Tag Archives: Genomics

Network Analysis and Creationism

Biological networks are hot these days in genomics, genetics, and bioinformatics. It’s obvious that with the rise of the modern-day ~omics technologies huge amounts of biological data are available for biologists (for example gene expression data, protein, metabolites measurements). In the living cell those key components of life interact with each other and form biological networks.

Most of the time biologists only looked for a long time at one gene or some more genes, at one time. In a system approach this a more (w)holistic approach is used. For example one could study the adaption to a certain environmental stress and look at transcription factors that up-regulate or down-regulate in specific conditions. Those data could be then be coupled with protein measurements, metabolites that play a role in that particular process, and more types of biological relevant data.

There are several characteristics that are important for biological network analysis. Below I summarized two of them.

  1. First of all, biological networks are neither random and have a certain design principles (which we do not know yet). From an evolution perspective that means that biological networks in diverse organisms will share common functional sub-systems. When inferring a network, one could do that based on homology, and so identify large parts of biological networks in different organisms. This would perfectly make sense in a creationist view.
  1. Second, Biological systems are modular. An inherent quality of biological systems is, that it is more likely that a set of genes performs their task as a module. So when we see an organism as a system, that means that many modules are able to perform a different task. For example you could cluster co-expressed genes which all play a role in the metal homeostasis.

What creationists could learn from biological network analysis is quite obvious. First of all, the fact that genomes are modular could be seen as a design principle. In organism A the module could behave in the same way as in organism B. Our Creator would have used modules to create everything. As a way of speaking only fine-tuning is then needed for God to ‘made the beast of the earth after its kind’ (Genesis 1:24–25).

For a creation biology model it is important to think about what homology means. It is important that creationists do not stop by just a linear view on the genome, but take in these matters a system approach, where all elements are considered.

Baraminology is the field in which a system of classification is used to identify created kinds. As is pointed out by Todd Wood (2006), the use of molecular data is kind of a  problem. Of course this problem is also known in mainstream science, when molecular data are used for tree building, that as a consequence,  trees become unreliable (for which they invented phylogenetic networks).
When I was thinking about how the systems approach could apply to baraminology I came to the following speculations. First of all, what if biological modules share homology between organisms, instead of just looking at one gene. One would expect that such a biological network module would give a more reliable estimate of similarity or dissimilarity between different baramins. The main reason for looking at morphological characters is because they give a more overall means of similarity. The hybridization criteria is used because hybridization is a ‘all-in’ character. After all for hybridization to happen between two species much of the developmental processes should be the same.

These are just theoretical speculations. For comparison of biological networks between mammals a huge amount of data is needed. And not only the genomes need to be sequenced, but also information about the regulatory of modules and of the system (organism) as a whole.

Currently, baraminology takes into account mainly morphological characters. But it would be nice, and I think everyone would agree, to also take into account molecular data. This modular homology approach could possible stimulate research into baraminology.

Any thoughts? Please let me know in the comments!


Bonneau, R., 2008. Learning biological networks: from modules to dynamics. Nature Chemical Biology, Vol. 4, 11,

Wood, T.C., 2006, The Chimpanzee Genome and the Problem of Biological Similarity. Occasional papers of the BSG, 7



Human genome
Human genome (Photo credit: Wikipedia)

This period I am following an advanced course in genomics, while at the same time starting with a minor thesis in genetics. It will be interesting to learn about the latest developments in genomics.

Last year it was 10 years ago that the human genome was maps and a huge celebration issue of nature gives insight into the many implications of the human genome project. Even now, scientists are still improving the annotation (giving a function to parts of the genome) and everyday new genes are discovered and researched.

Part of the excitement about genomics lies in the fact that genomics has become cheaper and cheaper in the previous year. The capacity to sequence doubles every five months and a decent genome can be sequenced for almost 10000 dollars.

It is waiting until the moment comes when sequencing a genome does not cost more than 1000 dollars. That is when personal genomics comes into play. Personally I do not think that having your own genome sequenced is of much interest but from a scientific point of view it is very interesting to compare different individual genomes.

Comparing multiple genomes of several individuals will give tremendous insight into the genetic part of diseases and for example studies into cancer could benefit by cheap genomics.

Some people argue that the Human Genome Project is an investment beyond rational analysis. Its costs estimate from 0.5 billion to 2 billion US dollars. I think that the Human Genome project has led the way to deciphering more genomic secrets than we would have without it.

In the future either people will call us stupid to invest so much, or they will be thankfully that we were willing to spend more money on science than on anything else.