subject: EMERGENCE - Continuous immunity, redefine the notion of symmetry [print this page] EMERGENCE - Continuous immunity, redefine the notion of symmetry
Symmetry is immunity to a possible change, immune to the change I know & understand.The different aspects of nature are really different aspects of the same thing.Duality is a simple case of two possible effects of a cause, where when an observer is measuring the effect, can only get one out of the two possible effects. The observer can only measure effect one, its probability is higher relative to effect number two, apart from the fact that the way the observer is measuring, can influence the high probability of effect number one. The outcome is that, effect number two is not even being consider.
Measurements of the amplitude (amplitude defined the variable amino acid in a highly conserved sequence) in an immune regulatory response are proportional to the change in oscillation pressure during the frequency (frequency of rotation) of each response. Oscillation in an immune response, imply repetitive variation in time, location, and concentration in the global symmetry of the system, and which often imply a change in local discrete symmetries.For an asymmetric wave (disturbance), the amplitude becomes ambiguous. The immune regulatory response is proportional to oscillation pressure (where the response is a periodical pulses/discrete in one direction).
Variable settings within a nexus(Center of Connected Nodes (CCN))
In any network system of many nexuses there are conserved parts and variable parts.The variable amino acids within a conserved region, are regarded as small fluctuations acting on the regulatory immune system and by crossing a critical point decide a system fate. Symmetry breaking plays a major role in pattern formation for fine regulation of the system. Disorder is more symmetric in the sense that small variation to it doesn't change its overall appearance, but the symmetry gets "broken".
EMERGENCE
Behaviour emerges when multiple agents operate in an environment and collectively form more complex behaviours. We need to understand the laws of collective pattern and organization and we need to think in terms of patterns "reductionism alone is not adequate as a way of understanding reality".An idea of a system composed of multi-network levels of regulation all appearing to be interconnected and interdependent is very complex to accept as true, because it is not simple to prove it. The overall is an emergence of the final output it is like the photon that emerge from an electron (it was not there to start with). Sequence sharing patterns (SSP) arethe idea that a hidden pattern is the key to how network system interact and exchange non linear information between Nexuses. And the change is based on the statistics of the different inputs and outputs of the different nexuses of a system.Often to solve a problem one must change variables. (self propagating variables arise suddenly and gradually grow beyond a certain threshold, where a new behaviour emerge from a new statistical pattern of the network in a system).
NETWORKS
Networks involve communication. The ratio of positive and negative inputs that reaches a Nexus has to attain a threshold level to send an output. If only one connection input is changed, all the others will change.Shifting Interrelationship of the different parts (regulatory proteins) are the basis for network communication and so the basis for "learning". Learning is the "hidden layer" that is located within the complex interaction and interconnection between all the Nexuses of the system. The hidden layer doesn't exist as a discrete interface or specific anatomic structure, rather it resides within the multiple connections.
Hidden assumption entail that, one cannot predict complex adaptive systems (CAS) outcomes. CAS can settle into any of many physiological or pathological equilibrium states (attractors). Ref. 2007 Ezerzer et al. recognizing that a dynamic CAS has many possible states of equilibria, one should search for ways in redirecting a CAS to a physiological state of equilibria. The basic evidence of CAS theory is that there are hidden directions to a behavior.CAS suggests that it is highly improbable to work out how a complex system will behave (unknown unknowns "there are things we do not know we don't know"). The question then is, what use CAS might have, if it has no way to predict the outcome of the many different events in a system. Once we acknowledge the fact that CAS cannot predict in what way the system will respond, we can start to focus on ways that can control a CAS.
Tools of CAS (identifying the rules of CAS)
Networks - (PPI: Protein-Protein Interaction and SSP: Sequence Sharing Patterns) PPI = visible connectivity and SSP = hidden connectivity (SSP is a recursive network hidden in the PPI network of Disease Associated Proteins (DAPs)).
Nonlinearity - What makes the nonlinearity in a CAS? Linear relationships are easy to see (PPI) Vs. Nonlinear relationships are not easy to see (SSP). SSP peptides makes so that CAS are nonlinear systems, it creates a new behavior, in a set of DAP, that do not occur in the linear PPI (DAP & Disease Non Associated Protein (DNAP)) network. PPI and SSP networks are inherent to the CAS emergence networks of proteins interaction in a cause and effect manner.
Modularity - Modules are the building blocks of a CAS- Clusters of protein networks organized into Modules many Local Modules (LM) into Global Modules (GM), and many GMs into CAS. Modules interact with other modules in Local and Global Clusters. Modularity reduces energy expenditure in a CAS. Modular upgradability in a CAS allows the system to explore new knowledge that could improve on existing knowledge.Modular Network Topology (MNT) defines proteins boundaries, specifying the sets of proteins associated in a cluster. MNT limit the amount of information that clusters manage in a CAS. But in CAS boundaries can shift; "shifting limits" in response to shared information, SSP peptides, between the different DAP, and creating change, behavior, in the system.
Hierarchical modularity - Implies the one to many ratio interaction and not many to many ratio interaction like in the case of CAS. So hierarchical modularity dose not apply for CAS.
Attractors (A CAS can reach in many different ways any one of the attractor states- Physiological Equilibrium State (PhES) or Pathological Equilibrium State (PaES). Is it a question we should ask ourselves? What is the mechanism of events? CAS can use many ways in reaching an attractor (physiological or pathological). The question should be, what are the sets of transformation (could be many proteins: DAPs) that caused the shifting of the limits (asymmetrical) to cause a swing in the system attractors? Different sets of proteins can lid to the same attractor (biological outcome), so the attractor (physiological or pathological) does not reflect the set of proteins from which the attractor has emerged.
Ways for "controlling" (regulating) CAS
Changing variables- The question is- what variables to change? And how they should be changed? -Using SSP peptides (in a "trail and error" way) to change variables for the system to settle in one of the many PyES. (The idea is to guide a CAS, using SSP peptides, to settle into on of the many PhES). It question the way SSP peptides (external input) set about influencing CAS changes. Changing variables has an infinite number of possibilities. By identifying the DAP, known as the most influential variables in a specific disease, we are creating a finite number of combinatorial possibilities of changing variables. The idea is to identify sequence patterns found to be shared by as many DAPs as possible. (This pattern should influence a go between protein or proteins, and so shifting or flips the equilibrium state of the system).
The trajectory of CAS towards an attractor does not have any special constraints, variant sets, except for remaining in the physiological attractor. The same is for the pathological attractor once it is there. It requires energy from outside the system to shift from one attractor to the other (thermodynamic losses or gains). A physiological attractor is a symmetrical function, that is, it does not change under some transformation, and the system appears exactly the same after the operation. A pathological attractor is an asymmetrical function. That is, under a series of transformation, chronic, the system may shift from physiological (symmetrical continued immunity) to pathological (asymmetrical loss immunity).
CAS can have either a Variation (changes within the type reaction (local changes)) or/and a Diversification (different type of reaction expending to other modules (Global changes)).
Discussion
CAS is in the in-between attractors?, once CAS lose the in-between attractors and it is found to be in one of the attractor domains then it is not a simple process, to change its ways, to shift over to the other attractor domain.???
The SSPpeptides derived from different proteins in a microenvironment, from the DAPs, can identify the junctions, the go between proteins (DAPs & DNAPs) or crossroads proteins, of the different possibilities that the system can chose from to reach equilibrium. The go-between proteins are the many possible branching, routes, that the system can use to respond to a cause.CAS has a Markov Property where the upcoming state of the system is not dependent on past events; it depends on only the present state of the system.
"No master plan, just sequence of events that leads to new association arising out of the multitude of nexus. Collectively generating emerging network of complex biological behaviour output"
