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Immune network theory

Immune network theory is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Immune network theory rather than just read about it. In short: The immune network theory is a theory of how the adaptive immune system works, that has been developed since 1974 mainly by Niels Jerne and Geoffrey W. Hoffmann.

Key takeaways

  • Immune network theory belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Immune network theory to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Immune network theory from memory before moving on to harder problems.

Reference excerpt

The immune network theory is a theory of how the adaptive immune system works, that has been developed since 1974 mainly by Niels Jerne and Geoffrey W. Hoffmann. The theory states that the immune system is an interacting network of lymphocytes and molecules that have variable (V) regions. These V regions bind not only to things that are foreign to the vertebrate, but also to other V regions within the system. The immune system is therefore seen as a network, with the components connected to each other by V-V interactions. It has been suggested that the phenomena that the theory describes in terms of networks are also explained by clonal selection theory. The scope of the symmetrical network theory developed by Hoffmann includes the phenomena of low dose and high dose tolerance, first reported for a single antigen by Avrion Mitchison, and confirmed by Geoffrey Shellam and Sir Gustav Nossal, the helper and suppressor roles of T cells, the role of non-specific accessory cells in immune responses, and the very important phenomenon called I-J. Jerne was awarded the Nobel Prize for Medicine or Physiology in 1984 partly for his work towards the clonal selection theory, as well as his proposal of the immune network concept. The immune network theory has also inspired a subfield of optimization algorithms similar to artificial neural networks.

The symmetrical immune network theory Heinz Kohler was involved in early idiotypic network research and was the first to suggest that idiotypic network interactions are symmetrical. He developed a detailed immune network theory based on symmetrical stimulatory, inhibitory and killing interactions. It offers a framework for understanding a large number of immunological phenomena based on a small number of postulates. The theory involves roles for B cells that make antibodies, T cells that regulate the production of antibodies by B cells, and non-specific accessory cells (A cells). Antibodies called IgG have two V regions and a molecular weight of 150,000. A central role in the theory is played by specific T cell factors, which have a molecular weight of approximately 50,000, and are postulated in the theory to have only one V region. Hoffmann has proposed that for brevity specific T cell factors should be called tabs. Tabs are able to exert a powerful suppressive effect on the production of IgG antibodies in response to foreign substances (antigens), as was demonstrated rigorously by Takemori and Tada. Hoffmann and Gorczynski have reproduced the Takemori and Tada experiment, confirming the existence of specific T cell factors. In the symmetrical network theory tabs are able to block V regions and also to have a stimulatory role when bound to a tab receptor on A cells. Symmetrical stimulatory interactions follow from the postulate that activation of B cells, T cells and A cells involves cross-linking of receptors. The symmetrical network theory has been developed with the assistance of mathematical modeling. In order to exhibit immune memory to any combination of a large number of different pathogens, the system has a large number of stable steady states. The system is also able to switch between steady states as has been observed experimentally. For example, low or high doses of an antigen can cause the system to switch to a suppressed state for the antigen, while intermediate doses can cause the induction of immunity.

I-J, the I-J paradox, and a resolution of the I-J paradox The theory accounts for the ability of T cells to have regulatory roles in both helping and suppressing immune responses. In 1976 Murphy et al. and Tada et al. independently reported a phenomenon in mice called I-J. From the perspective of the symmetrical network theory, I-J is one of the most important phenomena in immunology, while for many immunologists who are not familiar with the details of the theory, I-J "does not exist". In practice I-J is defined by anti-I-J antibodies, that are produced when mice of certain strains are immunized with tissue of certain other strains; see Murphy et al. and Tada et al., op cit. I-J was found by these authors to map to within the Major Histocompatibility Complex, but no gene could be found at the site where I-J had been mapped in numerous experiments. The absence of I-J gene(s) within the MHC at the place where I-J had been mapped became known as the "I-J paradox". This paradox resulted in regulatory T cells and tabs, which both express I-J determinants, falling out of favour, together with the symmetrical network theory, that is based on the existence of tabs. In the meantime however, it has been shown that the I-J paradox can be resolved in the context of the symmetrical network theory. The resolution of the I-J paradox involves a process of mutual selection (or "co-selection") of regulatory T cells and helper T cells, meaning that (a) those regulatory T cells are selected that have V regions with complementarity to as many helper T cells as possible, and (b) helper T cells are selected not only on the basis of their V regions having some affinity for MHC class II, but also on the basis of the V regions having some affinity for the selected regulatory T cell V regions. The helper T cells and regulatory T cells that are co-selected are then a mutually stabilizing construct, and for a given mouse genome, more than one such mutually stabilizing set can exist. This resolution of the I-J paradox leads to some testable predictions. However, considering the importance of the (unfound) I-J determinant for the theory, the I-J paradox solution is still subject to strong criticism, e.g.Falsifiability.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Immune network theory

Start with the simplest possible case. Write down what Immune network theory claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Immune network theory before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Immune network theory ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Immune network theory

In research
Immune network theory appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Immune network theory in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Immune network theory is common in secondary-school and first-year university syllabi. It links to neighbouring topics Immunology theories, so understanding it makes those chapters shorter.
In everyday life
Look for Immune network theory outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Immune network theory in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Immune network theory means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Immune network theory out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Immune network theory in simple terms?

The immune network theory is a theory of how the adaptive immune system works, that has been developed since 1974 mainly by Niels Jerne and Geoffrey W. Hoffmann.

Why does Immune network theory matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Immune network theory?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Immune network theory.

Tags

  • Immunology theories

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