Network security has always been an area of priority and extensive research. Recent years have seen a considerable growth in experimentation with biologically inspired techniques. This is a consequence of our increased understanding of living systems and the application of that understanding to machines and software. The mounting complexity of telecommunications networks and the need for increasing levels of security have been the driving factor. The human body can act as a great role model for its unique abilities in protecting itself from external, foreign entities. Many abnormalities in the human body are similar to that of the attacks in wireless sensor networks (WSN). This paper presents basic ideas drawn from human immune system analogies that can help modelling a system to counter the attacks on a WSN by monitoring parameters such as energy, frequency of data transfer, data sent and received. This is implemented by exploiting two immune concepts, namely danger theory and negative selection. Danger theory aggregates the anomalies based on the weights of the anomalous parameters. The objective is to design a cooperative intrusion detection system (IDS) based on danger theory. Negative selection differentiates between normal and anomalous strings and counters the impact of malicious nodes faster than danger theory. We also explore other human immune system concepts and their adaptability to Wireless Sensor Network Security.

Les stratégies de traitement biologiques ciblant le TNF-α se sont avérées efficaces pour réduire l'inflammation et les symptômes cliniques dans plusieurs maladies inflammatoires chroniques et sont maintenant couramment utilisées pour les patients qui ne répondent pas aux AINS au cours de la spondyloarthrite (SpA). Cependant, 30 à 40% des patients ne répondent pas aux anti-TNF, et il est actuellement impossible de prédire la réponse des patients à ces biomédicaments. Pour améliorer les résultats cliniques, nous avons besoin d’une part d’une meilleure compréhension des mécanismes d’action des anti-TNF sur le système immunitaire, et d’autre part de biomarqueurs permettant de prédire la réponse à ces biomédicaments afin de guider la décision thérapeutique. Mon projet de doctorat a porté sur deux objectifs complémentaires: (i) l'objectif principal était de progresser dans notre compréhension des mécanismes pathogéniques impliqués dans la SpA axiale et de définir de quelle façon les anti-TNF-α affectent les réponses immunitaires des patients, (ii) de développer des biomarqueurs pour prédire la réponse thérapeutique aux inhibiteurs du TNF. En collaboration avec l'équipe du Pr. Dougados à l'Hôpital Cochin, nous avons recruté deux cohortes indépendantes de patients SpA ayant une maladie active et pour lesquels nous avons collecté des échantillons de sang avant l'initiation du traitement par anti-TNF puis 1 semaine et 3 mois après le début du traitement. Les réponses immunitaires de ces patients ont été analysées à l'aide de tests hautement standardisés réalisés ex-vivo sur sang circulant. Ces tests "TruCulture" se présentent sous forme de seringues, dans lesquelles 1 ml de sang total est mis à incuber avec un stimulus spécifique ; 20 stimuli différents ont été testé et validé avant et après traitement dans les deux cohortes de patients. Nous avons observé une réduction très significative de la sécrétion de IL-1ra, IL-1β, IL-8, and MIP-1β en réponse à des stimuli microbiens et à des agonistes des TLR dans les échantillons de sang prélevés 7 jours et/ou 3 mois après le début du traitement. Pour identifier les bases moléculaires de l’action des inhibiteurs du TNF nous avons analysé l'expression des gènes dans ces différentes conditions de stimulation. L'analyse bioinformatique quantitative de l'expression des gènes (QuSAGE) a révélé que les gènes les plus modulés par le traitement anti-TNF étaient NF-KB et les gènes cibles de NF-kB, y compris le TNF lui-même et l’IL1B. Nos données suggèrent que les inhibiteurs du TNF agissent principalement en perturbant une boucle autorégulatrice pilotée par NF-kB. Afin d'identifier les signatures immunologiques de réponse aux anti-TNF avant le début du traitement, nous avons corrélé les réponses immunitaires chez les patients analysés au temps 0 à la réponse thérapeutique aux anti-TNF mesurée à 3 mois. Nos résultats suggèrent que les patients atteints de SpA et exprimant des niveaux inférieurs de PAX5 et des niveaux supérieurs de SPP1 en réponse à la stimulation avec SEB avant l'initiation de la thérapie anti-TNF ont les meilleures réponses thérapeutiques. Notre recherche montre que les tests TruCulture sont un outil efficace pour étudier les fonctions immunitaires chez les patients atteints de SpA et que les effets du traitement anti-TNF peuvent être mesurés lorsque les cellules immunitaires sont stimulées. En terme de recherche translationnelle, nous avons identifié des molécules qui pourront être utilisés comme biomarqueurs pour aider les cliniciens à prédire les réponses thérapeutiques aux traitements anti TNF / The introduction of anti-TNF therapy has proven effective to reduce inflammation and clinical symptoms in several chronic inflammatory diseases. However, 30-40% of patients do not respond to TNF blockers and it is currently not possible to predict responsiveness of patients to anti-TNF therapy. Furthermore, their impact on the immune system is incompletely understood. The goals of my PhD project were (i) to define the impact of anti-TNF therapy on immune responses to microbial challenges and stimuli targeting specific immune pathways in spondyloarthritis (SpA) patients, and (ii) to identify immunological correlates associated with therapeutic responses to TNF-blockers.Using a set of whole-blood, syringe-based assays to perform ex vivo stimulation while preserving physiological cellular interactions (TruCulture assays), we have performed a pilot study in SpA patients and investigated immune responses to 20 different stimuli before and 3 months after initiation of anti-TNF therapy. These findings were validated in a replication cohort, also assessing the effects of anti-TNF agents after only one week of treatment. We observed a highly significant reduction of the secretion of IL-1ra, IL-1β, IL-8 and MIP-1β in response to selected stimuli after 3 months of treatment compared to the baseline. Interestingly, these changes were already detectable after a single injection of an anti-TNF agent. To gain insight into the molecular mechanism of TNF blockers, we profiled gene expression in the stimulation cultures from all patients. Quantitative set analysis for gene expression (QuSAGE) revealed that the gene modules most affected by anti-TNF therapy are NF-kB transcription factors and inhibitors and NF-kB target genes, including TNF itself and IL1B. Our data suggest that TNF-blockers primarily act by disrupting an autoregulatory loop driven by NF-kB. We also tested whether there is a correlation between the responses of immune cells to specific stimuli and the clinical response to TNF-blockers. The decision tree model that we trained and validated suggests that SpA patients who expressed lower levels of PAX5 and higher levels of SPP1 in response to SEB stimulation before initiation of anti-TNF therapy had the best therapeutic responses. Our study shows that TruCulture assays are an efficient and robust tool to monitor immune functions in SpA patients and that the effects of anti-TNF therapy can be measured when immune cells are challenged, but not at steady state. Our data also indicate that analyzing immune responses in patients before therapy is a promising strategy to develop biomarkers for prediction of therapeutic responses to TNF-blockers

Les modèles animaux ont largement contribué à notre compréhension de l’immunologie humaine et des mécanismes pathologiques associés au développement des maladies. Cependant, les modèles murins ne permettent pas de reproduire toute la complexité des pathologies humaines. Les souris à système immunitaire humain (HIS), par leur capacité à récapituler l’hématopoïèse humaine et à être infectées par des pathogènes humains, constituent une solution de choix pour combler ce fossé inter-espèce. Après greffe de cellules souches hématopoïétiques humaines, des souris hôtes sévèrement immunodéprimées permettent un haut niveau de développement du système hémato-lymphoïde humain tout au long de leur vie. Cependant, certains types cellulaires, comme les cellules lymphoïdes innées, ne parviennent pas à se différencier et à fonctionner normalement dans les modèles murins HIS actuels. Ici, nous décrivons le développement d’un modèle souris HIS original, nommé BRGSF, montrant une amélioration de la maturation, de la fonction et de l’homéostasie des cellules natural killer (NK) humaines et des autres ILCs. De plus, en récapitulant les différentes étapes du développement des ILCs humaines, ce modèle souris BRGSF nous a permis d’identifier pour la première fois un précurseur d’ILC (ILCP) présent à la fois dans notre modèle HIS ainsi que dans le sang périphérique et plusieurs organes lymphoïdes et non-lymphoïdes humains. Cette population circulante d’ILCPs pourrait constituer un substrat pour la production d’ILCs matures dans les tissus périphériques en réponse à des stress environnementaux, inflammatoires et/ou infectieux. Dans une seconde partie de ce travail de thèse, nous avons utilisé ces souris BRGS afin de tester l’efficacité de deux immunothérapies reposant sur les lymphocytes innés pour le traitement d’un carcinome colorectal exprimant EGFR et muté pour KRAS. La première approche a consisté en la co-administration des cellules NK dérivées de sang de cordon ombilical et d'anticorps monoclonal cetuximab afin de promouvoir le mécanisme de cytotoxicité cellulaire dépendante des anticorps (ADCC) contre la tumeur. La seconde stratégie a reposé sur l’injection de nanobodies VHH combinant l’inhibition de l’EGFR et l’activation spécifique du récepteur Vγ9Vδ2 des cellules T effectrices. Les résultats de cette étude soulignent l’importance des modèles murins HIS pour la compréhension du développement des lymphocytes innés humains et pour mieux les mettre à profit dans les thérapies anti-tumeurs / Animal models have extensively contributed to our understanding of human immunobiology and to uncover the underlying pathological mechanisms occurring in the development of the disease. However, mouse models do not always reproduce the genetic complexity inherent in human disease conditions. Human immune system (HIS) mouse models that are susceptible to human pathogens and can recapitulate human hematopoiesis provide one means to bridge the interspecies gap. Severely immunodeficient host mice support life-long, high level human hematolymphoid development after engraftment with human hematopoietic stem cells (HSC). However, the differentiation and function of some blood cell types, including innate lymphoid cells (ILCs), is poorly characterized in current HIS mice. Here we describe the development of a novel HIS mouse model, named BRGSF, which demonstrate enhanced maturation, function and homeostasis of human natural killer (NK) cells and other ILCs. Furthermore, the BRGSF-based HIS mouse model recapitulated the developmental stages of human ILCs. We could identify for the first time an ILC precursor (ILCP) population that is present both in HIS mice and in human peripheral blood as well as in several lymphoid and non-lymphoid human tissues. This circulating human ILCP population may provide a substrate to generate mature ILCs in tissues in response to environmental stressors, inflammation and infection. In a second part of the thesis we used BRGS immunodeficient mice to assess two innate lymphocyte-based immunotherapeutic approaches for treating EGFR-expressing KRAS-mutated colorectal carcinoma in vivo. The first model used a combination of umbilical cord blood (UCB)-derived NK cells and the monoclonal antibody cetuximab to promote antibody dependent cell cytotoxicity (ADCC) against the tumors. In a second model, we evaluated the therapeutic suitability of novel bispecific VHH constructs that combine inhibition of the EGFR with the target-specific activation of effector Vγ9Vδ2-T cells. These studies highlight the utility for HIS-based mouse models to understand human innate lymphocyte development and to harness these potent effectors for anti-tumor therapies.

Huge amount of medical data has been generated in practical experiments which makes data analysis a challenging problem. This requires novel techniques to be developed. The improvements in computational power suggest to use computerbased modeling approaches to process a large set of data. One of the important systems in the human body to be investigated is the immune system. The previous studies of medical scientists and ongoing experiments at Karolinska Institute provide information about the human immune system. This information includes attributes of human immune system’s blood cells and the interactions between these cells. This interactions are provided as ‘if-then’ logical rules. Each rule verifies a condition on the attribute of one cell and it may initiate interaction processes to modify the attributes of other cells. A specific temporal value is associated to each process to quantify the speed of that process in the body (i.e., slow, medium, fast). We propose an agent-based model (ABM) to study human immune system cells and their interactions. The ABM is selected to overcome the complexity of large amount of data and find emergent properties and behavior patterns of the cells. Immune system cells are modeled as autonomous agents which have interactions with each other. Different values of a cell attributes define possible states of the cell and the collection of states of all cells constructs the state of the whole agent-based model. In order to consider the state transitions of the cells, we used a finite state machine (FSM). The first state is constructed from the input initial values for the cells and considering the logical time of 1. In each step, the program goes one time unit further and computes next state by applying the changes based on the cells’ interactions rules. This evolution of states in time is similar to game of life (GOL) automaton. The final model based on three modeling approaches of ABM, FSM and GOL are used to test medical hypothesis related to human immune system. This model provides a useful framework for medical scientists to do experiments on the cells’ attributes and their interaction rules. Considering a set of cells and their interactions, the proposed framework shows emergent properties and behavior patterns of the human immune system.

The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model for evaluation, conducts a robust set of experiments to ascertain the efficacy of the AIS-IDS, and answers key research questions aimed at evaluating the validity of the AIS-IDS. Finally, two expansions to the AIS-IDS are proposed with the goal of further infusing the HIS into AIS-IDS design.

Cytotoxic T-lymphocytes (CTLs) are important components of the adaptive immune system and function by scanning the intracellular environment so as to detect and de-stroy infected cells. CTL responses play a major role in controlling virus-infected cells such as in HIV or influenza and cells infected with intracellular bacteria such as in tuberculosis. To do so they require the antigens to be presented to them, which is fulfilled by the major histocompatibility complex (MHC), commonly known as human leukocyte antigen or HLA molecules in humans. Recognition of antigenic peptides to Class-1 HLA molecules is a prerequisite for triggering CTL immune responses. Individuals differ significantly in their ability to respond to an infection. Among the factors that govern the outcome of an infection, HLA polymorphism in the host is one of the most important. Despite a large body of work on HLA molecules, much remains to be understood about the relationship between HLA diversity and disease susceptibility. High complexity arises due to HLA allele polymorphism, extensive antigen cross-presentability, and host-pathogen heterogeneity. A given allele can recognize a number of different peptides from various pathogens and a given peptide can also bind to a number of different individuals. Thus, given the plurality in peptide-allele pairs and the large number of alleles, understanding the differences in recognition profiles and the implications that follow for disease susceptibilities require mathematical modelling and computational analysis. The main objectives of the thesis were to understand heterogeneity in antigen presentation by HLA molecules at different scales and how that heterogeneity translates to variations in disease susceptibilities and finally the disease dynamics in different populations. Towards this goal, first the variations in HLA alleles need to be characterized systematically and their recognition properties understood. A structure-based classification of all known HLA class-1 alleles was therefore attempted. In the process, it was also of interest to see if understanding of sub-structures at the binding grooves of HLA molecules could help in high confidence prediction of epitopes for different alleles. Next, the goal was to understand how HLA heterogeneity affect disease susceptibilities and disease spread in populations. This was studied at two different levels. Firstly, modelling the HLA genotypes and CTL responses in different populations and assessing how they recognized epitopes from a given virus. The second approach involved modelling the disease dynamics given the predicted susceptibilities in different populations. Influenza H1N1 infection was used as a case study. The specific objectives addressed are: (a) To develop a classification scheme for all known HLA class-1 alleles that can explain epitope recognition profiles and further to dissect the physic-chemical features responsible for differences in peptide specificities, (b) A statistical model has been derived from a large dataset of HLA-peptide complexes. The derived model was used to identify the interdependencies of residues at different peptide and thereby, rationalize the HLA class-I allele binding specificity at a greater detail, (c) To understand the effect of HLA heterogeneity on CTL mediated disease response. A model of HLA genotypes for different populations was required for this, which was constructed and used for estimating disease response to H1N1 via the prediction of epi-topes and (d) To model disease dynamics in different populations with the knowledge of the CTL response-grouping and to evaluate the effect of heterogeneity on different vaccination strategies. Each of the four objectives listed above are described subsequently in chapters 2 to 5, followed by Chapter 6 which summarises the findings from the thesis and presents future directions. Chapter 1 presents an introduction to the importance of the function of HLA molecules, describes structural bioinformatics as a discipline and the methods that are available for it. The chapter also describes different mathematical modelling strategies available to study host immune responses. Chapter 2 describes a novel method for structure-based hierarchical classification of HLA alleles. Presently, more than 2000 HLA class-I alleles are reported, and they vary only across peptide-binding grooves. The polymorphism they exhibit, enables them to bind to a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern due to multiplicity in their associations. Thus, a powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. The study reports a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification with 7 supergroups was derived, each further categorized to yield a total of 72 groups. An independent clustering scheme based only on the similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explains the basis for the observed clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues that contribute to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants. The results are presented in an interactive web- server http://proline.iisc.ernet.in/hlaclassify. In Chapter 3, the knowledge of structural features responsible for generating peptide recognition specificities are first analysed and then utilized for predicting T-cell epi-topes for any class-1 HLA allele. Since identification of epitopes is critical and central to many of the questions in immunology, a study of several HLA-peptide complexes is carried out at the structural level and factors are identified that discriminate good binder peptides from those that do not. T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics, critically dependent on experimentally obtained training data. Analysis of the training set from IEDB for several alleles indicate that sampling of the peptide space is extremely sparse covering only a tiny fraction of all possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. A new epitope prediction method is therefore developed. The method has four distinct modules, (a) structural modelling, estimating statistical pair-potentials and constraint derivation, (b) implicit modelling and interaction profiling, (c) binding affinity prediction through feature representation and (d) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles . HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any HLA Class-1 allele, by estimating binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It stands on the strength of mechanistic understanding of HLA-peptide recognition and provides an estimate of the total ligand space for each allele. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy. In chapter 4, the effect of genetic heterogeneity on disease susceptibilities are investigated. Individuals differ significantly in their ability to respond to an infection. Among the factors that govern the outcome of an infection, HLA polymorphism in the host is one of the most important. Despite a large body of work on HLA molecules, much remains to be understood about how host HLA diversity affects disease susceptibilities. High complexity due to polymorphism, extensive cross-presentability among HLA alleles, host and pathogen heterogeneity, demands for an investigation through computational approaches. Host heterogeneity in a population is modelled through a molecular systems approach starting with mining ‘big data’ from literature. The in-sights derived through this is used to investigate the effect of heterogeneity in a population in terms of the impact it makes on recognizing a pathogen. A case study of influenza virus H1N1 infection is presented. For this, a comprehensive CTL immunome is defined by taking a consensus prediction by three distinct methods. Next, HLA genotypes are constructed for different populations using a probabilistic method. Epidemic incidences in general are observed to correlate with poor CTL response in populations. From this study, it is seen that large populations can be classified into a small number of groups called response-types, specific to a given viral strain. Individuals of a response type are expected to exhibit similar CTL responses. Extent of CTL responses varies significantly across different populations and increases with increase in genetic heterogeneity. Overall, the study presents a conceptual advance towards understanding how genetic heterogeneity influences disease susceptibility in individuals and in populations. Lists of top-ranking epitopes and proteins are also derived, ranked on the basis of conservation, antigenic cross-reactivity and population coverage, which pro- vide ready short-lists for rational vaccine design (flutope). Next, in Chapter 5, the effect of genetic heterogeneity on disease dynamics has been investigated. A mathematical framework has been developed to incorporate the heterogeneity information in the form of response-types described in the previous chap-ter. The spread of a disease in a population is a complex process, controlled by various factors, ranging from molecular level recognition events to socio-economic causes. The ‘response-typing’ described in the previous chapter allows identification of distinct groups of individuals, each with a different extent of susceptibility to a given strain of the virus. 3 different approaches are used for modelling: (i) an SIR model where different response types are considered as partitions of each S, I and R compartment. Initially SIR models are developed, such that the S compartment is sub-divided into further groups based on the ‘response-types’ obtained in the previous chapter. This analysis shows an effect in infection sweep time, i.e., how long the infection stays in the population. A stochastic model incorporates the environmental noise due to random variation in population influx, due to birth, death or migration. The system is observed to show higher stability in the presence of genetic heterogeneity. As the contagion spreads only through direct host to host contact. The topology of the contact network, plays major role in deciding the extent of disease dynamics. An agent based computational framework has been developed for modelling disease spread by considering spatial distribution of the agents, their movement patterns and resulting contact probabilities. The agent-based model (ABM) incorporates the temporal patterns of contacts. The ABM is based on a city block model and captures movement of individuals parametrically. A new concept of system ‘characteristic time’ has been introduced in context of a time-evolving network. ‘Characteristic time’ is the minimum time required to ensure, every individual is connected to all other individuals, in the time aggregated contact network. For any given temporal system, disease time must exceed ‘characteristic time’ in order to spread throughout the population. Shorter ‘characteristic time’ of the system is suggestive of faster spread of the disease. A disease spread network is constructed which shows how the disease spreads from one infected individual to others in the city, given the contact rules and their relative susceptibilities to that viral strain. A high degree of population heterogeneity is seen to results in longer disease residence time. Susceptible individuals preferentially get infected first thereby exposing more susceptible individuals to the disease. Vaccination strategies are derived from the model, which indicates that vaccinating only 20% of the agents, who are hub nodes or highly central nodes and who also have a high degree to susceptible agents, lead to high levels of herd immunity and can confer protection to the rest of the population. Overall, the thesis has provided biologically meaningful classification of all known HLA class-1 alleles and has unravelled the physico-chemical basis for their peptide recognition specificities. The thesis also presents a new algorithm for estimating pep-tide binding affinities and consequently predicting epitopes for all alleles. Finally the thesis presents a conceptual advance in relating HLA diversity to disease susceptibilities and explains how different populations can respond differently to a given infection. A case study with the influenza H1N1 virus identified populations who are most susceptible and those who are least susceptible, in the process identifying important epitopes and responder alleles, providing important pointers for vaccine design. The influence of heterogeneity and response-typing on disease dynamics is also presented for influenza H1N1 infection, which has led to the rational identification of effective vaccination strategies. The methods and concepts developed here are fairly generic and can be adapted easily for studying other infectious diseases as well. Three new web-resources, a) HLAclassify, b) HLaffy and c) Flutope have been developed, which host pre-computed results as well as allow interactive querying to an user to perform analysis with a specific allele, peptide or a pathogenic genome sequence.