Fruit fly nervous system provides new solution to fundamental computer network problem

Jan 13, 2011
Fruit fly nervous system provides new solution to fundamental computer network problem
In this confocoal miscroscope image of the pupal stage of fruit fly development, nerve cells that self-select to become sensory organ precursors (SOPs) are identified by arrows. These cells send chemical signals to neighboring cells, blocking them from becoming SOPs and causing them to fluoresce red in the image.

(PhysOrg.com) -- The fruit fly has evolved a method for arranging the tiny, hair-like structures it uses to feel and hear the world that's so efficient a team of scientists in Israel and at Carnegie Mellon University says it could be used to more effectively deploy wireless sensor networks and other distributed computing applications.

With a minimum of communication and without advance knowledge of how they are connected with each other, the cells in the fly's developing manage to organize themselves so that a small number of cells serve as leaders that provide direct connections with every other nerve cell, said author Ziv Bar-Joseph, associate professor of machine learning at Carnegie Mellon University.

The result, the researchers report in the Jan. 14 edition of the journal Science, is the same sort of scheme used to manage the distributed that perform such everyday tasks as searching the Web or controlling an airplane in flight. But the method used by the fly's nervous system to organize itself is much simpler and more robust than anything humans have concocted.

"It is such a simple and intuitive solution, I can't believe we did not think of this 25 years ago," said co-author Noga Alon, a and computer scientist at Tel Aviv University and the Institute for Advanced Study in Princeton, N.J.

Bar-Joseph, Alon and their co-authors — Yehuda Afek of Tel Aviv University and Naama Barkai, Eran Hornstein and Omer Barad of the Weizmann Institute of Science in Rehovot, Israel — used the insights gained from to design a new distributed computing algorithm. They found it has qualities that make it particularly well suited for networks in which the number and position of the nodes is not completely certain. These include , such as environmental monitoring, where sensors are dispersed in a lake or waterway, or systems for controlling swarms of robots.

"Computational and mathematical models have long been used by scientists to analyze biological systems," said Bar-Joseph, a member of the Lane Center for Computational Biology in Carnegie Mellon's School of Computer Science. "Here we've reversed the strategy, studying a biological system to solve a long-standing computer science problem."

Today's large-scale computer systems and the nervous system of a fly both take a distributive approach to performing tasks. Though the thousands or even millions of processors in a computing system and the millions of cells in a fly's nervous system must work together to complete a task, none of the elements need to have complete knowledge of what's going on, and the systems must function despite failures by individual elements.

In the computing world, one step toward creating this distributive system is to find a small set of processors that can be used to rapidly communicate with the rest of the processors in the network — what graph theorists call a maximal independent set (MIS). Every processor in such a network is either a leader (a member of the MIS) or is connected to a leader, but the leaders are not interconnected.

A similar arrangement occurs in the fruit fly, which uses tiny bristles to sense the outside world. Each bristle develops from a nerve cell, called a sensory organ precursor (SOP), which connects to adjoining , but does not connect with other SOPs.

For three decades, computer scientists have puzzled over how processors in a network can best elect an MIS. The common solutions use a probabilistic method — similar to rolling dice — in which some processors identify themselves as leaders, based in part on how many connections they have with other processors. Processors connected to these self-selected leaders take themselves out of the running and, in subsequent rounds, additional processors self-select themselves and the processors connected to them take themselves out of the running. At each round, the chances of any processor joining the MIS (becoming a leader) increases as a function of the number of its connections.

This selection process is rapid, Bar-Joseph said, but it entails lots of complicated messages being sent back and forth across the network, and it requires that all of the processors know in advance how they are connected in the network. That can be a problem for applications such as wireless sensor networks, where sensors might be distributed randomly and all might not be within communication range of each other.

During the larval and pupal stages of a fly's development, the nervous system also uses a probabilistic method to select the cells that will become SOPs. In the fly, however, the cells have no information about how they are connected to each other. As various cells self-select themselves as SOPs, they send out chemical signals to neighboring cells that inhibit those cells from also becoming SOPs. This process continues for three hours, until all of the cells are either SOPs or are neighbors to an SOP, and the fly emerges from the pupal stage.

In the fly, Bar-Joseph noted, the probability that any cell will self-select increases not as a function of connections, as in the typical MIS algorithm for computer networks, but as a function of time. The method does not require advance knowledge of how the cells are arranged. The communication between cells is as simple as can be.

The researchers created a computer algorithm based on the fly's approach and proved that it provides a fast solution to the MIS problem. "The run time was slightly greater than current approaches, but the biological approach is efficient and more robust because it doesn't require so many assumptions," Bar-Joseph said. "This makes the solution applicable to many more applications."

Explore further: Powerful new software plug-in detects bugs in spreadsheets

Related Stories

Cells are like robust computational systems

Jun 16, 2009

Gene regulatory networks in cell nuclei are similar to cloud computing networks, such as Google or Yahoo!, researchers report today in the online journal Molecular Systems Biology. The similarity is that each system keeps ...

New technique accelerates biological image analysis

May 01, 2008

Researchers in Carnegie Mellon University’s Lane Center for Computational Biology have discovered how to significantly speed up critical steps in an automated method for analyzing cell cultures and other biological specimens.

Wireless sensors learn from life

Aug 25, 2008

(PhysOrg.com) -- European and Indian researchers are applying principles learned from living organisms to design self-organising networks of wireless sensors suitable for a wide range of environmental monitoring purposes.

Recommended for you

Researchers developing algorithms to detect fake reviews

Oct 21, 2014

Anyone who has conducted business online—from booking a hotel to buying a book to finding a new dentist or selling their wares—has come across reviews of said products and services. Chances are they've also encountered ...

User comments : 2

Adjust slider to filter visible comments by rank

Display comments: newest first

ChiRaven
5 / 5 (2) Jan 13, 2011
I understand the computational efficiency, but how is this more robust? If a member of the MIS drops off the network entirely, are the members that are connected to it then effectively "leaderless" and unable to function, thus degrading the network? In the fly, it appears that all SOP selection takes place prior to the adult stage, so once in the adult stage the failure of one SOP would eliminate the effectiveness of the cells dependent on it. Is there a provision for replacement of an MIS member in the computer network, to make it self-repairing?
Pyle
not rated yet Jan 13, 2011
Sweet! Except it might make us lazy.

Just because something adapted to fit a niche and it works efficiently, doesn't mean that there isn't a 1000x better method.

Hopefully we can use this method and still have people searching for alternatives that are more effective.

It is solutions like this that lead to vastly inefficient solutions three or four generations into the future. But, maybe by then we will direct the neural networks to develop their own algorithms to make their successors more efficient.