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.
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.
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.
Send in the Substitutes
If there were no bench for second-string players on a football team, who would substitute for tired or injured team members? Weizmann Institute scientists have found that, if the team were made up of genes, they might pull athletes who can play a little football in a pinch from nearby basketball or rugby teams.
Dr. Yitzhak (Tzachi) Pilpel and graduate students Ran Kafri and Arren Bar-Even, of the Institute’s Molecular Genetics Department, knew from previous studies that up to 80% of the genes in yeast, a common model for genetics research, have potential stand-ins in various spots around the genome. Though not identical to the original gene, they make a protein that is sufficiently similar to the one it produces to pass muster. Many scientists believed that both genetic substitutes and the main gene were expressed simultaneously so as to supply the organism with needed quantities of proteins. But Pilpel and his team showed in a study published in Nature Genetics that in fact when the original gene is up and running, the others are off playing at their own sports. Only when that gene is damaged or deleted do the substitutes get called onto the “football field,” where they play as best they can.
The scientists reached this conclusion after analyzing data from some 40 studies of yeast cells by different research teams around the world. Using the latest bioinformatics techniques to identify patterns and trends in the enormous flux of data supplied by these studies and by the sequencing of the yeast genome, they proposed a “football coach” mechanism that knows when to call up the substitute players.
This “coach” gets the lowdown on the players’ performance from the raw materials genes use to make proteins. When a gene is working at full capacity, it will use up most of the raw material available to it, leaving little in its original state. But if it’s not making sufficient quantities of protein, or producing defective proteins that are missing bits, a relatively larger amount of the raw material will be left over. Raw material that is sitting around activates a special set of proteins, called transcription factors, whose job is to turn on genes. The transcription factors then bind to, and activate, the substitute genes.
Why evolve genes to make proteins that are similar but not identical, and thus imperfect substitutes? Pilpel’s group proposed that the small variations between exchangeable genes, such as differences in the conditions that cause them to be activated, impart to each a unique function. These differences in function make them sufficiently vital to be preserved by evolution, yet allow them, when necessary, to step in for a gene on a different team as a substitute player.
Dr. Yitzhak Pilpel’s research is supported by the Leo and Julia Forchheimer Center for Molecular Genetics; Mr. Nathan Kahn, Riverdale, NY; the Ben May Charitable Trust; the Dr. Ernst Nathan Fund for Biomedical Research; the Rosenzweig-Coopersmith Foundation; the Samuel M. Soref and Helene K. Soref Foundation; and Mr. Walter Strauss, Switzerland.