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King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.G.K., Bryant, C.H., Muggleton, S.H., et al. (2004) Functional Genomic Hypothesis Generation and Experimentation by a Robot Scientist. Nature, 427, 247-252.
https://doi.org/10.1038/nature02236

has been cited by the following article:

• TITLE: Relational Learning in Microbial Ecology

  AUTHORS: Andrei Doncescu

  KEYWORDS: Logical Model, E coli, Default Logic, Binary Decision Diagram, Expectation Maximization, Simulation

  JOURNAL NAME: Natural Resources, Vol.16 No.13, December 29, 2025

  ABSTRACT: This paper introduces a methodology that enables the relational learning framework to incorporate quantitative data derived from experimental studies in microbial ecology. The focus of using Default Logic in microbial ecology is to enhance the comprehension of cellular physiological states and the interpretation of interactions among metabolites and signaling networks. To illustrate our approach, a logical model is proposed as model of the glycolysis and pentose phosphate pathways in E. coli. This method constructs a symbolic model based on kinetics, utilizing the Michaelis-Menten equation, by discretizing the concentration variations of specific metabolites over time based on relevant levels to be integrated into our Logic Inference framework. Additionally, we generate logical formulas for concentrations of metabolites that are difficult to measure during dynamic states through logical abduction. Given the resulting large set of conclusions/extensions, we employ an expectation maximization algorithm operating on binary decision diagrams for ranking.