Development of bioinformatic tools to design DNA sequences that can be used control elements to optimize the production of desired compounds in industrial microorganisms.
In recent years, DNA synthesis technology has become drastically cheaper. This allows us to engineer life at increasingly large scales, combining genes found in various organisms to construct new biological functions in microorganisms such as bacteria or yeast. Remarkable breakthroughs of such synthetic biology have been reported. However, a key remaining challenge is that while we know how to write the DNA, we do not know exactly what to write. Attempts at designing biological circuits often still take many man-years of development or even fail completely, because it is not well understood how DNA sequences of genes found in other organisms should be adapted and combined. There is a clear need for rational design methods for synthetic biology, allowing carefully constructed DNA building blocks to be combined for optimal, robust and predictable functionality in a variety of organisms. In this project, we will address one of the main needs in synthetic biology applications: to obtain predictable and robust gene expression at desired levels in industrially relevant microorganisms. To this end, we will develop a new class of bioinformatics algorithms to design full DNA sequences for promoters, the regions that control the expression of genes. The key novelty of our approach is (a) to exploit machine-learning at the core of these algorithms, rather than heuristics or approximate biological models; and (b) to use yeast, the model eukaryotic microorganism, as a "skunk works" for constructing promoters for less easily accessible microbes. We will generate a specific set of thousands of promoters tested for activity in yeast to learn to predict expression given a DNA sequence; once this predictor is available, we can then use optimization techniques to design DNA sequences yielding desired relative levels. These designs will be validated, both in yeast and in the industrially highly relevant fungus Aspergillus niger.
Biocatalytic, one-pot diterminal oxidation and esterification of n-alkanes for production of α,ω-diol and α,ω-dicarboxylic acid esters
Metabolic Engineering 44 (2017). - ISSN 1096-7176 - p. 134 - 142.
Draft genome sequence of the oleaginous green alga Tetradesmus obliquus UTEX 393
Genome Announcements 5 (2017)3. - ISSN 2169-8287
Expansion of the ω-oxidation system AlkBGTL of Pseudomonas putida GPo1 with AlkJ and AlkH results in exclusive mono-esterified dicarboxylic acid production in E. coli
Microbial Biotechnology 10 (2017)3. - ISSN 1751-7907 - p. 594 - 603.
Gene silencing of stearoyl-ACP desaturase enhances the stearic acid content in Chlamydomonas reinhardtii
Bioresource Technology 245 (2017)B. - ISSN 0960-8524 - p. 1616 - 1626.
Monascus ruber as cell factory for lactic acid production at low pH
Metabolic Engineering 42 (2017). - ISSN 1096-7176 - p. 66 - 73.
SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles
Bioinformatics 34 (2018)8. - ISSN 1367-4803 - p. 1401 - 1403.