Immune Network Model Research Articles

A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept

The natural immune system provides an effective defense mechanism against foreign substances via complex interactions among various cells and molecules. Jerne introduced the immune network theory to model the relation between immune cells and molecules. The immune system like the neural system is able to learn from experience. In this paper, a multi-epitopic immune network model is proposed. The proposed model is hybridized with Learning Vector Quantization (LVQ) and fuzzy set theory to present a new supervised learning method. The new method is called Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach (HFNINME). To evaluate the performance of the proposed method several experiments on benchmark classification problems are carried out and the results are compared with two prominent immune-based classifiers as well as several versions of the LVQ algorithm. The results of the experiments reveal that the proposed method yields a parsimonious classifier that can classify data more accurately and more efficiently.

Global analysis of the immune response

The immune system may be seen as a complex system, characterized using tools developed in the study of such systems, for example, surface roughness and its associated Hurst exponent. We analyze densitometric (Panama blot) profiles of immune reactivity, to classify individuals into groups with similar roughness statistics. We focus on a population of individuals living in a region in which malaria endemic, as well as a control group from a disease-free region. Our analysis groups individuals according to the presence, or absence, of malaria symptoms and number of malaria manifestations. Applied to the Panama blot data, our method proves more effective at discriminating between groups than principal-components analysis or super-paramagnetic clustering. Our findings provide evidence that some phenomena observed in the immune system can be only understood from a global point of view. We observe similar tendencies between experimental immune profiles and those of artificial profiles, obtained from an immune network model. The statistical entropy of the experimental profiles is found to exhibit variations similar to those observed in the Hurst exponent.

Multiple‐valued immune network with apoptosis system

AbstractIn this paper, we describe a new model of immune network based on biological immune response network. We propose an immunity like multiple‐valued network with apoptosis mechanism. The model is based on the interaction between B cells and T cells and the biological apoptosis mechanism in the human body. With the mechanism, a naturally immune system can be reproduced. The model is also applied to pattern recognition. It becomes possible with a conventional model to restrict the category increase of memory patterns. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(3): 51– 57, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20320

A Large Scale Dynamical System Immune Network Model with Finite Connectivity

We study a model of an idiotypic immune network which was introduced by N. K. Jerne. It is known that in immune systems there generally exist several kinds of immune cells which can recognize any particular antigen. Taking this fact into account and assuming that each cell interacts with only a finite number of other cells, we analyze a large scale immune network via both numerical simulations and statistical mechanical methods, and show that the distribution of the concentrations of antibodies becomes non-trivial for a range of values of the strength of the interaction and the connectivity.

Multiple-Valued Immune Network with Apoptosis System

In this paper, we describe a new model of immune network based on biological immune response network. We propose an immunity like multiple-valued network with apoptosis mechanism. The model is based on the interaction between B cells and T cells and the biological apoptosis mechanism in human body. With the mechanism, a naturally immune system can be reproduced. The model is also applied to pattern recognition. It gets possible with a conventional model to restricting categories increase of memory patterns.

Idiotypic and Anti‐Idiotypic Antibodies Produced in Immune Response to Bacteria Lactobacillus acidophyllus

Lactobacilli are nonpathogenic gram‐positive inhabitants of the normal human intestine known for their health‐promoting effects. In our earlier work, it is shown that human monoclonal antibody isolated from sera of a patient with Waldenstrom macroglobulinaemia possess innate antibody characteristics and binds to lactic acid bacteria. According to the immune network model, immunization with this bacteria could induce the perturbations in immune system that might result in production of anti‐Lactobacillus antibodies, human monoclonal antibody like (Ab1) and anti‐idiotypic antibody (Ab2). In this study, BALB/c mice were immunized with two doses of bacteria Lactobacillus acidophilus in complete and incomplete Freund's adjuvant and phosphate‐buffered saline (PBS), respectively. Seven days after the last immunization, sera from immunized mice were collected and the presence of Lactobacillus‐specific Ab1 and Ab2 were determined by ELISAs. In the sera of immunized mice, antibodies specific to bacteria Lactobacillus acidophilus were shown. The concentration of Lactobacillus‐specific antibodies was higher in the sera of hyperimmunized mice (mice immunized with 1 mg of IgM DJ) than in sera of mice immunized with 100 times lower doses of immunogen (0.01 mg per doses). Moreover, Ab1 and Ab2 antibodies were detected in the sera of Lactobacillus‐hyperimmunized mice. In this study, we have shown the idiotypic network interactions in mice immunized with bacteria Lactobacillus acidophilus.

Dynamics of an artificial immune network1

Artificial immune systems (AIS) are adaptive systems developed by taking inspiration from theoretical and experimental immunology and targeted at solving complex problems. Theoretical models are basically aimed at aiding and providing a more qualitative and quantitative description of the biological system they model, sometimes allowing the prediction and design of experiments, and the recovery of information from experimental set-ups. This paper describes the dynamics of one artificial immune network model based on artificial immune systems, named aiNet, and demonstrates through empirical results that even these highly simplified models may present several qualitative behaviours typical of the biological systems and theoretical models; thus being worthy of attention by the more theoretical community. In particular, clonal selection and expansion, affinity maturation, primary, secondary and cross-reactive responses are studied, and demonstrated to be observable in aiNet. Immune network features, such as self-organization, dynamics, connectivity and metadynamics are also discussed.

Long term and short term effects of perturbations in an immune network model

In this paper we review the trajectory of a model proposed by Stauffer and Weisbuch in 1992 to describe the evolution of the immune repertoire and present new results about its dynamical behavior. Ten years later this model, which is based on the ideas of the immune network as proposed by Jerne, has been able to describe a multi-connected network and could be used to reproduce immunization and aging experiments performed with mice. The immunization protocol is simulated by introducing small and large perturbations (damages), and in this work we discuss the role of both. Besides its biological implications, the physical aspects of the complex dynamics of this network is very interesting per se. In a very recent paper we studied the aging effects by using auto-correlation functions, and the results obtained apparently indicated that the small perturbations would be more important than the large ones, since their cumulative effects may change the attractor of the dynamics. However our new results indicate that both types of perturbations are important. It is the cooperative effects between both that lead to the complex behavior which allows to reproduce experimental results.

Blind Search for Optimal Wiener Equalizers Using an Artificial Immune Network Model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.

On the aging dynamics in an immune network model

Recently we have used a cellular automata model which describes the dynamics of a multi-connected network to reproduce the refractory behavior and aging effects obtained in immunization experiments performed with mice when subjected to multiple perturbations. In this paper we investigate the similarities between the aging dynamics observed in this multi-connected network and the one observed in glassy systems, by using the usual tools applied to analyze the latter. An interesting feature we show here is that the model reproduces the biological aspects observed in the experiments during the long transient time it takes to reach the stationary state. Depending on the initial conditions, and without any perturbation, the system may reach one of a family of long-period attractors. The pertrubations may drive the system from its natural attractor to other attractors of the same family. We discuss the different roles played by the small random perturbations (noise) and by the large periodic perturbations (immunizations).

Web information visualization method employing immune network model for finding topic stream from document-set sequence

A Web information visualization method based on the document set-wise processing is proposed to find the topic stream from a sequence of document sets. Although the hugeness as well as its dynamic nature of the Web is burden for the users, it will also bring them a chance for business and research if they can notice the trends or movement of the real world from the Web. A sequence of document sets found on the Web, such as online news article sets is focused on in this paper. The proposed method employs the immune network model, in which the property of memory cell is used to find the topical relation among document sets. After several types of memory cell models are proposed and evaluated, the experimental results show that the proposed method with memory cell can find more topic streams than that without memory cell.

IMMUNE AND NEURAL NETWORK MODELS: THEORETICAL AND EMPIRICAL COMPARISONS

This paper brings a detailed mathematical description of an artificial immune network model, named aiNet. The model is implemented in association with graph concepts and hierarchical clustering techniques, and is proposed to perform machine learning, data compression and cluster analysis. Pictorial representations for the aiNet basic units and typical architectures are introduced. The proposed immune network was primarily compared on a theoretical basis with well-known artificial neural networks. Then, the aiNet was applied to a non-linearly separable benchmark and a real-world problem, and the results were compared with that of the self-organizing feature map and with others already presented in the literature.

Francisco J. Varela (1946–2001)

It was with great sadness that we received the news of the death of Francisco Varela, who passed away at his home in Paris, on 28 May, 2001.Varela was well known for his work in a number of scientific fields. He was the co-originator (with Humberto Maturana) of the theory of autopoiesis 1xSee all References1, and was one of the first proponents of the embodied or ‘enactive’ approach to cognition 2xVarela, F.J., Thompson, E., and Rosch, E. See all References2. He was known for his work on autonomous-network models of the immune system 3xSecond generation immune networks. Varela, F.J. and Coutinho, A. Immunol. Today. 1991; 12: 159–166Abstract | Full Text PDF | PubMed | Scopus (233)See all References3. In recent years, he provided groundbreaking studies using multiple-electrode recordings and mathematical analysis of large-scale neuronal integration during cognitive processes 4xThe brainweb: phase synchronization and large-scale integration. Varela, F.J. et al. Nat. Rev. Neurosci. 2001; 2: 229–239Crossref | PubMed | Scopus (2228)See all References4, showing for the first time that the human perception of meaningful complex forms is accompanied by phase-locked, synchronous oscillations in distinct brain regions 5xPerception's shadow: long-distance synchronization of human brain activity. Rodriguez, E. et al. Nature. 1998; 397: 430–433See all References5, and that nonlinear analysis can be used to predict seizures in epileptic patients prior to the onset of symptoms 6xEpileptic seizures can be anticipated by non-linear analysis. Martinerie, J. et al. Nat. Med. 1998; 4: 1173–1176Crossref | PubMed | Scopus (278)See all References6. He was also known for his original philosophical and empirical studies of the ‘neurophenomenology’ of human consciousness 7xNeurophenomenology: a methodological remedy for the hard problem. Varela, F.J. J. Conscious. Stud. 1996; 3: 330–350See all References, 8xThe specious present: a neurophenomenology of time consciousness. Varela, F.J. : 266–314See all References.Varela was born on 7 September, 1946 in Chile. As a child and teenager, he received a strong classical education from the German Lyceum in Santiago, which instilled in him a deep and lifelong appreciation of literature, art, philosophy and science. He received his M.Sc. (Licenciatura) in Biology in 1967 from the University of Chile, and his Ph.D. in Biology from Harvard University in 1970. Although he was offered positions at Harvard and other American universities, he chose to return to Chile in 1970 to help build a scientific research community there. Forced to flee with his family after the military coup of 1973, he taught at the University of Colorado Medical School in Denver until 1978, spent a year at the Brain Research Laboratories of the New York University Medical School in 1979, and then returned to Chile in 1980. In 1986 he moved to Paris, where he was based at the Institut des Neurosciences and at CREA (Centre de Recherche en Epistemologie Applique). In 1988 he was appointed to be Director of Research at CNRS, a position he held until his death.Varela's years in Paris, right up to the month of his passing, were remarkably full and productive by any standard. That he suffered from Hepatitus C from the early 1990s onwards, receiving a liver transplant in 1998, makes his life and work during this time truly inspiring.Varela died calm and at peace, in the loving embrace of his family. He leaves his wife, Amy Cohen Varela, and their son Gabriel; and his former wife Leonor, and their daughters Alejandra and Leonor, and son Javier. He will be deeply missed.

Response to Invasion by Antigens and Effects of Threshold in an Immune Network Dynamical System Model with a Small Number of Degrees of Freedom

We study a dynamical system model of an idiotypic immune network with a small number of degrees of freedom, mainly focusing on the effect of a threshold above which antibodies can recognise antibodies. The response of the system to invasions by antigens is investigated in the both models with and without the threshold and it turns out that the system changes in a desirable direction for moderate magnitude of perturbation. direction for moderate magnitude of perturbation. Also, the propagation of disturbance by an antigen is investigated in the system of one-dimensionally connected basic units taking the closed 3-clone system as a unit, and it is clarified that the threshold of the system has effects to enhance the stability of the network and to localise the immune response.

Topic-based Intelligent Support System for Information Retrieval

We propose a new concept of intelligent support systems for topic-based information retrieval. As information retrieval (IR) on the World Wide Web (WWW) becomes widespread, new types of tools and systems that do not only find specific pages the user wants, but also and helping the user learn about a particular field of interest are increasingly needed. Two systems based on this consideration are introduced in this paper. One is the Fish View system for supporting document-ordering. It focuses on the user’s document-ordering (making diagrams) while reading, and the user’s viewpoint is represented by a combination of a small number of concepts taken from the existing concept structure dictionary. The extracted viewpoint can be used for measuring the similarity among documents, using fisheye matching, the extended Vector Space Model. The other is the query network for visualization of the topic distribution through WWW IR, and its concept employing the Immune Network model is introduced with preliminary experiments.

Analysis of an Immune Network Dynamical System Model with a Small Number of Degrees of Freedom

We numerically study a dynamical system model of an idiotypic immune network with a small number of degrees of freedom. The model was originally introduced by Varela et.al., and describes antibodies interacting in a body in order to prepare for the invasion of external antigens. The main purpose of this paper is to investigate the direction of change in the network system when antigens invade it. We investigate three models, original model, a modified model and a modified model with a threshold of concentration over which each antibody can recognize other antibodies. In all these models, both chaotic and periodic states exist. In particular, we find peculiar states organized in the network, the clustering state.

Natural and Induced Tolerance in an Immune Network Model

It has been proposed that the immune system can be partitioned into central and peripheral immune systems. Recently, Carneiroet al. (1996a, b) proposed a network model incorporating B and T lymphocytes that explicitly accounts for that partition. This model, however, had some limitations that are tackled here. Two main changes were introduced: the average idiotypic connectivity is now an explicit function of time based on empirical evidence; and the activation of T lymphocytes by antigen is described by a log-bell shaped dose response curve. The new model, which also accounts for the CIS and PIS distinction, shows more reasonable results since the frequencies of tolerant, immune or autoimmune responses to an antigen are now correct. The model provides a new interpretation for tolerance induction during the neonatal period, and for the adult tolerance induction by low or high doses of antigen. It predicts that natural tolerance for antigens available during the neonatal period can be kept indefinitely upon their removal, while tolerance induced in the adult stages is rapidly lost upon transient removal of the antigen. A semiquantitative analysis of the model provides a simple explanation for the different results in terms of the frequency at which a limited set of canonical connectivity structures emerge during ontogenesis.

Stable State Analysis of an Immune Network Model

The paper analyzes a model of immune system developed by different authors (Perelson, De Boer, Weisbuch and others). The model describes interactions among B-lymphocytes. It does not consider antibodies as interaction intermediaries, although it uses a typical activation curve. The relevant parameters are: an influx term, a threshold value, a proliferation rate, and a decay parameter. The study of the n-dimensional extension of the model and a bifurcation analysis of the stationary states with respect to the influx parameter show that the influx value for which biologically acceptable solutions exist decreases as n increases. When the influx term is neglected the stationary states are obtained analytically and their stability is discussed. Moreover, it is discussed how the stable solutions can be considered as "selective states", that is, with only one high idiotypic concentration, when we suppose a complete connectivity.

Inverse analysis of empirical matrices of idiotypic network interactions.

The concept of shape space proposed by Perelson and Oster (1979, J. Theor. Biol. 81, 645-670) has been a useful tool for the theoretical immunologists, who have invoked it to model idiotypic binding, which plays a significant role in mathematical models of immune networks. The actual construction of such a space from its definition requires specialized experimental information, which is not completely available. In this article, we discuss, with illustrative examples, how graphical representations similar to the idea of shape space can be derived by analyzing real affinity matrices, and the relative merits of such representations to approximations that might be obtained by the approach of Perelson and Oster. We also give directions for future research with a view toward applications.

Inverse analysis of empirical matrices of idiotypic network interactions

The concept of shape space proposed by Perelson and Oster (1979, J. Theor. Biol. 81, 645–670) has been a useful tool for theoretical immunologists, who have invoked it to model idiotypic binding, which plays a significant role in mathematical models of immune networks. The actual construction of such a space from its definition requires specialized experimental information, which is not completely available. In this article, we discuss, with illustrative examples, how graphical representations similar to the idea of shape space can be derived by analyzing real affinity matrices, and the relative merits of such representations to approximations that might be obtained by the approach of Perelson and Oster. We also give directions for future research with a view toward applications.