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Parallel computation with molecular-motor-propelled agents in nano fabricated networks

by
Dan V. Nicolau,, Mercy Lardc, Till Kortend,, Falco C. M. J. M. van Delftf,, Malin Perssong, Elina Bengtssong, Alf Månssong, Stefan Diezd,e, Heiner Linkec, and Dan V. Nicolauh,

"Significance

Electronic computers are extremely powerful at performing a high number of operations at very high speeds, sequentially. However, they struggle with combinatorial tasks that can be solved faster if many operations are performed in parallel. Here, we present proof-of-concept of a parallel computer by solving the specific instance {2, 5, 9} of a classical nondeterministic-polynomial-time complete (“NP-complete”) problem, the subset sum problem. The computer consists of a specifically designed, nanostructured network explored by a large number of molecular-motor-driven, protein filaments. This system is highly energy efficient, thus avoiding the heating issues limiting electronic computers. We discuss the technical advances necessary to solve larger combinatorial problems than existing computation devices, potentially leading to a new way to tackle difficult mathematical problems.

Abstract
The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which 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, molecular-motor-propelled agents 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. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology."

http://bit.ly/1RfbQxC

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What is a finite state machine

YouTube 

http://bit.ly/1HdJFx0

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What is a biological microprocessor?
bit.ly/1BHEgzc

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On YouTube

What are biological microprocessors good for?

http://bit.ly/1Nh38in

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Control unit; central processing unit: 
A final state machine

A final state machine, as shown here, is a theoretical model which can help to understand what is going on in the central processing unit. Simplified: Symbols a and b are written on a tape, which is read by the machine letter by letter from left to right. In this example the tape ends with the final letter b. Each letter provides the instruction to the machine into which state (S1, or S0) it should move; here a means move to state state 0 (S0) and b codes the instruction move to state 1 (S1). The final state of the machine in this example is thus S1. http://bit.ly/YI13bF
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Self-replication of DNA rings

 http://bit.ly/1S1BYQ0
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A new magazine about biological computing curated by me

THE BIOLOGICAL MICROPROCESSOR

http://flip.it/Pb3xL
via #flipboard  

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RNA that activates transcription

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Nicole Rusk

"Synthetic small RNA transcriptional activators can regulate gene transcription in Escherichia coli.

'Learn from nature and copy what it does' is one of the guiding principles in the laboratory of Julius Lucks at Cornell University, but in their recent work, the researchers developed a strategy that seemingly expands what nature has to offer.

“We want to leverage our ability to model and measure RNA structures to make gene networks,” says Lucks. His team focuses on transcriptional control, and they aim to have RNA inputs control RNA outputs without involving proteins such as transcription factors. “The big conceptual advantage of RNA over proteins is that you can do design,” explains Lucks. “We know a lot more about RNA folding than we do about protein folding.”

The strategy of the Lucks team has been to observe RNA design principles in nature, characterize their structure and then apply these designs to the engineering of genetic circuits. The limitation is that whereas nature very efficiently uses small RNAs to repress transcription, there are to date no known instances of small RNAs alone activating transcription. “But,” says Lucks, “if you want to build networks, you need to turn things on as well as off.”

Melissa Takahashi, a graduate student in the lab, first focused on characterizing the function of a natural RNA transcriptional repressor mechanism: a special sequence upstream of a gene's coding region that can form RNA structures that allow or prevent progression of the RNA polymerase. These structures are switchable: in one case transcription is stopped by a transcriptional terminator RNA hairpin, and in the other case transcription is allowed by an antiterminator sequence that sequesters the terminator and prevents the formation of the blocking hairpin. Takahashi looked at the structural transitions needed in order to undergo the switch from active to inactive transcription; she then came up with a strategy to invert this repression mechanism into one that activates transcription by adding yet another layer of structural transitions using a small transcription activating RNA (STAR)...."

http://bit.ly/1OZAWTi

Comment to:
Creating small transcription activating RNAs

by
James Chappell, Melissa K Takahashi & Julius B Lucks

"We expanded the mechanistic capability of small RNAs by creating an entirely synthetic mode of regulation: small transcription activating RNAs (STARs). Using two strategies, we engineered synthetic STAR regulators to disrupt the formation of an intrinsic transcription terminator placed upstream of a gene in Escherichia coli. This resulted in a group of four highly orthogonal STARs that had up to 94-fold activation. By systematically modifying sequence features of this group, we derived design principles for STAR function, which we then used to forward engineer a STAR that targets a terminator found in the Escherichia coli genome. Finally, we showed that STARs could be combined in tandem to create previously unattainable RNA-only transcriptional logic gates. STARs provide a new mechanism of regulation that will expand our ability to use small RNAs to construct synthetic gene networks that precisely control gene expression."
http://bit.ly/1HjhZW7
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Mammalian synthetic biology: emerging medical applications

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Zoltán Kis , Hugo Sant'Ana Pereira , Takayuki Homma , Ryan M. Pedrigi , Rob Krams

"In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON–OFF switches, categorized into transcriptional, post-transcriptional, translational and post-translational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes."

http://bit.ly/1CU8GN0
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