Scientific Computing at LNBio
The substantial growth of experimental data of biological macromolecules as well as the reduction of the cost of high-performance processing equipment has led to the development of increasingly accurate theoretical computer models, allowing researchers to better understand the biological function of macromolecular systems. These computational models allow the elaboration and analysis of hypotheses prior to the realization of bench experiments, accelerating the production of accurate results and reducing costs.
The convergence of Computer Sciences and Edge-Biotechnology at LNBio
In the last decades, biotechnological areas have benefited from scientific and technological advances that have led to the obtaining of large amounts of information on biological systems. Today, a biological process can be approached from the scope of genetic diversity and architecture, which allows the characterization of the functional potential of the system, through genomics, through the spatial, temporal and environmental manifestation, exposed by transcriptome and proteome data. The use of sophisticated analytical techniques such as nuclear magnetic resonance and mass spectrometry allowed the evaluation of substrates and products derived from the activity of biological systems. This information had allowed us to study the biological systems of interest as actually a SYSTEM, where the components interact and modify molecular species for phenotypic manifestation. While this myriad of data has the potential for a better understanding of the biological system, it carries with it such a complexity that it requires parallel development of data analysis and modelling strategies, as well as computational infrastructure compatible with the magnitude of the problem.
At LNBio we have a clear insight of the importance to use advanced protocols in Computational Biology, Bioinformatics and Chemoinformatics to take advantage of the valuable biological information generated by our scientists and to aid the creation of biological models that can be used to provide biotechnological assets to the community.
For example, drug discovery is one of the most important biotechnological processes, which includes identifying the target, validating the target, identifying the starting compound, optimizing the starting compound and introducing the new drug to the public. This process is very complex, involving the analysis of the causes of diseases and the search for mechanisms to attack these causes. The most common problems faced in this process are: i) the accuracy of identifying correct targets; ii) cost, which can reach millions of dollars and take about ten years. Both steps can be aided and improved by the targeted application of computational methods. It is understood as directed to the realization of bench experiments whose experimental design can be benefited by the extensive use of computational simulations that can reduce the number of variables involved in the biological process, facilitating their understanding, separating cause of the effect. These steps can be well addressed by structural bioinformatics/chemoinformatics to support the prediction in the discovery and optimization of the starting compounds to hit/lead. Also, bioinformatics application for systems biology can help to select targets in specific biological processes and not just an isolated protein.
On the other hand, in the areas of diagnosis and prognosis, the understanding of the genotype-phenotype correlation plays the leading role. This understanding involves searching for candidate genes using well-defined experimental constraints, which can be performed on different data sets (genome, transcriptome, proteome and metabolome). It is necessary to use all the knowledge about structural biology and protein behaviour so that such assumptions can take form and show real predictive value for molecular markers.
In conclusion, the knowledge in several sub-areas of Scientific Computing in the unique environment coupled with well-established experimental groups in several areas can be an accelerator in the successful creation of biotechnological processes in Brazil.
Structural computational biology is our flagship
Structural Bioinformatics deals with the analysis and prediction of the three-dimensional structure of biological macromolecules. The creation of computational models allows making inferences about macromolecular systems in which the experimental data are incomplete or little understood. These models allow the understanding of the biological function through the structural evaluation taking into account thermodynamic parameters obtained from the conformational evaluation by molecular mechanics and statistical methods.