A study of related molecular interactions found a modular collection of interconnected regulatory networks that could be used for diagnosis, characterisation and possibly treatment of disease. An inflammation network was considered to be instrumental in healing processes and a common disease signature was identified. The networks form an effective cognitive system.
Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient’s autoregulatory capacity? – Alyssa Goldman et al
Main themes
- A health/disease continuum characterised by network states
- Modularity of sub-networks
- Communication of biological information across multi-scale networks
- Stability in response to a perturbation
- A dedicated inflammatory network
- Inflammation as an auto-regulatory healing response
- Existence of a common disease state signature – the diseasome
The authors found organised networks at every scale of biological reality from the molecular up to the organism level, whose correct functioning is essential to maintain healthy homeostasis. These are largely self-regulatory systems which are stable to perturbations from both internal and external sources. Communication by various means exists within and between networks, the system as a whole forming an adaptive learning system responsible for maintaining orderly operations within the body.
The Core Diseasome.
Disease, within this formulation, is dysfunctional regulation:
“Disease occurs when an individual’s autoregulatory abilities are compromised. We encounter this scenario when accumulated stresses overpower the autoregulatory abilities, thereby impinging tissue robustness. These persistent perturbations can manifest as disease over time“
Suthram et al identified 4,620 functional modules in the human protein network and found that a subset of 59 of them were dysregulated in at least half the diseases studied thereby representing a common disease state signature. “Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs,“
Janjić and Pržulj found a subnetwork of the human Protein-Protein Interaction network whose ‘topology is the key to disease formation‘. The network was rich in ‘genes associated with disease and known drug targets‘. They labelled this network the Core Diseasome. “We show that the topology of the Core Diseasome is unique in the human PPI network suggesting that it may be the wiring of this network that governs the mutagenesis that leads to disease“
So a common regulatory state is identified that is associated with many known diseases and is responsive to many existing drugs. It therefore makes sense to incorporate knowledge of this state into characterisation and diagnosis of disease.
Inflammation as a healing response: “Bioregulatory systems medicine embraces inflammation as an extension of the autoregulatory capacity of the body capable of restoring a healthy tissue’s functional state. Inflammatory mechanisms are switched on by various stressors that aim to eliminate initial stressors and adjust to a changed environment by establishing new homeostatic set-points“
The inflammatory response is often mistaken for the disease itself but is regarded here as orchestrated by the innate healing system of the body as an attempt to restore homeostasis. Inflammatory pathways operate as a communication system between a cell and its own micro-environment and from there they can bubble up to the higher levels of the system.
The inflammatory process is initiated by the body and is in general not dangerous as it can be resolved by the body and it is only chronic inflammation that will eventually lead to disease. Treatment should focus not upon alleviating symptoms by suppressing inflammation but rather supporting the process in a controlled manner in order to stimulate resolution.
Biological information. Central to the bio-regulatory model is the requirement that there exist communication channels within a network and between networks. Communication takes many forms and can be found at every scale of biology and, importantly, exists between networks at different scales so that the molecular can meaningfully impact the cellular and vice-versa.
Various mechanisms of communication are proposed:
- Bio-chemical and bio-physical signals
- Non-coding RNA
- Extracellular matrix characterised as an information highway
- Prion transfer
- Genomic dark matter
These seem rather vague and any mention of dark matter is a clearly way of trying to avoid saying that “We really have no idea!”. The central problem is that information concerning the regulatory state of a cell, say, must eventually be translated to information concerning a molecular reaction and then back again to stimulate a network response somewhere else. The molecule and the network are worlds apart in both the scale and the nature of the information that each requires.
From the paper:
“Participants believed that these molecular machineries support the notion that a cell’s interaction with its immediate environment is in fact a coherent pattern, and is not a disordered or otherwise chaotic flow of molecules as some might assume. This coherence is thought to be sustained in part by a computational matrix that directs action within and across molecular networks“
That’s better! We still don’t know what format this information is in or even how to represent a ‘network state’ but here is an acknowledgment that some sort of coding scheme (computational matrix) is necessary and presumably some physical (or otherwise) apparatus to effect that translation.
What is being described here is a distributed organisational system albeit on the cellular scale. The task is to take multiple inputs and construct a mapping that creates multiple outputs, where there is no single dependence of any output upon any single input; a truly multi-causal system. Not only this but the communications system must somehow translate between different types of information often held at different physical scales; physical changes at the molecular level must somehow filter up to the level of an organ and back down again.
An engineering control system with non linear coupling is what we are looking at. These are well known to engineers and theoretical physicists and can be simulated by mathematical equations, wiring diagrams, computer software or even sometimes buckets of water,
The net result is the same whatever the implementation, so the behaviour of the system can be studied independently of the physical substance that it is made of. Such systems show behaviour that is typically complex but usually displaying several characteristic patterns:
- Amplification: A small input can sometimes result in a large output
- Stability: The system can return to a set point after a large disturbance
- Hormesis: A non-linear dose-response relationship
- Hysteresis: Time lag between cause and effect
- Synchronisation: Networks can sometimes respond to rhythmic input by resonant entrainment , resulting in rhythmic behaviour themselves
- Pattern recognition: The response to an input can be different if that input has been seen before
- Adaptation: The system can learn to self-stabilise when subjected to repeated insults
- Attractor dynamics: Certain states are preferred and perturbations of the system quickly return to one of these states
- Dynamic stability: A state as described here is not a static but a dynamic configuration, a stereotypical pattern of behaviour
Controllability and Observability: Two key concepts in control systems engineering are controllability and observability (Wikipedia). Roughly speaking a system is said to be observable relative to a set of outputs if the entire internal network state can be inferred from those observations and controllable if any desired network state can be achieved by manipulation of the inputs available.
Now clearly a system that is not observable cannot be used for diagnosis as ignorance of only part of such a network means ignorance of the whole network. Similarly, any network that is not controllable is untreatable by definition.
Adaptive cognition. The bio-regulatory systems take as input, information from the external world and from other internal networks. They interpret that information, retain it and respond to it as a network. The network is now permanently changed and any new input will necessarily be interpreted according to this new configuration.
The network thus has a memory, it has adapted and future responses will reflect that adaptation somehow.
In the case of alcohol for example, initial exposure to small amounts can have serious consequences but adaptation soon dampens the response to minimise immediate damage. In the case of allergies however, the response is enhanced in future exposures but can be trained out of the system (Energy Medicine – James Oschman).
Similarly, responses to psychological stress can be strengthening or weakening and this can depend upon whether they are experienced earlier or later in life, so part of who we are consists of how cultural practices are assimilated and stored within the system.
The regulatory systems then are configured to a large degree by what happens to them during their lifetime, they consist in large part, of information from outside of themselves (food, diurnal rhythm, physical exercise, disease..) that has been interpreted as meaning and stored in a network configuration.
This is otherwise known as ‘cognition’.
Naturopathy. The relationship to naturopathy is now clear The regulatory systems form an interconnected and ‘intelligent’ healing system, the nature and complexity of which tend to discourage intervention, as being unpredictable, but to encourage enablement, that is to say the removal of impediments to the natural functioning of the networks.
“The complexity of a systems approach challenges common reductionist thinking, and paves the way for medicine that works with rather than against the inherent interconnectivity of biological organization.“
“Bioregulatory systems medicine embraces this interconnectivity among networks as the global autoregulatory network, and posits that the state of an individual’s autoregulatory network is a key determinant and indicator of patient health“
‘Terrain theory‘ is now being proposed as an alternative to so-called ‘germ theory’ but with no clear picture of what the terrain actually looks like. Whatever description eventually emerges will need to include the ideas outlined above or we are just left with a herbalists check list: “This is good for that and this other may cure something else..”,
The ‘terrain’ is an intelligent system of biological networks and all interventions must be considered within this framework.
Psychosomatic and placebo effects. Disease is characterised here as a network state and such a state is described as having cognitive abilities, but cognition also happens in the brain and if we believe neuro-scientists is associated with molecular networks of its own, namely neural networks.
Now if there exists some common format for cognition itself then we have a possible mechanism for sophisticated communication between mental and somatic states, in other words, thoughts and health are using a similar language structure which is that of a network state.
Related pages:
References:
Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient’s autoregulatory capacity? – Alyssa Goldman et al
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541032/pdf/fphys-06-00225.pdf
The Core Diseasome – Vuk Janjić , Nataša Pržulj
https://pubmed.ncbi.nlm.nih.gov/22820726/
Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets – Suthram et al
https://pubmed.ncbi.nlm.nih.gov/20140234/
Hormesis in health and chronic diseases – Xin Li, Tingting Yang, Zheng Sun
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6875627/pdf/nihms-1538175.pdf
Wikipedia on Controllability
https://en.wikipedia.org/wiki/Controllability
A Broader Perspective about Organization and Coherence in Biological Systems
Author: Martin Robert
https://arxiv.org/ftp/arxiv/papers/1212/1212.0334.pdf
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