Ken E. Whelan

Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition

This paper presents a qualitative logic-based method for the steady-state analysis and revision of metabolic networks with inhibition. The approach is able to automatically revise an initial metabolic model -- through the addition and removal of whole reactions or individual substrates, products and inhibitors -- in order to ensure the existence of a steady-state behaviour consistent with a set of experimental observations. We show how this can be done in a nonmonotonic logic programming setting and discuss the challenges that arise when metabolic cycles or mutual inhibitions occur in the underlying network.

Using a logical model to predict the growth of yeast

BackgroundA logical model of the known metabolic processes in S. cerevisiae was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement.ResultsCompared to the FBA model, the logical model has information on an additional 263 putative genes and 247 additional reactions. The correctness of this model was evaluated by comparison with iND750 (an updated FBA model closely related to iFF708) by evaluating the performance of both models on predicting empirical minimal medium growth data/essential gene listings.ConclusionROC analysis and other statistical studies revealed that use of the simpler logical form and larger coverage results in no significant degradation of performance compared to iND750.

A Nonmonotonic Logical Approach for Modelling and Revising Metabolic Networks

his paper describes a new logic-based approach for representing and reasoning about metabolic networks.First it shows how biological pathways can be elegantly represented in a logic programming formalism able to model full chemical reactions with substrates and products in different cell compartments, and which are catalysed by iso-enzymes or enzyme-complexes that are subject to inhibitory feedbacks.Then it shows how a nonmonotonic reasoning system called XHAIL can be used as a practical method for learning and revising such metabolic networks from observational data.Preliminary results are described in which the approach is validated on a state-of-the-art model of Aromatic Amino Acid biosynthesis.

Automatic revision of metabolic networks through logical analysis of experimental data

This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks through the logical analysis of experimental data. The method extends previous work in two respects: by suggesting revisions that involve both the addition and removal of information; and by suggesting revisions that involve combinations of gene functions, enzyme inhibitions, and metabolic reactions. Our proposal is based on a new declarative model of metabolism expressed in a nonmonotonic logic programming formalism. With respect to this model, a mixture of abductive and inductive inference is used to compute a set of minimal revisions needed to make a given network consistent with some observed data. In this way, we describe how a reasoning system called XHAIL was able to correctly revise a state-of-the-art metabolic pathway in the light of real-world experimental data acquired by an autonomous laboratory platform called the Robot Scientist.

Representation, simulation, and hypothesis generation in graph and logical models of biological networks.

This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.

17th International Conference on Inductive Logic Programming

Mode declarations are a successful form of language bias in explanatory ILP. But, while they are heavily used in Horn systems, they have yet to be similarly exploited in more expressive clausal settings. This paper presents a mode-directed ILP procedure for full clausal logic. It employs a first-order inference engine to abductively and inductively explain a set of examples with respect to a background theory. Each stage of hypothesis formation is guided by mode declarations using a generalisation of efficient Horn clause techniques for inverting entailment. Our approach exploits language bias more effectively than previous non-Horn ILP methods and avoids the need for interactive user assistance.