LifeOS: exploring the system that executes DNA

February 10, 2009

Networks in Complex Systems

Biological systems process matter, energy and information. Every bit that moves in the process follows a path. Those pathways form a map of the interactions between the various elements of the system. These maps describe networks.

Computers have given us the reason, as well as the tools, to understand, build and maintain networks. They have given us the memory necessary to track their evolution. They have given us the power to analyze all kinds of information and discover the universal patterns of network functionality and growth. This is an extra bonus of the information age. Besides, the explosion of information technologies and the increased efficiency they have brought to all human endeavors, besides the exponential increase in the availability of information to all, besides the awesome power we have acquired to input the global system, the most important aspect may be our realization that information processing is fundamental to living systems. The study of networks in complex systems may be a Rosetta stone that will help us decipher the code of life.

It is becoming a truism that we’re living in the era of networks. Just about anywhere we turn, we encounter one. We have the World Wide Web and the internet; we have social networks, genetic networks, and biochemical networks. These things – web pages, genes, chemicals in our cells – are nothing new. What is new is that everybody’s waking up to the fact that there is a network behind all of these systems, and we need to think about networks as a common feature of all complex systems.
–Albert-László Barabási
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Throughout this discussion we have been shifting our focus from objects to process. LifeOS is a process of motion rather than a static structure. The study of networks gives us another method for understanding what a system does.

Household Networks

A house is a basic system that provides benefits for its inhabitants. The structure and its subsystems protect the inhabitants from the elements while networks provide the energy, materials and information necessary for it to function as a household.

The walls are subsystems that support a roof that consists of subsystems that channel rain water around the structure, keeping the interior dry. Pipes and wires bring incoming essential elements like water, gas, electricity and carry away the waste.

The pipes and wires are easy to see as networks, as their blueprints clearly show their web of relationships, but there are other functions that fit the pattern, as well. Windows and doors are openings that regulate and filter incoming light and air, while allowing or preventing access by insects and such. Groceries follow channels that are not as constrained as pipes and wires, but can be diagramed, as well. First there is the network that supplies the groceries to the market place and then the pathways the goods follow once in the house. A map of these pathways is complex and dynamic. These networks interact to provide the needs for each functional element to carry out its task.

Informational Networks

Besides the material and energy, we have purely informational networks that participate in the household dynamic. Phone calls make changes in other networks, like “Pick up some blueberries on the way home from work”, or calling a plumber. Radio, TV and of course, the internet, provide information that has real effects in the household.

Essentially we are looking at this household as if it were a giant cell. There are openings in the membrane that regulate and filter intake and output from the local environment. Internal networks route elements to their respective processing stations, etc. Purely informational networks influence and control process behavior at every turn. The way that a cell’s internal networks and systems function over time, determines its success.

Network View

This is just some of the magic of using the network view to analyze a system. The flow of energy, material and information throughout the system, is directly related to its state. Blockages and congestion in this flow are fundamental to system failure. Network view can expose potential bottlenecks and disturbances before failure becomes an issue. Like when you notice the sluggish drain, and call the plumber before the toilet overflows.

Besides being an excellent way to analyze a specific process, networks are a common denominator in all complex systems. Network view offers a way to understand the life process at all levels.


Networks grow in very specific patterns. Growth is accomplished by adding nodes to existing networks, one node at a time. The nodes, their connections and carrying capacity, adapt to demands; that is, they grow to accommodate use. This pattern is observable in such diverse systems as cells, organs, social groups, memory/learning, the internet, prairie dog trails and our household example. The rules that govern network evolution in complex systems are like the rules that govern information processing in general, in that the rules apply across the board, regardless of the platform, language or medium employed.

Network Types

Basically networks come in types that are characterized by the number of links per node, degree of separation(how many hops to connect any two nodes) and their shape.
Random Networks have nodes with nearly the same number of links. Older nodes will have more links than newer ones, but variation will be small. The number of hops necessary to connect any two nodes will be proportional to the size of the network. So, large scale random networks will get slower as they grow. They also tend to be unstable when stressed by heavy loads.

If steeped in neodarwinism, one might expect biological networks to be random in their construction. However, random networks rarely appear in living systems. When a node is added, decisions are made as to how it will be connected, based on a combination of network design criteria(preferential attachment) and usage. In fact, this network design criteria, employed at the cellular level, is well ahead of our current understanding. We are still learning.

Biological systems and most manmade networks fall into two categories: scale-free and hierarchical.

Scale-free Networks are characterized by a very low number of hops to connect any two nodes, regardless of network size. The structure that results has a wide variation in the number of links per node. Some nodes collect considerably more links, becoming hubs, while others have only a few connections. Scale-free networks are much more robust than random networks. Many more nodes can be disabled without disrupting traffic. They excel in the efficient distribution of information.

Hierarchical Networks form clusters that have many internal links, and limited external links. This is the basis for modules, organs and agents. These networks are best at processing information. Hierarchical networks are characteristic of entities.

These two types of networks are analogous to conventional and relational databases, or parallel and series circuits in electronics. If you can see the yin and yang of the two, or the split brain model, you’ve got it. These two fundamental modes of operation interact to weave our reality at all levels.

Invisible Criteria

It is obvious that the formation of networks is guided by some invisible criteria. At the critical stage where a node is added to a network, called preferential attachment, decisions are made that affect the future efficiency and stability of the system. Just as in dopamine neurons, where future expectations play a role in their behavior, the growth of biological networks exhibit the same ability to plan ahead, like something “knows” what it is doing.

In human networks we know where the “intelligence” is applied. It is at this same crucial step in the process. When a node is added, humans choose how it is connected. Yet, until we learned about networks, we were powerless to predict how they would function. Biological systems have known how to manage networks all along. They have a way to apply knowledge to the growth of networks. Seems to me that is a job for our coherent fields.

Holographic Fields are similar to networks in that every node is connected to every other node, in one hop. However, the nodes aren’t connected by “lines”, as in the others. No matter nor energy is exchanged, only information. There are no pathways, rather a field that contains the potential for all pathways. It becomes a medium that information traverses in waves. Any disturbance within the field traces patterns between affected nodes. These trails are the beginnings of physical network linkages to come. From these patterns grow combinations of hierarchical modules linked seamlessly into scale-free networks that process to a targeted outcome.

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