Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p

On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries

Systems technologies emerge with a powerful potential to support the deployment and design of future biorefineries. The chemical industry experiences a steady growth in the use of renewables induced by the gradual depletion of oil, uncertainties in energy supplies and a commanding requirement to reduce GHG emissions and save the planet. Renewables introduce an impressive range of options with biorefining at the center of attention as an emerging industrial concept, uniquely attached to chemical engineering and aiming to transform plant-derived biomass into a variety of products including transport fuels, platform chemicals, polymers, and specialty chemicals. In competing with conventional processes, biorefineries should match maximum efficiencies with better design and process integration. The paper highlights the pivotal role of systems technology to foster innovation, preview options, and support high-throughput computational experimentation, arguing that systems tools are largely under-deployed. Systems-enabled platforms could instead function as powerful environments to generate ideas for integrated designs and offer tremendous services to the complex and large problems produced by the numerous portfolios of feedstocks, unknown portfolios of products, multiple chemistries, and multiple processing paths. Complexities certainly exceed capabilities of previous methodologies but established achievements and experience with similar problems are excellent starting points for future contributions. Besides a general discussion, the paper outlines opportunities for innovation in design, concept-level synthesis, process integration, and the development of supply chains

On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries

Systems technologies emerge with a powerful potential to support the deployment and design of future biorefineries. The chemical industry experiences a steady growth in the use of renewables induced by the gradual depletion of oil, uncertainties in energy supplies and a commanding requirement to reduce GHG emissions and save the planet. Renewables introduce an impressive range of options with biorefining at the center of attention as an emerging industrial concept, uniquely attached to chemical engineering and aiming to transform plant-derived biomass into a variety of products including transport fuels, platform chemicals, polymers, and specialty chemicals. In competing with conventional processes, biorefineries should match maximum efficiencies with better design and process integration. The paper highlights the pivotal role of systems technology to foster innovation, preview options, and support high-throughput computational experimentation, arguing that systems tools are largely under-deployed. Systems-enabled platforms could instead function as powerful environments to generate ideas for integrated designs and offer tremendous services to the complex and large problems produced by the numerous portfolios of feedstocks, unknown portfolios of products, multiple chemistries, and multiple processing paths. Complexities certainly exceed capabilities of previous methodologies but established achievements and experience with similar problems are excellent starting points for future contributions. Besides a general discussion, the paper outlines opportunities for innovation in design, concept-level synthesis, process integration, and the development of supply chains