Abstract
With the accumulation of scientific information in natural science, even experts can find difficult to keep integrating new piece of information. It is critical to explore modelling solutions able to capture information scattered in publications as a computable representation form. Traditional modelling techniques are important in that regard, but relying on numerical information comes with limitations for integrating results from distinct studies, high-level representations can be more suited. We present an approach to stepwise construct mechanistic explanation from selected scientific papers using the Qualitative Reasoning framework. As a proof of concept, we apply the approach to modelling papers about cellulose hydrolysis mechanism, focusing on the causal explanations for the decreasing of hydrolytic rate. Two explanatory QR models are built to capture classical explanations for the phenomenon. Our results show that none of them provides sufficient explanation for a set of basic experimental observations described in the literature. Combining the two explanations into a third one allowed to get a new and sufficient explanation for the experimental results. In domains where numerical data are scarce and strongly related to the experimental conditions, this approach can aid assessing the conceptual validity of an explanation and support integration of knowledge from different sources.
Highlights
Keeping up to date in some fields of natural science is getting more and more difficult for the domain specialists since the accumulation of scientific information has been inexorable[1]
We strived for selecting publications addressing the most basic conditions, involving common cellulosic substrates with common hydrolytic cellulase, typically exo-active cellulase Cel7A from Trichoderma reesei (Tr) fungus, with processive activity
We developed two baseline Qualitative Reasoning (QR) models to map the structure of the two kinetic models presented above
Summary
Keeping up to date in some fields of natural science is getting more and more difficult for the domain specialists since the accumulation of scientific information has been inexorable[1]. The published scientific models, in particular differential equations models, represent a fair amount of formalised understanding of the cellulose hydrolysis, expressing distinct, complementary and even competing ideas It is highly relevant for the domain to analyse those scientific models and to determine which representations are the most promising for further developing an encompassing understanding of the natural system under investigation. QR models can map quantitative model structures as well as assemble information from the literature in a computational cause-effect model For this reason, QR models can be used to test the validity of domain theories and scientific models at the qualitative level[8]
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