Modeling of Naïve Lymphocyte Signaling Pathway

Isaac Barjis

, Aliyah Amin

, Amber Barjis

and Saif Amin

Department of Biological Sciences, New York City College of Technology, 300 Jay St, Brooklyn, U.S.A.

Syosset High School, 70 S Woods Rd, Syosset, U.S.A.

Department of Biology, Stony Brook University, 100 Nicolls Rd, Stony Brook, U.S.A.

Keywords: System Biology, T Helper Cell Activation, Modeling of Naive T Helper Cell, Petri Net and System Biology,

CPT Modeling of Naive T Helper Cell Activation.

Abstract: The immune system in general and T cells, in particular, play a critical role in protecting the organism from

infection and repairing damaged tissue. T cells are not only key components of the immune system but are

also central in mobilizing the adaptive immune responses at all stages of fighting infection. Furthermore,

studies have shown that subsets of T helper cells are critical for the activation of antitumor responses. T cells

have been intensively studied by both experimental immunologists and modelers. However, none of the

research papers represent the complete process that will show the link between antigen-presenting cells,

subtypes of T cells, and other signaling pathways. In this paper, we illustrate the first steps of an automation

process for quantitative modeling, of the naïve T lymphocytes activation pathway by discrete modeling

language using colored Petri nets (CPN). Modeling, simulation, and analyzing T cell activation signaling

pathways will improve our understanding of the structure and dynamics of these pathways considerably. Petri

nets have been proposed as an effective formalism for Systems Biology and modeling of metabolic pathways.

1 INTRODUCTION

Advances in experimental techniques and

biotechnology have revolutionized our understanding

of cellular and molecular processes. These techniques

have generated vast amounts of empirical data that

shed light on the intricate structure and mechanisms

of signaling pathways. This wealth of experimental

data provides valuable insights into the behavior of

cells and molecules under various conditions,

allowing scientists to decipher the complexities of

biological systems (Mueller SN et. al. 2013). To make

sense of this vast amount of experimental data and

perform computational analysis on biological

systems, it is necessary to represent and encode this

data in a way that can be easily processed by

computers. This data representation challenge is a

well-known problem in the field of bioinformatics

and has been extensively studied using various

knowledge representation techniques from computer

models. One approach that has proven to be

particularly useful in modeling molecular and

immune signaling pathways is based on a

mathematical concept called Petri Nets. Petri Nets,

that were initially developed in the early 1960s by

Carl Adam Petri and have since been adapted and

extended in many directions. They provide a formal

framework for representing and analyzing the

dynamic behavior of concurrent systems (Petri C.A.

1962) and has subsequently been adapted and

extended in many directions (Murta T. 1989). Petri

Nets offer a powerful way to model and simulate the

intricate interactions and dynamics of molecules and

immune signaling pathways. They provide a

graphical representation of the system's components

and their interactions, allowing researchers to study

the behavior of the system over time. This

mathematical approach enables the exploration of

complex biological processes, such as signal

transduction, gene regulation, and immune response,

in a computationally tractable manner.

By employing Petri Nets, researchers can capture

the essential elements of immune signaling pathway,

including molecular species, their states, and the rules

governing their interactions and activation. This

modeling approach enables the simulation of various

conditions and perturbations to better understand the

immune system's behavior and predict its response to

external stimuli such as pathogens. The utilization of

Petri Nets in the modeling of molecular and immune

Barjis, I., Amin, A., Barjis, A. and Amin, S.

Modeling of NaÃ

rve Lymphocyte Signaling Pathway.

DOI: 10.5220/0012127200003546

In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023), pages 385-392

ISBN: 978-989-758-668-2; ISSN: 2184-2841

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

385

signaling pathways has proven to be a valuable tool

in bridging the gap between experimental data and

computational analysis. It provides a systematic and

quantitative framework to study the dynamics and

behavior of complex biological systems, aiding in the

development of novel therapeutic strategies and the

advancement of our understanding of cellular

processes (Rzosinska, K. 2017).

1.1 Petri Net

In the realm of systems analysis and modeling, Petri

nets have emerged as a powerful mathematical tool

for representing, simulating, and analyzing various

complex systems. Petri nets are particularly valuable

in modeling biological systems, such as T cell

activation, where dynamic interactions and events

play a crucial role. The fundamental components of a

Petri net include circles, known as "places,"

rectangles, known as "transitions," and directed arcs

that connect the places and transitions. Each place

represents a state of the system, while transitions

represent events or actions that can occur within the

system.

transition representing an operation (action)

regular Petri net place

regular Petri net arc

regular Petri net place with a token

Figure 1: Legend of Petri nets.

The unique graphical representation of Petri nets

allows for the depiction of complex systems in a

visually intuitive manner. Places are used to represent

different states or conditions of the system, while

transitions represent the events or actions that can

cause a change in the system's state. The arcs

connecting the places and transitions represent the

flow of tokens, which signify the occurrence or

availability of certain resources or conditions.

By utilizing Petri nets, we can model and simulate

the behavior of complex biological systems, such as

naïve T helper cell activation. In the context of naïve

T helper cell activation, Petri nets can capture the

intricate interactions between signaling molecules,

receptors, and other cellular components. The

transitions in the Petri net represent the activation

events and the places depict the various states of the

T cell during the activation process. One approach of

modeling and simulating naïve T helper cell

activation is to use a Colored Petri net (Motta S. et. All

2013).

A colored Petri net is a variant of a Petri net that

assigns colors or values to the tokens (i.e., the

markings) that represent the state of the molecular

and biochemical signaling pathways. This allows for

a more detailed representation of a system's behavior,

as different tokens with different colors can represent

different types of molecules, receptors, enzymes,

chemical signals, conditions, changes in gene

expression, and so on.

Table 1: Petri net terms in the metabolic pathway.

Petri net

Terms

Biochemical, and molecular

signaling pathway term

Place State of metabolite, signal,

enzyme, gene expression…

Transition Chemical reaction, metabolic

process, gene expression,

secretion…

Input arcs

Reagents and substrates of a

chemical or metabolic reaction,

activation of transcription factor

Output arcs

Products or outputs of a chemical

or metabolic reaction, or gene

expression…

Initial

marking

Initial state of metabolic process

e.g. Naïve T cells

State of reaction at a giving time

Color Concentration of molecule, type

of chemical, enzyme, molecule

1.2 Colored Petri Net

In Colored Petri nets, sets of places, transitions and

arcs are pairwise disjoint: P ∩ T = P ∩ A = T ∩ A = ∅

 Σ is a set of color sets. This set contains all

possible molecules, enzymes, transcription

factors, signals, operations, and functions

used within the color Petri net.

 C is a finite set of color sets (closets). It maps

places in P into colors in Σ, and they define

the functions.

 N is a node function. It maps A into (P × T)

∪ (T × P).

 E is an arc expression function. It maps each

arc a ∈ A into the expression e. The input

and output types of the arc expressions must

correspond to the type of nodes the arc is

connected to.

 V is a finite set of variables v ∈ V of

colors c ∈ C. Arc expressions and guards

contain variables v ∈ V of the suitable types.

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

386

 P is a finite set of places {p1,…, pm} ∈ P,

depicted by ellipses (Fig. 12). Each

place p possesses a color c_p ∈ C and a

bag b_p

⊆

B of tokens of color c_p.

 G is a guard function. It maps each

transition t ∈ T to a guard expression g. The

output of the guard expression should

evaluate to a Boolean value (true or false). If

false, t cannot be fired.

 I is an initialization function. It maps each

place p into an initialization expression i.

The initialization expression must evaluate a

multiset of tokens with a color

corresponding to the color of the place C(p).

Colored Petri nets offer an enhanced

representation that allows for a more detailed and

nuanced analysis of complex systems, particularly in

the context of molecular and biochemical signaling

pathways. Colored Petri nets take the concept of

traditional Petri Nets a step further by introducing the

notion of colors or values assigned to tokens. Unlike

traditional Petri nets, which use uniform tokens,

colored Petri nets allow for the assignment of

different colors to tokens, enabling the representation

of various types of molecules, receptors, enzymes,

chemical signals, gene expression changes, and more

(Lee D-Y. 2006).

The introduction of colors or values to tokens in

colored Petri nets enhances the ability to represent

and model the intricate behavior of molecular and

biochemical signaling pathways. By assigning

specific colors to tokens, researchers can differentiate

between different molecular species, their functional

states, concentrations, or other relevant properties.

This enables a more comprehensive and detailed

representation of the system's behavior, taking into

account and concentration of the diverse components

and their interactions within the biological system.



With the capability to differentiate tokens based on

colors, colored Petri nets provide a powerful tool for

analyzing complex biological systems. Researchers

can investigate the dynamics and behavior of

molecular and biochemical signaling pathways,

observe the effects of different inputs or

perturbations, and gain insights into the overall

functioning of the system. The use of colored Petri

nets in modeling molecular and biochemical

signaling pathways offers numerous advantages. It

allows for a more precise representation of the

system, facilitating the identification of crucial

interactions, bottlenecks, feedback loops, and other

key characteristics. Additionally, the ability to assign

colors to tokens aids in the analysis of specific

molecules, signaling cascades, and regulatory

mechanisms, contributing to a deeper understanding

of complex biological processes (Liu F. 2012).

2 AN OVERVIEW OF IMMUNE

SYSTEM

The immune system plays a crucial role in protecting

our bodies against pathogens, such as bacteria and

viruses. One of the key components of the immune

system is the T helper cell, specifically the naive T

helper cell. Naive T helper cells are a type of white

blood cell that circulates in the bloodstream,

constantly surveying for potential threats. Naive T

helper cells are "naive" because they have not

encountered any specific antigens or foreign

substances before. Their activation is a complex

process that involves interactions with antigen-

presenting cells (APCs) and the recognition of

specific antigens (Gullo F. et all 2015).

The process of naive T helper cell activation

begins when an APC, such as a dendritic cell or

macrophage, encounters a foreign antigen. The APC

internalizes the antigen and presents small fragments

of it on its surface using a protein called major

histocompatibility complex II (MHC II). This MHC

II-antigen complex serves as a signal for the naive T

helper cell to recognize the antigen. When a naive T

helper cell encounters an APC presenting an antigen

that matches its specific T cell receptor (TCR), a

series of molecular interactions occur. The TCR on

the T helper cell binds to the MHC II-antigen

complex on the APC, initiating a signaling cascade

within the T cell (Paul, W. E et all 2010).

This signaling cascade leads to the activation of

the naive T helper cell. The T helper cell undergoes

proliferation, or rapid cell division, to produce a

population of activated T helper cells specific to the

antigen. This clonal expansion ensures a robust

immune response to the pathogen. Furthermore,

during activation, the naive T helper cell receives

additional signals from the APC in the form of co-

stimulatory molecules, such as CD28, CD4 and B7.

These co-stimulatory signals are necessary for full

activation and optimal function of the T helper cell.

Once activated, T helper cells differentiate into

various subsets, such as Th1, Th2, or Th17 cells. Each

subset has specialized functions and produces

specific cytokines to regulate different aspects of the

immune response. Activated T helper cells play a

central role in orchestrating the immune response by

secreting cytokines that activate other immune cells,

such as B cells, cytotoxic T cells, and macrophages.

Modeling of NaÃ

rve Lymphocyte Signaling Pathway

387

They also provide help to B cells for the production

of antibodies and help in the recruitment and

activation of other immune cells to the site of

infection (Daniel B. et all 2002).

In summary, naive T helper cell activation is a

crucial step in initiating an effective immune

response. It involves the recognition of antigens

presented by APCs, signaling between the T cell and

APC, clonal expansion of activated T helper cells, and

their subsequent differentiation into specific subsets.

This activation process ultimately leads to the

coordination of the immune response to eliminate

pathogens and maintain immune homeostasis in the

body (Yates AJ et al 2014).

2.1 T Helper Cell and Antigen

Presenting Cell Interaction

T helper cells, also known as CD4+ T cells, play a

critical role in the immune response by helping to

coordinate and regulate the activities of other immune

cells. Studies have shown that the subsets of TH cells

may play a critical role in the activation of anti-tumor

response either directly by themselves or by

stimulating cytotoxic T cell activity (Tay R.E. et all

2020 & Borst J et al 2018).

T helper cells become activated when they

recognize foreign antigens presented on the surface of

antigen-presenting cells (APCs), such as dendritic

cells or macrophages.

The process of T helper cell activation can be

divided into several key steps:

1. Antigen presentation: APCs present foreign

antigens on their surface, along with

molecules called major histocompatibility

complex (MHC) molecules. T helper cells

recognize these antigens when their T cell

receptor (TCR) binds to the antigen-MHC

complex

2. Costimulation: In addition to TCR binding,

T helper cell activation also requires

costimulatory signals provided by APCs.

These costimulatory signals are delivered by

molecules such as CD80 and CD86, which

bind to receptors on the surface of T helper

cells.

3. Activation and differentiation: Once a T

helper cell has received both TCR and

costimulatory signals, it becomes activated

and begins to proliferate. Activated T helper

cells also differentiate into different

subtypes based on the cytokines present in

their environment. The two main subtypes of

T helper cells are Th1 and Th2 cells, which

produce different cytokines and play

different roles in the immune response.

4. Effector function: Activated T helper cells

can then help to activate other immune cells,

such as B cells and cytotoxic T cells, by

secreting cytokines that promote their

activation and differentiation.

Figure 2: Activation of Naïve T helper cell by Antigen

Presenting Cell.

2.2 Petri Net Model of T Cell Activation

The use of modeling approaches in the field of

immunology has attracted interest due to an

increasing awareness among immunologists of the

need for modeling to increase insights into complex

biological processes. The behavior of Naive T helper

cells can be modeled using a colored Petri net. The

components of the model include Naive T helper

cells, antigens, antigen-presenting cells (APCs), T

cell Receptors (TCR), co-stimulants (CD28, B7), and

cytokines. The places represent the states of the

system, and the transitions represent the actions that

can change the system's state. The places may include

"Naive T helper cells," "APCs," "antigens,"

"activated T cells," "memory T cells," and

"cytokines". The transitions may include "antigen

presentation," "T cell activation," "cytokine

production," and "T cell differentiation”. The initial

marking specifies the initial state of the system, i.e.,

the initial distribution of tokens among the places.

Initially, the "Naive T helper cells" place will have a

token representing the population of Naive T helper

cells, while the other places will be empty. The color

functions determine which tokens can participate in a

firing of a transition, based on their colors. For

example, the "antigen presentation" transition may

require tokens representing an APC and an antigen to

be present in the "APCs" and "antigens" places,

respectively, before it can fire.

The model can be simulated to observe the

behavior of T-naive cells under different conditions,

such as the presence of different antigens or

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

388

Figure 3: Petri net model of Naïve T helper cell activation

by Antigen Presenting Cell. CYT=Cytokine, TCR = T Cell

Receptor, MHCAG = MHC protein bound to Antigen. If

there is an infection, then Antigen Presenting Cells will take

peptide from infectious agent and present it (bind to) Naïve

T helper cell’s receptor. Co-stimulators such as CD 4,

CD28, and B7 which are critical to allow full activation,

sustain cell proliferation of Naïve T helper cells also bind.

If all the conditions are right, then this binding will result in

the release of chemical messenger cytokine. Cytokines in

turn will bind to Naïve T helper cells and will activate them.

In our experiment we started with an initial concentration

of 100 (100 TCR, 100 CD4, 100 CD28…).

variations in the concentration of cytokines. The

model can also be analyzed using mathematical

techniques to study the properties of the system, such

as the threshold antigen concentration required for T-

cell activation.

In our colored Petri net model of naive T helper

cells, we aimed to simulate the initial state of the

system by representing it with a set of tokens. Tokens

in Petri nets symbolize the state or condition of the

entities being modeled. In this case, we began with

100 tokens, where each token represented a naive T

helper cell and an antigen-presenting cell (APC).

The representation of the system with 100 tokens

reflects the initial population of naive T helper cells

and antigen-presenting cells in the simulated

environment. This initial state serves as the starting

point for the subsequent events and transitions within

the Petri net, simulating the dynamic behavior of the

system as it progresses.By employing a colored Petri

net approach, we have the flexibility to assign specific

colors or values to the tokens representing naive T

helper cells and antigen-presenting cells. This color

assignment can help differentiate between different

subtypes or properties of these cells, providing a more

detailed and accurate representation of the biological

system.

2.3 T Helper 1, 2 and 17 Activation

T helper (Th) cells are differentiated from one another

based on the expression of subset-specific

transcription factors (such as T-bet, GATA3, and

RORγt), and the secretion of cytokines such as

interferons and interleukins. When naïve T helper

cells are activated, they differentiate into several sub-

types, in this paper we will focus on three major sub-

types of Th (Th1, Th2, and Th17). Each of which is

specialized for protecting against certain infections.

As mentioned earlier, antigen-presenting cells

(APCs), such as dendritic cells, macrophages, or B

cells, present antigens to naive T cells in the lymph

nodes. In addition to antigen presentation, APCs also

provide co-stimulatory signals to activate T cells.

This involves the interaction of molecules on the

surface of APCs, such as CD80 and CD86, with

receptors on the surface of T cells, such as CD28.

Upon binding of APC and naïve T, if the antigen

is present, then cytokines are released. Depending on

the type of cytokines released, naïve T cells will

differentiate into effector cells that can migrate to

sites of infection and inflammation, where they

release cytokines and directly attack infected or

cancerous cells. Depending on the cytokine, naïve T

cells could differentiate into either T helper 1 cells, T

helper 2, cells, T helper 17 cells, and so on. For

example:

 If Naïve T cells are exposed to Interleukin

12 (IL12), then “STAT 4’ (Signaling

Transducer and Activator of Transcription)

is phosphorylated and consequently

regulates gene transcription, and Naïve T

cells will differentiate to T helper 1 cells.

 If Naïve T cells are exposed to Interleukin 4,

then “STAT 6’ and “GATA3” transcription

factors are phosphorylated and consequently

they regulate gene transcription, and

Naïve

T cells will differentiate into T helper 2 cells.

 If Naïve T cells are exposed to Interleukin 6

then “STAT 3’ and “RORyt” transcription

factors are phosphorylated and consequently

they regulate gene transcription, and Naïve

T cells will differentiate into T helper 17

cells.

Overall, the activation of naïve T helper cells is a

complex process that involves multiple steps and

requires the coordinated interaction of several

different cell types and signaling molecules as shown

in our Petri net models. Three major subsets of

Th cells exist (Th1, Th2, and Th17), and each of these

cells is specialized for protecting our body against

certain infections.

Modeling of NaÃ

rve Lymphocyte Signaling Pathway

389

Differentiated T helper cells in turn release

different cytokines for example:

 Th1 cells primarily produce cytokines such

as interferon-gamma (IFN-gamma) and

interleukin-2 (IL-2), which activate other

immune cells such as macrophages and

cytotoxic T cells and are associated with

protection against intracellular microbes

(predominantly viruses) and the onset of

anti- or pro-tumorigenic effects (Kaiko GE,

et al 2008, and Zhu J et al 2010).

 The 2 cells release cytokines such as IL 4, IL

5, and IL 13, which promote the production

of antibodies, which are important for

neutralizing extracellular pathogens and

eliminating parasites (Walker JA et al 2017).

Figure 4: Detailed Petri Net model of naïve T helper cell

activation. The first step in activation of Naïve T helper cell

is binding of Antigen Presenting Cells to naïve T helper

cell. Binding of cells will initiate release cytokine by

Antigen Presenting Cells. Depending on the type of

cytokine releases (IL 12, IL 6, IL 4…) different genes

within naïve T helper cells will be expressed. Expression

of these genes will initiate transcription and translation,

thus naïve T helper cell will differentiate into active T

helper 1, or T helper 2 or T helper 17 cells.

 Th17 cells can migrate to sites of infection

and inflammation, where they release

cytokines such as IL 22, and IL 17 which

fight microbial pathogens and promote

inflammation and tissue damage (Kaiko GE,

et al 2008, and Zhu J et al 2010).

With a detailed model of naïve T helper cells in

the form of a Petri net model, it is easy to analyze and

simulate it with the common techniques for analyzing

and simulating the behavior of Petri nets. But it

should be noted that it is not the final step towards

simulation and analysis. Within the overall task of

simulation, there are three primary sub-fields: model

design, model execution, and model analysis. Models

can take many forms including declarative,

functional, constraint, spatial, or multi-model. A

multi-model is a model containing multiple integrated

models each of which represents a level of granularity

for the physical system.

The next task, once a model has been developed,

is to execute the model on a computing platform. This

step involves running the model using appropriate

software or tools that can simulate and analyze the

behavior of the system described by the model. For

this purpose, a computer program can be created to

run through the whole process while updating the

state and event variables in the mathematical model.

In the Petri net model of T cell activation, functional

programming languages can be leveraged to represent

the various components of the system as functions

and define the transitions between states. The places

in the Petri net, which represent the different

molecular entities and their concentration and

quantities, can be encoded as variables or data

structures within the functional programming

language. The transitions in the Petri net, which

signify the events and interactions occurring in T cell

activation, can be implemented as functions that take

the current state and produce a new state as a result.

These functions can encapsulate the complex logic

involved in the activation process, including the

binding of antigens to T cell receptors, the activation

of intracellular signaling pathways, and the

subsequent release of cytokines. By utilizing

functional programming languages, the Petri net

model of T cell activation can be simulated and

analyzed efficiently. The declarative and modular

nature of functional programming allows for easy

composition and abstraction, enabling researchers to

focus on specific aspects of the system while

maintaining a clear overall representation.

Furthermore, functional programming languages

provide a strong foundation for formal verification

and rigorous analysis of the Petri net model. The

mathematical nature of functional programming

aligns well with the underlying principles of Petri

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390

nets, allowing for the application of formal methods

to verify properties, detect potential issues, and

explore different scenarios.

For ease of understanding, we used the module-

programming technique. It means that for each core

operation of the naïve T helper cell activation process,

there is a separate program module written. For

example, module M1 (in Figure 5) programs all

operations necessary for Cytokine production after

antigen-presenting cell (APC) binds to naïve T helper

cell. The language used in this figure is a pseudo-

functional language, which can be easily extended

into an executable program.

Figure 5: The Petri net model of the T cell activation

described by functional programming language.

Finally, to construct an entire program for the

naïve T cell activation processes, one should

assemble all program modules indicated in Figure 5

into one program. The resulting program is

represented below.

PROGRAM Naïve T cell activation

BEGIN

IF cell_state=

infection:

THEN CALL M1, M2, M3, M4;

IF antigen is bound to MHC=

Cytokine secretion

THEN CALL M2, M3, M4;

Phosphorylate

Transcription Factors

CONVERSE;

ELSE CONTINUE;

END

3 CONCLUSION

In conclusion, the modeling of naive T helper cells

using CPN provides a valuable approach to

understanding the complex dynamics and behavior of

these cells within the immune system. CPNs offer a

formal and graphical representation that captures the

interactions, states, and transitions of the T helper

cells and their associated signaling pathways. By

employing CPN modeling, researchers can simulate

and analyze the behavior of naive T helper cells under

various conditions, such as different antigen

presentations, co-stimulation, or regulatory

mechanisms. This enables a deeper understanding of

the underlying mechanisms that govern T cell

activation, proliferation, differentiation, and cytokine

production.

The advantage of using CPNs lies in their ability

to handle the dynamic nature of the immune response,

capturing both qualitative and quantitative aspects.

CPNs can integrate experimental data and help

generate hypotheses by simulating the effects of

genetic modifications or perturbations in the system.

This aids in the exploration of different scenarios and

the identification of key factors influencing T cell

behavior.

Moreover, CPN modeling facilitates the

identification of critical control points or potential

interventions for therapeutic purposes. By

manipulating the CPN model parameters or

introducing virtual interventions, researchers can

assess the impact on T cell responses and potentially

guide the development of novel immunotherapies or

vaccine strategies. However, it is important to

acknowledge that CPN modeling of naive T helper

cells is a simplification of the complex and dynamic

immune system. The accuracy and reliability of the

model depends on the quality of the data used to

construct it and the assumptions made during the

modeling process. Therefore, validation of the model

against experimental observations is crucial to ensure

its fidelity and usefulness in understanding and

predicting T cell behavior.

In summary, CPN modeling of naive T helper cells

provides a powerful tool for investigating the intricate

processes involved in immune responses. It offers

insights into the dynamics of T cell activation and

differentiation, helping to unravel the underlying

mechanisms and identify potential therapeutic targets

and further enhancing our understanding of T cell

biology and its implications in health and disease.

Modeling of NaÃ

rve Lymphocyte Signaling Pathway

391

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