Biomedical subjects

John Hallam

Publications and source records attributed to John Hallam.

3 recordsLinked to original sources

PubMed2005

Artificial intelligence and robotics in high throughput post-genomics.

The shift of post-genomics towards a systems approach has offered an ever-increasing role for artificial intelligence (AI) and robotics. Many disciplines (e.g. engineering, robotics, computer science) bear on the problem of automating the different stages involved in post-genomic research with a view to developing quality assured high-dimensional data. We review some of the latest contributions of AI and robotics to this end and note the limitations arising from the current independent, exploratory way in which specific solutions are being presented for specific problems without regard to how these could be eventually integrated into one comprehensible integrated intelligent system.

Artificial Intelligence↗

PubMed2005

An analysis of neural models for walking control.

A large space of different neural models exists from simple mathematical abstractions to detailed biophysical representations with strongly differing levels of complexity and biological relevance. Previous comparisons between models have looked at biological realism or mathematical tractability rather than expressive power. This paper, however, investigates whether more sophisticated models are better suited to a complex sensorimotor control task than simpler ones, or whether the more general nature of groups of the simpler neurons allows them to collectively solve complex tasks better despite their individual simplicity. Many models have been proposed or used for sensorimotor control tasks such as the control of locomotion. Four such neural models with varying levels of complexity were chosen. Controllers made of networks of each neural type were evolved to generate locomotion in a simulated dynamically stable four-legged robot using a genetic algorithm. The problem domain was chosen as one for which no simple solution could be hand crafted and which, with its tight sensorimotor coupling, had strongly time-dependent properties as is common in many biological control tasks. Analysis of the results shows that the most complex and biologically based model is significantly better at walking control, even producing recognizable gaits.

Algorithms↗

PubMed2004

Kinase peptide specificity: improved determination and relevance to protein phosphorylation.

Specificity of phosphorylation is critical to signal transduction. Recent emphasis on colocalization of substrate and kinase has eclipsed emphasis on peptide specificity, i.e., kinase preference for particular amino acids surrounding the phosphorylation site. We describe an approach to determining peptide specificity by using positional scanning of biotinylated oriented peptide libraries and insights emerging from those determinations. We accurately determine preference (or disfavor) for residues at a given substrate position (such as P+2) by comparison of in vitro phosphorylation of peptide libraries differing by a single residue at that position. By analysis of all positions near the phosphorylation site, position-specific scoring matrices are generated and used both to understand the basis of specificity and to predict phosphorylation. PKC-delta and -zeta predictions have been validated rigorously by comparisons with measured phosphorylation. The results demonstrate specificity and sensitivity (80-90%) much better than the previous predictive method. These predictions can be accessed at http://mpr.nci.nih.gov. The accuracy of the specificity determination allows identification of an important difference in peptide specificity between these closely related kinases; Ile/Leu at the P-1 position is disfavored by PKC-zeta but not PKC-delta. Our findings and visual representation of peptide specificity highlight the importance of disfavored residues. Finally, analysis of 124 experimentally determined PKC sites from the literature demonstrates a very strong role of peptide specificity in many of those sites. Thus, position-specific scoring matrices generated by this method provide a foundation for quantitative analyses of kinase specificity and improved predictions of previously determined physiologically relevant phosphorylation sites.

Cyclic AMP-Dependent Protein Kinases↗