Attempting Transdisciplinary Research

Over recent years, the field of “systems biology” has been emerging.  It is throwing light on areas that have long been a mystery.  But how should systems biology develop into a mature discipline?

Slippery Biology

Biology is often described as an empirical science with limitless detail, but every instantiation of experiment, observation, and classification is closer akin to an island embedded within an archipelago of unifying theory and commonalities. The most lasting innovations in biology are those that bridge the gap between (seemingly) disparate phenomena, crossing the threshold into new paradigms of insight and synthesis. The present vista of possibilities for advances in mathematical biology extends outward e.g protein structure to metabolic pathways to quorum sensing to population dynamics.  Princeton University. 

According to this viewpoint, complex mathematics, big data and machine learning are seen as the solution to ‘the messy problem that is biology’.

But there is also the potential for an additional approach.  This would be to make use of qualitative research approaches.  For qualitative research looks at words rather than numbers, and scientific research contains as many words as numbers – particularly in the field of biology.

This makes use of an existing methodology for seeking and identifying patterns in test.

“Developing sets of patterns requires working from practice back up to theory. Individual case studies gathered together will show commonalities, making it possible to describe and refine patterns.  Finding patterns involves looking over the recent past, sorting out which aspects of the work being reviewed are both useful and reusable, and devleoping a model to describe how those patterns fit together.” (J Governor 2009).

“When a pattern recurs in different systems which bear no obvious relationship to one another, we must suspect a common causative principle, one which can be understood in the most general terms, without reference to the specifics of this or that case. J Satinover 2001.

A Mixed Inductive and Deductive Approach

Qualitative research is inductive rather than deductive.  Rather than simply seeking to use data to confirm existing theories via hypothesis, concepts are developed inductively from the data and raised to a higher level of abstractions and their inter-relationships then traced out.

Qualitative research does not sit in opposition to quantitative research, rather the two research types can be mixed. The range of instruments for qualitative data is very wide and open to extension.  For example qualitative theory can also involved a series of alternating inductive and deductive steps.

A general description of qualitative analysis (Hammersley and Arkinson 1995) is given below.

  1. An initial definition of the phenomenon to be explained is formulated
  2. Some cases of these phenomenon are investigated, documenting potential explanatory features.
  3. A hypothetical explanation is framed on the basis of analysis of the data designed to identify common factors across the cases
  4. Further cases are investigated to test the hypothesis.
  5. If the hypothesis does not fit the facts from the new cases, either the hypothesis is reformulated or the phenomenon to be explained is redefined (so negative cases are excluded)
  6. The procedure of examining cases, reformulating the hypothesis, and/or redefining the phenomenon is continued until new cases continually confirm the validity of the hypothesis, at which point it may be concluded that the hypothesis is correct (though this can never  be known with absolute  certainty).

Incidents or cases are compared establishing similarities or differences in order to define categories and concepts.

Positivism would then look for differences that disproved the theory, but biological experiments are extremely difficult to replicate as biology is in a state of constant change and adaption.  Physics has also evolved since the positivism was first conceptualised. Researchers should also be aware of concepts such as similarity, renormalisation, and self-organisation/ emergence.

Central to the structure of such models is self-similar scaling …. a broad range of different detailed, microscopic interactions, on coarse-graining, lead to the same collective behavior, thus one expects the same essential phenomenology to be ubiquitousNicholas W. Watkins 2015.  

What we call matter emerges as clusters created out of smaller clusters – that interact with other clusters at the same scale, but also scales above and below.  Some clusters are loose, others are very tight, but they are all being subjected to the same forces.  Nothing is allowed to remain the same for ever. This is a dynamic system, which is constantly introducing new elements to the clusters, breaking them apart, reforming them.   Dynamic research methods are needed to reflect this.

The Researcher as ‘Biology’

Qualitative analysis operates in as naturalistic environment as possible, without ‘expectations’ of the final outcome and so is very open to new ideas and findings.  It requires the researcher to be highly reflective and continually questioning themselves and asking how far they are influenced by existing (personal/social) preconceptions. This potentially opens the researcher to recognising their own ‘thought’ boundaries and going beyond them.

Biology allows us to observe what is taking place at our own scale – this provides an opportunity for direct observation that is not offer at other scales (i.e those scales normally studied by physicists). And when we see evidence of dynamics at our own scaling, similar dynamics may be taking place at other scales. It can be difficult for scientists who are used to operating on the basis of theory and laboratory based experiments to open their eyes and see what is happening in their own environment. And on a human level, it is difficult for us to move pass what we perceive as “reality” (and so societal and mental constructions).

Qualitative research recognises that the researcher themselves are ‘biology’, part of their own environment – subject to its influences, but also its strengths (e.g the ability to adapt to noisy input, sudden change, etc).  The researcher is seen as the primary instrument for data collection and analysis.

Challenges to Silo Thinking

Silo thinking had been encouraged in science for many decades for a number of reasons including: a mechanistic view of the universe, a focus on falsification, protected specialisms, laboratory based experimentation, and the sheer challenge of trying to read and understand diverse complex information.

More recently this has changed due to:

  • New thinking calling for transdisciplinary approaches e.g cybernetics and ‘Artificial Life’.  These draw very strongly on pattern analysis approaches which cut across disciplines.  ‘ In short, a number of reoccurring patterns and themes appear prevalent across both real and virtual worlds.’  P Tetlow 2007.
  • Commercial pressures, and the creation of cross cutting fields such as biotechnology and computing.  Here there is an awareness of highly complex adaptive systems, and the need to focus on “making it work”- rather than falsification.
  • The increasing availability of scientific research i.e on the internet and globally available.
  • The increasing questioning of some scientific models, which no longer stand up to close scrutiny – as research reveals new challenges to those models.  These challenges are not necessarily simple falsification, rather they can suggest that models may need to be extended.
  • Growing awareness of the validity of “self organisation”.
  • The emergence of cross disciplinary teams.

Applying Inductive Reasoning 

By applying inductive reasoning to ranges of scientific findings it could be possible to identify similarities or differences in order to define categories and concepts.

I have done this myself by looking at a range of research across what are consider separate scientific fields and seeking similarities, differences and associations.

I started with my own experience as a dyslexic and knowing other people with various neurological conditions. I then made the association between –

Neurology – non linear neurology – non-linear physics (classical and quantum).

I then looked several themes concurrently:

  • Quantum biology – magnetoreception and photosynthesis (including radical pair mechanisms).  Magnetoreception is the only theorised process for animals and therefore I explored the idea that magnetoreception might support quantum consciousness in some way.
  • Morphogenesis, reaction diffusion and the BZ reaction.
  • Possible interaction between quantum and classical.
  • Various neurological conditions and the similarities between them.  I explored neural oscillations (including non linear oscillations and biological rhythms as part of this)
  • Different biological navigational strategies (sitting alongside magnetoreception).
  • Biological Clocks (as cryptochrome is a clock gene that is also implicated in magnetoreception).

More recently I have become more interested in computing (due to the intersect with neurology, non-linear, quantum, navigation, a global clock, etc), semi-conducting and superconducting, and so have associated these with the above.

The results of this work are included on this website and focus on associations between:

  1. Redox/metabolism and circadian rhythms .
  2. Host and microbial interactions (focusing on circadian rhythms) and the implications for human health.
  3. The role of biological rhythms in neurology.
  4. Biological clock and compass interactions.
  5. The similarities between the properties of organic and biological semiconductors and materials under investigation in quantum biology. 
  6. How quantum and classical/reaction diffusion oscillations might interact within biology and beyond.
  7. Timing in collective behaviours.
  8. Forms of creativity.

Overall I would ask – as a result of these studies:

  • Are quantum effects (through the existence of biological semiconductors operating in nature and coupled to biorhythms) influencing growth, neurology/memory, and health?
  • Are such quantum effects the source of creativity  in human?
  • Is collective behaviour also a result of the coupling of classical and quantum effects?

I have also created a pinterest site ( that looks at imagery across different scaling (e.g neurology and superconductors) that may indicate relationships. And I have always been interested in a wide range of art/creativity – as image, book, belief systems, etc.

I have become particularly interested in oscillatory/rhythmic activity occurring in all biology and this has given me a centre on which to pin other elements. For the periodic physiological phenomena that exist in nature have been quantified, and there is a developing consensus that these rhythms are synchronised from the level of the cell to that of swarms, from periods of millisecond duration to rhythms spanning millennia.  T Glonek et al edited by A G Chila 2010.  In the terms of qualitative analysis circadian rhythms have been my ‘core category’ –  and I have systematically relating it to other categories, validating those relationships and filling in categories that need further development and refinement.

But at biological and universe scale, there must be an integration of space-time, magnetism, gravity, etc.  Phase transition may be at the heart of this and I do not currently have the understanding to properly explore this.


Universality and Specialism 

We reached an extreme in “specialist” thinking and are now being drawn back towards universal approaches.  This will not be an end to it.  Once an extreme is reached in “universality”, it will be time to start focusing on the differences again.  All human philosophies are subject to ebbs and flows: political systems, economies, etc – scientific thinking is part of this system.

2016.  This article merely joins up other peoples work into an overall system.  These works have been referenced so it is clear that others have provided the individual pieces of evidence that have been used to shape a specific systems approach.    


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