Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions.Biomineralization in diatoms,unicellular algae that use silica to construct ...Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions.Biomineralization in diatoms,unicellular algae that use silica to construct micron-scale cell walls with nanoscale features,is an attractive candidate for functional synthesis of materials for applications including photonics,sensing,filtration,and drug delivery.Therefore,controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field.In this work,we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning.An artificial neural network(NN)was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein(Thaps3_21880),resulting in 94% detection accuracy.Class activation maps visualized physical changes that allowed the NNs to separate diatom strains,subsequently establishing a specific gene that controls pores.A further NN was created to batch process image data,automatically recognize pores,and extract pore-related parameters.Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool,called CrossVis,and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.展开更多
基金The research by J.K.M.,T.J.M.,O.S.O.,A.A.P.,A.A.T.,S.M.and M.H.was sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory,managed by UT-Battelle,LLC,for the U.S.Department of EnergyThis paper has been authored by UT-Battelle,LLC,under Contract no.DE-AC0500OR22725 with the U.S.Department of Energy.
文摘Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions.Biomineralization in diatoms,unicellular algae that use silica to construct micron-scale cell walls with nanoscale features,is an attractive candidate for functional synthesis of materials for applications including photonics,sensing,filtration,and drug delivery.Therefore,controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field.In this work,we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning.An artificial neural network(NN)was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein(Thaps3_21880),resulting in 94% detection accuracy.Class activation maps visualized physical changes that allowed the NNs to separate diatom strains,subsequently establishing a specific gene that controls pores.A further NN was created to batch process image data,automatically recognize pores,and extract pore-related parameters.Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool,called CrossVis,and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.
