Navigation Links
Programming smart molecules
Date:12/12/2013

Cambridge, Mass. December 12, 2013 Computer scientists at the Harvard School of Engineering and Applied Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering at Harvard University have joined forces to put powerful probabilistic reasoning algorithms in the hands of bioengineers.

In a new paper presented at the Neural Information Processing Systems conference on December 7, Ryan P. Adams and Nils Napp have shown that an important class of artificial intelligence algorithms could be implemented using chemical reactions.

These algorithms, which use a technique called "message passing inference on factor graphs," are a mathematical coupling of ideas from graph theory and probability. They represent the state of the art in machine learning and are already critical components of everyday tools ranging from search engines and fraud detection to error correction in mobile phones.

Adams' and Napp's work demonstrates that some aspects of artificial intelligence (AI) could be implemented at microscopic scales using molecules. In the long term, the researchers say, such theoretical developments could open the door for "smart drugs" that can automatically detect, diagnose, and treat a variety of diseases using a cocktail of chemicals that can perform AI-type reasoning.

"We understand a lot about building AI systems that can learn and adapt at macroscopic scales; these algorithms live behind the scenes in many of the devices we interact with every day," says Adams, an assistant professor of computer science at SEAS whose Intelligent Probabilistic Systems group focuses on machine learning and computational statistics. "This work shows that it is possible to also build intelligent machines at tiny scales, without needing anything that looks like a regular computer. This kind of chemical-based AI will be necessary for constructing therapies that sense and adapt to their environment. The hope is to eventually have drugs that can specialize themselves to your personal chemistry and can diagnose or treat a range of pathologies."

Adams and Napp designed a tool that can take probabilistic representations of unknowns in the world (probabilistic graphical models, in the language of machine learning) and compile them into a set of chemical reactions that estimate quantities that cannot be observed directly. The key insight is that the dynamics of chemical reactions map directly onto the two types of computational steps that computer scientists would normally perform in silico to achieve the same end.

This insight opens up interesting new questions for computer scientists working on statistical machine learning, such as how to develop novel algorithms and models that are specifically tailored to tackling the uncertainty molecular engineers typically face. In addition to the long-term possibilities for smart therapeutics, it could also open the door for analyzing natural biological reaction pathways and regulatory networks as mechanisms that are performing statistical inference. Just like robots, biological cells must estimate external environmental states and act on them; designing artificial systems that perform these tasks could give scientists a better understanding of how such problems might be solved on a molecular level inside living systems.

"There is much ongoing research to develop chemical computational devices," says Napp, a postdoctoral fellow at the Wyss Institute, working on the Bioinspired Robotics platform, and a member of the Self-organizing Systems Research group at SEAS. Both groups are led by Radhika Nagpal, the Fred Kavli Professor of Computer Science at SEAS and a Wyss core faculty member. At the Wyss Institute, a portion of Napp's research involves developing new types of robotic devices that move and adapt like living creatures.

"What makes this project different is that, instead of aiming for general computation, we focused on efficiently translating particular algorithms that have been successful at solving difficult problems in areas like robotics into molecular descriptions," Napp explains. "For example, these algorithms allow today's robots to make complex decisions and reliably use noisy sensors. It is really exciting to think about what these tools might be able to do for building better molecular machines."

Indeed, the field of machine learning is revolutionizing many areas of science and engineering. The ability to extract useful insights from vast amounts of weak and incomplete information is not only fueling the current interest in "big data," but has also enabled rapid progress in more traditional disciplines such as computer vision, estimation, and robotics, where data are available but difficult to interpret. Bioengineers often face similar challenges, as many molecular pathways are still poorly characterized and available data are corrupted by random noise.

Using machine learning, these challenges can now be overcome by modeling the dependencies between random variables and using them to extract and accumulate the small amounts of information each random event provides.

"Probabilistic graphical models are particularly efficient tools for computing estimates of unobserved phenomena," says Adams. "It's very exciting to find that these tools map so well to the world of cell biology."


'/>"/>

Contact: Caroline Perry
cperry@seas.harvard.edu
617-496-1351
Harvard University
Source:Eurekalert  

Related biology news :

1. Fetal programming of sweet tastes elicited pleasure
2. CNIO researchers identify a new gene that is essential for nuclear reprogramming
3. Programming cells: The importance of the envelope
4. Insight into DNA reprogramming during egg and sperm cell development
5. How to make stem cells - nuclear reprogramming moves a step forward
6. Discovery of reprogramming signature may help further stem cell-based regenerative medicine research
7. Heart Damage Repaired By Reprogramming Resident Fibroblasts into Functioning Heart Cells
8. Keep Track of Your Children & Pets With TRAX - the New Smart GPS-Tracker
9. Bait research focused on outsmarting destructive beetle
10. Social networks make us smarter
11. Fujitsu Launches Four Smartphones and Two Tablet PCs With FPC Embedded Fingerprint Technology for the Japanese Market
Post Your Comments:
*Name:
*Comment:
*Email:
Related Image:
Programming smart molecules
(Date:5/16/2016)... , May 16, 2016   EyeLock LLC ... today announced the opening of an IoT Center of ... strengthen and expand the development of embedded iris biometric ... unprecedented level of convenience and security with unmatched biometric ... one,s identity aside from DNA. EyeLock,s platform uses video ...
(Date:5/3/2016)... Lithuania , May 3, 2016  Neurotechnology, ... released the MegaMatcher Automated Biometric Identification System ... of large-scale multi-biometric projects. MegaMatcher ABIS can process ... accuracy using any combination of fingerprint, face or ... MegaMatcher SDK and MegaMatcher Accelerator ...
(Date:4/26/2016)... India and LONDON ... Infosys Finacle, part of EdgeVerve Systems, a product ... and Onegini today announced a partnership to integrate ... solutions.      (Logo: http://photos.prnewswire.com/prnh/20151104/283829LOGO ... to provide their customers enhanced security to access ...
Breaking Biology News(10 mins):
(Date:6/23/2016)... 2016  The Prostate Cancer Foundation (PCF) is pleased to announce ... cures for prostate cancer. Members of the Class of 2016 were selected from ... Read More About the Class of 2016 PCF Young Investigators ... ... ...
(Date:6/23/2016)... , June 23, 2016   EpiBiome , a ... $1 million in debt financing from Silicon Valley Bank ... automation and to advance its drug development efforts, as ... facility. "SVB has been an incredible strategic ... services a traditional bank would provide," said Dr. ...
(Date:6/23/2016)... ... , ... STACS DNA Inc., the sample tracking software company, today announced that ... joined STACS DNA as a Field Application Specialist. , “I am thrilled that ... of STACS DNA. “In further expanding our capacity as a scientific integrator, Hays brings ...
(Date:6/23/2016)... June 23, 2016 Apellis Pharmaceuticals, Inc. ... clinical trials of its complement C3 inhibitor, APL-2. ... multiple ascending dose studies designed to assess the ... subcutaneous injection in healthy adult volunteers. ... as a single dose (ranging from 45 to ...
Breaking Biology Technology: