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The Immune System - Complexity In Action


Science gives way to art in the face of complexity. The immune system provides a good example of this in action. As this text book highlights, the problem is one of unpredictability. No matter the extent to which we understand the principles that determine how an immune response unfolds, we are still unable to predict what that will look like in any individual case.




This week I am studying the lymphatic system and immunity. As a former military geek and student of warfare, this is right up my street, for that is exactly what is going on when an immune response is initiated. I find this topic fascinating.

It is also extremely complicated. We have several layers of defence – physical barriers, chemical, a first response, a second wave of reinforcements, followed by mobilisation of the big guns, deployment of weapon systems, lessons learnt and capability development, de-escalation. We also have a variety of functions being performed, from maintaining security, intruder detection, raising the alarm, calling for back up, monitoring of the battlespace, reconnaissance by fire, troop training and selection, prisoner processing, identification of friend and foe, plus weapon selection decisions. We have mass assaults, suicide attacks, self-destruct buttons, scorched earth programs, alongside a high volume of communication in a variety of formats and mediums running right across the body.

Of special interest is the fact that all of this is self-organising – there is no command and control (C2) – and yet the system manages to be highly adaptable, both in terms of the scale and the type of response carried out.

Scientific inquiry has given us an understanding of the immune system. It is by no means complete, but we do know a lot and the picture is building. This is helpful, and even if it wasn’t, knowledge has value in its own right. Science does not need any further justification other than the pursuit of truth.

On a practical level, however, this understanding does not get us very far. This is a result of the system’s complexity, which makes its actions impossible to predict in any particular situation. For Modern medicine this becomes a problem when we are trying to transplant organs and need to override the immune system’s response, without destroying it completely. This text book, which approaches the subject from a network centric point of view, presents the difficulty in these terms,


Humans inherently think in a linear, stepwise (logical) fashion, and unconsciously impose linear, stepwise patterns on the things that they study. Unfortunately, networks are multifactorial, nonlinear arrays of interactive elements, making them confusing (illogical) to humans.” (p.126)


The problem, therefore, as I have discussed elsewhere, is the absence of a linear track of causality that can be captured in mechanical models and used in a predictive matter to assist an intervention such as a graft.


Tiers of evolutionary developments have provided a panorama of response options, all of which are generally effective, though not all equally efficient. The presence of multiple response options implies that there is no single, prototypic mechanism of immune response to any given set of foreign antigens...

The actual mechanism, or combination of mechanisms, employed to reject an allograft by any given individual depends on the unique panorama of immunologic and physiologic conditions associated with both the allograft and its recipient.” (p.128)


The key words here of course are ‘individual’ and ‘unique’. The text goes on,


“Thus, the same degree of antigenic disparity can evoke a different panorama of immune responses in different individuals. By extension, the mechanisms of acute allograft rejection in outbred populations must be highly individualized and quite unpredictable.” (p.128)


Here science reaches its limit, understanding must give way to art, or as the Ancient Greeks would have put it ‘phronesis’, which we can loosely translate as ‘knowing the right thing to do’ under a unique set of circumstances. Aristotle discussed this at length in his Ethics, and Gadamer places the same idea in a Modern medical context (see here). For our text author, who is involved in transplant medicine, the dilemma posed here takes this form. He begins by stating explicitly that a scientific or technological approach is of no use,


Given these unpredictable, individualized acute rejection processes, it is improbable that a single, standardized immunosuppressive strategy can operate effectively in all patients (contrary to the current premise of clinical transplantation).” (p.128)


In fact, he goes further, and explains how in this situation a linear or mechanical model fails, for even though a ‘cause’ has been removed by deleting certain genes, the ‘effect’ remains, the system compensating for the removal through another route.


Indeed, a major principle of clinical immunosuppression is the pharmacologic elimination of cytokine production or function (for review, see MacGregor and Bradley [29]). Nevertheless, experimental allograft studies with genetically engineered mice have demonstrated that cardiac allograft rejection develops efficiently despite the deletion of genes encoding the supposedly critical proinflammatory cytokines, IL2 [28] or IFNg [39].” (p.128)


The problem is not restricted to mice, the human immune system also has this same element of unpredictability, the same cytokines have an adaptive capacity which means there is no way of knowing how they will react to immunosuppressive drugs. This can also apply to the same person on different occasions. The point here is that the barrier is NOT one of understanding, it CANNOT be solved by further gains in knowledge – the system IS unpredictable by its very nature.


Because of the Mendelian distribution of these polymorphic cytokine genes, there is a broad continuum of cytokine production patterns among humans, resulting in some individuals with a high proinflammatory predisposition (high TNF, IFNg/low IL10, TGFb production), some with a high anti-inflammatory predisposition (high IL10. TGFb/low TNF, IFNg production), and a majority of individuals who fall somewhere in between. This clearly influences the design options for an immune response in any given individual, and therefore the efficacy of various immunosuppressants in populations of such individuals. Cytokine gene polymorphism helps to explain why many patients respond poorly to immunosuppressive strategies that appear to work well in others [20].” (pp.129-9)


The author, Charles Orosz, has no choice but to conclude – art rather than science is required to solve this problem. However, he also has to acknowledge that Modern medicine is unable to make such a transition. He states,


If there is no universal therapeutic strategy that works in all patients, then physicians have only two choices: continue to use the existing therapeutics arid tolerate the large number of patients for whom it is relatively ineffective, or develop individually tailored therapeutics. This alternative is currently considered to be impractical, because it requires an understanding of immune functions in a given individual, and an understanding of immunosuppressive drug effects in that same individual. Both of these requirements are well beyond the reach of current medicine, which has an understanding of neither.” (p.129)


Well beyond the reach’ are the key words here. Orosz goes on to detail what such a personalised approach would involve, before returning to his main theme – an investigation of the design principles that underpin the immune system, one that is very interesting too. But for us here, our purpose is served, the point should be clear enough. Science has its place, it has its value, but its also has its limits, and once we reach those limits we have to turn elsewhere, to art, philosophy, and tradition. (See my series on these here)






Orosz, C. G., An Introduction To Immuno-ecology and Immuno-informatics, in Cohen, I. R., & Segel, L. A. eds (2001). Design Principles for the Immune System and Other Distributed Autonomous Systems. Oxford: Oxford University Press.






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