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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
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Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 11 to 12 of 12 (0.045 seconds)Spelling suggestions: "subject:"läkemedelsbioteknik"" "subject:"läkemedelsbiverkning""
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Deep learning prediction of Quantmap clustersParakkal Sreenivasan, Akshai January 2021 (has links)
The hypothesis that similar chemicals exert similar biological activities has been widely adopted in the field of drug discovery and development. Quantitative Structure-Activity Relationship (QSAR) models have been used ubiquitously in drug discovery to understand the function of chemicals in biological systems. A common QSAR modeling method calculates similarity scores between chemicals to assess their biological function. However, due to the fact that some chemicals can be similar and yet have different biological activities, or conversely can be structurally different yet have similar biological functions, various methods have instead been developed to quantify chemical similarity at the functional level. Quantmap is one such method, which utilizes biological databases to quantify the biological similarity between chemicals. Quantmap uses quantitative molecular network topology analysis to cluster chemical substances based on their bioactivities. This method by itself, unfortunately, cannot assign new chemicals (those which may not yet have biological data) to the derived clusters. Owing to the fact that there is a lack of biological data for many chemicals, deep learning models were explored in this project with respect to their ability to correctly assign unknown chemicals to Quantmap clusters. The deep learning methods explored included both convolutional and recurrent neural networks. Transfer learning/pretraining based approaches and data augmentation methods were also investigated. The best performing model, among those considered, was the Seq2seq model (a recurrent neural network containing two joint networks, a perceiver and an interpreter network) without pretraining, but including data augmentation.
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Stabil och antibiotikafri läkemedelsproduktion i rekombinant Escherichia coliBenevides, Kristina, Broström, Oscar, Elison Kalman, Grim, Swenson, Hugo, Vlassov, Andrei, Ågren, Josefin January 2017 (has links)
Den här rapporten presenterar ett antibiotikafritt, stabilt och kromosombaserat expressionssystem för läkemedelsproduktion i Escherichia coli på beställning av företaget Affibody AB. E. coli-stammen BL21(DE3) valdes som värdorganism för expressionssystemet. Systemet består av en genkassett som innehåller en T7-promotor, en 5′-UTR från genen ompA och en terminatorsekvens från RNA-operonet rrnB. Fyra kopior av genkassetten ska integreras i pseudogenerna caiB, yjjM, hsdS och yjiV. En datormodell som modellerar det egentliga kopietalet i cellerna har skapats i mjukvaran MATLAB, vilket visar att det uppskattas vara maximalt 32 kopior av genkassetten per cell på grund av replikation av kromosomen. Ett högt pH i fermentorn; att använda fed-batch och blandade kolhydratkällor; och att använda stammen BL21(DE3) minskar acetatproduktionen i cellen. En lägre acetatproduktion kan leda till en högre produkthalt. En proteinutbytesmodell för mjukvaran MATLAB har konstruerats för att uppskatta koncentrationen av Affibody®-molekylen i en E. coli cell.
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