Modeling and Simulation of the Innate Immune System

Anilkumar Krishnan

MS Project - Report

Department of Computer Science University of Colorado at Colorado Springs

Colorado, USA

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Table of Contents ABSTRACT ................................................................................................................................................................................. 3

1. Introduction: ............................................................................................................................................................................... 4 2. Related Research: ....................................................................................................................................................................... 6 3. Immune System Overview: ....................................................................................................................................................... 7 4. Innate Immune System: ............................................................................................................................................................ 9 6. Software Architecture: ............................................................................................................................................................ 14 7. Program Descriptions:............................................................................................................................................................. 17 8. How to Run the Programs: ..................................................................................................................................................... 21 9. Simulation Results: .................................................................................................................................................................. 22 10. Conclusion and Future Work: ............................................................................................................................................. 25 11. Acknowledgements: .............................................................................................................................................................. 26 12. References: .............................................................................................................................................................................. 27

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ABSTRACT When a foreign substance or microbe (antigen) is introduced into our body, the immune system acts to neutralize or destroy the foreign body. The natural immune system provides a multilevel defense against the intruders through the collective and coordinated response of approximately 1012 cells. Computer models and simulations are beginning to play a role in immunology with the establishment of a new experimental technique called ‘in machina’ or ‘in silico’. Our primary focus here is to model and simulate the innate immune response of the body through the interactions of Complement Proteins, various immune cells like Macrophages, Neutrophils, Natural Killer cells etc. and pathogens like Bacteria and Viruses. The overall computer model is developed using an object-oriented language (C++) and the simulation provides the numerical and statistical information of the innate immune response. The current model gives a very simplified simulation of the innate immune system.

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1. Introduction: The natural immune system is a complex system, which efficiently employs several mechanisms for defense against foreign intruders. It provides a multilevel defense against the invaders through collective and coordinated response of approximately 1012 cells. Computer models and simulations are increasingly becoming popular in biology, specifically in the field of immunology using the technique, called ‘in machina’ or ‘in silico’ [2, 3]. Immune systems of higher life forms like vertebrates provide three levels of defense.

1. Physical Barriers (skin and mucus membrane) 2. Innate Immune System and 3. Adaptive Immune system

About 99% of animals get along with only physical barriers and the innate immune system to defend them [1, 12]. Adaptive immunity that is usually found only in vertebrates is mediated by B- and T-lymphocytes, clonally distributed and characterized by specificity and memory. In contrast, innate immunity is characterized by its non-specificity, and is mediated by the actions of Phagocytes, Natural Killer (NK) Cells and Complement proteins. Most of the research in the immune response has primarily concentrated on adaptive immunity and not on innate immunity. However, some studies have demonstrated that many aspects of the innate immunity play a crucial role in the immune response of higher organisms [4]. They also play an important role in the stimulation of the adaptive immune response. The main function of the immune system is to recognize all cells and molecules within the body and categorize them as self or non-self. Our body maintains a large number of immune cells, which circulates throughout the body. All these immune cells are developed in the bone marrow and travel to different part of the body through the blood vessels. Different types of immune cells have different roles to play in the overall immune response. Altogether, we can see the immune system as a collection of independently operating cells without any central control. Chemical signals are also involved in the system to provide communication between these cells. In this study we have attempted to model several features of the innate immune response like self-nonself recognition, different phases of a macrophage’s life and their functions, attraction of neutrophils and Natural Killer cells to the region of attack and pathogen invasion and proliferation. All immune cells are modeled as classes in the object-oriented technology. Cellular interactions are modeled based on cellular automata methodology. A graphical user interface is provided so that the user can change parameters to model the immune cells and pathogen (bacteria) and run the simulation with a variety of inputs. The graphical display of the simulation is created using the SDL package and that provides visual images of what is going on in the simulation. The organization of this report is as follows. Section 2 provides a brief overview of the related research. Section 3 outlines the overview. Section 4 outlines the biology of the innate immune system and explains the features and functions of which we can model to provide the simulation. Section 5 gives a brief overview of the systems approach we are using to develop the model. The basic software architecture used to simulate the model and object-oriented class structure is discussed in Section 6.

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Program descriptions and the hierarchy of the class structure are included in section 7. Section 8 gives a brief discussion on how to run the program. In Section 9 we analyze the simulation results and finally in Section 10, we conclude the paper by discussing the results and future directions for research.

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2. Related Research: A broad coverage of mathematical modeling of immune response is available in [15]. An overview of system analysis in the field of immunology can be found in [12]. In this paper particular attention is devoted to the mathematical models of the immune response including ordinary differential equation models. A cellular automaton model of the immune system called IMMSIM is described in [2]. IMMSIM has a running simulation that includes B-cells, T-cells, APCs (Antigen Presenting Cell), antigens and antibodies. This model attempts to incorporate the humoral side of the immune response that is part of adaptive immunity. SIMMUNE [3] presents a new approach to the simulation and analysis of immune system behavior. The simulations that can be performed with SIMMUNE are based on immunological data that describe the immune system players on a microscopical scale by defining cellular stimulus response mechanism. [5] discusses a number of immunological problems in which the use of physical concepts and mathematical methods has increased our understanding. The suitability of cellular automata models for simulating the dynamics of immune systems is described in detail in [6, 7, and 8]. All of these studies and research are mostly focused on adaptive immune responses.

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3. Immune System Overview: The defense mechanisms used by the body against an attack from a foreign substance (antigen) include physical barrier, innate immune cells and adaptive immune cells. Some of these mechanisms are present prior to exposure to antigens and their action is non-specific: that is, they do not discriminate between different antigens and their response doesn’t change upon further exposure to the same antigen. This is an example of natural immune response. The other mechanism with more specific behavior is induced by antigens and their response increases in magnitude and defense capabilities with successive exposure to the same antigen. This is part of the adaptive immune response. The three levels of defense provided by higher life forms are discussed in detail below. The physical barrier including skin and mucus membrane that line our digestive, respiratory and reproductive tracts provides the first line of defense against the foreign invaders. The physical barrier provides a large area that is defended against attacks by invaders like bacteria and viruses. So to cause trouble these foreigners must first get past the physical barrier. The second line of defense is provided by the innate immune system, which meets the invaders who get past the physical barrier. Main players of this innate system are macrophages, neutrophils, Natural Killer (NK) cells, and complement protein. Macrophages are made in the bone marrow and circulate through the blood (2 * 109 young macrophages are present at any point in time) looking for a place to escape to the tissue, where they normally act as garbage collectors. They are also able to sense chemical signals from invaders and reach out to grab and engulf them (phagocytosis). Macrophages some times eject some components of the ingested invaders out into the tissues, and the debris can signal other immune system players. Also, they produce cytokines (a kind of protein that functions as a chemical messenger that facilitates communication between other cells of the immune system). Cytokines can alert other cells such as neutrophils traveling in nearby capillaries, and the battle is on. Complement proteins are usually very abundant in the blood and tissue. The complement complex consists of 20 different proteins that work together to destroy invaders by punching holes in the surface membrane of a pathogen and signal other immune system players to join the attack. Another important player in the innate immune system is the NK cell. NK cells resemble hyper activated macrophages, and they can kill the invaders by secreting proteins such as perforin. Fig 1 shows the overview of interactions between players of innate immune response.

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Fig 1 The third level of defense, usually seen only in vertebrates, is the adaptive immune system. The main players of adaptive immunity are B-cells, T-cells, and antibodies. B-cells are the cells stemming from the bone marrow and they make antibodies that can recognize specific antigens, which are the foreign substances that invade the body such as virus, bacteria. Clonal selection is the process by which the immune system remembers a response to a particular antigen so that it can respond to the antigen immediately and effectively if it encounters it again. T-cells are the cells that generally perform the function of killing infected cells and regulating the production of antibodies by B-cells. The adaptive system has B cells that can make antibodies to fit every possible antigen, but antibodies have to be custom made according to the principle of clonal selection. It will take a week or two to make these antibodies. In the mean time, the body needs something to fight off the invader or at least hold it until the antibodies arrive. This is where the innate immune system comes in. Innate immune players are already in place, and they are ready to defend against the common invaders our body meets on a day to day basis. In fact, in many instances, the innate immune system is so effective and so fast that the adaptive immune system never even kicks in. Until recently, immunologists thought that the only real function of the innate system was to provide a rapid defense that would hold off pathogens until the adaptive immune system could get started. However, it is now clear that the innate system does much more than that. The innate system has evolved receptors that detect the presence of the common pathogens we encounter in daily life. In contrast to the innate system, whose receptors are precisely tuned to detect common invaders, the adaptive immune system’s receptors are totally unfocussed- they are so diverse that they can probably recognize any organic molecules in the universe. As a result, the adaptive system is clueless as to which molecules are dangerous and which are not. The adaptive system depends on the innate system to recognize this. Not only does the innate system alert the adaptive system to danger, the innate system also instructs the adaptive system on which weapons to use and which part of the body these weapons should be deployed.

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4. Innate Immune System: The importance of the innate system in the immune response is easy to understand from its quick response to common invaders. The adaptive system takes some time to kick off the response. So, without a quick innate immune response, pathogens like bacteria entering our body can multiply very quickly. For example, a single bacterium doubling in number every 30 minutes could give rise to roughly 100 trillion bacteria in one day. The innate immune system protects against many extra cellular bacteria or free viruses that are found in blood plasma, lymph, tissue fluid or interstitial space between cells. However innate immunity normally is not adequate for defeating microbes that burrow into cells, such as viruses, intracellular bacteria etc. The innate immune system includes three types of agents namely: phagocytes, Natural Killer Cells and Complement Proteins. Phagocytes: Professional phagocytes include Macrophages, Neutrophils, and Eosinophils. When a phagocyte encounters a bacterium it first engulfs the bacterium in a pouch called a phagosome. This phagosome is then taken inside the cell where it fuses with another vesicle called a lysosome that contains powerful chemicals and enzymes that can destroy the bacteria. This process is called phagocytosis. Macrophages are the most versatile of the professional phagocytes. They can exist in three stages. In resting state their primary function is garbage collection. They engulf anything which is marked as non-self. Self/non-self recognition is achieved by having every cell display a marker based on the major histocompatibility complex (MHC). Any cell not displaying the marker is treated as non-self and attacked. Macrophages can live for months in this state and just collect garbage. When some of these resting macrophages receive signals that alert them that invaders are present, they enter a primed stage. In this stage when the macrophages engulf invaders, they can function as APCs (Antigen Presenting Cells) and can display fragments of the invader’s protein (peptides) on their surfaces. The signal that primes a resting macrophage is a cytokine (intercellular communication molecule). When a macrophage receives some direct signals (like LPS – lipopolysacharide, found on the outer cell wall of Gram negative bacteria), they become hyperactivated. In the hyperactivated stage, macrophages grow larger and increase their rate of phagocytosis. Hyperactivated macrophages secrete the cytokine TNFα (tumor necrosis factorα) which can kill virus infected cells and inform other immune cells that the battle is on. Neutrophils are the most abundant and also the most important type of phagocyte. They constitute 68 – 70 % of white blood cells, and they are produced in bone marrow at a rate of 100 billion/day. They live for a very short time compared to other immune cells, and are programmed to die in 2 to 5 days. Neutrophils usually circulate through the capillaries in an inactive state and are swept along by the blood at a rate of 1000 microns/sec. When a pathogenic attack occurs, the cytokines produced by macrophages and lipopolysaccharide (LPS) released by bacteria can attract neutrophils to the region of infection. There are biological/chemical mechanisms which can stop the flow of neutrophils and attract them to the region. One characteristic of neutrophils is that they will exit the blood and come to tissue only if the infection is serious. Only when sustained expression of alarm from many phagocytes occurs does neutrophil recruitment take place.

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Natural Killer Cells: These are the other important players in the innate immune system. There are different kinds of NK cells with somewhat different properties. Natural killer (NK) cells apparently do not act directly on extracellular bacterial invaders. They can act on host cells that have been invaded by intracellular bacteria. They also act on cancer cells and host cells that harbor various types of viruses. The signal which activates NK cells to attack is a deficiency of MHC on the surface of the abnormal cells. Like neutrophils, NK cells use the biological/chemical mechanisms which cause them to leave the blood and enter tissue at a site of invasion. NK cells can kill abnormal cells, and they have two methods of killing. 1) They can produce pores in the target cell by secreting perforin molecules. NK cells also release a variety of enzymes (granzymes A-G) and tumor necrosis factor-α (TNF-α) which lead to cell lysis. 2) NK cells can use a protein called Fas ligand (FasL) that is expressed on the NK cell surface. FasL can interact with a protein called Fas on the surface of the target. When these two proteins connect, they can signal the target cell to commit suicide by apoptosis. Activated NK cells can also produce interferon-γ which protects normal cells from virus invasion. Complement System: Complement system consists of 20 different proteins that work together to destroy invaders and signal other immune system players to join the attack. Complement proteins start to be made during the first trimester of fetal development. They are produced by the liver, and they are very abundant in blood and tissues. The complement system must be activated before it can function. There are two types of complement activation, the classical pathway which depends upon antibodies, and the alternative pathway which involves interactions with various types of bacteria, bacterial wall components and other agents. In this pathway the most abundant complement protein C3 is continuously being clipped to yield C3b, which can bind to amino or hydroxyl groups (many of the cell surface of invaders have amino or hydroxyl groups). Another complement protein B binds to C3b, and complement protein D comes along and clips off part of B to yield C3bBb complex. This C3bBb acts like a chain saw that can activate C3 proteins and convert them to C3b. Once the C3 molecule is cut to produce C3b, it can bind to an amino or a hydroxyl group in the bacterial surface. This process is continuous, so finally there will be a lot of C3bBb that can cut even more C3 molecules. C3bBb can cut off part of another complement protein, C5, and the clipped part C5b can combine with other complement proteins such as C6, C7, C8 and C9 to make a membrane attack complex (MAC). The MAC can open up a hole in the surface of the bacteria. Unlike invaders, our own cells are equipped with proteins that inactivate complement proteins. The main features of the complement system include its quickness and its presence at high concentration in blood and tissue. It will attack any invaders with cell surfaces having amino or hydroxyl groups that are unprotected. The main functions of complement protein include building MAC to kill invaders and acting as chemo attractants, that recruit other immune system players to the site of infection. The main role of the immune system is to recognize all cells and molecules within the body and categorize them as self or non-self. The non-self cells are further categorized in order to induce an appropriate type of defensive mechanism [10]. All discrimination between self and non-self in the immune system is based upon chemical bonds that form between protein chains, and we can model these protein chains as binary strings of fixed length [9]. The key features of the innate immune systems which provide the several important aspects for modeling and simulation may be summarized as follows:

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Recognition: The immune system can recognize and classify different patterns and generate selective responses. This is achieved by intercellular binding. Self-nonself discrimination is one of the main tasks of recognition. Distributed presence: The immune system is inherently distributed. Immune cells originate in the bone marrow and they circulate throughout the body through blood, lymph, lymphoid organs and tissue spaces. As these immune cells (phagocytes and NK cells in the case of innate response) circulate, if they encounter any attack from invaders, they stimulate specific immune responses. Self-regulation: The mechanisms of immune response are self-regulatory in nature. There is no central control organ in the immune system that turns on or turns off the immune response. Threshold mechanism: Immune response and the proliferation of the immune cells take place above a certain threshold.

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5. Systems Approach and Simulation Immune system response to invasion by infectious microbes is a dynamic process in which the potentially uncontrolled growth of invader is countered by various protective mechanisms. A system-level approach [11] can be used to enable us to understand the immune system in it entirety by investigating: (1) the structure of the system, (2) the dynamics of the system and (3) methods to control the systems. Fig (2) gives the high level view of the innate immune system.

Fig 2 In this work we are attempting to simulate a small region of a tissue with two/three blood capillaries. All the phagocyte cells such as macrophages and neutrophils and NK cells are created in the bone marrow. Rate equations can be used to model the creation of these cells. The current simulation uses a constant number of macrophages, which enter the simulation and are allowed to interact with other cells. Neutrophils are the most abundant (and important) of all phagocytes. Neutrophils can be recruited to the region of attack based on the concentration of the chemoattractants (cytokines), reaching the endothelial cells of the blood vessels. In our simulation we can model this process as number of neutrophils attracted to the region as a function of the cytokine concentration produced by the macrophages in the region, which in turn depends on the number of pathogens present in the tissue. Complement proteins and other chemicals like cytokines in the blood and tissues can be modeled using diffusion equations. Current simulation uses concentrations of different chemicals as a property of the grid cells. Interactions between the immune cells and pathogens can be modeled using Cellular Automata. Cellular Automata (CA) can be used as an alternative to differential equations for modeling of physical and biological systems. Using ordinary differential equations to model the immune system has several limitations [2]. For example, differential equation models assume sufficiently large population sizes from which the properties of essentially identical entities can be calculated. Each type of cell of the immune system has a unique life history that defines their particular interaction with the environment. The application of the cellular automata methodology to model the system will overcome some of the limitations of differential equations, and moreover, it enables the representation of the components and processes of interest in biological language and characters [2]. A cellular automaton is a dynamic system whose evolution is completely described by local interactions and is discrete in both space and time [14]. Fundamental characteristics of cellular automata include:

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1) They consist of a discrete lattice of sites. 2) They evolve in discrete time steps. 3) Each site takes on a finite set of possible values. 4) The value of each site evolves according to the same deterministic rules. 5) The rules for the evolution of a site depend only on the local neighborhood of sites around it.

Cellular automata were invented to investigate how simple building blocks could locally cooperate to produce aggregates with interesting behavior. The building blocks are automata living on a grid. Their rules define how the change in state of a single automaton at the next time step depends on its own current state and the states of its direct neighbor automata. In the immune system, all activities are based on the actions of cells reacting to their direct neighbor cells and molecules. There is no central supervision of immune responses. In this work we developed a CA model of the innate immune system where the cells are simulated by automata that may carry their state information with them while they are moving on the three dimensional grid. Depending on the objects they encounter they may change their state.

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6. Software Architecture:

Fig 3 High level architecture of the software for simulating the immune system is shown in fig 3. The main driver which starts the modeler, brings up the graphical user interface (GUI) through which the user can input the parameters to model the pathogens and all the immune cells. GUI is a java application with tabbed panels. The user can select the corresponding panels for each cell type and change the parameters. There is one panel in which the user can change the grid size and other properties of the grid. (fig 4a-e). When parameters are changed and then one clicks on ‘create/run’, the modeler will start the simulator. The simulator will create all the immune cells and pathogens and place them on the grid/middle layer and control all the interactions between them. The middle layer represents the lattice of site in the CA model, which is a three dimensional grid, where the simulator can put all the objects (cells and pathogens). The display engine can read information from the middle layer and create graphics out of this information so that the user can view the interactions between the objects. A separate panel for the statistical graph is also provided in the display (user can turn-off this feature) which provides the statistics of the immune cells and pathogens in the grid.

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Fig 4a-e Both immune cells and pathogens are modeled as objects of the respective classes using object-oriented methodology. Properties and methods of the object are provided to interact with other objects in the simulation class hierarchy of the object in the simulation is shown in fig 5. Functions of each of the simulated immune cells and bacteria in the simulation are explained below. Bacteria Because the bacterial membrane lacks an MHC marker of self, it will be identified as non-self. Living and reproduction are two of the main functions of bacteria. In the normal environment a bacterial cell will divide when it reaches the mature age, or die if it cannot divide and when it reaches its life span. Dead bacteria are able to release LPS molecules in the grid so that immune cells can sense and migrate to the area of invasion.

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Endothelial Cell The cell membrane is marked as self cell (MHC). These are the static cells in the simulation that represent the tissue. They don’t have any other function assigned to them. Macrophage The macrophage cell membrane is marked with an MHC marker which identifies it as a self cell. Macrophages can recognize and categorize other objects with which they interact as self/non-self. They engulf and ingest all non-self cells. They can produce chemo attractants like TNF, IL1 AND IL6 when they ingest bacteria. They can sense and follow LPS. Normally they move randomly and behave like garbage collectors. They are relatively long-lived phagocytes. Neutrophil The cell membrane of a neutrophil is marked with an MHC marker that identifies it as a self cell. Neutrophils can recognize and categorize other objects with which they are interacting as self/non-self. The can engulf and ingest all non-self pathogens. They follow chemoattractants and migrate to the region of infection. Natural Killer Cell The NK cell membrane is marked with an MHC marker which identifies it as a self cell. NK cells can recognize and categorize other abnormal cells with which they interact whenever these cells have below normal levels of MHC. They kill cancer cells, virus-infected cells and cells infected with intracellular bacteria. Blood Vessel These are basically the endothelial cells on the blood capillary wall. They can sense the chemicals diffused in the grid and when the chemical concentration reaches the threshold value, the endothelium will send out appropriate immune cells to the grid.

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7. Program Descriptions: For the innate immune system we tried to model some features and behavior of Phagocytes (Macrophages & Neutrophils), Natural Killer Cells and a pathogen (a general harmful bacteria) as objects and the class structure of these objects in the simulation are as shown below.

Important methods in these objects are briefly described below: Bacteria: live () move () die () reproduce () trailChem () – Bacteria leaves a specific concentration of LPS in the grid. BloodVessel: createNaturalKiller () createNeutrophils () inspectForChemicals () – Blood vessel always check the concentration of chemo attractant in the grid cells in the simulation

BasicCell

Bacteria ImmuneCell EndothelialCell

Phagocytes NaturalKillerCell

Macrophage Neutrophil

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Macrophage: start () eat () – Engulfing non-self cells. produceChemoattr () – Produce chemo-attractants and leaves them in the grid cells to attract other immune cells. move () live () moveMacrophage () – Follows the LPS. Usually macrophages move randomly but when they sense the presence of LPS they become active and start moving in the direction of LPS concentration. NaturalKillerCell: start () kill () – Kill nonself cells and abnormal self cells. live () moveNaturalKiller () – This is the activated move towards a target to kill moveNKRandom () – Regular movement of NK cells Neutrophil: start () kill () move () moveNeutrophil () – This is the movement of a neutrophil towards the pathogen following the concentration gradient. Grid: This class has methods to check whether the grid cell is already occupied by some entities (isOccupied () ) and also to set the entities in the grid (setOccupied () ). loadChem () – This is to set the concentration of chemicals in the grid cells. getConcentration () – This is to get the concentration of a specific chemical n the grid. The basic algorithm used in the simisys framework is:

1. Setup the 3 dimensional grid to represent the small portion of a tissue 2. Instantiate blood capillaries (in the current simulation 3 capillaries will be created) 3. Place Endothelial cells (these are to represent the static cells in the tissue) in the grid 4. Place some initial number (it can be specified in the GUI part of the modeler) of bacteria in

some random positions of the grid. 5. Place some macrophages (population can be specified through GUI) in the grid at some random

positions distributed in the grid. 6. When a macrophage engulf a bacteria it produces chemo attractants 7. Chemo attractants and other chemicals can diffuse through the grid cells.

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8. When blood capillary cells identify the chemo attractants, they start sending out Neutrophils & NK cells.

9. Neutrophils follow the chemical gradient and move towards the pathogen (bacteria) and start destroying them.

10. NK Cells can also follow the chemical gradient to move towards the pathogen. Bacteria, Macrophages, Neutrophils and NK cells are running on their own threads (One thread per cell type). Similarly, there is a separate thread for the chemical diffusion process also. Providing separate threads for each cell type have the advantage of simulating at least the behavior of each cell type independently. Display Engine: An important feature of the current framework is the emphasis placed in the visualization aspect of the simulation. We have developed a graphical display that allows us to see the events and process in the innate immune response in real time in a simplified and scaled manner. This feature of the framework definitely helps to improve the educational value of the current simulation. We have used the openGL and SDL technology to develop this display engine. The display engine is coded to display the Cellular Automata characteristics of the model. A provision for statistical display is also available in the current simulation. The visualization aspect of the simulation is a very important part of development as the focus of the project has been to differentiate itself from other simulations by having a display that can be used for educational purposes such as the students can use it to learn the working of the immune system. With that aspect in mind we have developed a tightly integrated display engine that is easy to set up and can be adapted to handle introduction of entities in further releases. The engine is based on the SDL library (www.libsdl.org) , that is an open source package suitable for direct screen manipulation. The advantage of using the SDL package is the ability to paste an image on the screen at a specified location. The SDL library can be used for normal 3D operations. The engine operates as the following:

1. The user specifies the images that are to be specified for each type of entity present in the simulation in the initial portion of the program. The engine ignores display of any entity that has not been specified.

2. The engine then formats the image loaded for transparent background color and performs scaling of each of the entities loaded to have a series of increasingly larger images

3. Based on the user’s key presses, the engine decides the area of the simulation that is to be displayed.

4. The engine then scans the section of the grid that has been specified. In the current version of the simulation, the engine scans a 3D section of the grid based on hard-coded values, and recognizes each of the entities present in that grid position.

5. For each of the entities recognized in the grid, the engine computes the distance of the entity from the front of the screen, that is, the closest grid position to the user. Based on this distance, the image is pasted on screen such that a smaller image is pasted for an entity further than an entity closer to the user. This gives it the impression of a 3D engine, without the computational expense.

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Features of the engine are: 1. Uses the SDL library that comes preloaded in Red Hat Linux 8.0+. 2. Features six directions of scrolling 3. Can toggle between different tissues (different grids). 4. Can easily handle further entity types.

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8. How to Run the Programs: The source code and binary of the current version of simisys is available in the Dirac machine at CS lab. From a terminal, go to the simisysv01 directory and type run.pl (Perl code to start the java GUI). This will bring up the java GUI where the user can enter parameters like the initial number of cells and their maturity age etc. Once we enter these data and click to ‘create’ icon on the GUI, the simulation will start running. Arrow keys can be used to navigate the 3D grid vertically and horizontally and page up and page down key can be used to navigate through the depth of the 3D grid.

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9. Simulation Results:

Fig 5

The current simulation has endothelial cells, macrophages, neutrophils, NK cells and bacteria. This is a simplified model of the innate immune system. Most of the cellular mechanisms in this model are strong simplifications of the processes in real innate immune system. When the simulation starts, the initial number of bacteria selected will be placed somewhere in the grid where they start proliferating. In this simulation macrophages act as garbage collectors, and they engulf everything except self cells. When a macrophage ingests bacteria it produces some chemicals that diffuse into the grid cells. When the concentration of the chemicals reaches some threshold value at the blood vessel cells, they start to send out neutrophils to the infected region, and these neutrophils can follow the chemical concentration gradient to reach the bacteria. Similarly, when NK cells encounter abnormal host cells, they kill them. The dead bacteria release LPS molecules into the grid. Macrophages are able to sense and follow the LPS concentration gradient. Also, blood vessels are able to send out more neutrophils to the region when LPS concentration reaches certain threshold value. The following graphs represent the results of the simulation run for various initial conditions. Fig 6 shows the uncontrolled proliferation of bacteria in the absence of any immune response.

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l Fig 6 Figs 7 - 9 represent the simulation results when macrophages and neutrophils are present at the site of infection. In some results the bacteria are not getting a chance to proliferate and in other results the simulation can keep their number low for sometime (equivalent to 1-2 days) of simulation cycles. The differences in the results can be explained as follows: All the simulation results are based on the initial conditions. In the first graph probably all the bacteria are placed near a blood vessel. So when a macrophage engulfs a bacterium, the chemicals produced can attract more neutrophils to the region quickly, and the bacteria don’t get a chance to proliferate.

Fig 7

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Fig 8 In the other case it takes some time to recruit other cells to the region, and by that time the bacteria get a chance to start to proliferate. Then, the accumulation of immune cells at the infected region brings the bacterial number down. Later the bacterial number goes up uncontrollably. This can be explained by postulating that some bacteria escaped to a remote corner in the grid, where blood vessels are not present, so they can multiply to a large number when neutrophils are busy with other bacteria nearer the blood vessels.

Fig 9

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10. Conclusion and Future Work: The current simulation model of the innate immune system simulates the self/non-self recognition, the garbage collection of macrophages, attraction of neutrophils to the region of infection, and the proliferation and destruction of bacteria. The graphical part of the simulation software provides a statistical display, which can be used to show the numbers of various cells in the response over a different time period. This is a very simplified model of the innate immune response. There are many other complex process and players involved. As we discussed earlier, even though it seems the immune system is a large complex system, it is the resultant of the activities and process of 1012 cells of different kinds. Each cell type has its own behavior and attributes. Also, each independent cell behaves independently according to its own attributes and its own local context. It’s very difficult to model each of the cells in the immune system. Even though each cell behaves separately on its own, but as a result of all their activities, a global behavior of the immune response is revealed. One of the goals of the current work was to come up with a frame work which can be used and developed further to understand the global behavior of the immune system by modeling the relevant features of individual cells or cell types. In the future we can add other types of immune cells and molecules and thus make the model more realistic. Also, the GUI can be improved so that users can enter parameters of the model as well as different types of cells and molecules to make the simulation more flexible and useful. The integration of this part of simulation with the adaptive immune simulation will provide the more realistic immune response. The use of XML like languages to describe the cellular and molecular behavior in the modeling and simulation will greatly benefit to modify the system behavior very easy. This would allow experiments with specific infections under specified conditions. We can also improve the graphical aspect by making it visually more realistic. This would make the software have more educational value, so that students can use the simulation as a tool to learn and do experiments with. Moreover we need to spend more time with biologists and mathematicians to figure out the exact model of the system. Accuracy of the simulation depends on the accuracy of the model. The model needs to be validated with biological data as well.

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11. Acknowledgements: This work utilizes the display engine developed by SIMISYS group of Department of Computer Science, University of Colorado at Colorado Springs. I thank Kaushal Chandrashekar for his help in integrating the basic simulation with the display engine.

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12. References: [1] L. Sompayrac, How the immune system works, Blackwell Science, Inc, 1999 [2] S.H.Kleinstein and P.E. Seiden, Simulating the Immune Systems, Computing in Science and Engineering, Jul/Aug 2000. [3] M.Meier-Schellersheim and G.Mack, SIMMUNE, a tool for simulating and analyzing Immune System Behavior, http://arxiv.org/PS_cache/cs/pdf/9903/9903017.pdf, 2001 [4] S.Akira, Toll-like receptors: lessons from knockout mice, Biochemical Society Transactions, vol 28, part 5, 2000 [5] A.S. Perelson and G. Weissbuch, Immunology for physicists, Rev. Mod. Physics, vol. 69, No. 4 1997 [6] M. Bezzi, Modeling Evolution and Immune system by Cellular Automata, Dec, 2000, Researchindex.com [7] M. Bernaschi, F. Castiglione and S. Succi, An high performance simulator of the Immune Response, Researchindex.com [8] A. Grilo, A. Caetano, and A.Rosa, Immune System Simulation through a Complex Adaptive System Model, Researchindex.com [9] S.A.Hofmeyr and S.Forerest, Architecture for an Artificial Immune System, To appear in the journal of evolutionary Computation. [10] D. Dasgupta, Artificial Immune systems and their applications, Springer, 1999. [11] H.Kitano, Foundations of System Biology, MIT Press, 2001 [12] R. Mohler, C. Bruni and A. Gandolfi, A systems Approach to Immunology, Proceedings of the IEEE, vol. 68, No 8, August 1980 [13] R.S. Desowitz, The Thorn in the Starfish – How the immune system works, WW Norton & Company, 1987. [14] S. Wolfram, Theory and Applications of Cellular Automata, World Scientific Press, Singapore 1986.