Immune Network Theory Research Articles

Cellular and Molecular Immunity to Influenza Viruses and Vaccines.

Immune responses to influenza (flu) antigens reflect memory of prior infections or vaccinations, which might influence immunity to new flu antigens. Memory of past antigens has been termed "original antigenic sin" or, more recently, "immune imprinting" and "seniority". We have researched a comparison between the immune response to live flu infections and inactivated flu vaccinations. A brief history of antibody generation theories is presented, culminating in new findings about the immune-network theory and suggesting that a network of clones exists between anti-idiotypic antibodies and T cell receptors. Findings regarding the 2009 pandemic flu strain and immune responses to it are presented, including memory B cells and conserved regions within the hemagglutinin protein. The importance of CD4+ memory T cells and cytotoxic CD8+ T cells responding to both infections and vaccinations are discussed and compared. Innate immune cells, like natural killer (NK) cells and macrophages, are discussed regarding their roles in adaptive immune responses. Antigen presentation via macroautophagy processes is described. New vaccines in development are mentioned along with the results of some clinical trials. The manuscript concludes with how repeated vaccinations are impacting the immune system and a sketch of what might be behind the imprinting phenomenon, including future research directions.

Jerne's "immune network theory", of interacting anti-idiotypic antibodies applied to immune responses during COVID-19 infection and after COVID-19 vaccination.

Niels Kaj Jerne has proposed the "immune network theory" of interactions among anti-idiotypic antibodies, able to interfere with humoral responses to certain antigens. After the occurrence of the primary generation of antibodies, against an antigenic epitope, idiotypes of these antibodies induce anti-idiotypic antibodies that modulate the intensity of the first response, and so on. Adverse effects following SARS-COV-2 COVID-19 vaccines are occasionally similar to the symptoms of COVID-19 infection. Rare events linked to SARS-CoV-2 vaccines also resemble some rarely reported COVID-19 complications. Safety data from product information by European Medicines Agency suggest that spectra do overlap for four main vaccines. The proposition is that vaccine events and COVID-19 complications are related to anti-idiotypic antibodies whose spatial shape can lead to interactions with ACE2 molecules, in individuals with a prolonged Spike protein synthesis. The vaccines target cells by their affinity to the vaccine vector, or to engulf lipid nanoparticles. Anti-idiotypic antibodies shaped similarly to the Spike protein possibly interact with ACE2 molecules and cause diverse signs and symptoms.

Targeted innovative design of Bt Cry toxin insecticidal mimics

Bt Cry toxin is the mostly studied and widely used biological insect resistance protein, which plays a leading role in the green control of agricultural pests worldwide. However, with the wide application of its preparations and transgenic insecticidal crops, the resistance to target pests and potential ecological risks induced by the drive are increasingly prominent and attracting much attention. The researchers seek to explore new insecticidal protein materials that can simulate the insecticidal function of Bt Cry toxin. This will help to escort the sustainable and healthy production of crops, and relieve the pressure of target pests' resistance to Bt Cry toxin to a certain extent. In recent years, the author's team has proposed that Ab2β anti-idiotype antibody has the property of mimicking antigen structure and function based on the "Immune network theory" of antibody. With the help of phage display antibody library and specific antibody high-throughput screening and identification technology, Bt Cry toxin antibody was designed as the coating target antigen, and a series of Ab2β anti-idiotype antibodies (namely Bt Cry toxin insecticidal mimics) were screened from the phage antibody library. Among them, the lethality of Bt Cry toxin insecticidal mimics with the strongest activity was close to 80% of the corresponding original Bt Cry toxin, showing great promise for the targeted design of Bt Cry toxin insecticidal mimics. This paper systematically summarized the theoretical basis, technical conditions, research status, and discussed the development trend of relevant technologies and how to promote the application of existing achievements, aiming to facilitate the research and development of green insect-resistant materials.

Immune systems in computer science

A simplified description of the immune system is as follows: this is an organic system intended for protecting the host organism from the threats posed to it from pathogens and toxic substances. The architecture of the immune system is such that a series of defensive layers protect the host. Once the pathogen makes it inside the host, it must contend with the innate and adaptive immune system. These two immunological sub-systems are comprised of several types of cells and molecules produced by specialized organs and processes to address the self non-self problem. Artificial Immune Systems (AIS) are computational models, based on natural immune systems. They tend to solve specific problems in computer science by resembling the natural mechanisms. These systems are more widely applied within problem domains like clustering, pattern recognition, classification, optimization, and machine learning. Modern AIS are inspired by one of three sub-fields: clonal selection, negative selection, and immune network algorithms. In computer science, the clonal selection pattern can be used for pattern matching and optimization. The negative selection algorithm was designed for change detection, novelty detection, and intrusion detection. Immune network algorithms are inspired by the immune network theory of the acquired immune system and it is an upgrade of the clonal selection theory. The objective of the immune network process is to prepare a repertoire of discrete pattern detectors for a given problem domain, where better performing solutions suppress low affinity solutions within the same network. This is an interactive process of exposing the pattern to external information to which it responds. This article explains the biological background, the mechanisms of AIS, and presents their real-world applications. It presents an overview of those important applications of AIS for solving problems from problem domains like data analysis, anomaly detection, intrusion detection, and others.

A Prefix Hijacking Detection Model Based on the Immune Network Theory

The prefix hijacking problem is an urgent security issue that need to address in the Border Gateway Protocol (BGP) security research. In order to solve the problem of prefix hijacking in BGP, we propose (a) new (p)refix (h)ijacking (d)etection model based on the immune network theory in this paper, called aPHD. To be specific, aPHD uses real BGP UPDATE messages for pre-training and has the ability to detect UPDATE messages in real time after pre-training. The aPHD (1) can effectively detect prefix hijacking attacks with high accuracy; (2)is easy to deployment; and (3) has a low false positive rate and low overhead. Extensive performance evaluation shows that our solution is secure and feasible. The aPHD improved the accuracy rate by 6.2% and reduced the false positive rate by 85.7%.

Towards an HIV Vaccine Based on Immune Network Theory II

We have designed immunization protocols based on the co-section model to induce a broadening antigen antibody response against HIV1. As antigen were various antigen-antibody complexes used, including the monoclonal antibody 1F7 that is expressed on anti-HIV1 antibodies with different antigen specificities. Rabbits were used to test this concept using. Antibodies that neutralized HIV-1 of the same strain as that of the gp120 used were detected, but they did not include broadly neutralizing antibodies (BnAbs). Immunization with gp120 in adjuvant induced antibodies binding to gp120, gp41, Nef and p24, a finding predicted by the immune network theory. Furthermore a complex of 1F7 and B12 induced antibodies against gp120 and gp41, demonstrating an antigen broadening response towards achieving the goal of inducing BnAbs through immunization.

An immune network based distributed architecture to control public bus transportation systems

Although many Public Transportation Control Systems (PTCS) were developed to support the management of public transportation systems, there is still a need to improve the capability of PTCS to deal with a variety of disturbance types, which may degrade performance (e.g. earliness/delay) and quality of service (QoS, e.g. punctuality, frequency and utilization). This paper extends our previous works on artificial immune systems to control Public Transportation Systems by means of buses (PTS). The main contribution consists in developing a distributed reaction mechanism based on the immune network theory and its related mechanisms, such as cell co-stimulation, co-suppression, activation and coordination. The immune network reaction mechanism is implemented within a distributed multi-agent architecture, and its performance is assessed using traffic simulation software. The immune network reaction mechanism is benchmarked against other control strategies (holding at station, skip station, and their combination), and performance is evaluated based on three classes of criteria: time oriented criteria, passenger oriented criteria and service oriented criteria. The performance of the suggested system with respect to passenger and service oriented criteria was remarkably higher than the performance of other control algorithms. With respect to time oriented criteria, the performance of the suggested system was found slightly better than the combination of holding and skipping stations.

A new model of vehicular ad hoc networks based on artificial immune theory

Vehicular ad hoc networks (VANETs) are highly mobile and wireless networks intended to aid vehicular safety and traffic monitoring. To achieve these goals, we propose a VANET model based on immune network theory. Our model outperforms the delay tolerant mobility sensor network (DTMSN) model over a range of node numbers in terms of data packet arrival delay, arrival ratio, and throughput. These findings held true for the on-demand distance vector and connection-based restricted forwarding routing protocols. The model performed satisfactorily on a real road network.

A new model of vehicular ad hoc networks based on artificial immune theory

Vehicular ad hoc networks (VANETs) are highly mobile and wireless networks intended to aid vehicular safety and traffic monitoring. To achieve these goals, we propose a VANET model based on immune network theory. Our model outperforms the delay tolerant mobility sensor network (DTMSN) model over a range of node numbers in terms of data packet arrival delay, arrival ratio, and throughput. These findings held true for the on-demand distance vector and connection-based restricted forwarding routing protocols. The model performed satisfactorily on a real road network.

An artificial immune network to control interrupted flow at a signalized intersection

To monitor and control interrupted flow at signalized intersections, several Traffic Signal Control Systems (TSCSs) were developed based on optimization and artificial intelligence techniques. Although learning can provide intelligent ways to deal with disturbances, existing approaches still lack concepts and mechanisms that enable direct representation of knowledge and explicit learning, particularly to capture and reuse previous experiences with disturbances. This article addresses this gap by designing a new TSCS based on innovative concepts and mechanisms borrowed from biological immunity. Immune memory enables the design of a Case-Based Reasoning (CBR) System in which cases provide a direct representation of knowledge about disturbances. Immune network theory enables the design of a Reinforcement Learning (RL) mechanism to interconnect cases, capture explicit knowledge about the outcomes (success and failure) of control decisions and enable decision-making by taking advantage of previous outcomes in reaction to new occurrences of disturbances. We provide a detailed description of new learning algorithms, both to create the case-base and to interconnect cases using RL. The performance of the suggested TSCS is assessed by benchmarking it against two standard control strategies from the literature, namely fixed-time and adaptive control using the Longest Queue First – Maximal Weight Matching (LQF-MWM) algorithm. The suggested TSCS is applied on an intersection simulated using VISSIM, a state-of-the-art traffic simulation software. The results show that the suggested TSCS is able to handle different traffic scenarios with competitive performance, and that it is recommended for extreme situations involving blocked approaches and high traffic flow.

Toward a New Kind of Vaccine: A Logical Extension of the Symmetrical Immune Network Theory.

BackgroundThe symmetrical immune network theory, developed in 1975, is based on the existence of specific T cell factors and hypothesizes that normal IgG immune responses comprise the production of 2 kinds of antibodies, namely antigen-specific antibodies and anti-idiotypic antibodies.ObjectiveThe aim of this study was to confirm the existence of specific T cells factors and to show that immunization of C3H mice with BL/6 skin or using nominal antigen for immunization (Tetanus Toxoid) induced production of antigen-specific (anti-BL/6 or antitetanus) antibodies plus anti-idiotypic antibodies (C3H anti-anti-C3H). Subsequently, we investigated the role of combinations of antigen-specific and anti-idiotype antibodies in a variety of animal models of clinical diseases.MethodsAntigen-specific antibodies were produced by conventional immunization of mice (eg, with tetanus toxoid or by skin allografting). Subsequent anti-idiotypic antibodies were derived by exhaustive absorption of antigen-specific antibody, with confirmation of anti-idiotypic specificity by binding to relevant target antigen-specific antibodies in an enzyme-linked immunosorbent assay (ELISA). Antigen-specific plus anti-idiotypic antibodies were then used to modulate skin allograft survival, dextran sulfate sodium (DSS)-induced colitis, ovalbumin (OVA)-induced IgE production, and breast cancer growth in mice.ResultsInfusions of anti-BL/6 antibodies together with BL/6 anti-anti-BL/6 antibodies specifically suppressed (>85%) an immune response to BL/6 lymphocytes in C3H mice. The two kinds of antibodies with complementary specificity are hypothesized to stimulate 2 populations of T lymphocytes. Coselection of these 2 populations leads to a new stable steady state of the system with diminished reactivity to BL/6 tissue. A combination of anti-C3H and C3H anti‑anti-C3H IgG antibodies down-regulated inflammation in a mouse model of inflammatory bowel disease (>75%) and attenuated anti-IgE production and sensitization to produce IL4 cytokines (>70%) in an OVA-allergy model. Combination of C3H anti‑BL/6 and BL/6 anti-anti-BL/6 antibodies decreased tumor growth and metastases (>705) in an EMT6 transplantable breast cancer model.ConclusionsUse of a combination of antigen-specific and anti-idiotypic antibodies has potential as a new class of vaccines.

Artificial immune algorithm applied to distribution system reconfiguration with variable demand

This paper presents a new methodology to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demand, using the artificial immune algorithm Copt-aiNet (Artificial Immune Network for Combinatorial Optimization). This algorithm is an optimization technique inspired by immune network theory (aiNet). The reconfiguration problem with variable demand is a complex problem of a combinatorial nature. The goal is to identify the best radial topology for an EDS in order to minimize the cost of energy losses in a given operation period. A specialized sweep load flow for radial systems was used to evaluate the feasibility of the topology with respect to the operational constraints of the EDS and to calculate the active power losses for each demand level. The algorithm was implemented in C++ and was evaluated using test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes. The obtained results were compared with those in the literature in order to validate and prove the efficiency of the proposed algorithm.

Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research.

Compared with controlled terminologies (e.g., MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network (i.e., OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research.

An immunity-based hybrid genetic algorithms for permutation flowshop scheduling problems

This paper investigates novel GA-based hybrid artificial immune system (AIS) for the permutation flowshop scheduling problems (PFSP) that minimizes the makespan. The proposed approaches aim to show that the efficiency of GAs in solving flowshop problems can be improved significantly by tailoring the various AIS operators to suit the problem structure. The proposed hybridization scheme is applied in two ways: (1) the first hybrid of GA and AIS introduces vaccination (Jiao and Wang, IEEE Trans Syst Man Sybernetics Part A Syst Hum 30(5):552–561, 2000) into the field of GAs based on the theory of immunity in biology, (2) the second takes its inspiration on the immune network theory (Perelson, Immunol Rev 110(1):5–36, 1989), and applied it to the field of GAs. The proposed hybrid metaheuristics produce high quality solutions as proved by the tests performed over Taillard’s (Eur J Oper Res 64(2):278–285, 1993) well-known flowshop scheduling benchmarks and corroborated by the comparisons we did with the most frequently referred in the related literature and recently developed hybrid GAs, including genetic algorithms, particle swarm optimization, and other advanced and recent techniques. Furthermore, the effects of some parameters are discussed.

Artificial immune networks Copt-aiNet and Opt-aiNet applied to the reconfiguration problem of radial electrical distribution systems

This paper presents two new approaches to solving the reconfiguration problem of electrical distribution systems (EDS) using the Copt-aiNet (Artificial Immune Network for Combinatorial Optimization) and Opt-aiNet (Artificial Immune Network for Optimization) algorithms. The Copt-aiNet and Opt-aiNet algorithms are efficient optimization techniques inspired by the immune network theory (aiNet). The reconfiguration problem is a complex combinatorial problem that aims at identifying the best radial topology for the EDS in order to minimize power losses. A specialized forward/backward radial power flow was used to evaluate each proposed solution proposal in order to determine its power losses and its feasibility regarding the operational constraints of the EDS. The algorithms were developed in the C++ programming language and test systems of 33, 70, 84, 119, and 136 nodes, along with a real system of 417 nodes, were used to validate the proposed method. The obtained results were compared with the best solutions found in the specialized literature in order to verify the efficiency of the proposed algorithms.

Consideration of dual characters of exosomes in the tumour immune response

Efforts to get a strong and sustained anti-tumour immune response induced by a tumour specific antigen have failed, but sipuleucel-T has been approved by the US Food and Drug Administration (FDA). We noticed that exosomes secreted by tumour cells or immune cells may be crucially involved in the tumour immune response, whereas others have had inconsistent findings on exosome involvement. Based on immune network theory, we summarise research advances of exosomes and speculate that in the tumour immune response exosomes follow the immune response curve hypothesis. Exosomes activate simultabeously both immune activation and immune tolerance, but at different intensities. To obtain a desired anti-immune response, the initial point of immunity should be determined to achieve the strongest anti-tumour response, and repeated in vitro to extend and enhance this response. As a result, our hypothesis proposes that studies should now be directed at determining the exact time of exosome activity in maintaining a viable anti-tumour immune response in vivo.

Approaching mathematical model of the immune network based DNA Strand Displacement system

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot.

Adaptive Regulation and Partial Failure Recovery of Immune Network

In order to solve the problem that taking network reconfiguration when the circuit is partial failure leads to a waste of computing resources and long repair time, the article proposes a way to limite the failure influence in a local scope through adaptively regulating the immune network . On the basis reviewing of immune network theory, a immune network structured representation method is raised. In order to make the immune network have fault tolerant ability, adaptively adjusting the structure of the immune network can dynamically chang the immune network function. Lastly, a partial failure recovery experiment of the motor control circuits is applied to test the performance of immune network adaptive regulation. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.285 8

Immune network-based swarm intelligence and its application to unmanned aerial vehicle (UAV) swarm coordination

In this paper, we first investigate the fundamentals of the immune system and the principles of the antibodies and how antibodies work cooperatively to achieve swarm coordination and hence a successful immune response. Based on the study, we propose a multi-agent cooperative operation strategy for the immune system based on the immune network theory. In order to achieve successful swarm coordination, we design each agent to select its favorable strategy according to distributed sensing through local communication. Experiments show that this Immunology-inspired Cooperative Operation strategy performs well even under dynamically changing environment. Its effectiveness is also verified by numerical simulation on multiple unmanned aerial vehicle (UAV) coordination.

Multi-word-Agent Autonomy Learning Based on Adaptive Immune Theories

A multi-word-agent autonomy learning model as a new online learning model is presented based on clonal selection theory and idiotypic immune network theory, where words simulate B cells and antigens involved in adaptive immune responses to construct autonomic immune word-agents under the framework of autonomy oriented computing. Moreover, relations between words simulate specific relations between B cells and antigens, where attributes of words simulate receptors, and strengths of these relations are defined as affinities calculated by matching receptors. The environment simulates the region in which the word-agents interact with each other and share information. The goal of the model is to optimize these relations iteratively by increasing affinities with hyper-mutation of receptors of B cell word-agents. Experimental results on dependency parsing show that not only accuracies are improved continually, but also, compared with MSTParser, high accuracies can be gained with less learning samples.