Our Science – Aguda Website
Baltazar D. Aguda, Ph.D.
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Biography
Dr. Aguda was born in the Philippines, and obtained his PhD in Chemistry (Chemical Physics Program) from the University of Alberta in Canada. His doctoral and postdoctoral research focused on the mathematical and computational analysis of complex biochemical networks. After more than a decade of teaching physical chemistry courses in Canadian universities, he refocused his research program towards the analysis of gene regulatory networks relevant to cancer development. Prior to joining the NCI in June 2010, Dr. Aguda made substantial contributions to cancer systems biology, including the development of predictive network and kinetic models of mammalian cell cycle checkpoints such as the Restriction Point and the G2 DNA Damage Checkpoint Pathway. He recently wrote a graduate-level book called 'Models of Cellular Regulation' published in 2008 by Oxford University Press.Research
At the Neuro-Oncology Branch of the CCR/NCI, Dr. Aguda is developing network models involving genes, transcription factors and microRNAs associated with gliomagenesis. Current work is being focused on the interactions between myc and p53, two transcription factors - the first an oncogene and the second a tumor suppressor gene - that profoundly influence proliferative and differentiation pathways in cancer stem cells. How combinations of microRNAs work to finetune possible switching behaviors of the myc-p53 system is also a major question of interest. The ultimate goal is to generate hypotheses, amenable to experimental validation, on how to control and inhibit the propagation of glioma-initiating stem-like cells. His recent paper on the myc-p53 system has been featured in the Biophysical Journal (the paper is free online: Aguda BD et al., Qualitative Network Modeling of the Myc-p53 Control System of Cell Proliferation and Differentiation. Biophys. J. 101: 2082-91, 2011.)
Dr. Aguda also works in the field of network pharmacology, where his expertise in the analysis of complex biological networks and their control (with drug combinations) is yielding a very interesting algorithm.
This page was last updated on 2/26/2013.

