UCL Department of Science and Technology Studies


Aparicio De Narvaez, Alberto

About me

Alberto Aparicio is a PhD student in the Department of Science and Technology Studies (STS) of University College London, focusing on the governance of synthetic biology. Alberto is interested in governance and policy of life sciences and emerging technologies, and innovation for development.
In his doctoral research he is aims to better understand scientific practice in xenobiology (an area of research in synthetic biology), questioning its value choices, implicit assumptions, narratives and epistemological judgments, through engagement with scientists. In particular, he is interested in how the problems and promises of xenobiology are constructed, such as the engineering of organisms with built-in biological containment features. This I expected to how to provide insights on how to govern responsibly emerging technologies.  

Prior to his current PhD studies in STS (funded by Colciencias) Alberto worked at the Colombian government agency for technical and vocational education, SENA (translated as ‘National Learning Service’), as strategic advisor for the directorate of cooperation and international affairs. Before this post he received an M.Phil. in Technology from the University of Cambridge in 2013, with a scholarship from Colfuturo. Previously he worked as an innovation consultant at the consultancy firm Inventta (Colombia), supporting technology commercialization and transfer in Colombian universities, and implementation of innovation practices in organizations. Alberto received an M.Sc. in Biochemistry from the University of Saskatchewan, and a B.Sc in Microbiology from Universidad de Los Andes (Colombia).

During the Spring term of 2017, Alberto will be a Visiting Research Fellow at the Program on Science, Technology & Society at the Harvard Kennedy School of Government.


Barreto, K., Aparicio, A., Bharathikumar, V.M , DeCoteau, J.F., Geyer, C.R. 2012. Yeast two-hybrid screening of cyclic peptide libraries using a combination of random and PI-deconvolution pooling strategies. Protein Engineering, Design and Selection, 25(9):453-64



Twitter: @apadenz