Introduction To SIS-II

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Overview

This system of simulation requires that you imagine your self to be a cell that is facing the necessary decisions that must be taken before becoming a protective effector, and eventuall how your life will then end. The simulation begins with the single cell zygote that gives rise to an individual and the very first T and B cells formed in the embryo. The simulation proceeds to age and follow the rules you give it. You have a relatively long time to gather all the information (input conditions) needed to make a decision, but at the moment of cell division, a choice must be made. In other words, time has been reduced to the smallest decision making step, namely one cell division. Any event that takes less than one cell division (e.g., antigen binding to a receptor and signaling the cell) does not need to be measured, and if you like to think of a cell division taking 12 hours, then there are two division ticks to the day, or three ticks per day if division time is 8 hours.

SIS simulates a primitive synthetic immune system. The SIS is "born" as if in an embryo and it is forced to make a self-nonself discrimination before being allowed to respond to external nonself antigens. To keep the interface simple for the time being we have "hard-wired" a test of your rules that includes making a self-nonself discrimination, and then if that test is OK there is an automatic test of an immune response starting some time well after birth and lasting for 7 days. If at the end of these preliminary trials there are 400 - 500+ effector B cells, then the simulation can be called a success. However, if you are interested in following other rules for making the self-nonself discrimination, use the expert mode. In the expert mode you are presented with a newborn immune system and by adding antigen immediately you simulate the response to a self antigen. If you delay the addition of antigen, then you simulate the reponse to a nonself antigen.

The elements of this immune system are simply T and B cells. Antigen specificity is reduced to a single parameter, and for the time being it is fixed as a frequency of antigen reactive cells (3 per 1000 for B cells and 30 per 1000 for T cells), and the justification for these round figures is given below. The simulation covers a sample of one million cellular slots that are filled with either T or B cells that are either antigen-specific or not. The antigen is considered to be present uniformly throughout the million cells unit and any reaction that depends on antigen is switched ON or OFF by simply setting the antigen added or removed switch. There is no opportunity to encode the behavior of non-specific elements such as antigen-presenting cells and their products. Although Protecton Theory requires antigens to be recognized via multiple paratopes to induce the aggregation of soluble Ig, SIS treats these as a single specificity. A future model of SIS will allow multiple epitopes and multiple paratopes per antigen in order to incorporate more of the subtlety of Protecton behavior, along with antigens that grow and are eliminated by the immune response of SIS.

A set of default rules has been provided to show how T cells and B cells might behave in the presence and absence of antigen. The antigen that is added and subtracted by the operator, and only those T and B cells that can react with that antigen are tracked.  As some places it is suggested that you could choose to add either intracellular or extracellular forms of antigen, but for the time being only the extracellular form functions.

How to define the cells and their states and rules of interconversion.

The T and B cells are divided into three different states designated a, i, and e. Cells of any type or state are allowed 8 age steps that can be tweaked to an almost infinite number by tricking the rules (see later how this is done). The T cells are divided into two classes, one that is regulatory called H for helper, and another that is destructive called K for killer. For rather arcane reasons another state called x was added to the set of possibilities for B cells to allow a simulation of memory B cells.

Some special limit rules that are not adjustable.

Hard wired into the program are some important limitations. First, the effector functions of eTH are limited to those cells of any type that are within a chunk of 100 total cells. This effectively simulates a cell-cell interaction boundary for eTH. However, the eTH is able to secrete cytokines called IL-1 and IL-2 that act over a chunk of 10,000 total cells and any cell within that domain can respond to the secreted hormones. Second, to distinguish between intracellular and extracellular antigens an arbitrary decision was made to render the TK system unable to respond to extracellular antigens. Thus, two forms of antigen are designated, intracellular and extracellular, and these do or do not respectively activate the TK system.

 The whole system functions as a set of nested cellular automata with one million total cellular elements. This was a limit decided by the results of our studies on the behavior of a Protecton, which in essence simply states that there is a minimum number of cells that can be taken from an immune system and they can mimic the behavior of the total. The concept of a Protecton arises from the elephant tadpole paradox, which states that an immune system is made of an iterative unit that is present in varying copy number in differently sized species and differently sized individuals of different ages.

How to write and edit a rule.

The rules of syntax are straightforward.

On the left of the "=>" separator is a description of the starting state of a cell along with any conditions needed to effect a change in that cell. Each addition is separated by the "<" punctuation mark.

An implicit feature in every rule is that each cycle of the automata is the equivalent of a cell division, and at every cycle the cellular objects increase one unit of age.

The consequence of the stated input condition is given following the "=>" separator and if there are multiple consequences, these are separated by the ">" punctuation.

Not always is there only one possible output, and to accommodate this it is possible to distribute the outputs into three probability categories. If there is a distribution of possible outcomes this is indicated by preceding the description with P(0.x) such that the sum of the probabilities is unity. At the time of execution of the rule in the program one of the possible outcomes is chosen so that at different times different outcomes are produced with a probability defined in the rule.

The best way to understand the system is to simply run the rules offered as a default and then look over the entire set and begin making changes. Designing a new set of rules from scratch is difficult unless the vocabulary and structure of the rules system is well understood.

If you want to recover an edited rule table, simply use the "load rule" box on the authoring tool. However, as the user "guest" everyone uses this and your saved rule may be lost. If you want to play seriously, please email me and I will give you a private sandbox to play in.

The choice of "hard wired" variables that are not adjustable by the user.

 Finally, it is acknowledged that the rules are primitive and deal with a minimal set of features of the immune response. However, to have a system such as SIS that starts with an embryonic immune system and produces protective levels of antibody in a sufficiently short time to be protective against infection is a good starting point.

Expert Rules or Experts Rule

The special set of rules, just the first two lines, are sufficient to cause autoimmunity. Usually it is around 400 days later, but because the system is based on random arrangements, sometimes a new simulation will develop autoimmunity in less than 50 days.

These initial rules are to get you on the right track, but you should test you imagination.

If you use this page often, and you want to keep your rules, just copy them off the web page, save them in an ascii file, and paste them back in when you are ready to go again.



last updated January 12, 2010