Translating simulation approaches for immunology

This thesis presents a novel set of guidelines to convert between simulation modelling approaches, namely, Ordinary differential Equations (ODEs), System Dynamics (SD) and Agent-based Modelling and Simulation (ABMS). In our literature review we identify a gap in establishing translation techniques between these approaches. We therefore focus our research in developing these techniques and assessing the impact of these conversions in the simulation outcomes. In particular, our interest lies in investigating our techniques applied to simulation problems for the immune system, as we wish to aid immunologists with the choice of the most appropriate approach for a certain problem. The aims of this thesis are therefore defined as: (1) with no explicit guidelines available from the literature, we want to develop, test and validate our own set of guidelines for converting between approaches: from ODE models to SD, from SD to ABMS and from ABMS to SD; and (2) we seek to discuss the merits of SD and ABMS for Immunology to assist researchers with the choice between both approaches. The assessment of the effectiveness of the conversion guidelines is achieved by using a case study approach involving six cases of established mathematical models describing immunological phenomena. These case studies are chosen by considering aspects such as the behaviour of the entities of the model (whether they are static or interact with other entities and whether they have spatial representation or not), the type of hypothesis to be tested, the empirical embeddedness of real data, population sizes, number of elements involved and the modelling effort. In order to conduct our conversion for the case studies, we first convert their original ODE model into an SD model, and then perform the translation from SD to ABMS. For the last three case studies, we also test the conversion guidelines from ABMS to SD. Evidence from the experiments reveal that for all cases it was possible to obtain equivalent approaches by using the conversion guidelines developed. However, outcome differences occur given the intrinsic characteristics of each simulation modelling paradigm. By observing these differences we could conclude that (1) SD is incapable of reflecting exactly the same variability as that obtained from the agent-based simulation, as it is a deterministic approach; (2) SD variables change continuously in time and therefore population numbers over time might be different from those obtained by the agent-based simulation; (3) as the number of different agents and behaviours increase, the corresponding SD becomes very intricate and difficult to develop and understand; (4) there are cases where it is preferable not to convert from ABMS to SD, as the agent-based model is easier to conceptualise and implement; (5) For other circumstances, ABMS outcomes are the same as those produced by the ODEs and SD, with the disadvantage to be more resource consuming in terms of computational memory and processing capacity; and (6) For some cases SD is less informative than ABMS, as it does not produce multiple scenarios or variations over the course of more than one run within the same parameters

Translating simulation approaches for immunology

This thesis presents a novel set of guidelines to convert between simulation modelling approaches, namely, Ordinary differential Equations (ODEs), System Dynamics (SD) and Agent-based Modelling and Simulation (ABMS). In our literature review we identify a gap in establishing translation techniques between these approaches. We therefore focus our research in developing these techniques and assessing the impact of these conversions in the simulation outcomes. In particular, our interest lies in investigating our techniques applied to simulation problems for the immune system, as we wish to aid immunologists with the choice of the most appropriate approach for a certain problem. The aims of this thesis are therefore defined as: (1) with no explicit guidelines available from the literature, we want to develop, test and validate our own set of guidelines for converting between approaches: from ODE models to SD, from SD to ABMS and from ABMS to SD; and (2) we seek to discuss the merits of SD and ABMS for Immunology to assist researchers with the choice between both approaches. The assessment of the effectiveness of the conversion guidelines is achieved by using a case study approach involving six cases of established mathematical models describing immunological phenomena. These case studies are chosen by considering aspects such as the behaviour of the entities of the model (whether they are static or interact with other entities and whether they have spatial representation or not), the type of hypothesis to be tested, the empirical embeddedness of real data, population sizes, number of elements involved and the modelling effort. In order to conduct our conversion for the case studies, we first convert their original ODE model into an SD model, and then perform the translation from SD to ABMS. For the last three case studies, we also test the conversion guidelines from ABMS to SD. Evidence from the experiments reveal that for all cases it was possible to obtain equivalent approaches by using the conversion guidelines developed. However, outcome differences occur given the intrinsic characteristics of each simulation modelling paradigm. By observing these differences we could conclude that (1) SD is incapable of reflecting exactly the same variability as that obtained from the agent-based simulation, as it is a deterministic approach; (2) SD variables change continuously in time and therefore population numbers over time might be different from those obtained by the agent-based simulation; (3) as the number of different agents and behaviours increase, the corresponding SD becomes very intricate and difficult to develop and understand; (4) there are cases where it is preferable not to convert from ABMS to SD, as the agent-based model is easier to conceptualise and implement; (5) For other circumstances, ABMS outcomes are the same as those produced by the ODEs and SD, with the disadvantage to be more resource consuming in terms of computational memory and processing capacity; and (6) For some cases SD is less informative than ABMS, as it does not produce multiple scenarios or variations over the course of more than one run within the same parameters

In vivo Neutralization of Pro-inflammatory Cytokines During Secondary Streptococcus pneumoniae Infection Post Influenza A Virus Infection

An overt pro-inflammatory immune response is a key factor contributing to lethal pneumococcal infection in an influenza pre-infected host and represents a potential target for therapeutic intervention. However, there is a paucity of knowledge about the level of contribution of individual cytokines. Based on the predictions of our previous mathematical modeling approach, the potential benefit of IFN-γ- and/or IL-6-specific antibody-mediated cytokine neutralization was explored in C57BL/6 mice infected with the influenza A/PR/8/34 strain, which were subsequently infected with the Streptococcus pneumoniae strain TIGR4 on day 7 post influenza. While single IL-6 neutralization had no effect on respiratory bacterial clearance, single IFN-γ neutralization enhanced local bacterial clearance in the lungs. Concomitant neutralization of IFN-γ and IL-6 significantly reduced the degree of pneumonia as well as bacteremia compared to the control group, indicating a positive effect for the host during secondary bacterial infection. The results of our model-driven experimental study reveal that the predicted therapeutic value of IFN-γ and IL-6 neutralization in secondary pneumococcal infection following influenza infection is tightly dependent on the experimental protocol while at the same time paving the way toward the development of effective immune therapies

mockrobiota: a Public Resource for Microbiome Bioinformatics Benchmarking.

Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/. The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community

MedZIM: Mediation analysis for Zero-Inflated Mediators with applications to microbiome data

The human microbiome can contribute to the pathogenesis of many complex diseases such as cancer and Alzheimer's disease by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate in the context of microbiome data due to the excessive number of zero values in the data. Zero-valued sequencing reads, commonly observed in microbiome studies, arise for technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inflated mediators are still lacking largely because of challenges raised by the zero-inflated data structure: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel mediation analysis method under the potential-outcomes framework to fill this gap. We show that the mediation effect of the microbiome can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. The first component corresponds to the mediation effect attributable to a unit-change over the positive relative abundance and the second component corresponds to the mediation effect attributable to discrete binary change of the mediator from zero to a non-zero state. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the applications in two real microbiome studies demonstrate that our approach outperforms existing mediation analysis approaches.Comment: Corresponding: Zhigang L