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Hence, the successful novel and fit complex agent, organism or product can become an attractor, drawing more connections and copies from it than to it [ 10 ]. Sometimes successful novel and fit complex agents emerge from the merger of two existing agents, to form a new innovative and more competitive agent, or product, or organism.

Once successful, innovative agents replicate and diversify fast. So innovation plays an important role in the continual evolution of a complex system. Innovations can only become realized on the foundation of already existing, solidified and successful previous innovations [ 19 ].

Hence, as mentioned above, complex systems are organized in layers where each layer establishes a solid foundation for the next-order layer to be able to evolve. Another essential and related principle is information transfer. Information is constantly flowing, commonly compressed, decompressed and translated. Transmitters broadcast information, and then sensors intercept it.

Agents in complex systems not only have the ability to passively listen and adapt to their environment, but can also communicate with the environment and change the environment to match their need. Sensors pass information about the state of the environment into the internal central processing centres. Before information is passed to such centres, the signal can be amplified and filtered. In the processing centres, classifiers use the information intelligently, learning from experiences to make optimal decisions about responding and adapting to the state of the environment the next time they are exposed to a previously experienced state.

Hence, these classifiers use memory to determine the appropriate future response of the agent. Often this response is simply turning on or off a switch. Sensors, and other components that pass information, implement such switches as well as filters and amplifiers to convert noisy information from the environment to valuable and useful messages, often through the process of discretization or digitization. Tagging, symbolizing, grouping and classifying signals are ways to abstract many similar objects and observations related to forms from the environment into abstract simplified representations.

Groups and classes are labelled, converted from their physical reality to symbols encoded into messages. These symbols make it easier for the central processing unit to process information from the environment, and to compute the appropriate response, which involves transmitting information to other complex agents.

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To compute the right response, internal processing centres use learning, memory and adaptation. The ability to adapt to new environments is critical for the survival of the complex agent living in the complex environment. Robustness to fluctuations and changes in the environment is required for overall fitness and viability [ 24 ]. However, a balance between rigidity, robustness and tolerance to change versus flexibility to change is required for providing the necessary level of plasticity for proper adaptation [ 25 ].

When learning is successful, responses are commonly automated. Automation is also needed for efficient production.

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Efficient and sophisticated mechanisms are in place to manufacture many almost exact replicas of complex agents and their parts. This allows the cycle of birth—life—death to continue, and for the complex system type to continually proliferate. The birth—life—death concept is related to the observation that complex systems and their parts are dynamically replaced by new parts, while global patterns of the entire complex system and ecosystem remain. For example, proteins in a cell continually turn over, water molecules in a river are not the same but the river stays in constant flow, cars on a highway keep passing, blood cells travel through blood vessels, and people commute back and forth to and from work in and out of a big city; these are only some examples.

In some of those cases, these complex agents, or their parts, circulate. This is the case for blood cells, or the people that commute to work, while in other cases the flowing complex agents, or their parts, are completely replaced every time. Hence, complex systems have elaborate and efficient transportation systems that permit the transfer of resources and agents to remote locations quickly and efficiently.

An Introduction to Complex Systems Society, Ecology, and Nonlinear Dynamics

Such transportation systems are commonly organized in a tree-like hierarchical structure, where the leaves of the tree, the terminal locations on the tree-like system, often have a unique address encoded in a string of symbols. The hierarchical structure of transportation systems is common in complex systems.

To move around, locomotion is necessary. Locomotion is the ability of complex agents to move about in their complex environment.

Economic systems rely on planes, ships and trucks to transfer goods and workers from one unique terminal address to another address. Botanic plants lack the ability to move, and this handicap is compensated with an amazing ability to use solar energy, capacity to extract nutrients from the ground, and capability to pollinate and reproduce effectively without the need to travel. Plants and other complex natural systems have seeds that contain compressed information that can be used to create completely new copies of the same complex agents. Such seeds often have mechanisms to travel and diffuse to reach their target for optimal fertilization.

They are generated in many copies where each copy is slightly different, and where only a few will be selected to pollinate the next generation. Notable barriers are present to protect complex agents from other agents and the outside.

These containers, or modules, hide internals from exposed externals. The externals have an interface, facilitating the ability to communicate with the environment and other systems, using standard protocols, symbols and flags. Related to this is the plug-and-play design principle that allows reusability and generality.

This principle permits complex systems to work together to form higher-order systems. This modularity creates hierarchies. Interacting complex systems have the ability to switch between individual behaviour and behaviour once in a pack. When in a pack, complex systems often form distinct geometrical shapes. Shapes in complex systems commonly tessellate, forming elaborate mosaics [ 26 ]. Polymorphic complex systems in a pack behave randomly in parallel but often display amazing synchronicity.

Synchronicity can be achieved through governance, for example, by a conductor who signals to an orchestra, but often synchronicity does not require governance in complex systems. Randomness and noise are required for such emergent behaviour. Noise is also required for other aspects of dynamical behaviour that supports complexity and evolution. Noise is a mechanism needed for overcoming being stuck in an evolutionary minimum state.

Randomness and noise result in a constant search for homeostasis, but complex systems never settle at a steady state forever [ 27 ]. Complex systems continually grow, improve in fitness and increase in complexity because their environment is constantly changing in that direction [ 28 ].


Introduction to Complex Systems, Sustainability and Innovation

Phase transitions happen in short time periods where a system, being in a rather stable state, goes through one small change that induces many changes, turning the system into another new quasi-stable state [ 10 ]. Finding an improved fitness state is a design principle directly related to efficiency and energy utilization.

While most processes in complex systems use energy, and where complex agents compete for energy resources, the systems' utilization of energy is more concerned with overall fitness and less with energy conservation and energy efficiency [ 29 ]. This is one of many concepts that makes complex systems different from the typical systems that are studied in physics. However, energy conservation and efficiency can help complex systems to better compete.

It is interesting that often dead organisms become the energy resource for other organisms, while the most decomposed organic material, crude oil, serves as the major energy source for the initial phase of the technological evolution we see today. Most complex systems typically produce waste; in balanced ecosystems, the waste from one complex system is a resource for another.

However, technological man-made complex systems produce waste that is not well recycled.

Related to this are feedback loops which are important dynamical structures that set the creation of complex systems in motion. The primordial metabolic soup was made of simple enzymes forming competing feedback loops [ 18 ]. Competition involves taking action in markets, where trade makes two or more complex systems winners.

Successful trade requires diversity of products and specialization of services. Winners in trade are often the innovators, or the best listeners to innovation. Trade results in cooperation, which can develop into symbiosis: the codependency of two separate complex systems on each other in order to coexist.

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Unidirectional symbiosis is parasitism. Parasitic complex agents use the success of their hosts for their own survival needs. Successful complex agents must learn how to self-repair and fight parasites, while parasites engage in a game of creative evasion strategies. Parasites sometimes kill their hosts, but not before they replicate and have their copies jump to other hosts, so they can spread. All the concepts listed above briefly introduce some of the design principles of complex systems with some hinted relationships between them. But more detailed explanations are needed to describe all of these concepts with less ambiguity.

In addition, specific examples are required to illustrate how these concepts take shape in real-world natural and technological systems. Such detailed descriptions are beyond the scope of this review; here, however, we are concerned with thinking about how some of those general observations about complex systems apply to human cells and how such a perspective can inform systems biology. The human cell is a complicated living natural machine. Cells that together compose our bodies are a prototypical example of a natural complex system that was evolved and optimized over billions of years.

What partially makes human cells a typical complex system is that they are made of many different types of components with many copies of the same components, all working together, interacting in concert and in parallel to form a high-order functional entity that is a part of an organism. We are made of approximately 50 trillion cells. Almost all these cells contain the same genetic code which is made of long DNA molecules that are strings that hold the template and symbolic instructions that are needed to make an entire organism.

Information about how to construct a complete organism is well compressed in the nuclei of human cells. Although the DNA in all our cells is the same, the approximately different cell types constituting our body are markedly different from one another. This is because within each cell type, different sets of genes are expressed.

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This differential expression of genes is the result of the different extracellular signals that instruct cells how to behave. Cells receive extracellular signals from other cells telling them which genes to express, and in turn, what proteins to make and ultimately how to behave; which cell type they should become. Cells can form elaborate structures and become specialized due to such cell—cell communication protocols that result from either cell—matrix interactions, or from paracrine or endocrine signals coming from other cells carried by small molecules that can pass through the cell membrane, or bind to receptors at the cell surface.

These are the complex system sensors.