Skip to main content

Parallel network-based biocomputation: technological baseline, scale-up and innovation ecosystem

Periodic Reporting for period 2 - Bio4Comp (Parallel network-based biocomputation: technological baseline, scale-up and innovation ecosystem)

Reporting period: 2018-01-01 to 2019-06-30

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.
The most important achievement to date was the successful operation of an EXACT COVER device that solves a problem with 1000+ possible solutions. The network algorithm encoding this problem profited from several optimizations and was formally verified to give correct results. Operation of the respective device was enabled by optimizations to the network junctions as well as the biomolecular motor systems.

In parallel we have laid the foundations for the subsequent milestones which are (1) the decision on the layout- and (2) the successful operation of a 3-SAT device with > 10^6 possible solutions: We have formulated several network encodings including the respective fabrication layouts for networks solving 3-SAT. The encodings are of two fundamental types:
(i) Space-encoded networks that encode all information in the position of the filaments within the network. Consequently, these networks do not require filament tagging.
(ii) (ii) Filament-encoded networks that encode information within the filaments themselves. These networks are much more compact than space-encoded networks but require filament tagging. By photobleaching fluorescent dyes, we have demonstrated the feasibility of filament tagging.

We have made good progress towards further scale-up of network-based biocomputation devices. We have fabricated potentially error-free 3D pass-junctions with tunnels and bridges and demonstrated the compatibility of the respective material with the biomolecular motor systems. In addition to reduced errors, scaling analysis has identified
(i) filament multiplication as a key capability to enable favorable scaling of the number of filaments within networks and
(ii) filament encoded data as a key capability to enable favorable scaling of the networks themselves.

We have successfully demonstrated both methods filament multiplication as well as encoding data into filaments and are in the progress of demonstrating both methods within network devices.

In order to make network-based biocomputation more practically applicable, we are developing several methods to make the devices and the motor proteins programmable, as well as integrating detectors into the devices themselves.

The website bio4comp.org helps to communicate events such as workshops, awards and webinars. We have created a LinkedIn page in order to make use of an existing professional networking platform.
Furthermore, in order to increase the reach and impact of our next workshop, we will organize a symposium titled “Biocomputation: Materials, Algorithms, Devices and Fabrication” at the E-MRS spring meeting, 25-29 May 2020 in Strassbourg, France; see also: https://www.european-mrs.com/biocomputation-materials-algorithms-devices-and-fabrication-emrs
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.
Principle of network-based biocomputation. (Graphic/pictures: Till Korten, Cornelia Kowol)