathematical problems – such as the design and verification of circuits, the folding and design of proteins and optimal network routing – are intractable for conventional, serial computers. Therefore, a significant need exists for parallel-computing approaches that are capable of solving such problems within reasonable time frames. Recently, part of our consortium demonstrated a proof-of-principle for a parallel-computation system – termed network-based biocomputation (NBC) – in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. The problem is then solved by a large number of independent biological agents, namely molecular-motor-propelled protein filaments, exploring the network in a highly parallel fashion (PNAS 113, 2591 (2016)). Notably, this approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power-consumption and heat-dissipation.

Within Bio4Comp we
(i) will establish the technological and scientific basis for robust scale-up of this approach,
(ii) will demonstrate scalability by systematically increasing the problem size by several orders of magnitude, and
(iii) will develop new algorithms with the aim to open up a wide range of applications. Additionally, we will
(iv) foster and structure an ecosystem of scientists and companies that will accelerate the path to market success, including the creation of a joint technological roadmap.

Benefits to society will include the ability to solve hitherto intractable practical problems, the development of a sustainable and energy-efficient computing approach that is radically different from current information and communications technology and shedding light on key unsolved problems in computer science.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We have made very good progress that exceeded our planned progress in all major directions of the project:

1. Algorithms and simulations. By converting a new NP-complete problem (EXACT COVER) to network form, making it solvable with molecular motors. This is important because EXACT COVER has various industrial and other applications as a resource-optimisation problem. Thereby we have demonstrated that our NBC approach is applicable, in principle, to a wide range of practical applications. We have also developed a first version of a toolset allowing the verification of correctness for SUBSET SUM and EXCOV problems, using the NuSMV framework.

2. Architectural elements. Developing a complete optimization workflow using both, numerical Monte-Carlo simulations and experimental test devices, enabled us to reduce junction errors to a level where we will not have to worry this type of errors for the remainder of the Bio4Comp project. Furthermore, we made progress towards first prototypes 3D junctions (“overpasses/tunnels”). If these will work as designed, this will enable entirely error-free junctions in the future.

3. Scaling the biology. Optimization of the biological systems has (i) improved the fidelity of the protein-filament movement in the channels, improving scalability of NBC networks; (ii) enabled re-use of the network channels, reducing cost and environmental footprint of our technology; and (iii) made progress towards implementing self-multiplying filaments that will allow us to dynamically adjust the computing performance to the size of the problem to be solved.

4. Fabrication. Development of a scalable and rapid-prototyping optimized nanofabrication technology has enabled the flexible fabrication of large NBC networks and optimized the performance of biomolecular motors within these networks.

5. Community building. We organized the open workshop “New Directions in Biocomputation (Dresden, September 12-13, 2017)”, which attracted 48 participants from 27 organizations (including academic, institutes and industrial), and thus successfully engaged a wider scientific community in our NBC approach. The workshop also initiated several promising new collaborations. Furthermore, we have created the technological platform for a community portal which will serve as an information-hub for the growing scientific and industrial community around NBC.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We have successfully “pushed the envelope” of what is possible with network-based biocomputation in several directions (see main results achieved so far). In doing so, we already fulfilled one of the main goals of the Bio4Comp project, namely to develop new algorithms with the aim to open up a wide range of applications.

Furthermore, we have made very good progress towards two more goals:
(i) establishing the technological and scientific basis for robust scale-up of network-based biocomputation and
(ii) foster and structure an ecosystem of scientists and companies that will accelerate the path to market success.

Within the remainder of the project, this will enable us to fulfill our final goal: demonstrate scalability by systematically increasing the problem size by several orders of magnitude.

The Bio4Comp project will demonstrate the scale-up of an alternative parallel computing approach that uses orders of magnitude less energy than conventional computers. This approach will be an excellent complement to existing electronic computers, particularly because heat generation, small-scale e.g. quantum effects, and rising costs will prevent further miniaturization of electronic computers by about 2020. Therefore, Bio4Comp will lay the foundation for a new technology that has the potential to disrupt the market of electronic computers and help us solve important practical problems such as the design and verification of circuits, the folding and design of proteins and optimal network routing.

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