We investigate an alternate hypothesis [than the MVG one] that has been suggested, but heretofore untested, which is that modularity evolves not because it conveys evolvability, but as a byproduct from selection to reduce connection costs in a network (figure 1) [9,16]. Such costs include manufacturing connections, maintaining them, the energy to transmit along them and signal delays, all of which increase as a function of con- nection length and number [9,17 –19]. The concept of connection costs is straightforward in networks with physical connections (e.g. neural networks), but costs and physical limits on the number of possible connections may also tend to limit interactions in other types of networks such as genetic and metabolic pathways. For example, adding more connections in a signalling pathway might delay the time that it takes to output a critical response; adding regulation of a gene via more transcription factors may be difficult or impossible after a certain number of proximal DNA binding sites are occupied, and increases the time and material required for genome replication and regulation; and adding more protein–protein interactions to a system may become increasingly difficult as more of the remaining surface area is taken up by other binding interactions. Future work is needed to investigate these and other hypotheses regarding costs in cellular networks. The strongest evidence that biological networks face direct selection to minimize connection costs comes from the vascular system [20] and from nervous systems, including the brain, where multiple studies suggest that the summed length of the wiring diagram has been minimized, either by reducing long connections or by optimizing the placement of neurons [9,17 –19,21 –23]. Founding [16] and modern [9] neuroscientists have hypothesized that direct selection to minimize connection costs may, as a side-effect, cause modularity. [...]
Given the impracticality of observing modularity evolve in biological systems, we follow most research on the subject by conducting experiments in computational systems with evolutionary dynamics [4,11,13]. Specifically, we use a well- studied system from the MVG investigations [13,14,27]: evolving networks to solve pattern-recognition tasks and Boolean logic tasks (§4). [...]
After 25 000 generations in an unchanging environment (L-AND-R), treatments selected to maximize performance and minimize connection costs (P&CC) produce significantly more modular networks than treatments maximizing per- formance alone (PA)
Yeah, Alon briefly mentions that line of study as well, although he doesn’t discuss it much. Personally, I think connection costs are less likely to be the main driver of biological modularity in general, for two main reasons:
If connection costs were a taut constraint, then we’d expect to see connection costs taking up a large fraction of the organism’s resources. I don’t think that’s true for most organisms most of the time (though the human brain is arguably an exception). And qualitatively, if we look at the cost of e.g. signalling molecules in a bacteria, they’re just not that expensive—mainly because they don’t need very high copy number.
Connection costs are not a robust way to produce modularity—we need a delicate balance between cost and benefit, so that neither overwhelms the other. Given how universal modularity is in biology, across so many levels of organization and basically all known organisms, it seems like a less delicate mechanism is needed to explain it.
I do find it plausible that connection cost is a major driver in some specific systems—in particular, the sanity checks pass for the human brain. But I doubt that it’s the main cause of modularity across so many different systems in biology.
There is also the suggestion that having connection costs imposes modularity:
Yeah, Alon briefly mentions that line of study as well, although he doesn’t discuss it much. Personally, I think connection costs are less likely to be the main driver of biological modularity in general, for two main reasons:
If connection costs were a taut constraint, then we’d expect to see connection costs taking up a large fraction of the organism’s resources. I don’t think that’s true for most organisms most of the time (though the human brain is arguably an exception). And qualitatively, if we look at the cost of e.g. signalling molecules in a bacteria, they’re just not that expensive—mainly because they don’t need very high copy number.
Connection costs are not a robust way to produce modularity—we need a delicate balance between cost and benefit, so that neither overwhelms the other. Given how universal modularity is in biology, across so many levels of organization and basically all known organisms, it seems like a less delicate mechanism is needed to explain it.
I do find it plausible that connection cost is a major driver in some specific systems—in particular, the sanity checks pass for the human brain. But I doubt that it’s the main cause of modularity across so many different systems in biology.