Avani Dave, Jai Hind College
The fidelity of disease model predictions is based on precisely describing the incorporated processes and parameters. Several different models exist and help in describing the dynamics observed in the host during malaria infection. That being said, there is insufficient clinical data that can be utilized for the quantitative validation and successive establishment of confidence in the resulting predictions.
Along with it, the model parameters usually acquire a certain level of uncertainty and some degree of variations that seem to be existing between individuals, potentially undermining the reliability of these model predictions. In a study conducted by Horn, S., et al. the disease models were reproduced following which an analysis was performed to identify the robustness, uncertainty, local sensitivity, and local sensitivity robustness to determine confidence in their predictions.
The study:
Analysis of the disease models delineating the response of the immune system to malaria was conducted, the outcomes were studied to note the variations in model formalism and sensitivity to parameter values. The methods utilized in the current study might help in the indications of specific models being more pertinent for narrating disease dynamics than others. This study also provides an insight into the necessity for parameter value certainty. The finding highlighted that the Anderson model proved to be the most robust amongst all the other models displaying high resistance to variation in parameter values and gaining results with the least difference in outputs.
Results:
The results of the study reveal that several components of the immune system can be held accountable for the maximum uncertainty in model outputs. Further analysis allowed the identification of the variables associated with diseases demonstrating the highest level of sensitivity for the above-mentioned components. All the models involved in the study helped in visualizing a comparable degree of robustness but showed different ranges in their predictions. Amongst these different ranges, sensitivities were seen to be well-conserved in three of the four tested models.
Conclusion:
On analyzing the effects of parameter variations in models enable a comparative tool for evaluating the model predictions. It also helps in unveiling weak points in the model and they can be utilized in disease models to verify probable features for therapeutic intervention. The findings from local sensitivity analysis accentuate the role of immune response on the disease dynamics of malaria, along with highlighting specific parameters such as the death and proliferation rates of immune effectors that act as a potential focal point in the investigation of disease eradication which poses a large effect on disease variables.
The local sensitivity robustness analysis demonstrated that the corresponding findings are well conserved across a given population. This study aimed to design a measure of confidence in models that cover the foundational dynamics of a disease state, this can further be extrapolated to various deterministic models which share identical ODE structures, for malaria and different diseases. More such models can be constructed to involve variables of resistant and non-resistant parasites and treatment parameters. This will open new doors for the analysis of disease models.
Also read: Proteome plasticity at high temperatures!
References:
- Horn, S., Snoep, J.L. & van Niekerk , D.D. Uncovering the effects of heterogeneity and parameter sensitivity on within-host dynamics of disease: malaria as a case study. BMC Bioinformatics22, 384 (2021). https://doi.org/10.1186/s12859-021-04289-z
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Author info:
Avani Dave is currently in the final year of her bachelor’s degree, majoring in Life Sciences. Holding a good academic and extra-curricular record, she is on a constant journey of acquiring exposure in her field of interest while simultaneously not limiting herself to just that. Avani likes studying Diseases and Syndromes and everything under this umbrella! That being said, she is adept at working across departments and promises to deliver.
LinkedIn – https://www.linkedin.com/avani-dave/
Publications in BioXone:
- (2021). ECMO: An artificial heart-lung set for COVID-19 treatment. BioXone. https://bioxone.in/news/worldnews/ecmo-an-artificial-heart-lung-set-for-covid-19-treatment/
- Dave, A. (2021). Ultrasound-on-chip: a novel platform for medical imaging. https://bioxone.in/news/worldnews/ultrasound-on-chip-a-novel-platform-for-medical-imaging/
- Dave, A. (2021). A dash of sugar kelp for better health. https://bioxone.in/news/worldnews/a-dash-of-sugar-kelp-for-better-health/
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