IL-27R signalling mediates early viral containment and impacts innate and adaptive immunity after chronic lymphocytic choriomeningitis virus infection

Chronic viral infections represent a major challenge to host's immune response and a unique network of immunological elements, including cytokines, are required for their containment. By using a model persistent infection with the natural murine pathogen lymphocytic choriomeningitis virus clone 13 (LCMV Cl13) we investigated the role of one such cytokine, interleukin 27 (IL-27), in the control of chronic infection. We found that IL-27R signalling promoted control of LCMV Cl13 as early as day 1 and 5 after infection and that il27p28 transcripts were rapidly elevated in multiple subsets of dendritic cells (DCs) and myeloid cells. In particular, plasmacytoid DCs (pDCs), the most potent type-1-interferon (IFN-I) producing cells, significantly increased il27p28 in a TLR7 dependent fashion. Notably, mice deficient in IL-27 specific receptor (R), WSX-1, exhibited a pleiotropy of innate and adaptive immune alterations after chronic LCMV infection, including compromised NK cell cytotoxicity and antibody responses. While, the majority of these immune alterations appeared cell-extrinsic, cell-intrinsic IL-27R was necessary to maintain early pDC numbers, which, alongside lower IFN-I transcription in CD11b+ DCs and myeloid cells, may explain the compromised IFN-I elevation that we observed early after LCMV Cl13 infection in IL-27R-deficient mice. Together these data highlight the critical role of IL-27 in enabling optimal anti-viral immunity early and late after infection with a systemic persistent virus and suggest that a previously unrecognized positive feedback-loop mediated by IL-27 in pDCs might be involved in this process

Juxtaposition of system dynamics and agent-based simulation for a case study in immunosenescence

Advances in healthcare and in the quality of life significantly increase human life expectancy. With the aging of populations, new un-faced challenges are brought to science. The human body is naturally selected to be well-functioning until the age of reproduction to keep the species alive. However, as the lifespan extends, unseen problems due to the body deterioration emerge. There are several age-related diseases with no appropriate treatment; therefore, the complex aging phenomena needs further understanding. It is known that immunosenescence is highly correlated to the negative effects of aging. In this work we advocate the use of simulation as a tool to assist the understanding of immune aging phenomena. In particular, we are comparing system dynamics modelling and simulation (SDMS) and agent-based modelling and simulation (ABMS) for the case of age-related depletion of naive T cells in the organism. We address the following research questions: Which simulation approach is more suitable for this problem? Can these approaches be employed interchangeably? Is there any benefit of using one approach compared to the other? Results show that both simulation outcomes closely fit the observed data and existing mathematical model; and the likely contribution of each of the naive T cell repertoire maintenance method can therefore be estimated. The differences observed in the outcomes of both approaches are due to the probabilistic character of ABMS contrasted to SDMS. However, they do not interfere in the overall expected dynamics of the populations. In this case, therefore, they can be employed interchangeably, with SDMS being simpler to implement and taking less computational resources

Production of CXC and CC chemokines by human antigen-presenting cells in response to Lassa virus or closely related immunogenic viruses, and in cynomolgus monkeys with lassa fever.

International audienceThe pathogenesis of Lassa fever (LF), a hemorrhagic fever endemic to West Africa, remains unclear. We previously compared Lassa virus (LASV) with its genetically close, but nonpathogenic homolog Mopeia virus (MOPV) and demonstrated that the strong activation of antigen-presenting cells (APC), including type I IFN production, observed in response to MOPV probably plays a crucial role in controlling infection. We show here that human macrophages (MP) produce large amounts of CC and CXC chemokines in response to MOPV infection, whereas dendritic cells (DC) release only moderate amounts of CXC chemokines. However, in the presence of autologous T cells, DCs produced CC and CXC chemokines. Chemokines were produced in response to type I IFN synthesis, as the levels of both mediators were strongly correlated and the neutralization of type I IFN resulted in an inhibition of chemokine production. By contrast, LASV induced only low levels of CXCL-10 and CXCL-11 production. These differences in chemokine production may profoundly affect the generation of virus-specific T-cell responses and may therefore contribute to the difference of pathogenicity between these two viruses. In addition, a recombinant LASV (rLASV) harboring the NP-D389A/G392A mutations, which abolish the inhibition of type I IFN response by nucleoprotein (NP), induced the massive synthesis of CC and CXC chemokines in both DC and MP, confirming the crucial role of arenavirus NP in immunosuppression and pathogenicity. Finally, we confirmed, using PBMC samples and lymph nodes obtained from LASV-infected cynomolgus monkeys, that LF was associated with high levels of CXC chemokine mRNA synthesis, suggesting that the very early synthesis of these mediators may be correlated with a favourable outcome

The Equifinality of Archaeological Networks: an Agent-Based Exploratory Lab Approach

When we find an archaeological network, how can we explore the necessary versus contingent processes at play in the formation of that archaeological network? Given a set of circumstances or processes, what other possible network shapes could have emerged? This is the problem of equifinality, where many different means could potentially arrive at the same end result: the networks that we observe. This paper outlines how agent-based modelling can be used as a laboratory for exploring different processes of archaeological network formation. We begin by describing our best guess about how the (ancient) world worked, given our target materials (here, the networks of production and patronage surrounding the Roman brick industry in the hinterland of Rome). We then develop an agent-based model of the Roman extractive economy which generates different kinds of networks under various assumptions about how that economy works. The rules of the simulation are built upon the work of Bang (2006; 2008) who describes a model of the Roman economy which he calls the ‘imperial Bazaar’. The agents are allowed to interact, and the investigators compare the kinds of networks this description generates over an entire landscape of economic possibilities. By rigorously exploring this landscape, and comparing the resultant networks with those observed in the archaeological materials, the investigators will be able to employ the principle of equifinality to work out the representativeness of the archaeological network and thus the underlying processes

Mobilizing for change: simulating political movements in armed conflicts

Theories on the establishment and propagation of political movements through mobilization have emerged and evolved over the last half century. Among the major theoretical frameworks that have been advanced are resource mobilization theory, political process theory, and culture theory. However, despite these developments, relatively few methodological approaches have applied bottom-up computational modeling and simulation in explaining movement development in conflicts. With developments made in computational methods, the integration of social theory with modeling and simulation is a natural progression in creating tools that allow analysts, policy makers, and researchers the means to assess the successes or failures of political movements during armed struggles. This article presents an agent-based model and simulation that applies several frequently used theoretical approaches to political mobilization and explores the extent to which group resources and identity shaped conflicts in Central Asia. Given their historical, cultural, political, economic, and geographical circumstances, the authors seek to determine why different movements experienced contrasting political mobilization outcomes. Results show that receiving outside resources could help a relatively weak group, with limited mobilization, overcome opposition that is initially better mobilized, while shared identity and sufficient risk taking are shown to be potentially strong factors in producing successful mobilization. More broadly, the approach advanced enables analysts and researchers to better anticipate future mobilization events and projected paths of conflict by developing and understanding cause and effect relationships within relevant theoretical frameworks

Src family kinases Fyn and Lyn are constitutively activated and mediate plasmacytoid dendritic cell responses

Plasmacytoid dendritic cells (pDC) are type I interferon-producing cells with critical functions in a number of human illnesses; however, their molecular regulation is incompletely understood. Here we show the role of Src family kinases (SFK) in mouse and human pDCs. pDCs express Fyn and Lyn and their activating residues are phosphorylated both before and after Toll-like receptor (TLR) stimulation. Fyn or Lyn genetic ablation as well as treatment with SFK inhibitors ablate pDC (but not conventional DC) responses both in vitro and in vivo. Inhibition of SFK activity not only alters TLR-ligand localization and inhibits downstream signalling events, but, independent of ex-vivo TLR stimulation, also affects constitutive phosphorylation of BCAP, an adaptor protein bridging PI3K and TLR pathways. Our data identify Fyn and Lyn as important factors that promote pDC responses, describe the mechanisms involved and highlight a tonic SFK-mediated signalling that precedes pathogen encounter, raising the possibility that small molecules targeting SFKs could modulate pDC responses in human diseases

A Block-Free Distributed Ledger for P2P Energy Trading:Case with IOTA?

Across the world, the organisation and operation of the electricity markets is quickly changing, moving towards decentralised, distributed, renewables-based generation with real-time data exchange-based solutions. In order to support this change, blockchain-based distributed ledgers have been proposed for implementation of peer-to-peer energy trading platform. However, blockchain solutions suffer from scalability problems as well as from delays in transaction confirmation. This paper explores the feasibility of using IOTA’s DAG-based block-free distributed ledger for implementation of energy trading platforms. Our agent-based simulation research demonstrates that an IOTA-like DAG-based solution could overcome the constraints that blockchains face in the energy mar- ket. However, to be usable for peer-to-peer energy trading, even DAG- based platforms need to consider specificities of energy trading markets (such as structured trading periods and assured confirmation of transactions for every completed period)

Simulation and real-time optimal scheduling: a framework for integration

Traditional scheduling and simulation models of the same system differ in several fundamental respects. These include the definition of a schedule, the existence of an objective function which orders schedules and indicates the performance of a given schedule according to specific criteria, and the level of fidelity at which the items are represented and processed through he system. This paper presents a conceptual, object-oriented, architecture for combining a traditional, high-level, scheduling system with a detailed, process- level, discrete-event simulation. A multi-echelon planning framework is established in the context of modeling end-to-end military deployments with the focus on detailed seaport operations

Enhancing Digital Twins with Advances in Simulation and Artificial Intelligence: Opportunities and Challenges

The papers presented at this conference are made freely available at INFORMS website: https://informs-sim.org/wsc23papers/by_area.html .Simulations are used to investigate physical systems. A digital twin goes beyond this by connecting a simulation with the physical system with the purpose of analyzing and controlling that system in real-time. In the past 5 years there has been a substantial increase in research into Simulation and Artificial Intelligence (AI). The combination of Simulation with AI presents many possible innovations. Similarly, combining AI with Simulation presents further possibilities including approaches to developing trustworthy and explainable AI methods, solutions to problems arising from sparce or no data and better methods for time series analysis. Given the progress that has been made in Digital Twins and Simulation and AI, what opportunities are there from combining these two exciting research areas? What challenges need to be overcome to achieve these? This article discusses these from the perspectives of six leading members of the Modeling & Simulation community