Scale free networks
The evident surprise of many biologists when confronted with the unexpected nophenotype knockouts stems from, I believe, a profound lack of appreciation of non-linear effects in biochemical systems. It is ironic that standard wall charts of biochemical
reactions show hundreds of coupled reaction working together in networks, while
graduate students are tacitly encouraged to think in terms of linear cause and effect.
Linear cause-and-effect thinking roots in ancient Greek Philosophy, was adopted by
nineteenths century European scholars, and is still dominating most fields of science,
including biology. We cannot understand genetic redundancy and biological robustness
in linear terms of single causality, where A causes B causes C causes D causes E. Rather, biological systems are designed as redundant scale-free networks.
In a scale-free network the distribution of node linkage follows a power law, in that it contains many nodes with a low number of links, few nodes with many links and very few nodes with a high number of links.
The internet is an example of a scale-free robust network: the major
part of the websites makes only a few links, less make an intermediate number of links,
whereas a minor part makes the majority of links. Usually hundreds of routers routinely
malfunction on the Internet at any moment, but the network rarely suffers major
disruptions. As many as 80 percent of randomly selected Internet routers can fail and the
remaining ones will still form a compact cluster in which there will still be a path
between any two nodes [23]. Individual links are not essential for letting the system
function as a whole. We rarely notice the consequences of the errors that routinely occur
in our cells, because proteins operate in scale-free networks.
The protein networks found
in biology are the result of hundreds of cooperating genes, which ensure that would one
of them become inactivated by a mutation, essential pathways are not shut down
immediately. The scale-free networks of biology have the ability to by-pass the functions
of individual components of those networks and due to this buffering capacity provide
robustness to organisms. Scale-free networks govern intracellular signaling and
homeostasis. They also govern and fine-tune gene expression, regulate the maintenance
of genomes and provide regulatory feedback on gene expression. Biological systems rely
on scale-free networks that can incorporate small failures in order to withstand larger
failures. Natural selection does not act on the individual nodes of a scale-free network
and this is why we observe genetic redundancy. Genetic redundancy not associated with
genetic duplications provides independent evidence for a Grand Omnipotent Designer –
the great engineer that constructed multipurpose genomes with a high level of buffering
capacity to make sure life could propagate in time.
--------
Quotation from Genetic Redundancy
The Ultimate Evidence of the Design of Life
By Peter Borger
---------
Thanks a million supersport. This idea is absolutely brilliant.
The evident surprise of many biologists when confronted with the unexpected nophenotype knockouts stems from, I believe, a profound lack of appreciation of non-linear effects in biochemical systems. It is ironic that standard wall charts of biochemical
reactions show hundreds of coupled reaction working together in networks, while
graduate students are tacitly encouraged to think in terms of linear cause and effect.
Linear cause-and-effect thinking roots in ancient Greek Philosophy, was adopted by
nineteenths century European scholars, and is still dominating most fields of science,
including biology. We cannot understand genetic redundancy and biological robustness
in linear terms of single causality, where A causes B causes C causes D causes E. Rather, biological systems are designed as redundant scale-free networks.
In a scale-free network the distribution of node linkage follows a power law, in that it contains many nodes with a low number of links, few nodes with many links and very few nodes with a high number of links.
The internet is an example of a scale-free robust network: the major
part of the websites makes only a few links, less make an intermediate number of links,
whereas a minor part makes the majority of links. Usually hundreds of routers routinely
malfunction on the Internet at any moment, but the network rarely suffers major
disruptions. As many as 80 percent of randomly selected Internet routers can fail and the
remaining ones will still form a compact cluster in which there will still be a path
between any two nodes [23]. Individual links are not essential for letting the system
function as a whole. We rarely notice the consequences of the errors that routinely occur
in our cells, because proteins operate in scale-free networks.
The protein networks found
in biology are the result of hundreds of cooperating genes, which ensure that would one
of them become inactivated by a mutation, essential pathways are not shut down
immediately. The scale-free networks of biology have the ability to by-pass the functions
of individual components of those networks and due to this buffering capacity provide
robustness to organisms. Scale-free networks govern intracellular signaling and
homeostasis. They also govern and fine-tune gene expression, regulate the maintenance
of genomes and provide regulatory feedback on gene expression. Biological systems rely
on scale-free networks that can incorporate small failures in order to withstand larger
failures. Natural selection does not act on the individual nodes of a scale-free network
and this is why we observe genetic redundancy. Genetic redundancy not associated with
genetic duplications provides independent evidence for a Grand Omnipotent Designer –
the great engineer that constructed multipurpose genomes with a high level of buffering
capacity to make sure life could propagate in time.
--------
Quotation from Genetic Redundancy
The Ultimate Evidence of the Design of Life
By Peter Borger
---------
Thanks a million supersport. This idea is absolutely brilliant.
