Incremental identification of qualitative models of biological systems using inductive logic programming

The use of computational models is increasingly expected to play an important role in predicting the behaviour of biological systems. Models are being sought at different scales of biological organisation namely: sub-cellular, cellular, tissue, organ, organism and ecosystem; with a view of identifying how different components are connected together, how they are controlled and how they behave when functioning as a system. Except for very simple biological processes, system identification from first principles can be extremely difficult. This has brought into focus automated techniques for constructing models using data of system behaviour. Such techniques face three principal issues: (1) The model representation language must be rich enough to capture system behaviour; (2) The system identification technique must be powerful enough to identify substantially complex models; and (3) There may not be sufficient data to obtain both the model's structure and precise estimates of all of its parameters. In this paper, we address these issues in the following ways: (1) Models are represented in an expressive subset of first-order logic. Specifically, they are expressed as logic programs; (2) System identification is done using techniques developed in Inductive Logic Programming (ILP). This allows the identification of first-order logic models from data. Specifically, we employ an incremental approach in which increasingly complex models are constructed from simpler ones using snapshots of system behaviour; and (3) We restrict ourselves to "qualitative" models. These are non-parametric: thus, usually less data are required than for identifying parametric quantitative models. A further advantage is that the data need not be precise numerical observations (instead, they are abstractions like positive, negative, zero, increasing, decreasing and so on). We describe incremental construction of qualitative models using a simple physical system and demonstrate its application to identification of models at four scales of biological organisation, namely: (a) a predator-prey model at the ecosystem level; (b) a model for the human lung at the organ level; (c) a model for regulation of glucose by insulin in the human body at the extra-cellular level; and (d) a model for the glycolysis metabolic pathway at the cellular level.