2005 ACM Symposium on Applied Computing
CAFISS: A Complex Adaptive Framework for Immune System Simulation Joc Cing Tay and Atul Jhavar Nanyang Technological University Blk N4, #2a-32 Nanyang Avenue Singapore 639798 +65 6790 6266
[email protected] The immune system has a powerful and flexible information processing capability as a decentralized, intelligent system. It also has some important computational aspects such as self/non-self discrimination, learning, memory, retrieval, pattern matching and emergent behaviour. These factors make for a compelling argument for developing immune system simulations using CAS concepts.
ABSTRACT Currently most reported immune system simulations in literature involve the use of differential equations, genetic algorithm-based searching or simple cellular automata models. This limits the diversity in results obtained and thus provides fewer avenues for experimenting with behavioral responses of the immune system entities under exogenous stimulations. Complex adaptive systems (or CAS) by Holland provide a way of modeling natural systems with complex aggregation and nonlinear interactions to exhibit emergent behaviours. The immune system, being a powerful and flexible information processing system is particularly suited to being modeled using CAS. This paper describes a Java-based implementation of a framework for modeling the immune system, particularly Human Immunodeficiency Virus (or HIV) attack, using a CAS model. The credibility of the system is established through comparisons against available viral dynamics data. We show that it is feasible to achieve relatively accurate predictions of viral pathogenesis through agent-based discrete event simulations, the first steps towards improved automation of hypothesis verification.
This paper describes the design and implementation of a Complex Adaptive Framework for Immune System Simulation or CAFISS. The agent framework is based on the basic principles of complex adaptive systems as proposed by John Holland [8]. In addition to this, it incorporates features from cellular automata [19], genetic algorithms [5] and Holland classifier systems [6]. The simulation specifically targets the attack of the Human Immunodeficiency Virus (or HIV). It provides information about the dynamics of HIV infection and its subsequent progression towards full-blown AIDS. Results have been compared with biological data through classical HIV dynamics models and have been found to be credible.
Algorithms, Measurement, Design, Experimentation.
This paper is organized as follows: Section 2 presents some related work on immune system and HIV simulations. Section 3 discusses the design and implementation of CAFISS. Section 4 provides a brief theoretical background on complex adaptive systems, the human immune system and HIV. Section 5 presents the results obtained from CAFISS. Finally, Section 6 outlines some conclusions and future work.
Keywords
2. RELATED WORK
Complex Adaptive System, Artificial Immune System, Agentbased Simulation, Evolutionary Computation, HIV.
This Section will examine a few important works in the field of immune system simulation. We concentrate on cellular automata (or CA) and CAS-based simulations, rather than simulations based on ordinary differential equations (or ODEs) because CAFISS emphasizes on answering ‘why and how’ questions while ODE based simulations are used only in our empirical experiments for model-based verification of simulated results. The latter has the disadvantage of averaging details to produce global characteristics and hence, are more suitable for discovering ‘what and when’ characteristics of an observed biological phenomena.
Categories and Subject Descriptors I.6.5 [Model Development]: Modeling Methodologies.
General Terms
1. INTRODUCTION The vertebrate immune system [16][8] is extremely complex and computer simulations or experiments in silico are paving the way for subsequent genetic and biochemical experiments that promise to yield new information about how the immune system works. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SAC’05, March 13-17, 2005, Santa Fe, New Mexico, USA. Copyright 2005 ACM 1-58113-964-0/05/0003…$5.00.
As will be discussed in Section 4, in the natural immune system (or NIS), binding between for example, a B cell receptor and an antigen’s epitope will take place through the matching of amino acid strings comprising both the receptor and the epitope. This binding will occur only if there is a sufficient match between the
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obtained by them are interesting, there is no detailed description of the model’s implementation.
two amino acid strings. It is possible to represent each amino acid string using an alphabet of m characters, with each character representing one amino acid. String matching algorithms can then be used to determine the amount of match between the two amino acids. Typically, a binary bit-string model is used. This idea of using a bit-string model was first applied to cellular automaton modeling by Seiden and Celada [3][12][17]. They called their model IMMSIM. It was written in the APL2 language by Philip E. Seiden and requires the IBM APL2 runtime environment. In its original version, IMMSIM contained B-cells, T-cells and antigen presenting cells (or APC) as well as antigen and antibody molecules and is concerned only with the humoral response (see [14]). A later version of the model, IMMSIM3, added cellmediated response features as well.
3. DESIGN AND IMPLEMENTATION The simulation runs on an imaginary rectangular grid, which is representative of locations in the human immune system. Each location in the grid is capable of housing an arbitrary number of cells. These cells can be of different types, and are capable of autonomous behaviour so as to mimic their NIS counterparts. Over time, viral cells are injected into random nodes within the grid. The immune cells react with these virus cells and both sides try to eliminate each other. The results can be visualized graphically. The simulation also provides for ways to tune parameters of the system and observe the effects. New rules and behaviours can be added without too much effort, and new types of cells can also be incorporated.
However, it is inconvenient that IMMSIM is written in APL2. The language is essentially interpreted with no explicit dynamic memory allocation capability. This means that only relatively small scale simulations could be conducted. To remedy this problem, Bernaschi and Castiglione [2] came up with a parallel coded version of IMMSIM written in the C language. Finally, a C++ version of IMMSIM was developed by Steven Kleinstein [14]. In most bit-string models, such as the Caleda-Seiden model, the function that is used to match the two string representing amino acids is the exclusive-OR function. IMMSIM has been used by several researchers and provides a robust simulation of the humoral and cell-mediated immune system. However, there does not appear to have any provision for incorporating learning capabilities into the agents.
In order to allow the user to experiment with various scenarios of disease progression and control, several parameters of the simulation can be changed dynamically. These include:
Another group of researchers composed of mainly Stephanie Forrest, Ron Hightower, Steven Hofmeyr, Derek Smith and Alan Perelson seem to be coming up with interesting findings with a focus on using genetic algorithms. Specifically, they have used genetic algorithms to explore pattern recognition in the immune system [4]. Again, this is a specific and focused study and analyses only one aspect of the immune system, i.e. pattern recognition. However, the interesting results obtained by them indicate that employing genetic algorithm features in a broader immune system simulation will provide a good opportunity to explore this field further.
The length of the rules in terms of the number of bits.
The dimensions of the grid which simulates the immune system topology.
The initial number of cells of each type in the system.
The number of cells of each type that will be introduced (the burst size) at specified intervals of time.
The interval of time between which cells of each type will be introduced into the system.
The strength or life force of each type of cell which determines how easy it is for a cell of a particular type to be killed by another.
The limit of the size of a swarm (which is a collection of agents) within one location in the grid.
3.1 Multithreaded Asynchronous Updating There are some significant advantages in designing a program that supports a multi-threading capability as opposed to one that does not. Even on a single processor machine, this often leads to a more efficient utilization of CPU resources, depending on the application that is running. The real advantage though, lies in its scalability and accuracy in simulating physical phenomena [9][10].
We now move on to simulations that have focused on observing HIV infection-specific immune response. One recent work in this area was done by Zorentzen dos Santos and Coutinho [20]. Their model employed a simple CA approach. In it, every site in the two-dimensional CA grid represents a target cell for the HIV. Each cell can be in four states, namely healthy, infected-A1, infected-A2 or dead. A1 and A2 refer to two stages of the infection. This work has been analyzed critically by Catherine Beauchemin [1]. She concluded that the model used is far too simple to correctly model the immune system and thus leads to misleading results. It appears that the fact that the authors have chosen to have each site to represent a cell and to have no cell diffusion is responsible for patterns that are not expected in a real system.
The NIS is a classic example of a distributed detection system with no central control unit or singular point of failure. This is unlike the human nervous system where the brain assumes central control. All the billions of cells in the immune system perform their function based on simple local rules. In other words, as with other CAS instances, the system consists of a large number of autonomous and simple agents interacting continuously and asynchronously to create complex collective behaviours. One way that such asynchronous behaviours be replicated plausibly on a computer simulation is by using multiple threads. Using a single thread would mean that there is central control and all the cells react according to this central control. Cell functions would have to be performed by cycling through each cell in each time step and taking some action. In CAFISS however, each instance of each type of cell in the immune system runs in its own
Nowak and McMichael [13] obtained interesting results concerning the mutation of HIV in the human body and have proposed that this is the main factor that is responsible for crippling of the NIS. In fact, this result was also obtained via CAFISS, as will be described in Section 5. Although the results
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reduced in strength. This factor will come into play when more than one rule is selected for firing. Stronger rules will have a greater chance of being fired in such a case.
thread. Thus, there is no central control nor are there any global time steps. Each cell’s behaviour is based only the code which its own thread is running.
Bit string pattern matching is one way to simulate receptorstimulus representation in immune system simulations. A receptor is said to match an antigen if their bit strings match (in this context, matching could mean either that the bits in the same position are the same, or they are complimentary). But in reality, each antibody must match against several different types of antigens simultaneously. To achieve this, we do not require perfect matching. The degree of matching can be quantified by a matching function M: Antigen × Antibody ĺ R [4]. For example, M can simply calculate the number of matching bits in the two strings or M can identify the contiguous regions of matching bits in the two strings. Stadnyk [18] introduced a function M that computes the length li of the matching regions and then combines them in such a way so as to reward longer regions. Using this basic idea, many different types of combining functions can be explored, such as exponential, threshold, linear and so on [5]. In this simulation however, a simple pattern matching function which counts the number of matching bits is used, this has been found to be physiologically plausible.
3.2 Event-Processing Model One of the fundamental objectives in designing CAFISS was to ensure that the simulation was event driven and distributed rather than centrally controlled. This was in line with the requirements of CAS [8]. To do this, a relatively sophisticated event model had to be used. Java provided the solution in the form of the Delegation Event Model [11]. In CAFISS, the event source and event listener are objects that represent a cell. In the beginning of the simulation, any given cell object is registered as an event listener with other appropriate cell objects. This will depend on the current location of the two cells as well as their cell types (certain types of cells can send events to only certain other types). Also, proximity from the sending cell is essential for the receiving cell to get the message correctly. Each Cell object described has a set of bits which function as a variable length detector. Each bit in the detector string has some meaning assigned to it, depending on the type of cell and the type of messages it is likely to receive. For example, in a B-cell, several bits of the detector set will act as the B-cell receptor, that is, they will be used to accept an antigen’s molecular signature so that it can be processed and possibly matched. Other bits in the Bcell may be used for receiving stimulatory signals from T-helper cells so that they can begin the process of plasmic differentiation and antibody production.
The following are basic rules that have been programmed into each type of cell in the system. These rules are based on the information given in Section 4 and have the following grammar. [{detector bit number(s)} [detector bit function]] :: [{effecter bit number(s)} [effecter bit function]] T-Helper Cells: Since the primary function of a T-helper cell is to detect the presence of a specific antigen and send stimulatory signals to other immune cells, it requires just a single rule:
3.3 Agent Model This Subsection gives details on how a CAFISS cell processes its rules and reacts to simulated environmental inputs. Each agent in the simulation is an instance of a Cell object in Java. Further, each Cell object has a member of type RulesManager, which essentially encapsulates all the tasks related to processing incoming events and rule-based reactions.
[{0…n-1}[match antigen]]:: [{0}[activation signal][{1}[reproduce]] The above rule means that on the rule condition side, a bit string of length n is used to match the signal coming from a nearby antigen. On the rule action side, both an activation signal and a signal to reproduce will be produced. Thus, upon successful detection, the T-helper cell will not only produce a stimulatory signal, it will also begin to reproduce (through clonal selection, see Section 4).
Whenever the state of the cell’s detectors change, the rule manager is invoked to process the rules and suggest a suitable reaction. Rules for each type of cell are different, but what they essentially do is this: the rule manager checks the current state of the detectors (which will be a string of bits) and pattern-matches through its set of rules to see which ones will be activated given the current state of the detectors. The appropriate rule is then selected and its consequent action performed.
HIV Cell: The HIV cells go about their function of infecting host cells (especially T-helper cells) without any need for rule processing. However, the following two rules are still a minimum requirement:
Each CAFISS cell rule consists of two parts – a rule condition and a rule action. Both are represented by bit strings. The rule condition is used to match a rule to an incoming event and check its suitability for being activated. The rule action determines what the rule’s output will be. This output may appear in the form of an external event to another cell, or it may be an input to another rule in the same agent. Although this flexibility is provided, most cells in the immune system have functions that are well understood and are quite simple and reactive. Thus, there is no immediate need for a sophisticated internal rule chaining mechanism unless the granularity of the immune function model increases. An attribute associated with a rule is its strength. This is an important aspect of the Holland classifier model. Rules that provide a beneficial effect to the cell may be strengthened. Similarly, harmful rules may be
[{0}[fatal signal]]::[{0}[die]] [{1}[successful infection]]::[{0}[reproduce]] The first rule allows the immune cells to destroy HIV cells, while the second rule allows successful HIV infection of a host cell to begin reproduction within the infected cell. B-Cell: The primary function of B-cells is to release antibodies when they detect a corresponding antigen and receive a costimulatory signal from T-helper cells. To model this, the following rule is used: [{0…n-1}[match antigen] {n}[get [{0}[release AB]{1}[reproduce]]
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TH
activation]]::
fitness-proportionate reproduction cannot be applied. However, the concept that fitter individuals propagate their genes still applies. This is closer to the concept of Lamarckian evolution [7] in which useful traits in individuals are inherited by offspring. In CAFISS, individual agents, both immune cells and virus cells that manage to match and destroy virus cells or infect immune cells respectively, will proliferate and the average fitness of the populations will tend to improve over time. It should be noted that there is no explicit fitness function, and this is consistent with most CAS implementations. This makes the simulation very different from a simple genetic algorithm based search to find fitter individuals.
Here, AB stands for antibodies. Antibody: Although antibodies function indirectly by ‘marking’ foreign cells (see Section 4), a reasonable assumption has been made here. We assume that once antibodies tag a foreign cell, the latter will be killed. This is true in the human body. It has been established that once the body starts producing antibodies against a specific strain of a virus, it is usually eliminated quickly. Hence, only one rule is required: [{0…n-1}[match antigen]]::[{0}[fatal signal]]
4. THEORY 4.1 Complex Adaptive Systems
4.2 Immune Response to HIV Infection
Natural systems are in general, difficult to describe or model using traditional ODE-based techniques. Examples of these include economies, ecologies, weather, social organizations, and cultures, to name but a few. Typical of such systems are large amounts of non-linear interactions between a large number of system entities that obey simple rules. These systems are often referred to as complex systems. Further, the individual entities of some complex systems change their behaviours and configurations over time i.e. they evolve. Such systems are then referred to as complex adaptive systems [8].
Foreign molecules from viruses, fungi or parasites are known as antigens. More formally, an antigen is a substance that reacts with antibody molecules and antigen receptors on lymphocytes (these terms will be described later in this Section). The immune system, whose job is to fight these antigens, is divided into two classes: the non-specific (or innate) and the specific (or acquired, adaptive) immune response. Innate immunity refers to antigen-non-specific defence mechanisms that a host uses immediately or within several hours after exposure to an antigen. This is the immunity one is born with and is the initial response by the body to eliminate microbes and prevent infection. Adaptive (or acquired) immunity refers to antigen-specific defense mechanisms that take several days to become protective and are designed to remove a specific antigen. This is the immunity one develops throughout life.
The basic building blocks of CAS are ‘agents’. Agents are semiautonomous or autonomous units that seek to maximize some measure of goodness, or fitness, by evolving over time. These agents interact and connect with each other in unpredictable and unplanned ways. But from this mass of interactions regularities emerge and start to form a pattern which feeds back on the system and informs the interactions of the agents. For example in an ecosystem if a virus starts to deplete one species, this results in a greater or lesser food supply for others in the system which affects their behaviour and their numbers. A period of flux occurs in all the populations in the system until a new balance is established.
What follows is a brief introduction to the functions of the most important types of immune cells currently implemented in CAFISS. Antigen Presenting Cells (or APC): The primary function of these cells is to engulf foreign bodies and present the epitopes of the antigen on their surface. This presentation is done via the socalled MHC (Major Histocompatability Complex) molecules. This presentation is essential for the detection of the antigen by other immune cells. Cells that can perform the function of antigen presentation are Macrophages, Dendritic and B-Cells.
CAS have several identifiable properties like emergence, coevolution, aggregation, variety, connectivity, simple rules and self organization. Based on properties like these, several CAS models have been built. Holland classifier systems are one such model, which has inspired the design of CAFISS. It is an “adaptive, general purpose machine learning system designed to operate in noisy environments with infrequent and often incomplete feedback” [6]. Classifiers are simply if-then rules. A classifier system is a machine learning system that learns a set of such rules over time and uses these rules to guide its performance in an arbitrary environment. Information from the environment is received by the agent’s detectors (e.g. as binary strings). These messages are then processed by the rule base and the appropriate actions are carried out through the agent’s effectors. The apportionment of the credit system uses an algorithm such as the Bucket Brigade to update strength of rules based on their performance. The rule discovery system typically uses a genetic algorithm to discover and try out new rules for the agent. Each agent in CAFISS is an enhanced Holland classifier system.
T-Helper Cells (or TH): T-Helper cells recognize the antigen presented by APCs via the MHC molecules and then release substances that activate and trigger other important cells of the immune system. For example, they promote the differentiation of B-cells (see below), activate cytotoxic T-cells and macrophages to engulf and kill foreign bodies. B-Cells (or B): As mentioned earlier, B cells also act as APCs, thus aiding in the entire immune system’s detection and elimination procedure. However, more importantly, they produce antibodies. When B cells detect a particular antigen and are stimulated by T-helper cells, they differentiate into antibodyproducing plasma cells. Antibody Cells (or AB): Antibody cells are one of the most important immune cells during a specific response. They are essentially the soluble form of the receptors of the B cells from which they originate. When a B cells differentiates into a plasma cell it releases its surface receptors as antibodies. The antibodies are specific to a particular type of antigen (which they are able to
Although the evolution of most biological systems correspond closely to the genetic algorithm, it cannot be applied directly to how the immune system entities evolve. This is because immune cells reproduce asexually, hence, operators such a crossover and
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Another assumption with the context of modelling deficiencies is that all the viral epitopes produce the same reaction in the immune cells, provided the epitope can be matched of course. In reality certain epitopes are more stimulatory than others. This means that clonal selection will direct more antibodies against that epitope. Finally, a point to note is that the crucial problem of self/non-self discrimination has not been dealt with in this simulation. We are only interested in modeling immune responses to a specific virus, namely HIV; hence addressing the larger problem of self/non-self discrimination is not within the scope of this project.
match). Upon matching the corresponding antigen, they bind on to it. This has two effects. Firstly, it becomes difficult for the antigen to infect any more cells. Secondly, the antibodies which cover the cell surface act as tags, ‘marking’ it as foreign. Now, any phagocytic cell like a macrophage will engulf the antigen and destroy it. Clonal Selection: Clonal selection and clonal expansion are processes by which the adaptive immune system is able to create a requisite number of immune cells upon detection of an antigen. Essentially, upon specific detection of an antigen, the immune cells that detected it begin to multiply rapidly, creating a large population of effective cells. Furthermore, during cloning, they undergo mutations which eventually lead to affinity maturation and a more effective population of immune cells.
5.2 System Dynamics To demonstrate how CAFISS can be used for supporting clinical hypothesis verification, a simulation is shown below along with the values of some important parameters.
5. RESULTS AND DISCUSSION 5.1 Assumptions Cellular automata usage. First, a CA simulation is typically used for a finite system. Even today’s most powerful supercomputers cannot possibly simulate a system of realistic size without sacrificing much detail. Given that the simulated system will be much smaller than the real one, much attention must be given to whether the results are artifacts due to this restricted size. Second, it is generally more difficult to extract the essence of what is happening and to discover general laws about system behavior from a CA-based simulation. The field of cellular automata is young and much less is known about cellular automata, so our intuition is less well developed. Nevertheless, the intuitive match with biology and biological thinking greatly outweighs the problems in CA-based simulations.
Parameters:
Initial nos. (TH, HIV, B)
= [50, 5, 50]
Per burst = [10, 1, 0]
Abstraction. The very first simplification that was made was the modeling of rules, receptors and messages as bit strings. This was done with a view to allow flexibility in testing biological operators like those used in genetic algorithms. However, in reality, the process of receptor matching is extremely complex and involves the processes that occur at a molecular level and involved far more factors than just matching a few bits of data. At present, environmental factors such as the effect of biological solvents and mediators like interleukins have not yet been factored in. These aspects should be added subsequently for more realistic behaviour.
Figure 1. Simulation of HIV Pathogenesis with CAFISS With reference to Figure 1, we see that when the virus is introduced into the system, the T-helper cells detect the strain almost immediately. Thus, B cells get the critical co-stimulatory signal from them and are able to produce specific antibodies. For a considerable amount of time, the antibodies are able to keep the virus in check. However, the virus mutates constantly. Hence, several T-helper cells are killed without being able to match them. This is why, even though the body is churning out new T-helper cells (at the rate of about 2 per second), their level remains almost constant right until t=20. At this point, something interesting is observed. A gradual decline in the T-helper population begins with a correspondingly sharp drop in antibody population levels despite having a constant rate of virus mutation. What is surprising is that this occurs at a point in time where the HIV population is relatively low.
Limited Modeling. The immune system is a vast area of study, with very complex subject matter. In this project, only the basics have been modeled to provide a robust and feasible starting point for supporting automated hypothesis verification. There are still a large number of aspects of the immune system which have not yet been included. So far, the cells of the immune system that have been included are T-helper cells, B-cells and antibodies. These are the three most critical and essential cells although there are several more that perform auxiliary or helper functions. One important type of cell that should be modelled subsequently is the cytotoxic T cell. Also, as the system proves itself, more complexity can be added by reducing the level of abstraction and replicating the immune system processes more accurately. Thus, it should be understood that the current system models the immune system at a high level, with only the critical cells being simulated thoroughly.
We believe this is due to the constant mutation of the virus. Even though its population is in check, there are constantly more and more strains in the body. Starting from t=20, the number of strains becomes too high for the body to cope with (Note that antibodies are antigen specific, if the virus mutates, older antibodies are useless). The B cells and T-helper cells simply cannot keep up with so many different strains and thus, a few of the strains begin to proliferate. These mutant strains infect T-helper cells and begin to reproduce. This explains the sudden spike in the HIV population at t=30. By t=40, the T-helper population is down to a
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dangerously low level. Around this period, the patient will begin to show symptoms due to reduced immunity, and this is the beginning of the onset of AIDS. After t=40, another interesting observation is made. There is a drop in HIV population and at the same time an increase in Thelper and antibody populations. Although the effect is temporary, it does demonstrate that an important behaviour of the immune system has been modelled correctly. This is clonal selection (Section 4). The spike in T-helper population occurs because a particular family of T-helper cells must have detected a strain with a particularly large population. This caused T-helper cells with those receptors to reproduce according to the principle of clonal selection. Eventually however, both the T-helper and antibody populations drop to near zero and the virus population increases unchecked.
Parameters:
Figure 3. Typical three stage evolution of HIV illness According to Figure 3, the HIV infection evolution presents 3 distinct phases. During the first months after infection, there is an acute phase with a large increase in the viral population and a corresponding destruction of immune cells. This ends with the reprise of the immune system to the invasion and the decrease of the virus population to low levels. After the immune system reprise comes a long period of slowly increasing virus population and slowly decreasing immune cells population. At some stage the virus population rises exponentially and the immune system collapses resulting in an onslaught of other diseases and certain death. As was seen earlier in the simulation results, stages 2 and 3 were replicated accurately. Stage 1 however, was not replicated accurately. Stage 1 occurs because the body usually takes some time to detect the introduced strains and within this period the virus population spikes. With a larger receptor repertoire, this effect should be studied further.
Initial nos. (TH, HIV, B) = [50, 5, 50] Per burst = [10, 1, 0]
6. CONCLUSIONS AND FUTURE WORK
Viral mutation = OFF
In this project, a CAS model, based on ideas from Holland’s original CAS framework, Holland classifier systems, cellular automata and genetic algorithms, were applied to the task of developing a complex adaptive framework (called CAFISS) for immune system simulation. The model promises to overcome drawbacks of other immune system simulations in the literature, which are usually based on ODEs or straightforward cellular automata, and provides a framework which has the potential to provide new insights in HIV pathogenesis under adaptive immune response.
Figure 2. Dynamics when HIV is Unable to Mutate Next, we shall see what happens when the virus is hypothetically unable to mutate. In other words, upon initial detection, the virus is prevented from proliferating. From Figure 2, we can see that once the strain is detected, the antibody production is stepped up and it destroys all the virus cells in a short while. This confirms the hypothesis that HIV is extremely dangerous primarily because of its high rate of mutation within the body. This effect was also observed by Nowak & McMichael [13].
The most important contribution of this project was to show the feasibility of using multiagent-based immune system simulations for verification of HIV pathogenesis. The simulation itself only models the basic functionality of the immune system, with respect to attack by an HIV-like virus. Extensive tests were conducted to verify that the simulation results correlated with biological data for the prediction of the chronic and acute stages of HIV infection. In the process, some interesting results about the effects and importance of HIV mutation were also obtained. However, more experiments and analysis are required to verify the relative significance of known non-mutually exclusive HIV hypotheses while deriving new emergent behaviours through increasing model granularity.
5.3 Comparison with Biological Data The following figure (addapted from picture, Copyright Dr. R.E. Hurlbert, from the Washington State University Fundamentals of Microbiology 101 course home page:http://www.slic2.wsu.edu:82/hurlbert/micro101/pages/Chap 16.html#AIDS_course_disease) [15] shows biological data about how HIV infection usually progresses in the body.
The following are a few key features that could be added to the simulation itself. Currently, each epitope expressed by the virus
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Parallel Problem Solving from Nature (pp. 320-325). Berlin. Springer-Verlag (Lecture Notes in Computer Science), 1991.
causes a similar reaction from the immune system. In reality certain epitopes are more stimulatory than others. This means that clonal selection will direct more antibodies against that epitope. This is usually a good thing. But if such an epitope (one that evokes a higher response) were to mutate, the body’s defenses will find themselves blindsided. They will presumably get biased slowly towards the next most responsive epitope. If this situation continues, the dominant response might soon be too weak to cause significant harm to the virus; its numbers will then explode. This hypothesis needs to be tested and should be modeled into future versions of the system.
[6] Geyer-Schulz, A., Holland Classifier Systems. APL Quote Quad, 25(4): 43-55. Jun. 1995. Proc. of the International Conference on APL, San Antonio, TX, 1995. [7] Gould, S. J., The Panda’s Thumb. New York: Norton & Co, 1980. [8] Holland, J. H., Hidden Order: How Adaptation Builds Complexity, Reading, Mass.: Addison-Wesley 1995. [9] Huberman, B. A. & Glance, N. S., Evolutionary Games and Computer Simulations, Proc Natl Acad Sci USA; 90 (16): 7716-7718, 15 August 1993.
Additionally, the current system models the few basic immune cell entities that are important in the context of HIV infection. Essentially, the simulation is T- helper cell specific. Cytotoxic Tcells have not been modeled. Although their effects are less important with respect to HIV, they should be useful in getting a fuller picture of HIV dynamics. Also, because the system consists of autonomous agents, it is easy to add new cells to the system without any detrimental effects and with a relatively small amount of effort. The system’s core functionality is independent on the types of cells, their rules and behaviour. Hence, one interesting way in which the system may be used is by testing out artificially made cells with new behaviours. This can be useful in testing the effect of introducing artificial agents into the body which perform a particular, known function and interact with other cells in known ways. Such a facility would help biologists to develop and test new kinds of drugs and treatments once the simulation is made comprehensive enough to include most of the immune system’s features.
[10] Axtel, R. L., Effects of Interaction Topology and Activation Regime in Several Multi-Agent Systems, SFI Working Paper #00-07-039, 2000. [11] Java AWT: Delegation Event Model (1997). Retrieved March 1, 2004, from http://java.sun.com/j2se/1.4.2/docs/guide/awt/1.3/designspec /events.html [12] Kleinstein, S. H. & Seiden, P. E., Simulating the Immune System. Computing in Science and Engineering, 69-77. Jul.Aug. 2000. [13] Nowak, M. A. & McMichael, A. J., How HIV Defeats the Immune System, Scientific American, Aug. 1995. [14] Perelson, A. S. & Weisbuch, G., Immunology for Physicists. Reviews of Modern Physics, 69(4): 1219-1267, Oct. 1997. [15] R.E. Hurlbert, http://www.slic2.wsu.edu:82/hurlbert/micro101/pages/Chap1 6.html#AIDS_course_disease
7. ACKNOWLEDGMENTS Our thanks to ACM SIGAPP for allowing us to modify templates they had developed.
[16] Roitt I., Essential Immunology. Ninth Edition. Pub. Blackwell Science 1997.
8. REFERENCES
[17] Seiden, P. E. & Celada, F., A model for simulation cognate recognition and response in the immune system. Journal of Theoretic Biology, 158(3): 329-357, 1992.
[1] Beauchemin, C., Modelling the immune system. A reading course final report. Department of Physics, University of Alberta. December 2002. [2] Bernaschi, M. & Castiglione, F., Design and implementation of an immune system simulator. Computers in Biology and Medicine, 31(5): 303-331, 2001.
[18] Stadnyk, I., Schema Recombination in Pattern Recognition Problems. Proc. 2nd International Conference on Genetic Algorithms and their Applications, Hillsdale, N. J., 1987. Lawrence Erlbaum Assoc.
[3] Celada, F. & Seiden, P. E., A Computer Model of Cellular Interactions in the Immune System. Immunology Today, 13(2): 56-62, February 1992.
[19] Stephen Wolfram: Articles on Cellular Automata (1983). Retrieved March 1, 2004, from http://www.stephenwolfram.com/publications/articles/ca/
[4] Forrest, S. et. al., Using Genetic Algorithms to Explore Pattern Recognition in the Immune System. Evolutionary Computation, 1(3): 191-211, 1993.
[20] Zorzenon dos Santos, R. M. & Coutinho, S., Dynamics of HIV infection: A cellular automata approach. Physical Review Letters, 87(16) 2001.
[5] Forrest, S. & Perelson, A. S., Genetic Algorithms and the Immune System. In H. Schwefel & R. Maenner (Eds.).
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