Orangutans and Networks

Publish date:
Social count:

We can now add orangutans to the list of species whose genomes have been sequenced. ScienceDaily:

"The average orangutan is more diverse -- genetically speaking -- than the average human," says lead author Devin Locke, PhD, an evolutionary geneticist at Washington University's Genome Center. "We found deep diversity in both Bornean and Sumatran orangutans, but it's unclear whether this level of diversity can be maintained in light of continued widespread deforestation."

It appears that the orangutan genome is more stable than its cousins’.

"In terms of evolution, the orangutan genome is quite special among great apes in that it has been extraordinarily stable over the past 15 million years," says senior author Richard K. Wilson, PhD, director of Washington University's Genome Center, which led the project. "This compares with chimpanzees and humans, both of which have experienced large-scale structural rearrangements of their genome that may have accelerated their evolution."

This means that orangutans are probably closer to our common great ape ancestor than other species. The Nature article “Comparative and demographic analysis of orang-utan genomes” can be found here.

A press release from University of Veterinary Medicine – Vienna:

Comparing the new sequence with that of the human genome and other mammalian genomes thus provides unique insights into the evolution of man. [Dr Carolin] Kosiol has examined a total of nearly 14,000 human genes that are also found in the orang-utan, chimpanzee, macaque and dog genomes. She was able to show that genes involved in two processes have been particularly subject to natural selection in primate evolution: visual perception and the metabolism of glycolipids. Intriguingly, defects in glycolipid metabolism are known to be associated with a number of neurodegenerative diseases in humans. "Changes in lipid metabolism may have played a big part in neurological evolution in primates, as well as being involved in the diversity of diets and life history strategies," Kosiol believes. "Apes, especially orang-utans, have slower rates of reproduction and much lower energy usage than other mammals. It would be very valuable to sequence more primate genomes to enable more comparative analysis of this kind and thus help us understand the evolution of primates and our own species."

I want to put the above in context with an article from Nature Physics, “Impact of single links in competitive percolation.” As I have said many times, the fact that genes work in networks and that animals are examples of complex systems means that small changes can have major consequences. For example, predicting drug and disease response across species lines is going to problematic. The Nature Physics article discusses the impact of adding just one more connection to a network. ScienceDaily:

A single new connection can dramatically enhance the size of a network -- no matter whether this connection represents an additional link in the Internet, a new acquaintance within a circle of friends or a connection between two nerve cells in the brain.

Or if the connection is between genes or proteins.

The scientists found that after a certain number of new links, a sudden growth spurt occurs: The size of the largest network within the system is enhanced dramatically. "With respect to the size of the system, this jump is more dramatic in small systems than in large ones," says Nagler. However even in systems that consist of a huge number of elements -- comparable for example to the number of neurons in the brain -- the size of the largest network can double.

Ritsert C. Jansen wrote in Nature Reviews Genetics:

The recent progress in genomics and genetics, particularly in the high-throughput measurement of DNA transcript and protein levels, has led to a surge of interest in the fields of Systems Biology and Complex Traits Biology to study complex human diseases and disorders. Although such optimism is justified by the success that has been achieved in identifying the genetic basis of monogenic diseases and disorders, such as cystic fibrosis and Huntington disease, unravelling how genes “talk to each other” is likely to be an intimidating effort for more complex traits and processes. This is also true when analysing the genetic actions/interactions that occur in experimental systems: even organisms that have 10 [raised to the power of] 3 – 10 [raised to the power of] 4 genes, such as the budding yeast Saccharomyces cerevisiae, can produce more than 10 [raised to the power of] 4 – 10 [raised to the power of] 5 gene products, which can easily account for more than 10 [raised to the power of] 5 – 10 [raised to the power of] 7 interactions.1

(Sorry about the funny formatting of exponents. I could not make the exponents look like exponents so had to write it out that way.)

Humans have around 24,000 genes. With 24,000 genes, a pairwise matrix would yield 576 million combinations. If we assume a 100-gene network with each gene switched off or on, then the number of states the network can be is 2 [raised to the power of] 100. As Brian Goodwin describes in How the Leopard Changed Its Spots 2, if the network proceeded through every state in some sequence and stayed in that state for one microsecond, it would take billions of billions of years to get through all the states. With 24,000 genes that can be expressed between 0 and 1.0, the number of states is at least 2 [raised to the power of] 24,000. This should give some idea of the complexity of the system and why small changes in it can have enormous implications.

Small changes on the genetic level (involving these networks and or the regulation thereof) can lead to very large differences between species. Indeed, that is what evolution is all about. The claim that humans and rodents (or humans and any animal) are the same animal at the biochemical level, just dressed up differently, simply isn’t true. (Even rodents are not the same animal dressed up differently. There are profound differences between strains.) Moreover, it is irrelevant to point to observed similarities in genetic makeup between species (e.g., 94% similarity in sequences), since the details of the differences are in the interactions between conserved genes, not in the genes themselves. (See Animal Models in Light of Evolutionfor more.)

Yes, animals have similarities with humans. All are composed of essentially the same elements; all are made up of cells; and all share conserved processes. This is interesting for its own sake (and was important in the early days of biology research) but it is not sufficient for using animals to predict human response to drugs and disease—the reason animals are used in biomedical research and testing (or at least the reason given to society).

Vivisection activists cannot refute the argument from evolution, complex systems, and gene networks and it is this argument that allows us to place the empirical evidence (for examples see 3-31) in context. Just as many failures to make a perpetual motion machine, when put into the context of the 2nd law of thermodynamics, allow us to conclude a perpetual motion machine is impossible, so too should a thorough understanding of the above stop the practice of using animals as predictive models for humans.

Considering all the money involved, I doubt it will.


1.            Jansen, R.C. Studying complex biological systems using multifactorial perturbation. Nat Rev Genet4, 145-151 (2003).

2.            Goodwin, B. How the Leopard Changed Its Spots : The Evolution of Complexity, (Princeton University Press, 2001).

3.            Calabrese, E.J. Suitability of animal models for predictive toxicology: theoretical and practical considerations. Drug Metab Rev15, 505-523 (1984).

4.            Calabrese, E.J. Principles of Animal Extrapolation, (CRC Press, 1991).

5.            Collins, J.M. Inter-species differences in drug properties. Chem Biol Interact134, 237-242 (2001).

6.            Fletcher, A.P. Drug safety tests and subsequent clinical experience. J R Soc Med71, 693-696 (1978).

7.            Garattini, S. Toxic effects of chemicals: difficulties in extrapolating data from animals to man. Crit Rev Toxicol16, 1-29 (1985).

8.            Hackam, D.G. Translating animal research into clinical benefit. BMJ334, 163-164 (2007).

9.            Hackam, D.G. & Redelmeier, D.A. Translation of research evidence from animals to humans. JAMA296, 1731-1732 (2006).

10.            Heywood, R. Target organ toxicity II. Toxicol Lett18, 83-88 (1983).

11.            Heywood, R. Clinical Toxicity--Could it have been predicted? Post-marketing experience. in Animal Toxicity Studies: Their Relevance for Man (eds. CE Lumley & Walker, S.) 57-67 (Quay, Lancaster, 1990).

12.            Igarashi, T. The duration of toxicity studies required to support repeated dosing in clinical investigation—A toxicologists opinion. in CMR Workshop: The Timing of Toxicological Studies to Support Clinical Trials (ed. C Parkinson, N.M., C Lumley, SR Walker) 67-74 (Kluwer, Boston/UK, 1994).

13.            Igarashi, T., Nakane, S. & Kitagawa, T. Predictability of clinical adverse reactions of drugs by general pharmacology studies. J Toxicol Sci20, 77-92 (1995).

14.            Igarashi, T., Yabe, T. & Noda, K. Study design and statistical analysis of toxicokinetics: a report of JPMA investigation of case studies. J Toxicol Sci21, 497-504 (1996).

15.            Igarashi, Y. Report from the Japanese Pharmaceutical Manufacturers Association 1994 Seiyakukyo data.

16.            Johnson, J.I., et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer84, 1424-1431 (2001).

17.            Knight, A. Systematic reviews of animal experiments demonstrate poor human clinical and toxicological utility. Altern Lab Anim35, 641-659 (2007).

18.            Knight, A., Bailey, J. & Balcombe, J. Animal carcinogenicity studies: 1. Poor human predictivity. Altern Lab Anim34, 19-27 (2006).

19.            Koppanyi, T. & Avery, M.A. Species differences and the clinical trial of new drugs: a review. Clin Pharmacol Ther7, 250-270 (1966).

20.            Lindl, T., Voelkel, M. & Kolar, R. [Animal experiments in biomedical research. An evaluation of the clinical relevance of approved animal experimental projects]. ALTEX22, 143-151 (2005).

21.            Lindl, T., Völkel, M. & Kolar, R. Animal experiments in biomedical research. An evaluation of the clinical relevance of approved animal experimental projects: No evident implementation in human medicine within more than 10 years. [Lecture abstract.]. ALTEX23, 111 (2006).

22.            Litchfield, J.T., Jr. Symposium on clinical drug evaluation and human pharmacology. XVI. Evaluation of the safety of new drugs by means of tests in animals. Clin Pharmacol Ther3, 665-672 (1962).

23.            Lumley, C. Clinical toxicity: could it have been predicted? Premarketing experience. in Animal Toxicity Studies: Their Relevance for Man (eds. Lumley, C. & Walker, S.) 49-56 (Quay, 1990).

24.            Mahmood, I. Can absolute oral bioavailability in humans be predicted from animals? A comparison of allometry and different indirect methods. Drug Metabol Drug Interact16, 143-155 (2000).

25.            Perel, P., et al. Comparison of treatment effects between animal experiments and clinical trials: systematic review. BMJ334, 197 (2007).

26.            Salsburg, D. The lifetime feeding study in mice and rats--an examination of its validity as a bioassay for human carcinogens. Fundam Appl Toxicol3, 63-67 (1983).

27.            Shanks, N. & Greek, R. Animal Models in Light of Evolution, (Brown Walker, 2009).

28.            Shanks, N., Greek, R. & Greek, J. Are animal models predictive for humans? Philos Ethics Humanit Med4, 2 (2009).

29.            Shanks, N. & Pyles, R.A. Evolution and medicine: the long reach of "Dr. Darwin". Philos Ethics Humanit Med2, 4 (2007).

30.            Spriet-Pourra, C., Auriche, M. & (Eds). SCRIP Reports, (PJB, 1994).

31.            Wall, R.J. & Shani, M. Are animal models as good as we think? Theriogenology69, 2-9 (2008).


Popular Video