Search

Systems biology recognizes in particular the importance of interactions between biological components and the consequences of these interactions. Such interactions and their downstream effects are known as events. To computationally mine the literature for such events, text mining methods that can detect, extract and annotate them are required. This review summarizes the methods that are currently available, with a specific focus on protein-protein interactions and pathway or network reconstruction. The approaches described will be of considerable value in associating particular pathways and their components with higher-order physiological properties, including disease states.

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

Because they mainly do not involve chemical changes, membrane transporters have been a Cinderella subject in the biotechnology of small molecule production, but this is a serious oversight. Influx transporters contribute significantly to the flux towards product, and efflux transporters ensure the accumulation of product in the much greater extracellular space of fermentors. Programmes for improving biotechnological processes might therefore give greater consideration to transporters than may have been commonplace. Strategies for identifying important transporters include expression profiling, genome-wide knockout studies, stress-based selection, and the use of inhibitors. In addition, modern methods of directed evolution and synthetic biology, especially those effecting changes in energy coupling, offer huge opportunities for increasing the flux towards extracellular product formation by transporter engineering.

At present, the assignment of function to novel genes uncovered by the systematic genome-sequencing programmes is a problem. Many studies anticipate that this can be achieved by analysing patterns of gene expression via the transcriptome, proteome and metabolome. Thus, functional genomics is, in part, an exercise in pattern classification. Because many genes have known functional classes, the problem of predicting their functional class is a supervised learning problem. However, most pattern classification methods that have been applied to the problem have been unsupervised clustering methods. Consequently, the best classification tools have not always been used. Furthermore, the present functional classes are suboptimal and new unsupervised clustering methods are needed to improve them. Better-structured functional classes will facilitate the prediction of biochemically testable functions.

There is much interest in the exploitation of more-or-less sophisticated mathematical methods combining the signals from different sensors for measured variables for estimating the present or future state of bioprocesses. In many cases, however, the application of these methods has failed to take into account many important principles. We therefore summarize what we consider to be some of the key issues, and present them in the form of a guide for assisting the development of these methods and their integration into mainstream biotechnology.

Overall, the major themes coming from this workshop were that the large-scale technologies now being developed will allow us to ask and answer the truly major questions, both long-standing and novel, that will revolutionize our understanding of the workings of living organisms; that large-scale expression analyses show that most genes are functional (and thus contribute to fitness) most of the time; that horizontal gene transfer has been widespread; that our traditionally single-gene-based phylogenetic analyses are consequently no longer sustainable; and that the recent emphasis on the study of macromolecules at the expense of metabolism will need modifying. The post-genomic era will not be dull.

In this postgenomic era, there is a specific need to assign function to orphan genes in order to validate potential targets for drug therapy and to discover new biomarkers of disease. Metabolomics is an emerging field that is complementary to the other ‘omics and proving to have unique advantages. As in transcriptomics or proteomics, a typical metabolic fingerprint or metabolomic experiment is likely to generate thousands of data points, of which only a handful might be needed to describe the problem adequately. Extracting the most meaningful elements of these data is thus key to generating useful new knowledge with mechanistic or explanatory power.

In conclusion, it is evident that the trend towards miniaturization, the intelligent generation and deployment of chemical libraries, the innovative hardware and software, and the robust automation now available are major forces in the drive to develop new pharmaceuticals with novel targets, high efficacy and, of course, substantial commercial potential. However, for these hopes to be realized, good assays will remain paramount.

Many historical attempts to increase the yield of biotechnological processes have been at best semi-empirical. However, given the availability of modern techniques of genetic and protein engineering, the question arises as to how one might rationally seek to choose the most suitable genes to clone and/or modify for this purpose. The metabolic control theory of Kaeser, Burns, Heinrich and Rapoport allows one to decide quantitatively which enzymatic steps are (most) rate-determining to the flux through desired pathways (and why). An extension of these principles allows one rationally to identify optimal strategies for the improvement of microbial processes.