Engineering Approaches to Biomolecular Motors

Engineering Approaches to Biomolecular Motors: From in vitro to in vivo Poster Abstracts

14-POS Board 14 Parallel Biocomputational Devices Based on Molecular Motors in Nanostructures Frida W. Lindberg 1 , Till Korten 2 , Mercy Lard 1 , Mohammad A. Rahman 3 , Hideyo Taktsuki 3 , Cordula Reuther 2 , Falco Van Delft 4 , Malin Persson 5 , Elina Bengtsson 3 , Emelie Haettner 1 , Alf Månsson 3 , Stefan Diez 2 , Dan Jr. V. Nicolau 6 , Dan Nicolau 7 , Heiner Linke 1 . 1 Lund University, Lund, Sweden, 2 Technische Universität Dresden, Dresden, Germany, 3 Linnaeus University, Kalmar, Sweden, 4 High Tech Campus 4, Eindhoven, Netherlands, 5 Karolinska Institutet, Stockholm, Sweden, 6 Molecular Sense Ltd, Oxford, United Kingdom, 7 McGill University, Montreal, QC, Canada. Solving mathematical problems of a combinatorial nature requires the exploration of a large solution space. As the number of possible solutions grows, this task becomes intractable for traditional, serial computation and therefore, calls for parallel computation techniques. Here we demonstrate an approach to solve a combinatorial problem by parallel computation based on molecular-motor driven biomolecules to explore physical networks of nanoscaled channels in a highly energy-efficient manner (Nicolau et al. 2016). We solve a combinatorial problem known as the subset sum problem, by encoding it into physical networks of channels patterned by lithography. These networks encode binary addition computers. The channel floors are covered with molecular motors that propel protein filaments fed into the network at one end, exploring the network. The filaments' exit-points correspond to different solutions. Each filament explores one solution, thus, a large number of proteins can be used to compute problems in a massively parallel, energy-efficient manner. We present a proof-of-principle demonstration of the parallel-computation technique, and the status of our ongoing work to optimize and up-scale this system. We test different designs to optimize the individual architectural elements, reducing error rates and increase computing efficiency. We also aim to incorporate switchable junctions into the networks, providing programmable “gates” that can be switched on and off, controlling passage of protein filaments, enabling a high variability of networks. Furthermore, we develop different processing methods for fabricating devices. Our approach is scalable using existing nanofabrication technology. Because one NP complete problem can be converted into another, this technique can be used, in principle, to solve many NP complete problems, with applications in, drug design, scheduling activities, checking of electronic circuit designs, etc. Nicolau, D.V.J. et al., 2016. Massively-parallel computation with molecular motor-propelled agents in nanofabricated networks. PNAS 113(10), pp. 2591–2596.

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