Structural identifiability analysis of epidemic models based on differential equations: a tutorial-based primer

The successful application of epidemic models hinges on our ability to estimate model parameters from limited observations reliably. An often-overlooked step before estimating model parameters consists of ensuring that the model parameters are structurally identifiable from the observed states of the system. In this tutorial-based primer, intended for a diverse audience, including students training in dynamic systems, we review and provide detailed guidance for conducting structural identifiability analysis of differential equation epidemic models based on a differential algebra approach using differential algebra for identifiability of systems (DAISY) and Mathematica (Wolfram Research). This approach aims to uncover any existing parameter correlations that preclude their estimation from the observed variables. We demonstrate this approach through examples, including tutorial videos of compartmental epidemic models previously employed to study transmission dynamics and control. We show that the lack of structural identifiability may be remedied by incorporating additional observations from different model states, assuming that the system's initial conditions are known, using prior information to fix some parameters involved in parameter correlations, or modifying the model based on existing parameter correlations. We also underscore how the results of structural identifiability analysis can help enrich compartmental diagrams of differential-equation models by indicating the observed state variables and the results of the structural identifiability analysis.

Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies

The development of medical products that can delay or prevent progression to stage 3 type 1 diabetes faces many challenges. Of note, optimising patient selection for type 1 diabetes prevention clinical trials is hindered by significant patient heterogeneity and a lack of characterisation of the time-varying probability of progression to stage 3 type 1 diabetes in individuals positive for two or more islet autoantibodies. To meet these needs, the Critical Path Institute's Type 1 Diabetes Consortium was launched in 2017 as a pre-competitive public-private partnership between stakeholders from the pharmaceutical industry, patient advocacy groups, philanthropic organisations, clinical researchers, the National Institutes of Health and the Food and Drug Administration. The Type 1 Diabetes Consortium acquired and aggregated data from three longitudinal observational studies, Environmental Determinants of Diabetes in the Young (TEDDY), Diabetes Autoimmunity Study in the Young (DAISY) and TrialNet Pathway to Prevention (TN01), and used analysis subsets of these data to support the model-based qualification of islet autoantibodies as enrichment biomarkers for patient selection in type 1 diabetes prevention trials, including registration studies. The Type 1 Diabetes Consortium has now received a qualification opinion from the European Medicines Agency for the use of these biomarkers, a major success for the field of type 1 diabetes. This endorsement will improve product developers' ability to design clinical trials of agents intended to prevent or delay type 1 diabetes that are reduced in size and/or length, while being adequately powered.

The 3p21.31 genetic locus promotes progression to type 1 diabetes through the CCR2/CCL2 pathway

Multiple cross-sectional and longitudinal studies have shown that serum levels of the chemokine ligand 2 (CCL-2) are associated with type 1 diabetes (T1D), although the direction of effect differs. We assessed CCL-2 serum levels in a longitudinal cohort to clarify this association, combined with genetic data to elucidate the regulatory role of CCL-2 in T1D pathogenesis.

The Diabetes Autoimmunity Study in the Young (DAISY) followed 310 subjects with high risk of developing T1D. Of these, 42 became persistently seropositive for islet autoantibodies but did not develop T1D (non-progressors); 48 did develop T1D (progressors). CCL-2 serum levels among the three study groups were compared using linear mixed models adjusting for age, sex, HLA genotype, and family history of T1D. Summary statistics were obtained from the CCL-2 protein quantitative trait loci (pQTL) and CCR2 expression QTL (eQTL) studies. The T1D fine mapping association data were provided by the Type 1 Diabetes Genetics Consortium (T1DGC).

Serum CCL-2 levels were significantly lower in both progressors (p = 0.004) and non-progressors (p = 0.005), compared to controls. Two SNPs (rs1799988 and rs746492) in the 3p21.31 genetic locus, which includes the CCL-2 receptor, CCR2, were associated with increased CCR2 expression (p = 8.2e-5 and 5.2e-5, respectively), decreased CCL-2 serum level (p = 2.41e-9 and 6.21e-9, respectively), and increased risk of T1D (p = 7.9e-5 and 7.9e-5, respectively).

The 3p21.31 genetic region is associated with developing T1D through regulatory control of the CCR2/CCL2 immune pathway.

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Serum CCL-2 levels are lower in individuals with islet autoantibodies and type 1 diabetes compared to controls.

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Serum CCL-2 levels are associated with the 3p21.31 genetic locus.

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The 3p21.31 genetic locus is associated with type 1 diabetes.

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The 3p21.31 genetic locus is associated with gene expression of the CCL-2 receptor, CCR2.

Inverse probability weighting is an effective method to address selection bias during the analysis of high dimensional data

Omics studies frequently use samples collected during cohort studies. Conditioning on sample availability can cause selection bias if sample availability is nonrandom. Inverse probability weighting (IPW) is purported to reduce this bias. We evaluated IPW in an epigenome-wide analysis testing the association between DNA methylation (261,435 probes) and age in healthy adolescent subjects (n = 114). We simulated age and sex to be correlated with sample selection and then evaluated four conditions: complete population/no selection bias (all subjects), naïve selection bias (no adjustment), and IPW selection bias (selection bias with IPW adjustment). Assuming the complete population condition represented the "truth," we compared each condition to the complete population condition. Bias or difference in associations between age and methylation was reduced in the IPW condition versus the naïve condition. However, genomic inflation and type 1 error were higher in the IPW condition relative to the naïve condition. Postadjustment using bacon, type 1 error and inflation were similar across all conditions. Power was higher under the IPW condition compared with the naïve condition before and after inflation adjustment. IPW methods can reduce bias in genome-wide analyses. Genomic inflation is a potential concern that can be minimized using methods that adjust for inflation.

Using SDS-PAGE gel fingerprinting to identify soft-bodied wood-boring insect larvae to species

BACKGROUND

This paper describes the progress that we have made in assessing the feasibility of 'fingerprinting' using imaged SDS-PAGE gels of haemolymph proteins, to identify soft-bodied wood-boring insect larvae such as the Asian longhorn beetle, Anoplophora glabripennis (Motscholsky, 1853) (Coleoptera: Cerambycidae). Because of stringent import restrictions and difficulty in obtaining licences to work with these organisms, we opted to work with four species of scarab beetle, Mecynorhina polyphemus (Fabricius, 1781), Pachnoda sinuata (Fabricius, 1775), Eucidella shiratica (Csiki, 1909) and Eucidella shultzeorum (Kolbe, 1906) which have near identical larval morphologies.

RESULTS

We show that this technology when combined with an advanced pattern matching system (Digital Automated Identification SYstem - DAISY) can classify soft-bodied insect larvae that are almost identical morphologically to species at a level of accuracy is in excess of 98%. The study also indicates that the technology copes well with noisy data and small training sets.

CONCLUSION

The experience gained in undertaking this study gives us confidence that we will be able to develop a field deployable system in the medium term. We believe that as a high-throughput identification tool, this technology is superior to competitor technologies (e.g. fingerprinting of imaged DNA gels) in terms of speed, cost and ease of use; and therefore, is suitable for low-cost deployment in the field. © 2017 Society of Chemical Industry.

Approaches to identifying synthetic lethal interactions in cancer

Targeting synthetic lethal interactions is a promising new therapeutic approach to exploit specific changes that occur within cancer cells. Multiple approaches to investigate these interactions have been developed and successfully implemented, including chemical, siRNA, shRNA, and CRISPR library screens. Genome-wide computational approaches, such as DAISY, also have been successful in predicting synthetic lethal interactions from both cancer cell lines and patient samples. Each approach has its advantages and disadvantages that need to be considered depending on the cancer type and its molecular alterations. This review discusses these approaches and examines case studies that highlight their use.