Mapping the details and mechanisms of carbon secretion in diatoms through integrated multi-omic systems biology.

Background

Diatoms, a large group of photosynthetic unicellular microalgae, are major primary producers in aquatic environments and contribute to up to 20% to global CO2 fixation. Diatoms secrete large quantities of carbon compounds, e.g. extracellular polymeric substances (EPS) which serve as a major carbon source for microorganisms in marine environments. Despite their ecological importance, many aspects of secretion of carbon compounds in diatoms are still poorly understood. ‘Omics’ technologies including proteomics, metabolomics and transcriptomics are powerful tools for the analysis and discovery of molecular processes and metabolic pathways. However, the achievement of new knowledge through the integration of results from multiple omics technologies represents a key challenge in bioinformatics and systems biology due to data heterogeneity, large number of variables and typically limited numbers of biological samples. To address these problems, bioinformaticians increasingly apply data mining and machine learning techniques to integrate multi-omics data sets and derive new biological meaning from high-dimensional data.

Aims

This project aims to identify molecular and genetic aspects of carbon secretion systems in diatoms using a multi-omics approach. The PhD candidate will conduct experiments to study and assay conditional diatom carbon secretion in the laboratory, and conduct transcriptomics, metabolomics and proteomics to measure this property and its underlying mechanisms within specific diatom species. Subsequent data integration will be conducted by the student using data mining approaches to identify genetic and molecular elements that are implicated and involved in diatom secretion systems and carbon export.



Desirable skills and qualifications

We are seeking a highly motivated PhD candidate with a background in molecular biotechnology, algal genetics, microbiology or a related field, and a keen interest in bioinformatics, computational biology and data mining. In addition to meeting the general PhD entry requirements of UTS, the ideal candidate should have a first class Honours or Master’s degree and/or published work or research experience. A strong background in molecular biology and microbiology is desirable; experience with linux/UNIX systems, command line and programming skills preferred.



Overseas applications are welcomed (with current IELTS assessments), but the successful applicant must commence their studies no later than 31 August 2017 (visa approved).