A Cayley tree immune network model with antibody dynamics

A Cayley tree model of idiotypic networks that includes both B cell and antibody dynamics is formulated and analysed. As in models with B cells only, localized states exist in the network with limited numbers of activated clones surrounded by virgin or near-virgin clones. The existence and stability of these localized network states are explored as a function of model parameters. As in previous models that have included antibody, the stability of immune and tolerant localized states are shown to depend on the ratio of antibody to B cell lifetimes as well as the rate of antibody complex removal. As model parameters are varied, localized steady-states can break down via two routes: dynamically, into chaotic attractors, or structurally into percolation attractors. For a given set of parameters percolation and chaotic attractors can coexist with localized attractors, and thus there do not exist clear cut boundaries in parameter space that separate regions of localized attractors from regions of percolation and chaotic attractors. Stable limit cycles, which are frequent in the two-clone antibody B cell (AB) model, are only observed in highly connected networks. Also found in highly connected networks are localized chaotic attractors. As in experiments by Lundkvistet al. (1989.Proc. natn. Acad. Sci. U.S.A. 86, 5074–5078), injection ofAb ₁ antibodies into a system operating in the chaotic regime can cause a cessation of fluctuations ofAb ₁ andAb ₂ antibodies, a phenomenon already observed in the two-clone AB model. Interestingly, chaotic fluctuations continue at higher levels of the tree, a phenomenon observed by Lundkvistet al. but not accounted for previously.     

A continuous time Bayesian network model for cardiogenic heart failure

Continuous time Bayesian networks are used to diagnose cardiogenic heart failure and to anticipate its likely evolution. The proposed model overcomes the strong modeling and computational limitations of dynamic Bayesian networks. It consists of both unobservable physiological variables, and clinically and instrumentally observable events which might support diagnosis like myocardial infarction and the future occurrence of shock. Three case studies related to cardiogenic heart failure are presented. The model predicts the occurrence of complicating diseases and the persistence of heart failure according to variations of the evidence gathered from the patient. Predictions are shown to be consistent with current pathophysiological medical understanding of clinical pictures.     

Internal antigen and immune network

The network hypothesis postulates the existence of internal idiotopic structures which mimic nominal antigens. Experimental evidence for idiotope internal antigen is presented and its implication for the Network hypothesis is discussed. The exploitation of the internal idiotope antigens (or preparation of vaccines) is a realistic possibility.     

A drug-sensitive genetic network masks fungi from the immune system

Fungal pathogens can be recognized by the immune system via their beta-glucan, a potent proinflammatory molecule that is present at high levels but is predominantly buried beneath a mannoprotein coat and invisible to the host. To investigate the nature and significance of "masking" this molecule, we characterized the mechanism of masking and consequences of unmasking for immune recognition. We found that the underlying beta-glucan in the cell wall of Candida albicans is unmasked by subinhibitory doses of the antifungal drug caspofungin, causing the exposed fungi to elicit a stronger immune response. Using a library of bakers' yeast (Saccharomyces cerevisiae) mutants, we uncovered a conserved genetic network that is required for concealing beta-glucan from the immune system and limiting the host response. Perturbation of parts of this network in the pathogen C. albicans caused unmasking of its beta-glucan, leading to increased beta-glucan receptor-dependent elicitation of key proinflammatory cytokines from primary mouse macrophages. By creating an anti-inflammatory barrier to mask beta-glucan, opportunistic fungi may promote commensal colonization and have an increased propensity for causing disease. Targeting the widely conserved gene network required for creating and maintaining this barrier may lead to novel broad-spectrum antimycotics.     

Quantitative analyses of immune network components

Polyclonal immune network antibodies were quantitated and characterized in a syngeneic BALB/c murine system. Immunizations of BALB/c antifluorescein mAb 9-40 conjugated to keyhole limpet hemocyanin, produced anti-Id (anti-9-40, 39 to 190 micrograms/ml) as well as anti-fluorescein (anti-Fl, 12 to 109 micrograms/ml). Separately, immunizations of polyclonal anti-9-40, developed significant anti-Fl serum levels in the secondary (2 degrees) response (50 to 270 micrograms/ml), which decreased in the 3 degrees response (50 to 180 micrograms/ml) and thereafter, although levels of 9-40 idiotypically related antibodies increased. Polyclonal 2 degrees anti-anti-9-40 exhibited variant anti-Fl active sites, was antigenically more cross-reactive than polyclonal 2 degrees anti-Fl, but did not exhibit affinity maturation for fluorescein relative to 1 degrees anti-anti-9-40. In addition, the 9-40 idiotype constituted a small (less than 1.0%) percent of the 2 degrees and 3 degrees anti-Fl (ab1) immune response. When viewed within the context of an antigenic system that possesses widely diverse idiotypy, continued introduction of polyclonal anti-Id appears eventually to: 1) induce polyclonal ab3 with quantitative expression of idiotypically related antibodies in preference to ab3 of ancestral (9-40) antigenic specificity, 2) relative to ab1, induce a 100-fold increase in the level of ab3 antibodies that have both ancestral idiotype and ancestral antigen reactivity, and 3) induce polyclonal ab3 antibodies with a measurably wider range of antigenic reactivities than those of polyclonal ab1. These quantitative data may reflect the natural state of an immune network in a diverse antigenic response.     

Capacity of a model immune network

The capacity of a model immune network in terms of the number of different antigens that can be vaccinated against without any memory lost is computed and tested by numerical simulations. We also investigate memory loss and failure to vaccinate due to overcrowding the network with too many antigens. The computations are done for two different strategies for proliferation, one implying all the antigen specific clones and the second one being more thrifty.     

Evolution of specificity in an immune network

A dynamic antigen response of the immune network is discussed, based on shape-space modelling. The present model extends the shape-space modelling by introducing the evolution of specificity of idiotypes. When the amount of external antigen increases, a measure of stability of the immune network is lost and thus the network can respond to the antigen. It is shown that specific and non-specific responses emerge as a function of antigen amounts. A specific response is observed with a fixed-point attractor, and a non-specific response is observed with a chaotic attractor for the lymphocyte population dynamics. The network topology also changes between fixed-point and chaotic attractors. For some antigen amounts, chaotic attractors will vanish or become long-lived super-transient states. A dynamic bell-shaped response function will thus emerge. The relevance of long-lived chaotic transient states embedded in fixed-point attractors is discussed with respect to immune functions.     

On the activities in a continuous neural network

In this work an attempt is made to study the activities in a continuous neural system. The neural model considered here is a two dimensional continuous version of an earlier discrete model investigated in a series of papers [5–8]. The variations of the normalized firing rates in the present model are described by a nonlinear integro-partial differential equation. The conditions for the existence and uniqueness of the solutions of the describing equation subject to an initial condition are established and the steady-state solutions are investigated for inputs which are constant with respect to time. Depending on the parameters which are related to the self-inhibition and adaptation properties of the neural network, some of the oscillatory and stability properties of the solutions of the describing equation are discussed.     

Idiotypic network regulations of immune responses to HLA

The development of antiidiotypic autoimmunity with respect to HLA alloantigens provides an attractive explanation for the phenomenon of maternal tolerance to the fetus and for the tandem selection of two antagonistic traits, major histocompatibility complex polymorphism and alloreactivity. We have demonstrated that both T and B lymphocyte responses to allogeneic HLA antigens are subjected to feedback regulation by autologous antiidiotypic immunity. Idiotypic receptors for the alloantigen expressed by T lymphocytes induce antiidiotypic antibodies that are readily detectable in serum during pregnancy, and antiidiotypic T cells that can be revealed in the autologous mixed lymphocyte culture system. Such antiidiotypic T cells and antibodies inhibit specifically the alloimmune function of autologous T cell lines. Similarly, antiidiotypic antibodies (Ab2) to HLA antibody molecules (Ab1) block the binding of the latter to the immunizing HLA antigen. The prevalence of Ab2 over Ab1 during pregnancy may explain the maternal tolerance to the fetus.     

Towards an integrated network of coral immune mechanisms

Reef-building corals form bio-diverse marine ecosystems of high societal and economic value, but are in significant decline globally due, in part, to rapid climatic changes. As immunity is a predictor of coral disease and thermal stress susceptibility, a comprehensive understanding of this new field will likely provide a mechanistic explanation for ecological-scale trends in reef declines. Recently, several strides within coral immunology document defence mechanisms that are consistent with those of both invertebrates and vertebrates, and which span the recognition, signalling and effector response phases of innate immunity. However, many of these studies remain discrete and unincorporated into the wider fields of invertebrate immunology or coral biology. To encourage the rapid development of coral immunology, we comprehensively synthesize the current understanding of the field in the context of general invertebrate immunology, and highlight fundamental gaps in our knowledge. We propose a framework for future research that we hope will stimulate directional studies in this emerging field and lead to the elucidation of an integrated network of coral immune mechanisms. Once established, we are optimistic that coral immunology can be effectively applied to pertinent ecological questions, improve current prediction tools and aid conservation efforts.     

The discrete dynamics of monotonically decomposable maps

We correct errors in Propositions 11 and 14 of the paper in the title. Corollary 13 is withdrawn. The online version of the original article can be found under doi:.     

Neural network modeling in optimisation of continuous fermentation processes

The capability of self-recurrent neural networks in dynamic modeling of continuous fermentation is investigated in this simulation study. In the past, feedforward neural networks have been successfully used as one-step-ahead predictors. However, in steady-state optimisation of continuous fermentations the neural network model has to be iterated to predict many time steps ahead into the future in order to get steady-state values of the variables involved in objective cost function, and this iteration may result in increasing errors. Therefore, as an alternative to classical feedforward neural network trained by using backpropagation method, self-recurrent multilayer neural net trained by backpropagation through time method was chosen in order to improve accuracy of long-term predictions. Prediction capabilities of the resulting neural network model is tested by implementing this into the Integrated System Optimisation and Parameter Estimation (ISOPE) optimisation algorithm. Maximisation of cellular productivity of the baker's yeast continuous fermentation was used as the goal of the proposed optimising control problem. The training and prediction results of proposed neural network and performances of resulting optimisation structure are demonstrated.     

A radial basis function network approach for the computation of inverse continuous time variant functions

This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.     

An artificial immune network based algorithm for diabetes diagnosis

A novel artificial immune network based algorithm for the diagnosis of diabetes is presented. The algorithm's implementation includes: (1) creating the initial immune antibody network; (2) the network is evolved with the learning from foreign antigens; (3) diagnosis process is accomplished by majority vote of the k nearest neighbor antibodies.     

Extensive mapping of an innate immune network with CRISPR

The application of the CRISPR‐Cas9 system marks a major breakthrough for genetic screens, particularly in mammalian cells where high‐throughput targeted gene editing has been lacking. Parnas et al ( 2015 ) apply this screening technology to mouse bone marrow‐derived dendritic cells in order to study the regulation of the immune response triggered by PAMPs. Through integrated analysis of gene knockouts in conjunction with changes in protein and mRNA expression, CRISPR screens are facilitating dissection of immune regulatory networks at unprecedented resolution.     

Endoplasmic reticulum: one continuous network compartmentalized by extrinsic cues

The endoplasmic reticulum (ER) is an extensive three-dimensional network that stretches from the inner nuclear envelope to the cell cortex with a single, continuous membrane and a single, continuous lumen. Yet the ER contains specialized regions that carry out unique functions. The question that immediately arises is how the ER can be compartmentalized if it is continuous, and the answer to this is that cellular landmarks with unique sub-cellular distributions impose non-uniformity on the ER from outside, creating structural and functional sub-domains of the ER.