An Evolutionary Computing Model for the Study of Within-Host Evolution

Abstract

Evolution of an individual within another individual is known as within-host dynamics (WHD). The most common modeling technique to study WHD involves ordinary differential equations (ODEs). In the field of biology, models of this kind assume, for example, that both the number of viruses and the number of mouse cells susceptible to being infected change according to their interaction as stated in the ODE model. However, viruses can undergo mutations and, consequently, evolve inside the mouse, whereas the mouse, in turn, displays evolutionary mechanisms through its immune system (e.g., clonal selection), defending against the invading virus. In this work, as the main novelty, we propose an evolutionary WHD model simulating the coexistence of an evolving invader within a host. In addition, instead of using ODEs we developed an alternative methodology consisting of the hybridization of a genetic algorithm with an artificial immune system. Aside from the model, interest in biology, and its potential clinical use, the proposed WHD model may be useful in those cases where the invader exhibits evolutionary changes, for instance, in the design of anti-virus software, intrusion detection algorithms in a corporation’s computer systems, etc. The model successfully simulates two intruder detection paradigms (i.e., humoral detection, danger detection) in which the intruder represents an evolving invader or guest (e.g., virus, computer program,) that infects a host (e.g., mouse, computer memory). The obtained results open up the possibility of simulating environments in which two entities (guest versus host) compete evolutionarily with each other when occupying the same space (e.g., organ cells, computer memory, network).

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

“…Granulocytes and macrophages play a major role in the innate immune system, by attacking all foreign substances and reacting directly and immediately without distinction. Conversely, lymphocytes play an important role in the adaptive immune system, reinforcing innate immunity by making antibodies that can effectively remove antigens by remembering those that first invaded and reacting specifically once they reinvade [35,36]. The commonly used definition of immunity refers to the adaptive immune system.…”

“…AISs are biologically inspired algorithms that use the principle of the aforementioned immune system. On the basis of their classification and reasoning abilities, AIS algorithms help solve pattern recognition, optimization, machine learning, and computer network security problems [36]. The system largely employs positive selection (PS) and negative selection (NS) algorithms and a clonal selection (CS) process, a process in which B lymphocytes create more effective antigens to remove antibodies.…”

A Bio-Inspired Reaction Against Cyberattacks: AIS-Powered Optimal Countermeasures Selection

“…After a quite complex modeling phase, AIS have been successfully applied to various fields of digital knowledge, including optimization theory [29], data analysis [30], image recognition [31], and computer security [32]. More specifically, AIS-based proposals have arisen to solve cybersecurity challenges for anomaly detection, intrusion detection, malicious process detection, scan and flood detection, and fraud detection, among others [33], [34], [35], [36] others. Those proposals rely on the capabilities of four main bio-inspired techniques, namely, negative selection, clonal section, artificial immune networks, and Danger Theory [28], [37], [38].…”

AISGA: Multi-objective parameters optimization for countermeasures selection through genetic algorithm

“…Indeed, the GA has been proved successful in solving several optimization problems in different knowledge areas, including mathematics, informatics, etc. [6]. In this sense, the presented GA methodology performs a multi-objective optimization of the parameters, i.e., risk and execution time of the AIS-powered reaction.…”