Continuing on from my previous post, I will elaborate on some proposed components of an information-theoretic model of evolution (ITME). Here are some key points in the development of the ITME that I’ve touched on so far.
- Evolution is the spacetime of biology – without it we cannot construct an “M-Theory” – a mother-of-all theories.
- Information is the base unit of all evolutionary systems.
- Structure is the arrangement of information in at least 2 spatial dimensions.
- Complexity is the interaction between information and structure.
- Stability is complexity over time.
Intuitively, complexity is the integration of information and structure, and stability is the change in complexity over time. Since any change in the system will change its complexity score and pattern, stability represents the degree in which a system is unchanging – as Stephen J. Gould coined the term, in “stasis”. A perfectly stable system is unchanging and therefore is not actively evolving. As a system (in this case, a biological system) moves through niche space, experiencing different selective pressures and responding accordingly, its complexity metrics (score and pattern) will also change through time. The mean of this complexity score might represent some average niche, or most frequently encountered niche, for the particular organism.
Intuitively, complexity is the integration of information and structure, and stability is the change in complexity over time.
Similar to Einstein’s theory of gravity, space and time are also the base components of evolutionary systems. Information is rooted in space and the arrangement of that information (structure) changes through time. In his description of physics, Einstein understood the power of contextual levels (relativity) and how they framed our understanding of scientific systems. So how do we incorporate this same concept into an information-theoretic model? Using bits and structure instead of specific molecules (e.g., DNA) or organisms (e.g., nematodes) allows us to move through contextual levels (environments), but keep the same basic framework for assessing evolutionary trends in systems.
|Information||Base unit of structure (e.g., bit).|
|Structure||Spatial organization of information (e.g., atoms in a molecule).|
|Alphabet||Unique set of bits that make up an evolutionary system.|
|Complexity||Integration of information and structure (e.g., bits x structure).|
|Space||Base unit of spatial dimensions.|
|Time||Base unit of time.|
|Stability||Complexity over time; A unique timestamp of information and structure.|
|Environment||Boundary of the system.|
|System||Synonymous with environment.|
|Context||Contextual level of the system (e.g., chemical, biological, cultural).|
|Interactors||Give bits unique information due to spatial orientation (e.g., fields).|
What are some examples of contextual systems in evolutionary systems (ES’s)? For starters, we can look at the lowest level that might relative to ES’s – particles. Particle physics is the interface between quantum physics and chemistry. Although quantum physics might technically be a “lower” contextual level than particle physics (Newtonian mechanics), it would be difficult to construct an evolutionary system using purely quantum mechanics – mostly because we don’t really fully understand them! In the information-theoretic model of evolution, understanding an established set of rules for the system is critical to defining its base components – bits and structure. So without a formal definition of rules, we cannot work at that contextual level. For that reason, we would define the lowest working level in the information-theoretic model at the interface between chemistry and particle physics, with some allowances for quantum chemistry.
What type of systems could we construct at this chemical level – we’ll call it the quantum chemistry interface (QCI) for sake of creating a formal definition? In this system, the bits are atoms and their arrangement in space is structure. Bonds, interactions, and forcefields then become modifiers of complexity. It seems overly simplistic at first, but the simplicity is key to the capacity of the theory to expand out to higher contextual levels. But keeping focused at this level, we can begin to think about how the bits link together to form vastly different molecules with unique properties.
From only 118 known atomic elements, the total possible number of molecules that could be created is more than the total number of atoms in the observable universe. In other words, from only a very simple set of “bits” – a simple alphabet of base particles – vastly complex systems can be established. In Chemistry, the elements represent an Alphabet. Under the information-theoretic model, an alphabet is a unique set of bits that make up an evolutionary system.
In Chemistry, the elements represent an Alphabet – the unique set of bits that make up an evolutionary system.
In genetics, DNA and RNA serve as the Alphabet – a 4 to 5 letter system (ATGC in DNA and U in RNA) that defines all the properties of its systems. In proteomics, the 20 amino acids serve as its Alphabet. In tissue-scale biology, cell-types (e.g., Neurons, Astrocytes, and Glial cells in populations of neurons) serve as the Alphabet – so on and so forth. Each contextual level in the information-theoretic model has its own Alphabet, its own unique set of bits that defines the system.
It should become clear now that the information-theoretic model of evolution (ITME) has some extremely powerful constructional rule sets. Bits and structure assembled together to form complexity, complexity analyzed through time to form stability. Evolution is a process and will always be filled with moving parts. Contextual levels simply allow the observer (the researcher) to change what those moving parts are without changing their overall theoretical framework. This allows scientists to discover potential universal truths throughout evolutionary systems at different contextual levels; an end-goal in many fields of science like Physics and Chemistry that have been building contextual layers for nearly four centuries now – since Isaac Newton first peered into a telescope.
In the coming chapters I will continue to flesh out this idea and expand on the central themes of contextualizing evolutionary systems and identifying core truths shared at various contextual levels.
Follow along the entire series by following these links below:
Part 1: Developing the Information-Theoretic Model of Evolution: Towards an Information-Theoretic Model of Evolution (ITME)