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Networking Naturally

01.05.2003

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Dr. Uri Alon and students. basic principles from netwok motifs

 

 

Khad Gadya, the 16th-century song concluding the Passover Seder, describes a succession of allegorical events leading from the purchase of a young goat to the striking down of the Angel of Death:
 
  Father bought a young
   goat for two zuzim;
   a cat came and ate the goat;
   a dog then bit the cat;
   the dog was beaten by a stick;
   the stick was burned by fire;
   water quenched the fire;
   an ox drank the water;
   a shohet [ritual slaughterer]                              
   slaughtered the ox;
   the shohet was killed by the Angel of Death,
   who in punishment was destroyed by God.
 
Cause-and-effect chains, spanning all aspects of life, have often baffled the human mind, which is naturally inclined to find the “order” behind things. The task becomes more mind-racking as the number of factors increases. The verse above mentions a dozen factors. Now, take our DNA. How do 30,000 genes in our DNA work together to form a large part of who we are? How does one neuron operate in the context of the whole nervous system?
 
Decades of pinpointed biological research have yielded profuse bits of information. Increasingly, scientists are feeling the need to meld the many fragments together to understand how we work as a whole. In an article published in Nature Genetics, Dr. Uri Alon, a physicist at the Weizmann Institute’s Molecular Cell Biology Department, proposed a mathematical technique to seek out the basic principles of biological systems. Using this technique, he has uncovered several motifs underlying genetic, neural and food networks.   
 
Alon surmised that patterns serving an important function in nature might recur more often than statistically forecasted. This guiding principle, he thought, might help one find the “wiring” underlying biological systems. Using an algorithm that he devised, Alon analyzed the scientific findings existing on networks in some well-researched organisms. He noticed that some patterns in the networks were inexplicably more repetitive than they would be in randomized networks. This handful of patterns was singled out as a potential bundle of design principles.
 
“For a physicist, looking inside the cell is like witnessing a succession of miracles,” says Alon. “The field of physics offered many ‘miracles’ in the past as well – lightning, for instance, evoked an image of Zeus casting a fiery spear. When this belief gave way to an understanding of electric charges, lightning became an ‘interesting phenomenon.’ The basic laws of biology, however, are still unknown, making biology seem ‘miraculous.’ Unearthing these laws is what makes this kind of research so exciting.”
 
Alon’s team has succeeded in uncovering “basic laws” in genetic systems, neural systems and food webs. “Surprisingly, we found two identical laws in genetic and neural systems,” says Alon. “Apparently both information-processing systems employ similar strategies.” Exposing the backbones of such networks can thus help scientists classify systems generically (just as butterflies and ants both belong to the same “class,” neural and genetic systems would also belong to a generic category). This would function as more than just an organizing principle: “One might be able to learn about the neural system by studying the genetic system, which is usually more accessible,” says Alon.
 
In each of seven ecosystems scrutinized, seven recurring patterns relating to food webs were found. One demonstrates that predators will not eat components in the diet of their prey – a lion will eat a gazelle, which eats grass, but it will not eat grass (see below). Humans, who are avid omnivores, are an exception to the rule.
 
Alon’s method detects such patterns – which he calls network motifs – on the basis of their frequency. Any patterns that are functionally important but not statistically significant will not be detected by this method.

“The dream,” says Alon, “is to detect and understand all of the laws governing our bodies, rendering the workings of a cell fully evident and the means of repairing it clear-cut.” Scientists hope that one day in the distant future doctors’ work will be comparable to that of present-day electronic engineers, who analyze blueprints of their subjects and then set to work to put them back in shape.

 
Dr. Uri Alon is the incumbent of the Carl and Frances Korn Career Development Chair in the Life Sciences. His research is supported by the Charpak-Vered Visiting Fellowship, Canada; the Clore Center for Biological Physics; the Rita Markus Foundation Inc.; the Minerva Stiftung Gesellschaft fuer die Forschung m.b.H.; the James and Ilene Nathan Charitable Directed Fund; the Mrs. Harry M. Ringel Memorial Foundation; R.P.H. Promotor Stiftung; the Yad Abraham Center for Cancer Diagnostics and Therapy; and Yad Hanadiv.

Networking Naturally-scientific-1

1. Gene regulation and neural networks: The "feedforward loop." For Z (a gene or a neuron) to be activated, both X and Y must send it a signal (a protein in the case of genes, a pulse in the case of neurons). Y is activated by X, but only when the latter's signal is strong. Thus Z won't begin to be activated when X is present in low concentrations. This motif's function may be to filter noise (low concentrations of X are unimportant noise) and to allow rapid deactivation.

2. Gene regulation networks: Conveyor belt-like activation. X, in relatively small amounts, will produce V. As its amoung increases, it will produce W, Y and Z, respectively. Deactivation will follow the opposite sequence.

Networking Naturally

3. Food webs: Predators don't usually eat the same food as their prey (man-made products like cheese are not taken into account). Omnivores, such as humans, are the exception to the rule and are rare in ecosystems, conformint to the "feedforward loop" shown above.

4. Food webs and neural networks: Different species of prey of a given predator will often have a similar diet. Likewise, if two neurons are activated by the same neuron, they are both likely to be needed to activate a subsequent neuron.

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