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Networks in Complex Animal Systems

An article by Barabasi et al. titled “Network medicine: a network-based approach to human disease,” appeared in Nature Reviews Genetics in January. This is an important article. One of our arguments against using animals as predictive models for human response to drugs and disease is that genes act in networks with other genes that in turn interact with proteins and with the environment. The presence of such networks has implications in complex systems. The Barabasi article explores the implications of networks for disease. Barabási et al.:

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems . . . Most cellular components exert their functions through interactions with other cellular components, which can be located either in the same cell or across cells, and even across organs. In humans, the potential complexity of the resulting network — the human interactome — is daunting: with ~25,000 protein-coding genes, ~1,000 metabolites and an undefined number of distinct proteins (Zhao and Jensen 2009) and functional RNA molecules, the number of cellular components that serve as the nodes of the interactome easily exceeds 100,000. The number of functionally relevant interactions between the components of this network, representing the links of the interactome, is expected to be much larger (Venkatesan et al. 2009).

This inter- and intracellular interconnectivity implies that the impact of a specific genetic abnormality is not restricted to the activity of the gene product that carries it, but can spread along the links of the network and alter the activity of gene products that otherwise carry no defects. Therefore, an understanding of a gene's network context is essential in determining the phenotypic impact of defects that affect it (Goldstein 2009) (Schadt 2009). Following on from this principle, a key hypothesis underlying this Review is that a disease phenotype is rarely a consequence of an abnormality in a single effector gene product, but reflects various pathobiological processes that interact in a complex network. A corollary of this widely held hypothesis is that the interdependencies among a cell's molecular components lead to deep functional, molecular and causal relationships among apparently distinct phenotypes . . . Although much of our understanding of cellular networks is derived from model organisms, the past decade has seen an exceptional growth in human-specific molecular interaction data (Ideker and Sharan 2008) . . . Although the bulk of research on biological networks has focused on Escherichia coli and Saccharomyces cerevisiae, following the Human Genome Project the amount of data pertaining to networks in the human cells exceeds in richness and diversity the data that are available for model organisms . . . The emergence of a disease is therefore viewed as a combinatorial problem in which many different defects and perturbations result in a similar disease phenotype, provided that they alter the activity of the disease module . . .  (Barabasi, Gulbahce, and Loscalzo 2011)

This is the kind of article that, if properly understood, goes a long way toward invalidating the use of animals as predictive models for human response to drugs and disease. Granted, in order to properly understand it one needs a working knowledge of evolutionary biology, genetics, and complex systems but that’s science. You rarely get knowledge without expending quite a bit of effort. This article is not a knockout punch for using animals as predictive models. There is no one article or even one concept that does that. But this article represents, and explains, key concepts in the overall argument.

I want to make three points.

1. Vivisection activists usually do not understand, or perhaps acknowledge, the concepts in this article. Based on my conversations with them, I think they really do not understand the concepts. They have not kept up with science outside their own very small field and it shows. There are people who understand the concepts but who just lie about the implications, but in my opinion they are in the minority.

2. As I have often said, an understanding of complex systems and or evolutionary biology alone calls into question the use of animal models to predict human response to drugs and disease; even genetically modified animals. This article nicely illustrates why this is the case. Scientists who are also honest (and these scientists are the rule not the exception), and who do understand these concepts both appreciate such arguments and expect such arguments to be used when animal activists argue scientifically against using animals in research. Such scientists are not favorably impressed when such arguments are ignored and instead the animal activist hauls out the usual examples over and over again. There are a lot of scientists out there who, when these concepts are explained, are very willing to question whether animal models can predict human response to drugs and disease.

3. If the animal activist wants to argue vivisection with vivisectors or scientists in general, this is the kind of article she needs to understand. If she cannot read this article and get most of the points, then I would argue that she really does not know enough science to argue with scientists. That is not to say that the animal activist cannot quote from my books or use well-known examples of the failure of the animal model when discussing this issue with the general public in all the usual and various forums. But this article is an example of the concepts that competent scientists use when discussing the use of animals as predictive models. So if you do not understand these concepts and the Barabasi article in general, do not kid yourself that you can argue the science with people who do understand them. (As I have stated ad nauseam, the ethics and science are two separate topics. Maybe you think they should not be, but that they are. You can discuss one without the other.)

NOTE. The Barabasi article is probably not available free of charge. If you are an animal activist and want to read it, please contact me. Scientists can probably download it through their university or institution.


Barabasi, Albert-Laszlo, Natali Gulbahce, and Joseph Loscalzo. 2011. Network medicine: a network-based approach to human disease. Nat Rev Genet 12 (1):56-68.

Goldstein, David B. 2009. Common Genetic Variation and Human Traits. New England Journal of Medicine 360 (17):1696-1698.

Ideker, Trey, and Roded Sharan. 2008. Protein networks in disease. Genome Research 18 (4):644-652.

Schadt, Eric E. 2009. Molecular networks as sensors and drivers of common human diseases. Nature 461 (7261):218-223.

Venkatesan, Kavitha, Jean-Francois Rual, Alexei Vazquez, et al. 2009. An empirical framework for binary interactome mapping. Nat Meth 6 (1):83-90.

Zhao, Yingming, and Ole N. Jensen. 2009. Modification-specific proteomics: Strategies for characterization of post-translational modifications using enrichment techniques. PROTEOMICS 9 (20):4632-4641.


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