C Louw1; N Truter1; W Bergh1; M van den Heever1; S Horn1; D van Niekerk2; R Singh1;
1 Incubate.bio, UK; 2 Stellenbosch University, UK
AbstractIntroductionWe have developed the novel Adaptable Large-Scale Causal Analysis (ALaSCA) computational platform, which uses causal analysis and counterfactual reasoning. ALaSCA offers the ability to simulate the outcome of a number of different hypotheses, gaining insight into the complex dynamics of biological mechanisms prior to, or without, wet lab experimentation. We demonstrate the ability of ALaSCA to untangle the pivots and redundancies within biological pathways of various drivers to a specific phenotypic process. In this paper, we document results on a mainstream disease, namely Type 1 Diabetes (T1D), to quantify causal relationships between oxidative stress proteins and T1D progression. ALaSCA is benchmarked against associative analyses.Results and discussionUsing causal inference and counterfactual simulation within ALaSCA we replicated evidence for the protective effect that oxidative stress proteins, specifically Superoxide dismutase 1 (SOD1), have in T1D, a trend which is seen in literature. We were also able to replicate an unusual case from literature where oxidative stress proteins, specifically Catalase, do not have a protective effect on T1D. ConclusionBy analysing the disease mechanism and the inferred causal effects we identified the upstream HLA proteins, specifically the DR alpha chain and DR beta 4 chain proteins as causes of the protective effect of the oxidative stress proteins on T1D. In contrast, through analysis of the unusual case, in which the DR alpha chain and DR beta 4 chain proteins are not present in the model, we saw that the increasing effect that the oxidative stress proteins have on T1D is due to the HLA protein, DQ beta 1 chain, and not the oxidative stress proteins themselves.