Novel approaches to the automated assay of ß-glucanase and lichenase activity.

We report herein the development of a novel assay procedure for the measurement of ß-glucanase and lichenase (EC 3.2.1.73) in crude enzyme extracts. Two assay formats based on a) a direct cleavage or b) an enzyme coupled substrate were initially investigated. The 'direct cleavage' substrate, namely 4,6-O-benzylidene-2-chloro-4-nitrophenyl-ß-31-cellotriosyl-ß-glucopyranoside (MBG4), was found to be the more generally applicable reagent. This substrate was fully characterised using a crude malt ß-glucanase extract, a bacterial lichenase (Bacillus sp.) and a non-specific endo-1,3(4)-ß-glucanase from Clostridium thermocellum (EC 3.2.1.6). Standard curves were derived that allow the assay absorbance response to be directly converted to ß-glucanase/lichenase activity on barley ß-glucan. The specificity of MBG4 was confirmed by analysing the action of competing glycosyl hydrolases that are typically found in malt on the substrate. Manual and automated assay formats were developed for the analysis of a) ß-glucanase in malt flour and b) lichenase enzyme extracts and the repeatability of these assays was fully investigated.

Quantitative fluorometric assay for the measurement of endo-1,4-ß-glucanase.

There is a growing demand for research tools to aid the scientific community in the search for improved cellulase enzymes for the biofuel industry. In this work, we describe a novel fluorometric assay for cellulase (endo-1,4-ß-glucanase) which is based on the use of 4,6-O-benzylidene-4-methylumbelliferyl-ß-cellotrioside (BzMUG3) in the presence of an ancillary ß-glucosidase. This assay can be used quantitatively over a reasonable linear range, or qualitatively as a solution screening tool which may find extensive use in the area of metagenomics.

The statistical relationship between connectivity and neural activity in fractionally connected feed-forward networks.

It is desirable to have a statistical description of neuronal connectivity in developing tractable theories on the development of biological neural networks and in designing artificial neural networks. In this paper, we bring out a relationship between the statistics of the input environment, the degree of network connectivity, and the average postsynaptic activity. These relationships are derived using simple neurons whose inputs are only feed-forward, excitatory and whose activity is a linear function of its inputs. In particular, we show that only the empirical mean of the pairwise input correlations, rather than the full matrix of all such correlations, is needed to produce an accurate estimate of the number of inputs necessary to attain a prespecified average postsynaptic activity level. Predictions from this work also include distributional aspects of connectivity and activity as shown by a combination of analysis and simulations.