Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation

Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for an hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.publishersversionPeer reviewe

Efficient Algorithms for String-Based Negative Selection

Abstract. String-based negative selection is an immune-inspired classi-fication scheme: Given a self-set S of strings, generate a set D of detectors that do not match any element of S. Then, use these detectors to parti-tion a monitor set M into self and non-self elements. Implementations of this scheme are often impractical because they need exponential time in the size of S to construct D. Here, we consider r-chunk and r-contiguous detectors, two common implementations that suffer from this problem, and show that compressed representations of D are constructible in poly-nomial time for any given S and r. Since these representations can them-selves be used to classify the elements in M, the worst-case running time of r-chunk and r-contiguous detector based negative selection is reduced from exponential to polynomial.

Detecção de elementos estranhos em modelos inspirados em imunologia

Mestrado em Engenharia FísicaNeste trabalho é apresentado um algoritmo para detecção de elementos estranhos (nonself) baseado no mecanismo de Frustração Celular. Este mecanismo apresenta uma nova abordagem às interacções celulares que ocorrem no sistema imunológico adaptativo. O conceito é o de que qualquer elemento estranho estabelecerá interacções menos frustradas do que os restantes elementos do sistema, podendo por isso, através do seu comportamento anómalo, ser detectado. O algoritmo proposto possui vantagens em relação aos sistemas imunológicos artificiais mais conhecidos. Entre elas está a possibilidade de obter detecção perfeita com um número reduzido de detectores. Nesta tese, analisa-se comparativamente este algoritmo com algoritmos de selecção negativa existentes na literatura.In this work an algorithm for nonself detection is presented, based on the Cellular Frustration mechanism. This mechanism presents a novel approach to cellular interactions occurring in the adaptive immune system. The concept is that any nonself element will establish less frustrated interactions than the remaining elements of the system, can thus, by its anomalous behaviour, be detected. The proposed algorithm has advantages over the most know artificial immune systems. Among the advantages there is the possibility to achieve perfect detection using a reduced number of detectors. In this thesis, this algorithm is analysed comparatively to negative selection algorithms that can be found in literature

An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design

A novel artificial immune system algorithm with social learning mechanisms (AIS-SL) is proposed in this paper. In AIS-SL, candidate antibodies are marked with an elitist swarm (ES) or a common swarm (CS). Correspondingly, these antibodies are named ES antibodies or CS antibodies. In the mutation operator, ES antibodies experience self-learning, while CS antibodies execute two different social learning mechanisms, that is, stochastic social learning (SSL) and heuristic social learning (HSL), to accelerate the convergence process. Moreover, a dynamic searching radius update strategy is designed to improve the solution accuracy. In the numerical simulations, five benchmark functions and a practical industrial application of proportional-integral-differential (PID) controller tuning is selected to evaluate the performance of the proposed AIS-SL. The simulation results indicate that AIS-SL has better solution accuracy and convergence speed than the canonical opt-aiNet, IA-AIS, and AAIS-2S