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PhD Lecture by Nicolas Rapin, CBS
Bioinformatics and simulation of the immune system Thursday May 7, 2009 at 13:00 CBS, DTU, Lyngby, Building 208, Auditorium 062
The immune system has evolved to protect the body against pathogens. Clearance of pathogens is usually taken care of by pathogen-nonspecific mechanisms of the innate immune system, but if needed, the more specific mechanisms of the adaptive immune system are triggered, and the appropriate type of response is normally made. Cells of the adaptive immune system are able to recognize and target a wide range of antigens, such as viruses or bacteria.
The study of the immune system presented herein relies on the use of models and theories, and falls under a field of biology known as theoretical biology. The core of the work attempts to link two fields of theoretical biology, namely, immunological bioinformatics and theoretical immunology. Theoretical immunology is an established field, which relies on formal mathematical language to frame biological models. Concurrently, the somewhat newer field of immunological bioinformatics aims at using data-driven computer algorithms to gain a detailed view of the immune system. It makes extensive use of mathematics, information science, computer engineering, genomics, proteomics and immunological methods to bridge immunology and informatics. Together, these approaches advance our understanding of immune responses, escape and evolution of pathogens under immune pressure. A key step for the simulation of the immune response is the prediction of immunogenicity at the molecular level. Only the immunogenic parts of an invading pathogen also known as epitopes will trigger an immune response in the affected host. In this context, one has to remember that the immune response is host-specific. This is handled using methods from theoretical biology to capture the essence of the immune system and bioinformatics to add information about the variation between individuals (i.e. tissue type). In this way, it is possible to get the best of both approaches.
In this thesis, the first part deals with the creation of a matrix method to predict major histocompatibility complex class I and II epitopes with the ability to cover a large part of the human population. The method, although less accurate than others such as a neural network-based predictions, has the advantage of being very fast, computationally speaking. It was subsequently incorporated into a simulation program of the immune system, C-ImmSim, providing it with a new genetic dimension, closer to reality, as the host and pathogens are represented by their DNA sequences.\\ In the second part, the role of memory cells in infection with the human immunodeficiency virus (HIV) is explored using mathematical models and computer simulations. first, a mathematical model is developed. This model predicts that if HIV did not have the ability to mutate, it would be cleared by the immune system. Having established this, the model is used in a general framework where the virus is allowed to mutate. The model shows that the virus can establish a chronic infection by mutating to stay one step ahead of the immune system. By combining host-specific epitope predictions and mathematical models describing the interaction between the host immune system and infectious agents, it is possible to make predictions of the evolution of diseases on a patient basis, an important step towards personalized medicine.
Everybody is welcome. Registration is not necessary. |