Demystifying water microbiomes in engineered ecosystems: from drinking water distribution systems to saline activated sludge
| PhD ceremony: | Ms A. (Asala) Mahajna |
| When: | April 08, 2025 |
| Start: | 14:30 |
| Supervisors: | prof. dr. B. (Bayu) Jayawardhana, prof. dr. G.J.W. (Gert-Jan) Euverink, prof. dr. K. Keesman |
| Where: | Academy building RUG / Student Information & Administration |
| Faculty: | Science and Engineering |
Asala Mahajna has worked on the integration of machine learning with meta-omics datasets to advance our understanding of microbial dynamics wastewater microbiomes. While these advancements have provided valuable insights, applying them to optimize treatment processes remains a challenge. Mahajna's thesis addresses this gap by exploring the operational applicability of meta-omics data in water and wastewater treatment, leveraging machine learning approaches to bridge the divide between theory and practice.
A comprehensive literature review first synthesizes current applications of data-driven metagenomics in drinking water distribution systems (DWDS), highlighting microbial community dynamics and knowledge gaps. Next, Mahajna presents a case study on microbiomes in saline activated sludge, providing a detailed metatranscriptomic dataset from wastewater treatment plants (WWTPs) and insights into microbial functions. In her thesis, Mahajna introduces the innovative Diversity-Informed Valuation of Ecosystem Functioning (DIVE) framework, which links microbial community structure to ecosystem functionality, extending biodiversity-ecosystem functioning (BEF) theory to wastewater microbiomes.
Additionally, Mahajna applies recursive feature elimination (RFE) with machine learning models to identify operational parameters that influence microbial diversity and treatment efficiency. Analysis of microbiome composition and metabolic diversity reveals functional redundancy in saline activated sludge bacteriota as a key factor in system performance. Furthermore, Mahajna analyzes stress response genes in saline activated sludge to elucidate microbial adaptation mechanisms under extreme conditions.
The findings of this research provide a framework for microbiome-informed optimization of wastewater treatment, with broader implications for microbial ecosystem management and bioremediation.