Alexandre Bittencourt Pigozzo

On the computational modeling of the innate immune system

In recent years, there has been an increasing interest in the mathematical and computational modeling of the human immune system (HIS). Computational models of HIS dynamics may contribute to a better understanding of the relationship between complex phenomena and immune response; in addition, computational models will support the development of new drugs and therapies for different diseases. However, modeling the HIS is an extremely difficult task that demands a huge amount of work to be performed by multidisciplinary teams. In this study, our objective is to model the spatio-temporal dynamics of representative cells and molecules of the HIS during an immune response after the injection of lipopolysaccharide (LPS) into a section of tissue. LPS constitutes the cellular wall of Gram-negative bacteria, and it is a highly immunogenic molecule, which means that it has a remarkable capacity to elicit strong immune responses. We present a descriptive, mechanistic and deterministic model that is based on partial differential equations (PDE). Therefore, this model enables the understanding of how the different complex phenomena interact with structures and elements during an immune response. In addition, the models parameters reflect physiological features of the system, which makes the model appropriate for general use.

A three-dimensional computational model of the innate immune system

The Human Immune System is a complex system responsible for protecting the organism against diseases. Although understanding how it works is essential to develop better treatments against diseases, its complexity makes this task extremely hard. In this work a three-dimensional mathematical and computational model of part of this system, the innate immune system, is presented. The high computational costs associated to simulations lead the development of a parallel version of the code, which has achieved a speedup of about 72 times over its sequential counterpart.

Implementation of a computational model of the innate immune system

In the last few years there has been an increasing interest in mathematical and computational modelling of the human immune system (HIS). Computational models of the HIS dynamics may contribute to a better understanding of the complex phenomena associate to the immune system, and support the development of new drugs and therapies for different diseases. However, the modelling of the HIS is an extremely hard task that demands huge amount of work to be performed by multi-disciplinary teams. In this scenario, the objective of this work is to model the dynamics of some cells and molecules of the HIS during an immune response to lipopolysaccharide (LPS) in a section of the tissue. The LPS constitutes the cellular wall of Gram-negative bacteria, and it is a highly immunogenic molecule, which means that it has a remarkable capacity to elicit strong immune responses.

Computational Modeling of Microabscess Formation

Bacterial infections can be of two types: acute or chronic. The chronic bacterial infections are characterized by being a large bacterial infection and/or an infection where the bacteria grows rapidly. In these cases, the immune response is not capable of completely eliminating the infection which may lead to the formation of a pattern known as microabscess (or abscess). The microabscess is characterized by an area comprising fluids, bacteria, immune cells (mainly neutrophils), and many types of dead cells. This distinct pattern of formation can only be numerically reproduced and studied by models that capture the spatiotemporal dynamics of the human immune system (HIS). In this context, our work aims to develop and implement an initial computational model to study the process of microabscess formation during a bacterial infection.

On the use of multiple heterogeneous devices to speedup the execution of a computational model of the Human Immune System

The Human Immune System (HIS) is responsible for protecting the body against diseases, but the mechanisms used in this task are not completely understood. Mathematical and computational tools can be used for this purpose, and due to the costs involved in simulating the HIS, GPUs (Graphics Processing Units) are frequently used as the computational platform. The frequency in which GPUs are used for tasks like this is so high that some processing units, such as the APUs (Accelerated Processing Units), have integrated then into the CPU chip. This work presents the implementation on an APU of a mathematical model that describes part of the HIS. A load balancing strategy was implemented to distribute data with the objective of equalizing the load at each computational device, since GPU and CPU are heterogeneous. Gains up to 6.0 × , 1.28 × and 3.7 × were obtained by the balanced version of the code, when compared to the same parallel versions that execute exclusively on CPU, GPU, and on both of them, but without using load balancing, respectively.

Modelling the Innate Immune System

The Human Immune System (HIS) is a complex network composed of specialized cells, tissues, and organs that is responsible for protecting the organism against diseases caused by distinct pathogenic agents, such as viruses, bacteria and other parasites. The first line of defence against pathogenic agents consists of physical barriers of skin and the mucous membranes. If the pathogenic agents breach this first protection barrier, the innate immune system will be ready for recognize and combat them. The innate immune system is therefore responsible for powerful non-specific defences that prevent or limit infections by most pathogenic microorganisms.

Performance Evaluation of a Human Immune System Simulator on a GPU Cluster

The Human Immune System HIS is a complex system that protects the body against several diseases. Some aspects of such complex system can be better understand with the use of mathematical and computational tools. Huge computational resources are required to execute simulations of the HIS, so the use of parallel environments is mandatory. This work presents a parallel implementation of a 3D HIS simulator on a GPU cluster that uses CUDA, OpenMP and MPI to speedup the execution of the application. A performance evaluation is then carried out, and the impact of the use of InfiniBand, a low latency network, and GPUs Error-Correcting Code ECC are measured. Speedups upi¾?to 956 were obtained by the parallel version that uses Infiniband and turns off ECC.

Parallel Implementation of a Computational Model of the HIS Using OpenMP and MPI

Primary immune responses are initiated when foreign pathogenic microorganisms move past the front-line defense system of the body. The primary immune responses consist of a) the production of antibody molecules that are specific for the pathogenic microorganism and b) the expansion and differentiation of special-purpose defensive cells, the lymphocytes. In this scenario, our work aims to develop and implement a mathematical and computational model for this primary immune response in a microscopic section of a tissue. However, solving the set of equations related to the mathematical model requires a large amount of computation. Therefore, in this work we present an initial attempt to improve the performance of the computational implementation via the use of parallel computing.

Modeling Human Immune System Using a System Dynamics Approach

The Human Immune System (HIS) consists of a complex network of cells, tissues and organs that protects the body against invaders. Understanding how the HIS works is therefore essential to obtain new insights into its nature and to deal effectively with diseases. Computational modeling can be used for this purpose: it allows researchers to perform virtual experiments in silico, that can speed up the discovery of new drugs against diseases and reduce their cost. To create a computational model of the HIS, the behavior of its components and their relationships must be modeled using a formal language, such as those used in Mathematics. This paper presents the JynaCore, an API that uses System Dynamics instead of pure mathematical equations to model complex systems. In particular, this API is used to model and simulate the dynamics of the immune response to lipopolysaccharide (LPS) in a microscopic section of tissue.

Parallel implementation of a computational model of the human immune system

Our work aims to develop and implement a mathematical and computational model for the primary immune system response in a microscopic section of a tissue. A large amount of computation is required to solve the set of equations related to the mathematical model, for this reason in this work we present an initial attempt to improve the performance of the implementation via the use of parallel computing.