Experimental and computational studies have revealed that T-cell cross-reactivity is a widespread phenomenon that can either be advantageous or detrimental to the host. In particular, detrimental effects can occur whenever the clonal dominance of memory cells is not justified by their infection-clearing capacity. Using an agent-based model of the immune system, we recently predicted the “memory anti-naïve” phenomenon, which occurs when the secondary challenge is similar but not identical to the primary stimulation. In this case, the pre-existing memory cells formed during the primary infection may be rapidly deployed in spite of their low affinity and can actually prevent a potentially higher affinity naïve response from emerging, resulting in impaired viral clearance. This finding allowed us to propose a mechanistic explanation for the concept of “antigenic sin” originally described in the context of the humoral response. However, the fact that antigenic sin is a relatively rare occurrence suggests the existence of evolutionary mechanisms that can mitigate the effect of the memory anti-naïve phenomenon. In this study we use computer modeling to further elucidate clonal dominance and the memory anti-naïve phenomenon, and to investigate a possible mitigating factor called attrition. Attrition has been described in the experimental and computational literature as a combination of competition for space and apoptosis of lymphocytes via type-I interferon in the early stages of a viral infection. This study systematically explores the relationship between clonal dominance and the mechanism of attrition. Our results suggest that attrition can indeed mitigate the memory anti-naïve effect by enabling the emergence of a diverse, higher affinity naïve response against the secondary challenge. In conclusion, modeling attrition allows us to shed light on the nature of clonal interaction and dominance.

L'idea imprenditoriale da cui prende origine la start-up ProNeuro, nasce come conseguenza del lavoro di ricerca svolto dai soci fondatori presso il Consiglio Nazionale delle Ricerche (CNR). Questo lavoro ha portato negli ultimi 3 anni al deposito di due domande di brevetto italiano, di cui una già estesa in PCT, che proteggono l'utilizzo della molecola ProNGF-A per scopi terapeutici mirati alla cura di patologie neurologiche e infiammatorie (domanda di brevetto Nr. 102018000003279 del 05/03/2018 e PCT/IB2019/051753 del 05/03/2019) e la produzione di una forma mutata di ProNGF-A e il suo utilizzo per terapia neurologica e di patologie cutanee (domanda di brevetto numero 102019000014646 del 12/08/2019). Tali brevetti sono di proprietà del CNR, mentre ProNeuro ha messo a punto un sistema di offerta finalizzato alla loro valorizzazione. Attraverso attività di Ricerca e Sviluppo, ProNeuro individua principi attivi farmacologici con attività protettiva e riparativa per il sistema nervoso, ne modifica la struttura per renderli maggiormente efficaci, sicuri e biocompatibili, mette a punto i metodi produttivi ed esegue le prime fasi di caratterizzazione dei loro effetti, prima di proporli ad aziende farmaceutiche per un successivo sviluppo come farmaci destinati al mercato. ProNeuro commercializza, quindi, i diritti di utilizzo della proprietà intellettuale e una serie di prodotti collegati alle attività di discovery, produzione (trasferimento tecnologico) e prima validazione sia predittiva che biologica di nuovi neurofarmaci. ProNeuro avrà la forma giuridica di Società a responsabilità limitata e si configura come spin-off CNR. Come tale, il rapporto tra la società ProNeuro e il CNR è regolato dal "Regolamento per la costituzione e la partecipazione del CNR alle Imprese Spin off, Del,18/2019". I brevetti sopracitati, attualmente di proprietà del CNR, verranno concessi in licenza a ProNeuro, con possibilità di sub-licenziare a terzi, sulla base del suddetto Regolamento. Questo prevede, infatti, la cessione a condizioni agevolate delle licenze sui brevetti di proprietà CNR, la messa a disposizione di risorse logistiche e strumentali in fase di start-up e l'autorizzazione al proprio personale a svolgere attività a favore delle spin-off, con copertura dei costi salariali per un terzo del tempo lavorativo per tre anni. La sede dell'impresa è stata individuata presso l'Istituto di Farmacologia Traslazionale del CNR, via del Fosso del Cavaliere 100, 00133 Roma

Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statis- tical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationally- cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.

GOLIAT is an offshore production field that spans from the subsea wells up to a complete process plant installed on a FPSO. Due to the comprehensive instrumentation installed on the plant, it is the perfect case study to test an innovative agent based software architecture able to support production management. The modularity and the scalability provided by the agent based architecture guarantees the applicability of the method to any part of the plant. Each agent is in charge of supervising a specific or a group of equipment and is fed by the real-time data coming from the field. These data are then analysed through Machine Learning and Deep Learning algorithms which are incorporated within the agents. The machine learning algorithms estimate the current state of the equipment and provide a set of KPIs in order to understand both the production efficiency and the health status of the machines. Furthermore, learning from the observations of the state transition paths which happened in the past, the agents are capable of predicting the most likely future state. The latter capability is fundamental to prevent unplanned shutdowns and optimize the maintenance plans. On the basis of the estimated current state, each agent can also provide a list of actions targeted to maximize the efficiency from an "equipment" point of view. The actions coming from all the agents can then be collected and negotiated in order to maximize the production from a "plant" point of view. The negotiating algorithms are implemented in a super-agent that can support a human operator in the day-by-day management tasks of the plant. Even though the negotiating capabilities will be implemented in the future version of the application, the modular nature of the system guarantees an easy integration of the super-agent inside the agent's framework. The paper will present the results of the agent framework in terms of the robustness of state estimation and the correctness of the computed KPIs.

Background. Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. Aim. In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. Results. The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.

The protozoan parasite Trypanosoma cruz causes the Chagas disease, which final outcome can be morbidity or death. The complexity of this infection is due to the many kinds of players involved in the immune response and to the variety of host cells targeted by the parasite. We built an ordinary differential equation model which includes aspects of innate and adaptive immune response to study the T. cruzi infection. The model also includes cardiomyocytes to represent how the infection affects the heart. We used parasitemia experimental data of infected wild-type mice to estimate the model parameters. We investigated how the number of parasites and infected cardiomyocytes were affected by changes of parameters controlling the survival rates of the parasite. We thus introduce a 20% variation in either macrophages, CDS+T cells, or anti- parasite specific antibody activity. This resulted in a change of the parasitemia as expected, and produced a broader variation in the number of parasites around the peak of parasitemia. Moreover, the same three model modifications were enabled one at a time to simulate a knockout effect in the host. The results of the knockout effects were a faster parasite growth and death of the host in all three cases, in agreement with in vivo experimental data. The model also is corroborated by in vivo data from the literature where the inhibition of macrophages, antibody, or CTL is not compensated by the other parasite killing mechanisms, and as a result lead to death of the host. Altogether these results indicate that the immune system plays a crucial role in controlling T. cruzi infection and impairment of one modality of action greatly reduces its efficiency and results in a much larger extensionof the infection of cardiomyocytes.

Infection by Leishmania can cause diseases ranging from self-healing cutaneous to visceral dissemination that can lead to death if untreated. In order to explore the early phase of the infection and the role of macrophages, we implement a system of differential equations involving the major players in the innate immune response to leishmaniasis (i.e., parasites in the intracellular and free form, infected and uninfected macrophages, and NO/ROS). The model was adjusted and validated using data from C57BL/6, KO and SCID mice published in the literature. The key findings were the surprisingly more active macrophages in the mice knockouts for IL-12 and IFN-g. This result can be interpreted as an indication of an M2b polarization of the macrophages in these mice. Sensitivity Analysis shows that NO/ROS secretion rate is more important to Leishmania control then the mechanisms of killing intracellular parasites. This model is a useful tool for comprehending the infection and treatments. Index Terms-leishmaniasis, cutaneous, innate-immune-response, macrophages, ODE

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically-activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively-activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely M2a, M2b and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b and M2c phenotypes. Using the software GINsim and BoolNet we analysed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behaviour of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.

Il contributo della modellistica matematica/computazionale alla bioinformatica

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The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized. Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine. Nonetheless, to date only a few mathematical models have been aiming at this specific purpose. We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise. Built upon an existing model, it allows to detail better both subjects' characteristics and physical exercise, thus determining to a greater extent the dynamics of the hormones and the metabolites considered.

Macrophages derived from monocyte precursors undergo specific polarization processes being influenced by the local tissue environment: classically-activated (M1) macrophages, showing a pro-inflammatory activity affecting effector cells in Th1 cellular immune responses; and alternatively-activated (M2) macrophages, with anti-inflammatory functions, involved in immunosuppression and tissue repair. At least three distinctive subsets of M2 macrophages, i.e. M2a, M2b and M2c, are characterized in the literature based on their eliciting molecular signals. The triggering and polarization of macrophages is attained through numerous, interweaved signaling pathways. To depict the logical relations among the genes involved in macrophage polarization, we utilized a computational modeling methodology, viz. Boolean modeling of gene regulation. We combined experimental data/knowledge from the literature to build a logical gene regulation network model driving macrophage polarization to M1, M2a, M2b and M2c phenotypes. Exploiting the GINsim software we studied the network dynamics under different settings and perturbations to comprehend how they affect cell polarization. Simulations of the network model, enacting the most significant biological conditions, showed consistency with the experimentally observed behaviour of in vivo macrophages. The model could properly replicate the polarization toward the four main phenotypes as well as to numerous hybrid phenotypes, known to be experimentally associated to physiological and pathological conditions. We speculate that shifts among different phenotypes in our model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the poles of a continuous succession of states. Our simulations also suggest that anti-inflammatory macrophages are more resilient to shift to the pro-inflammatory phenotype.

The change in interest from identifying individual molecules to several components in biological samples as well as how they interact is addressed by systems biology. Mathematical, statistical, and computational methods have emerged to deal with the biological complexity exposed in past years by the massive production of high-throughput data through "omics" technologies. This chapter discusses powerful mathematical networks and modeling to identify key components related to rheumatoid arthritis and how to predict the response of different individuals to infections. As the consequence of a better understanding of biological processes, this chapter also presents the creation of new specific devices to diagnosis, treat, and prevent diseases. These nonnatural systems, produced by the insertion of genetic devices into a cell or cell-free systems, or even by editing a genome, are part of the synthetic biology field. The use of synthetic molecules or systems is discussed as attempts to diagnosis and treat cancer, Lyme disease, Ebola, and human immunodeficiency virus, among others. The chapter describes a diversity of ways in which systems and synthetic biology may help understand and control diseases.

Interleukin-6 (IL-6) has been recently shown to play a central role in glucose homeostasis, since it stimulates the production and secretion of Glucagon-like Peptide-1 (GLP-1) from intestinal L-cells and pancreas, leading to an enhanced insulin response. In resting conditions, IL-6 is mainly produced by the adipose tissue whereas, during exercise, skeletal muscle contractions stimulate a marked IL-6 secretion as well. Available mathematical models describing the effects of exercise on glucose homeostasis, however, do not account for this IL-6 contribution. This study aimed at developing and validating a system model of exercise's effects on plasma IL-6 dynamics in healthy humans, combining the contributions of both adipose tissue and skeletal muscle. A two-compartment description was adopted to model plasma IL-6 changes in response to oxygen uptake's variation during an exercise bout. The free parameters of the model were estimated by means of a cross-validation procedure performed on four different datasets. A low coefficient of variation (< 10%) was found for each parameter and the physiologically meaningful parameters were all consistent with literature data. Moreover, plasma IL-6 dynamics during exercise and post-exercise were consistent with literature data from exercise protocols differing in intensity, duration and modality. The model successfully emulated the physiological effects of exercise on plasma IL-6 levels and provided a reliable description of the role of skeletal muscle and adipose tissue on the dynamics of plasma IL-6. The system model here proposed is suitable to simulate IL-6 response to different exercise modalities. Its future integration with existing models of GLP-1-induced insulin secretion might provide a more reliable description of exercise's effects on glucose homeostasis and hence support the definition of more tailored interventions for the treatment of type 2 diabetes.

Results: Tests were performed to select the appropriate doses of vaccine and infectious bacteria to set up the model. Simulation outputs were compared with the specific antibody production and the expression of BcR and TcR gene transcripts in spleen. The model has shown a good ability to be used in sea bass and could be implemented for different routes of vaccine administration even with more than two pathogens. The model confirms the suitability of in silico methods to optimize vaccine doses and the immune response to them. This model could be applied to other species to optimize the design of new vaccination treatments of fish in aquaculture. Motivation: A computational model equipped with the main immunological features of the sea bass (Dicentrarchus labrax L.) immune system was used to predict more effective vaccination in fish. The performance of the model was evaluated by using the results of two in vivo vaccinations trials against L. anguillarum and P. damselae.

Fever plays a role in activating innate immunity while its relevance in activating adaptive immunity is less clear. Even brief exposure to elevated temperatures significantly impacts on the immunostimulatory capacity of dendritic cells (DCs), but the consequences on immune response remain unclear. To address this issue, we analyzed the gene expression profiles of normal human monocyte-derived DCs from nine healthy adults subjected either to fever-like thermal conditions (39 degrees C) or to normal temperature (37 degrees C) for 180 minutes. Exposure of DCs to 39 degrees C caused upregulation of 43 genes and downregulation of 24 genes. Functionally, the up/downregulated genes are involved in post-translational modification, protein folding, cell death and survival, and cellular movement. Notably, when compared to monocytes, DCs differentially upregulated transcription of the secreted protein IGFBP-6, not previously known to be specifically linked to hyperthermia. Exposure of DCs to 39 degrees C induced apoptosis/necrosis and resulted in accumulation of IGFBP-6 in the conditioned medium at 48 h. IGFBP-6 may have a functional role in the hyperthermic response as it induced chemotaxis of monocytes and T lymphocytes, but not of B lymphocytes. Thus, temperature regulates complex biological DC functions that most likely contribute to their ability to induce an efficient adaptive immune response.

Infection by Leishmania protozoan parasites can cause a variety of disease outcomes in humans and other mammals, from single self-healing cutaneous lesions to a visceral dissemination of the parasite. The correlation between chronic lesions and ecto-nucleotidase enzymes activity on the surface of the parasite is addressed here using damage caused in epithelial cells by nitric oxide. In order to explore the role of purinergic metabolism in lesion formation and the outcome of the infection, we implemented a cellular automata/lattice gas model involving major immune characters (Th1 and Th2 cells, IFN-gamma, IL-4, IL-12, adenosine-Ado-, NO) and parasite players for the dynamic analysis of the disease progress. The model were analyzed using partial ranking correlation coefficient (PRCC) to indicate the components that most influence the disease progression. Results show that low Ado inhibition rate over Th-cells is shared by L. major and L. braziliensis, while in L. amazonensis infection the Ado inhibition rate over Th-cells reaches 30%. IL-4 inhibition rate over Th-cell priming to Th1 independent of IL-12 are exclusive of L. major. The lesion size and progression showed agreement with published biological data and the model was able to simulate cutaneous leishmaniasis outcomes. The sensitivity analysis suggested that Ado inhibition rate over Th-cells followed by Leishmania survival probability were the most important characteristics of the process, with PRCC of 0.89 and 0.77 respectively. The simulations also showed a non-linear relationship between Ado inhibition rate over Th-cells and lesion size measured as number of dead epithelial cells. In conclusion, this model can be a useful tool for the quantitative understanding of the immune response in leishmaniasis.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Abstract

Systems and synthetic biology for health