Applying Agent-Based Modeling to Studying Emergent Behaviors of the Immune System Cells

Huge amount of medical data has been generated in practical experiments which makes data analysis a challenging problem. This requires novel techniques to be developed. The improvements in computational power suggest to use computerbased modeling approaches to process a large set of data. One of the important systems in the human body to be investigated is the immune system. The previous studies of medical scientists and ongoing experiments at Karolinska Institute provide information about the human immune system. This information includes attributes of human immune system’s blood cells and the interactions between these cells. This interactions are provided as ‘if-then’ logical rules. Each rule verifies a condition on the attribute of one cell and it may initiate interaction processes to modify the attributes of other cells. A specific temporal value is associated to each process to quantify the speed of that process in the body (i.e., slow, medium, fast). We propose an agent-based model (ABM) to study human immune system cells and their interactions. The ABM is selected to overcome the complexity of large amount of data and find emergent properties and behavior patterns of the cells. Immune system cells are modeled as autonomous agents which have interactions with each other. Different values of a cell attributes define possible states of the cell and the collection of states of all cells constructs the state of the whole agent-based model. In order to consider the state transitions of the cells, we used a finite state machine (FSM). The first state is constructed from the input initial values for the cells and considering the logical time of 1. In each step, the program goes one time unit further and computes next state by applying the changes based on the cells’ interactions rules. This evolution of states in time is similar to game of life (GOL) automaton. The final model based on three modeling approaches of ABM, FSM and GOL are used to test medical hypothesis related to human immune system. This model provides a useful framework for medical scientists to do experiments on the cells’ attributes and their interaction rules. Considering a set of cells and their interactions, the proposed framework shows emergent properties and behavior patterns of the human immune system

Applying Agent-Based Modeling to Studying Emergent Behaviors of the Immune System Cells

Huge amount of medical data has been generated in practical experiments which makes data analysis a challenging problem. This requires novel techniques to be developed. The improvements in computational power suggest to use computerbased modeling approaches to process a large set of data. One of the important systems in the human body to be investigated is the immune system. The previous studies of medical scientists and ongoing experiments at Karolinska Institute provide information about the human immune system. This information includes attributes of human immune system’s blood cells and the interactions between these cells. This interactions are provided as ‘if-then’ logical rules. Each rule verifies a condition on the attribute of one cell and it may initiate interaction processes to modify the attributes of other cells. A specific temporal value is associated to each process to quantify the speed of that process in the body (i.e., slow, medium, fast). We propose an agent-based model (ABM) to study human immune system cells and their interactions. The ABM is selected to overcome the complexity of large amount of data and find emergent properties and behavior patterns of the cells. Immune system cells are modeled as autonomous agents which have interactions with each other. Different values of a cell attributes define possible states of the cell and the collection of states of all cells constructs the state of the whole agent-based model. In order to consider the state transitions of the cells, we used a finite state machine (FSM). The first state is constructed from the input initial values for the cells and considering the logical time of 1. In each step, the program goes one time unit further and computes next state by applying the changes based on the cells’ interactions rules. This evolution of states in time is similar to game of life (GOL) automaton. The final model based on three modeling approaches of ABM, FSM and GOL are used to test medical hypothesis related to human immune system. This model provides a useful framework for medical scientists to do experiments on the cells’ attributes and their interaction rules. Considering a set of cells and their interactions, the proposed framework shows emergent properties and behavior patterns of the human immune system

A Chimeric Vaccine Consisting of Highly Immunogenic Regions Form Escherichia coli Iron Regulated Outer-Membrane Proteins: An In Silico Approach

Background: Six pathogen-associated Outer Membrane Iron receptors (OMPs) reside in Uropathogenic strains of E. coli (UPEC): haem-utilization gene (ChuA), Heme acquisition protein (Hma), IrgA homologue adhesin (Iha), Iron-regulated virulence gene (IreA), IroN, and IutA. Cumulative concern over the prevalence of this bacteria in hospital environments, especially in Intensive Care Units (ICUs), highlights the significance of vaccination against this pathogen. In this study, we aimed to develop 3D models of ChuA, Hma, IutA, IreA, Iha, and IroN proteins by invoking various in silico methods and design a chimeric immunogen composed of highly immunogenic regions from these six Escherichia coli antigens as a chimeric vaccine. Materials and Methods: In the present study, homology modeling, fold recognition, Ab initio approaches, and their combination were invoked to determine the Three-Dimensional (3D) structures of ChuA, Hma, Iha, IreA, IroN, and IutA. Next, a set of biochemical, immunological, and functional properties were predicted using various bioinformatics tools. Results: The obtained results indicated that all six modeled proteins fold to a β-barrel structure. The results of biochemical, immunological, and functional analysis determined the regions of each antigen carrying the best immunogenic properties. These regions are employed to construct the final vaccine linked via flexible GGGGS linkers. Intriguingly, re-analyzing the properties of the final vaccine indicated its immunological advantage over individual proteins.  Conclusion: The strategy of this study to predict the protein 3D structure, followed by epitope prediction, could be adapted to design efficient vaccine candidates. Applying this approach, we designed a vaccine candidate harboring the most promising regions of six OMPs. This approach could lead to better functional, structural, and therapeutic outcomes in the context of vaccine design investigations