High prevalence of multi-domain proteins in many organisms has been attributed to higher stability and functional and folding advantages of the multi-domain proteins. In my talk I will present our learning on the evolution of domain architecture and evolution of domain structures of protein kinases I will elaborate on our recent studies on the influence of tethering of domains in multi-domain proteins on the structural, dynamics and energetics properties of the constituent domains and its implications on the functions of proteins. The analyses suggest that tethering influences the structural, dynamic and energetic properties of constituent protein domains. Our observations hint at regulation of protein domains by tethered domains in multi-domain systems, which may manifest as differential function observed between single-domain and homologous multi-domain proteins

The functions of biological macromolecules are dependent on their three-dimensional structures. Although the overall structure of these molecules are important for their functions, only a subset of residues are actively involved in a particular function mechanism. These functionally important residues are usually conserved as 3D motifs and are involved in molecular interactions as binding sites or catalytic mechanisms. In my research group, we have developed methods to identify and compare these 3D motifs, especially as a means to investigate the evolution of functional convergence in cases where similar motifs exist and carry out similar functions despite the overall structure being different with no detectable sequence or fold similarities. We then explore whether our methods could be deployed in applications such as drug development and synthetic biology.

It has been 15 years since the first human genome data was published, generating high hopes for improved medical care. The promise of genomic medicine and the inevitable end to human suffering were celebrated by bringing about early diagnostics, effective treatments and improved clinical outcomes. A decade and a half on, we are still waiting for the genomic medicine to be moved from the “bench” to the “bed”. Despite many setbacks and delays, the scientific community has made significant progress in unlocking genomic information and the creation of knowledge repositories. Medical researchers also learned to be more patient and realistic in their expectations, setting smaller but achievable goals. Personalised medicine was the first concept to emerge from the post-genomic era. It can be characterised as a subset of genomic medicine, related to diagnostics and treatments. It utilises genomic data to tailor interventions to specific individual needs. It has been successfully implemented in cancer management, the use of common drugs (pharmacogenetics), and recently extended to polygenic and multifactorial diseases. The transitions of genomic data to clinical care was possible thanks to the advances in computational sciences, allowing for fast and accurate analysis of large biological data. New and powerful bioinformatics tools are underway, capable of processing Big Data using a variety of Artificial Intelligence technologies, and perhaps fulfilling the original promise of genomic medicine.

This presentation aims at showcasing the main uses of medical genomics in the clinical practice, main technologies used, and most likely future directions.

Ecological informatics is a combination of information technology and ecological theory to assist in dissemination of ecological research involving (genomes, organisms, population, communities, ecosystem, landscape) to researchers and the community. It is an evolving field that uses ecological data to communicate results and decisions associated to exploration, preservation and resource management. Ecologists need appropriate tools to deal evolving nature and volume of ecological data and proper data management. Ecological data management must fulfil requirements of variety of ecological information sources , and appropriate to a wide range of spatial and temporal scales such as habitat-specific to global, and real-time to historical dataset. Ecological data management process begins with conceptualization of the project and ends when ecological data have been archived and the outcomes used for proper resource management, preservation, and decision-making using data mining approaches. Application of data mining methods to ecological data allows exploration, analysis, visualization and understanding of ecological data that can then be communicated to other scientists and the community. Ecological data analysis involves inferential and process based modelling techniques, and uses remote sensing and GIS-based tools. Principal component analysis (PCA) and Self Organising Maps (SOM) reduces data dimension and allows discovery of nonlinear relationships by ordination and clustering of complex ecological data. Conventional statistical methods are not suitable for analysis of multivariate nonlinear intrinsic characteristic of ecological data compared to, inferential models using artificial neural networks (ANN) and evolutionary algorithms (EA). Communicating and results generated by data analysis using data mining approach is important for recognizing feasible management of ecological data and long-term forecasts and decision-making involving ecological data.