Photo courtesy of Biflow Systems GmbH




The third open-innovation Bio4Comp Award was for novel ideas that conceptually or technically advance the field of parallel computing with biological agents in a substantial manner. The call deadline was on 17 July 2021. The third challenge was about

Applications for network-based and bio-inspired computation technologies.

Winning idea:

"Exploiting molecular motor propelled filaments for selective, ultrasensitive detection in a Lab-on-a-Chip”

by Tim Erichlandwehr and Irene Fernandez-Cuesta

(Hamburg, Germany)

Summary: Ultra-sensitive detection of biomolecules with high selectivity in physiological fluids (e.g. blood, saliva) is essential for applying nanosensors to real world applications. The development of a compact platform for detection and quantification of minute amounts of biomarkers would have an impact on several different fields such as cancer monitoring, detection of toxins or pathogens in real time. To achieve this, several technological challenges need to be addressed, since most of the available optofluidic devices work only for a very narrow concentration range, are single use, or hardly work in complex biofluids. We propose to combine functional molecular motors with micro- and nanofluidics for hybrid and selective biosensing. The motors would offer the possibility of continuously capturing molecules, and the fluidic circuit to control and manipulate the liquid sample. As a long term vision, we include parallelization of channel circuits for multiplexing, and for increasing statistics. Through the collaboration of our group with different Bio4Comp groups and thanks to the interdisciplinary background of the collaborators, we expect to exploit biocomputation technologies and methods to develop versatile Lab-on-a-Chip devices.


In the opinion of the Innovation System Committee, chaired by Danny Reuter (FHG), the proposal is quite straight forward from the state of the art. It is a sound description of an approach for motor protein based transport in a biotechnology application. The scientific quality is very high; the technological realization is very likely.

Congratulations to the winners!


Applications for network-based and bio-inspired computation technologies

The competition asks for

a) Applications for network-based computers using bio-molecular agents; or

b) Applications in the field of diagnostics or lab on a chip devices utilized by molecular motor propelled filaments; or

c) Applications or application scenarios which are enabled by network-based biocomputation and contribute to biotechnological challenges;

The combinatorial nature of many important mathematical problems, including so called NP-complete problems (nondeterministic-polynomial-time-complete  problems), places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. While a number of parallel-computation approaches, such as DNA computation, quantum computation, and microfluidics-based computation, have been reported in the past, these approaches have so far not proven to be scalable and practical from a fabrication and operational perspective.

One alternative is network-based computation (NBC) where a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent agents (e.g. cytoskeletal filaments propelled by molecular motor proteins) then solves the mathematical problem.

This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation (see: Nicolau et al., PNAS, 113(10), pp. 2591-2596, 2016).


Applications were evaluated according to:

(i) novelty and excellence of the idea,

(ii) potential impact for the research area, and (iii) feasibility.


Decision about the winner(s) of the contest was made by the Innovation System Committee in agreement with the Bio4Comp General Assembly (see above).