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I'm a master student working on networks analysis in general. A network is something that has nodes and there are links between the nodes. Nodes and links could have attributes. An evolving network is one that changes overtime (new nodes and links are added..etc). An example of that is Facebook. Nodes are users and links represent the friendship relationship. Users have attributes (gender, age, etc.). A Facebook network as you know is an example of a social network.

The issue is that so many people studied traditional evolving networks like social networks, the web, or transportation networks. Currently I'm looking for novel examples of evolving networks to study them. So I thought there might be some examples in biology that could represent some kind of an evolving network.

So my question: Can you give me examples in biology for evolving networks? I'm aware of metabolic networks, but that is also heavily studied, I need something else.

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I don't know if you can call them networks : Ecological relationships between organisms –  biogirl May 27 at 15:49

5 Answers 5

The following does not answer the question! It only gives some ideas of where I personnaly found some work involving network analysis in biology.

Most of the networks I've heard about in biology concern

  • network of species interactions
  • network of individual interactions within a population
  • network of subpopulation interactions within a metapopulation
  • neuronal network
  • anatomy network (blood vessels, skelleton, etc..)
  • network of metabolic pathways.

I give you below a bunch of article title that discuss networks in biology. I'll probably add the links later $ \ddot\smile$ but if you just copy-paste these titles on scholar.google or on WebOfKnowledge you will easily find the articles.

  • Evolution of a vertebrate social decision-making network
  • climate change, human impacts coral reefs
  • architecture of mutualistic increase biodiviversity
  • variation in migration propensity among individuals maintained by landscape structure
  • skeleton and fractal scaling in complex networks
  • Habitat modification alters the structure of tropical host-parasitoid food webs

This last article concerns the impact of various environment on the network of interactions between host and parasites. Fairly interesting for a biologist.

I would expect that most evolving networks in biology concerns the impact of environmental changes on the network of species, subpopulations and species interactions. You may have expected some stuff that are more related to evolutionary biology. I think most of this work concers evolutionary ecology and evolutionary processes in metapopulations. Here is a very interesting and very theoretical article (by stuart kauffmann) on evolutionary biology though that has nothing to do with ecology.

  • antichaos and adaptation
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1-actually what I need is to construct a dataset of an evolving network (which is recorded overtime) so I can study the data. My university has a great biology department, so is it possible to get the data of something that you mentioned? Or are the examples theoritical? I'm not good in biology at all, that's why I found them a bit difficult to understand. 2- I'm only concerned in evolving networks. So please can you remove the examples that don't concern evolving networks? –  Jack Twain May 27 at 19:42
    
Re-reading the only article I considered to offer evolving network I realize it does actually not give an evolving networks. It is only a comparison of networks between different ecosystems. Hum…so I don't know about any article that report the analysis of an evolving network in biology. There probably exist on though. I guess I won't delete my answer because it gives an idea of where we can find networks in biology which is always informative. –  Remi.b May 27 at 20:54
    
It is going to be even harder if you have to find a good dataset I guess. Once you'll find an article that presents an evolving network, you may always ask the authors if you can use their data for your project and of course don't forget to tell them you will cite their work. –  Remi.b May 27 at 20:58

I find this a very interesting question as I personally work with networks very frequently!

Based on your definition of evolving networks, it is feasible to consider protein-protein interaction networks as evolving since over time more and more interactions between different proteins are discovered and more novel proteins (nodes) are tested for their interaction with other proteins.

If this meets your definition, then you can use PP interaction databases such as BioGRID and look at the different releases and see what proteins are added over time and what new interactions have been added between different releases. Here is the archives page for BioGRID (http://thebiogrid.org/download.php). BioGRID has also a Cytoscape plugin which you can use, although I have never worked with dynamic networks in Cytoscape so perhaps have a look at Pavlopoulos et al 2008 BioData Min, which is a survey of visualisation tools for biological network analysis.

Hope this helps!

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mmhh interesting. And nice that the data are freely available with your example. But analyzing this evolving network would tell more about the history of discovery of new proteins than about the real biological underlying mechanisms. I am not sure though. It maight tell about how close we are to have a good definition of how the network of protein interactions looks like I guess. This data might indeed interest the OP. –  Remi.b May 27 at 21:01
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Yes, this dataset analysed in a dynamic sense can tell us how close we are to a good estimation of global PP interactions in a given organism, given the techniques/equipments used and what the progression rate of finding PP interactions has been over time, but non the less it is more of a historical account as opposed to a biological underlying. The data should more appropriately be correlated to equipments/techniques, although there are many other hidden variables such as the number of people that engaged in PP interaction discovery between the data releases etc. –  Bez May 27 at 21:19

Others have nicely summarized to different types of biological networks and how they can evolve. I would like to make a fine distinction between evolution and dynamic adaptation and add some comments about relative evolvabilities of different biological networks.

Given an architecture, a network can respond dynamically to different inputs. The preferred paths may change and the output characteristics can vary. Example cases include the effect of environment on metabolism. Personally, I would not consider this as network evolution because the architecture remains the same. Network evolution refers to rewiring or addition of new connections.

It would be apt to classify the biological networks like this:

  • molecular networks

    • Gene regulatory networks (GRN)
    • Molecular (protein-protein) interaction networks (PPI)
    • Cell signaling networks
  • cellular networks

    • Neural networks
    • Biofilms (?)
  • organismal networks

    • Ecological networks (food webs etc)

There are several ways in which a network can evolve: Edge addition, edge deletion, edge modification, node addition, node deletion and node duplication.

In molecular networks edge modification happens when the chemical interaction is affected. For e.g. in a gene regulatory network a promoter-Transcription Factor (TF) interaction can change if there is a mutation in promoter sequence or in the DNA binding domain of TF. Similar situation exists in the cases of PPI and ligand-receptor interactions (Though concept of mutation doesn't apply to ligands). Gene (node) duplications are also common mechanism of evolution of GRN. Gene deletions/inactivations also change the network and in some cases result in complex diseases. Metabolic networks evolve if an enzyme mutates and a new pathway is created. Usually node additions rarely happen; node duplications and edge modifications are more common in molecular networks.

In neural networks the plasticity is higher than molecular networks. There is a basic architecture that is essential to support life but the connections keep changing, nonetheless, in the non-core regions. Since active neurogenesis stops after a certain point, we can assume that number of nodes remains unchanged but edge addition (new synapse), edge deletion (synaptic pruning) and edge modification (long term potentiation/depression) is quite rampant and widespread. In neurodegenerative diseases neurons die and therefore nodes are lost.

Ecological networks are affected by environment and geological barriers. So there are several subnetworks (ecosystems) that are weakly or strongly connected with each other. Introduction of foreign species (node addition) causes immediate perturbation of the local network architecture. Long term effects arise because of extinction (node deletions), speciation (node evolution leading to edge modification i.e feeding habits etc).

It is also to be noted that in all the cases the effect of a change on a network depends on which node/edge is affected; hubs are under tight scrutiny and are protected from perturbations because any dysregulation can lead to network collapse. Some networks also follow the preferential attachment behavior (which appears contradictory to hub preservation as a mechanism). I guess that molecular networks do not employ preferential attachment whereas ecological networks do.

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Not sure if this meets your definition of a network, but there are several kinase cascades which transmit signals. For example, the basic MAPK cascade has evolved to serve different roles via the ERK, JNK, and p38 cascades

The evolution of the MAP kinase pathways: coduplication of interacting proteins leads to new signaling cascades. Caffrey et al., Journal of molecular evolution, 1997. Link here

Ancient signals: comparative genomics of plant MAPK and MAPKK gene families. Link here. Manning et al., Trends in Biochemical Sciences, 2002.

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Can you please add some references? –  Chris May 27 at 17:52
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added a couple of references -- reading wikipedia articles on MAPK would be very useful as well; these proteins are extremely important for a very wide range of cellular function –  user635185 May 28 at 15:41

Ants, slime molds, and brains.

Ants and slime molds use simple rules to generate pretty good transportation networks in an emergent way, and brains wire and rewire themselves constantly(adding/removing edges, but not usually nodes).

Evolutionary networks, metabolic networks, and ecological networks are much harder to get concrete data sets from, because of the time and space scales involved. (I mean the time scale over which node addition/deletion and edge modification takes place, which is in evolutionary time)

Slime mold shapes, ant paths, and neuron traces are all reproducible in the lab, however. Antlike algorithms are pretty common, so you could run one of those and then examine the graph it puts out as it works. Those aren't precisely biological networks, but certainly biomimetic. If the subtleties are likely to be important, you might have to physically get some ants.

If you're looking for premade databases of slime mold or ant experiments, you might try contacting the authors of the PNAS paper, but I don't think anyone is looking to model slime mold behavior as a graph. You could be the first!

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Could you provide some databases and references which have the evolution of these networks recorded overtime? Also neuronal rewiring/wiring is a static but dynamic network but it does not evolve in a sense that the number of neurons hence the nodes increases over time after birth as stated in WYSIWYG response but as stated, it can be only used as a deconstructive evolving networks in neurodegenerative conditions. Still some references would be great! –  Bez Jun 25 at 17:29
    
I'm not sure that the databases that you're asking for exist. They would definitely be useful for studies like this, though. I added some references to people working in relevant fields and some biomimetic approaches, which may work for you. –  Resonating Jun 25 at 18:24

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