Dr. Carlos F. Lopez, received his BSc and BLA degrees from the University of Miami and his PhD in Physical Chemistry from the University of Pennsylvania. He pursued a postdoctoral position at the University of Texas at Austin where he studied theoretical biophysics of protein solvation. He pursued further training at Harvard Medical school where he attained the prestigious HW Pierce/King Trust Research Fellowship and developed novel methods to bridge scales in the study of cellular processes. He subsequently moved to Vanderbilt University at the end of 2012 as an Assistant Professor of Cancer Biology, Biomedical Informatics, and faculty member of the Vanderbilt-Ingram Cancer Center, Center for Quantitative Sciences, Center for Structural Biology, Institute for Chemical Biology, and Quantitative Sys-tems Biology Center. In 2017, he moved to the Department of Biochemistry as part of an administrative reorganiza-tion within Vanderbilt University.

He has been the recipient of multiple awards, honors, and fellowships including the NIH K22 Transition Career Development Award (2011), American Association for Cancer Research – Minority Scholar in Cancer Research Award (2012), Vanderbilt-Ingram Cancer Center Young Ambassadors Award (2013), Vanderbilt Provost Office Outstanding URM Accomplishments Award (2014, 2015, 2016), Leadership Alliance SR-EIP Faculty Mentor Commendation (2015), and the National Science Foundation CAREER Award – the highest honor conveyed by this organization to junior faculty. He was appointed as the Vanderbilt University Liaison to Oak Ridge National Laboratory in 2017-2019, where he served in the ORNL steering committee to guide the interactions between the national lab and Vanderbilt University.

Recently, he has also become a member of the Cancer Systems Biology Consortium as part of his research activities with NIH/NCI. His work employs novel computational modeling tools in combination with experimental data to explain and predict how intracellular molecular interactions give rise to cell-decision processes and cell-community behaviors. The overall goal of his work is to attain a mechanistic and predictive understanding of dynamic cellular systems, how they are regulated in healthy cells, dysregulated in dis-eases such as cancer, and leverage this knowledge to guide experiments toward novel therapies. To this end he devel-ops novel theories and numerical methods to explain how systems-level biochemical interaction networks process biochemical signals and lead to a phenotypic outcome.

Title: How to Hang With a Modeler (using Python)

Abstract: Interdisciplinary work is now commonplace in many areas of biology. However, interaction between disciplines requires a common language. Here we discuss ongoing challenges in interdisciplinary collaboration and how general purpose programming platforms in Python can help unify the field. I will touch on aspects of rule-based modeling, model optimization, and data analysis, which would traditionally require multiple tools but that can be explored in a common environment to bridge the gap between data and knowledge.

Suggested Reading or Key Publications:

• • a
  • b
  • c
  • d

Links to Relevant Software: 

• • e
  • f
  • g
  • h

• • Question 1
  • Question 2