Metabolism is a primordial biological system responsible for interconversion of environmental chemicals to energy and cellular building blocks in all living species. Whenever organisms are genetically affected or facing new environment they do need to adapt their metabolism to satisfy their individual growth demands. At the same time, in natural environments, organisms never exist in isolation but rather constantly interacting with other species. Ranging from local competition for the same resources up to emergent global social interactions, metabolism shapes individual species behavior and provides a common communication platform of all living entities. At Zelezniak lab we are interested in studying how genetic, environmental factors affect operation and regulation of cellular metabolic networks. At the single species level, we want to understand how complex phenotypes emerge from the underlying molecular levels organized via central biological dogma. At the multicellular level, we want to understand what is the role of metabolism in the cell-to-cell interactions, in particular, its role in the co-existence of microbial species. Answering these questions will allow us not only to design organisms with desired metabolic properties for biotechnology purposes but also engineer synthetic microbial communities with specific healthbenefits.
We are combining best practices of data science with machine learning and metabolic modeling to develop novel technologies for getting insights about biological mechanisms directly from molecular data. Furthermore, together with our collaborators, we are actively involved in developing mass spectrometry technology to enable fast, robust, precise and inexpensive proteome acquisitions from any biological sample. At the same time, we are actively interested in expanding the scope of our computational methods applications with the goal to gain mechanistic insights about the following biological processes: the role of metabolic interactions in shaping healthy gut microbiome, designing stable microbial communities for bioremediation and other biotechnological applications, transcription and translationcontrol.
Jan Zrimec (Postdoc) FIlip Buric (PhD student)
Sara Jonason (MS student) Francisco Zorrilla (MS student)
Zelezniak A, Vowinckel J, Capuano F, Messner CR, Demichev V, Polowsky N, Muelleder M, Kamrad S, Klaus B, Keller M, Ralser M Machine learning predicts the yeast metabolome from quantitative proteome of kinase knockouts. Cell Systems, 7, 1-17,2018
Vowinckel J*, Zelezniak A*, Bruderer R, Mülleder M, Reiter L, Ralser M Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition. Scientific reports 8 (1), 4346, 2018
Campbell K, Herrera-Dominguez L, Correia-Melo C, Zelezniak A, Ralser M Biochemical principles enabling metabolic cooperativity and phenotypic heterogeneity at the single cell level. Current Opinion in Systems Biology, 1, 2017
Haas R, Zelezniak A, Iacovacci J, Kamrad, Townsend SJ, Ralser M Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology Current Opinion in Systems Biology 6, 37-45, 2017
Alam MP, Olin-Sandoval V, Stincone A, Keller MA, Zelezniak A, Luisi BF, Ralser M The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization. Nature communications, Vol(8), 16018, 2017