We are interested in software systems that automatically engineer regulatory networks that can perform and behave according to a desired function. The enormous possibilities of artificially-created genetic networks are currently limited to a few genes and simple functions due to the difficulty to design complex networks with many components and feedback loops. Using machines that can automatically design complex networks will pave the way for innovative engineering solutions to many current problems.
Inference of Dynamic Spatial GRN Models with Multi-GPU Evolutionary Computation
R. Mousavi, S.H. Konuru, D. Lobo
Briefings in Bioinformatics bbab104, 2021.
Kinetic modeling of microbial growth, enzyme activity, and gene deletions: an integrated model of β-glucosidase function in Cellvibrio japonicus
J. Hwang, A. Hari, R. Cheng, J.G. Gardner, D. Lobo
Biotechnology and Bioengineering 117, pp. 3876-3890, 2020.
Fluxer: a web application to compute, analyze, and visualize genome-scale metabolic flux networks
A. Hari, D. Lobo
Nucleic Acids Research 48, pp. 427-435, 2020.
MoCha: molecular characterization of unknown pathways
D. Lobo, J. Hammelman, M. Levin
Journal of Computational Biology 23(4): 291-297, 2016.
Evolutionary development of tensegrity structures
D. Lobo, F.J. Vico
BioSystems 101(3), pp. 167-176, 2010.
Reconfiguration algorithms for robotically manipulatable structures
D. Lobo, D.A. Hjelle, H. Lipson
Proceedings of the ASME/IFToMM Intern. Conf. on Reconfigurable Mechanisms and Robots
J.S. Dai, M. Zoppi, X. Kong (eds.)
ReMAR2009 pp. 13-22, London, UK, 2009.