ABSTRACT: This model picks up an already published mechanistic model by Chassagnole et al. (2001) for the threonine synthesis pathway model of E. coli and turns it into a hybrid model with a deep neural network, fully compatible with the SBML format
Project description:Recent advances in stem cell technology have led to the development of three-dimensional (3D) culture systems called organoids, which have fueled hopes to bring about the next generation of more physiologically relevant high throughput screens (HTS). However, the adaptation of established organoid protocols for HTS applications has so far been elusive. Here, we present a fully scalable, HTS-compatible workflow for the automated generation, maintenance, whole mount staining, clearing, and optical analysis of human neural organoids generated from neural precursor cells in a standard 96-well format. By combining organoid generation and analysis steps in an automated fashion, we can perform quantitative whole-organoid high content imaging with single cell resolution. The resulting organoids are highly homogeneous with regard to their morphology, size, global gene expression, cellular composition, and structure. Calcium imaging suggests organoid-wide synchronized functional coupling. The scalability of our approach has the potential to form the basis for 3D tissue-based screening in a variety of applications including drug development, toxicology studies, and disease modeling.
Project description:Deep sequencing of total RNA extracted from the genital discs of males for each of the following strains : Drosophila sechellia, Drosophila mauritiana, hybrid introgression line 3Q1(A) and hybrid introgression line Q1(A)
Project description:E. coli steady-state cultures were grown at different aerobiosis levels; transcriptomic profiling was carried out of these cultures to measure oxygen-dependent gene expression using a reference-design microarray format.
Project description:The design of large genetic circuits requires genetic regulatory devices capable of performing complex logic operations. Hybrid riboswitches, synthetically enhanced compact RNA elements (<100 nucleotides) that form a tertiary structure with the ability to specifically bind two different target molecules, can be used to design genetic regulators that emulate Boolean logic. When inserted into the 5' UTR of an mRNA, these devices can regulate translation initiation upon specific binding of one or both ligands. The goal of this study is to design hybrid riboswitches that emulate Boolean NAND logic in yeast. We propose a novel machine learning-based design framework combining high-throughput in vivo screening and deep Bayesian optimization. Through an initial screening, we discovered a hybrid riboswitch with NAND behavior. Using batch Bayesian optimization with an ensemble neural network as surrogate, we further improve the NAND functionality of our hybrid riboswitch with respect to a performance score, thereby achieving near digital NAND behavior. With its focus on model-based and score-driven design, our proposed method can complement experiment driven approaches by allowing fine grained adaptation of functionality, including constructs sensitive to single nucleotide changes.