The Lobo Lab aims to understand the regulation and mechanisms of biological growth, shape, and pattern formation with a systems biology approach. We combine both molecular and computational methods to gain a mechanistic understanding of development and regeneration, find personalized treatments for cancer, and streamline regulatory designs in synthetic biology. We have developed new techniques for the acquisition and formalization of microscopy spatial data, the formulation of mechanistic models of genetic, metabolic, and signaling networks, and high-performance machine learning methods for inferring such models. We combine these computational methods with molecular assays at the bench for obtaining quantitative spatial and dynamic data, such as gene expression and morphological outcomes, and for prediction validation including genetic, surgical, and pharmacological perturbations and the engineering of synthetic circuits. This ambitious research plan will have a long-term payoff for the streamline of our ability to infer and design dynamic mechanisms, which will be essential for advancing fundamental understanding in biology and developing new medical and industrial applications.
BIOL 737 - Research Seminar in Bioinformatics and Computational Biology
BIOL 615 - Systems Biology (graduate)
BIOL 415 - Systems Biology
BIOL 313 - Introduction to Bioinformatics and Computational Biology
Lobo, Daniel. (2022). Formalizing Phenotypes of Regeneration. Whole Body Regeneration 663-679 Springer.
Ogunsan, Oluwateniayo O., Lobo, Daniel. (2022). Automatic Generation of Interactive Multidimensional Phase Portraits. bioRxiv.
Wolff, Andrew, Cynthia, Ruth Wagner, Wolf, Julia B., Lobo, Daniel. (2022). In Situ Probe and Inhibitory RNA Synthesis Using Streamlined Gene Cloning with Gibson Assembly. STAR Protocols.
Mousavi, Reza, Konuru, Sri, Lobo, Daniel. (2021). Inference of Dynamic Spatial GRN Models with Multi-GPU Evolutionary Computation. bbab104. Briefings in Bioinformatics.
Ko, Jason, Mousavi, Reza, Lobo, Daniel. (2021). Computational systems biology of morphogenesis. Computational Systems Biology in Medicine and Biotechnology 343-365 Springer.
Hwang, Jeanice, Hari, Archana, Cheng, Raymond, Gardner, Jeffrey G., Lobo, Daniel. (2020). Kinetic modeling of microbial growth, enzyme activity, and gene deletions: an integrated model of β-glucosidase function in Cellvibrio japonicus. 117 3876-3890 Biotechnology and Bioengineering.
Hari, Archana, Lobo, Daniel. (2020). Fluxer: a web application to compute, analyze, and visualize genome-scale metabolic flux networks. 48 427-435 Nucleic Acids Research.
Roy, Joy, Cheung, Eric, Bhatti, Junaid, Muneem, Abraar, Lobo, Daniel. (2020). Curation and annotation of planarian gene expression patterns with segmented reference morphologies. 9. 36 2881–2887 Bioinformatics.
Ko, Jason M., Lobo, Daniel. (2019). Continuous dynamic modeling of regulated cell adhesion: sorting, intercalation, and involution. 11. 117 2166-2179 Biophysical Journal.
Herath, Samantha, Lobo, Daniel. (2019). Cross-inhibition of Turing patterns explains the self-organized regulatory mechanism of planarian fission. 485 110042 Journal of Theoretical Biology.
García-Quismondo, Manuel, Levin, Michael, Lobo, Daniel. (2017). Modeling regenerative processes with Membrane Computing. 381 229–249 Information Sciences.
Lobo, Daniel, Lobikin, Maria, Levin, Michael. (2017). Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus. 7 41339 Scientific Reports.
Lobo, Daniel, Levin, Michael. (2017). Computing a Worm: Reverse-Engineering Planarian Regeneration. Advances in Unconventional Computing: Emergence, Complexity and Computation 637–654 Switzerland: Springer International Publishing.
Lobo, Daniel, Morokuma, Junji, Levin, Michael. (2016). Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration. 17. 32 2681–2685 Bioinformatics.
Durant, Fallon, Lobo, Daniel, Hammelman, Jennifer, Levin, Michael. (2016). Physiological controls of large-scale patterning in planarian regeneration: a molecular and computational perspective on growth and form. 2. 3 78–102 Regeneration.
Hammelman, Jennifer, Lobo, Daniel, Levin, Michael. (2016). Artificial neural networks as models of robustness in development and regeneration: stability of memory during morphological remodeling. Artificial Neural Network Modelling 628 45–65 Springer International Publishing.
Lobo, Daniel, Hammelman, Jennifer, Levin, Michael. (2016). MoCha: Molecular Characterization of Unknown Pathways. 4. 23 291–297 Journal of Computational Biology.
Rubin, Beatrix P., Brockes, Jeremy, Galliot, Brigitte, Grossniklaus, Ueli, Lobo, Daniel, Mainardi, Marco, Mirouze, Marie, Prochiantz, Alain, Steger, Angelika. (2015). A dynamic architecture of life. 1288 F1000Research.
Emmons-Bell, Maya, Durant, Fallon, Hammelman, Jennifer, Bessonov, Nicholas, Volpert, Vitaly, Morokuma, Junji, Pinet, Kaylinnette, Adams, Dany, Pietak, Alexis, Lobo, Daniel, Levin, Michael. (2015). Gap Junctional Blockade Stochastically Induces Different Species-Specific Head Anatomies in Genetically Wild-Type Girardia dorotocephala Flatworms. 11. 16 27865–27896 International Journal of Molecular Sciences.
Lobikin, Maria, Lobo, Daniel, Blackiston, Douglas J., Martyniuk, Christopher J., Tkachenko, Elizabeth, Levin, Michael. (2015). Serotonergic regulation of melanocyte conversion: A bioelectrically regulated network for stochastic all-or-none hyperpigmentation. 397. 8 ra99 Science Signaling.