Neural Darwinism is a biological, and more specifically Darwinian and selectionist, approach to understanding global brain function, originally proposed by American biologist, researcher and Nobel-Prize recipient Gerald Maurice Edelman. Edelman's 1987 book Neural Darwinism introduced the public to the theory of neuronal group selection (TNGS) – which is the core theory underlying Edelman's explanation of global brain function.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.
B cells, also known as B lymphocytes, are a type of white blood cell of the lymphocyte subtype. They function in the humoral immunity component of the adaptive immune system. B cells produce antibody molecules which may be either secreted or inserted into the plasma membrane where they serve as a part of B-cell receptors. When a naïve or memory B cell is activated by an antigen, it proliferates and differentiates into an antibody-secreting effector cell, known as a plasmablast or plasma cell. In addition, B cells present antigens and secrete cytokines. In mammals, B cells mature in the bone marrow, which is at the core of most bones. In birds, B cells mature in the bursa of Fabricius, a lymphoid organ where they were first discovered by Chang and Glick, which is why the B stands for bursa and not bone marrow, as commonly believed.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
Evolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve.
In immunology, a memory B cell (MBC) is a type of B lymphocyte that forms part of the adaptive immune system. These cells develop within germinal centers of the secondary lymphoid organs. Memory B cells circulate in the blood stream in a quiescent state, sometimes for decades. Their function is to memorize the characteristics of the antigen that activated their parent B cell during initial infection such that if the memory B cell later encounters the same antigen, it triggers an accelerated and robust secondary immune response. Memory B cells have B cell receptors (BCRs) on their cell membrane, identical to the one on their parent cell, that allow them to recognize antigen and mount a specific antibody response.
In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.
The adaptive immune system, also known as the acquired immune system, or specific immune system is a subsystem of the immune system that is composed of specialized, systemic cells and processes that eliminate pathogens or prevent their growth. The acquired immune system is one of the two main immunity strategies found in vertebrates.
In immunology, affinity maturation is the process by which TFH cell-activated B cells produce antibodies with increased affinity for antigen during the course of an immune response. With repeated exposures to the same antigen, a host will produce antibodies of successively greater affinities. A secondary response can elicit antibodies with several fold greater affinity than in a primary response. Affinity maturation primarily occurs on membrane immunoglobulin of germinal center B cells and as a direct result of somatic hypermutation (SHM) and selection by TFH cells.
In immunology, central tolerance is the process of eliminating any developing T or B lymphocytes that are autoreactive, i.e. reactive to the body itself. Through elimination of autoreactive lymphocytes, tolerance ensures that the immune system does not attack self peptides. Lymphocyte maturation occurs in primary lymphoid organs such as the bone marrow and the thymus. In mammals, B cells mature in the bone marrow and T cells mature in the thymus.
Germinal centers or germinal centres (GCs) are transiently formed structures within B cell zone (follicles) in secondary lymphoid organs – lymph nodes, ileal Peyer's patches, and the spleen – where mature B cells are activated, proliferate, differentiate, and mutate their antibody genes during a normal immune response; most of the germinal center B cells (BGC) are removed by tingible body macrophages. There are several key differences between naive B cells and GC B cells, including level of proliferative activity, size, metabolic activity and energy production. The B cells develop dynamically after the activation of follicular B cells by T-dependent antigen. The initiation of germinal center formation involves the interaction between B and T cells in the interfollicular area of the lymph node, CD40-CD40L ligation, NF-kB signaling and expression of IRF4 and BCL6.
A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence.
Polyclonal B cell response is a natural mode of immune response exhibited by the adaptive immune system of mammals. It ensures that a single antigen is recognized and attacked through its overlapping parts, called epitopes, by multiple clones of B cell.
In molecular biology, a framework region is a subdivision of the variable region (Fab) of the antibody. The variable region is composed of seven amino acid regions, four of which are framework regions and three of which are hypervariable regions. The framework region makes up about 85% of the variable region. Located on the tips of the Y-shaped molecule, the framework regions are responsible for acting as a scaffold for the complementarity determining regions (CDR), also referred to as hypervariable regions, of the Fab. These CDRs are in direct contact with the antigen and are involved in binding antigen, while the framework regions support the binding of the CDR to the antigen and aid in maintaining the overall structure of the four variable domains on the antibody. To increase its stability, the framework region has less variability in its amino acid sequences compared to the CDR.
Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.
Somatic hypermutation is a cellular mechanism by which the immune system adapts to the new foreign elements that confront it. A major component of the process of affinity maturation, SHM diversifies B cell receptors used to recognize foreign elements (antigens) and allows the immune system to adapt its response to new threats during the lifetime of an organism. Somatic hypermutation involves a programmed process of mutation affecting the variable regions of immunoglobulin genes. Unlike germline mutation, SHM affects only an organism's individual immune cells, and the mutations are not transmitted to the organism's offspring. Because this mechanism is merely selective and not precisely targeted, somatic hypermutation has been strongly implicated in the development of B-cell lymphomas and many other cancers.
Immunology is the study of the immune system during health and disease. Below is a list of immunology-related articles.
Professor Emma Hart, FRSE is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evolutionary Computation, and D. Coordinator of the Future & Emerging Technologies (FET) Proactive Initiative, Fundamentals of Collective Adaptive Systems.
C-ImmSim started, in 1995, as the C-language "version" of IMMSIM, the IMMune system SIMulator, a program written back in 1991 in APL-2 by the astrophysicist Phil E. Seiden together with the immunologist Franco Celada to implement the Celada-Seiden model. The porting was mainly conducted and further developed by Filippo Castiglione with the help of few other people.