Stuart J. Dunbar

Functional expression of a locust tyramine receptor in murine erythroleukaemia cells

The LCR/MEL system (Locus Control Region/Murine Erythroleukaemia cells) was employed to express and characterize the Locusta migratoria tyramine receptor (TyrLoc), an insect G protein‐coupled receptor. Functional agonist‐dependent responses were recorded in stable, tyramine receptor expressing cell clones (MEL‐TyrLoc). Tyramine elicited a dose‐dependent increase of cytosolic Ca2+‐ions and an attenuation of forskolin‐induced cyclic adenosine monophosphate (AMP) production. Octopamine was shown to be a weak agonist for both responses. In addition, yohimbine proved to be a potent tyramine receptor antagonist. This study reports the first application of the LCR/MEL expression system in functional assays for G protein‐coupled receptors and therefore expands the capabilities of this system by exploiting the functionality of the signal transduction pathways.

Approaches to in-vitro lead generation for fungicide invention

There are increasing opportunities for the development of high-throughput in-vitro screens to aid the discovery of fungicides with novel modes of action. In the past, such screens were developed when biochemical targets were validated by fungicides with defined modes of action. However, genetic information is beginning to have a major impact both on the way in-vitro targets are selected and on the speed at which mode-of-action information is gained on current fungicides having an, as yet, undefined mode of action. This paper discusses issues concerning target selection and high-throughput screening, using examples taken from the current literature and from investigations at Zeneca Agrochemicals, using inhibition of fungal respiration as an example. Saccharomyces cerevisiae is discussed as model for fungicide research, both in terms of its sensitivity to known fungicides and its well defined molecular genetics, which makes it amenable to such techniques as gene dosage for mode of action determination.

Does multi-clause learning help in real-world applications?

The ILP system Progol is incomplete in not being able to generalise a single example to multiple clauses. This limitation is referred as single-clause learning (SCL) in this paper. However, according to the Blumer bound, incomplete learners such as Progol can have higher predictive accuracy while use less search than more complete learners. This issue is particularly relevant in real-world problems, in which it is unclear whether the unknown target theory or its approximation is within the hypothesis space of the incomplete learner. This paper uses two real-world applications in systems biology to study whether it is necessary to have complete multi-clause learning (MCL) methods, which is computationally expensive but capable of deriving multi-clause hypotheses that is in the systems level. The experimental results show that in both applications there do exist datasets, in which MCL has significantly higher predictive accuracies than SCL. On the other hand, MCL does not outperform SCL all the time due to the existence of the target hypothesis or its approximations within the hypothesis space of SCL.

WIBL: Workbench for Integrative Biological Learning

The construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. We present WIBL, a visualization and model development environment initially geared towards logic-based modelling of biological systems using integrated datasets. WIBL combines data integration, visualisation and modelling in a single portal-based workbench providing a comprehensive solution for interdisciplinary systems biology projects.

Variation of background knowledge in an industrial application of ILP

In several recent papers ILP has been applied to Systems Biology problems, in which it has been used to fill gaps in the descriptions of biological networks. In the present paper we describe two new applications of this type in the area of plant biology. These applications are of particular interest to the agrochemical industry in which improvements in plant strains can have benefits for modelling crop development. The background knowledge in these applications is extensive and is derived from public databases in a Prolog format using a new system called Ondex (developers BBSRC Rothamsted). In this paper we explore the question of how much of this background knowledge it is beneficial to include, taking into account accuracy increases versus increases in learning time. The results indicate that relatively shallow background knowledge is needed to achieve maximum accuracy.

Machine Learning and Text Mining of Trophic Links

Machine Learning has been used to automatically generate a probabilistic food-web from Farm Scale Evaluation (FSE) data. The initial food web proposed by machine learning has been examined by domain experts and comparison with the literature shows that many of the links are corroborated. The FSE data were collected using two different sampling techniques, namely Vortis and pitfall. The corroboration of the initial Vortis food web, generated by machine learning, was performed manually by the domain experts. However, manual corroboration of hypothetical trophic links is difficult and requires significant amounts of time. In this paper we review the method and the main results on machine learning of trophic links. We study common trophic links from Vortis and pitfall data. We also describe a new method and present initial results on automatic corroboration of trophic links using text mining.

Glucocorticoid-inducible gene expression vectors for use in Drosophila melanogaster Schneider 2 cells

Inducible, vector‐based, expression systems that allow fine control of transgene expression are gaining more and more use in fundamental research as well as in therapeutic applications. In an effort to develop a tightly regulated heterologous expression system for Drosophila Schneider 2 cells, three different inducible reporter constructs were compared. These comprised six copies of the glucocorticoid response element fused to one of three distinct types of Drosophila gene promoters: (1) a TATA‐box containing, (2) a TATA‐less and (3) a bidirectional core sequence. These were fused to a luciferase reporter gene. The promoter constructs displayed different basal as well as agonist‐induced activities. The implications of the observations made are discussed in the context of promoter properties and of induction of genes that may be studied in Drosophila.