(6ap) Computational Modeling in Cancer Systems Biology: Stochasticity, Complexity, and Multiscale Dynamics in Disease Progression and Drug Response

Biological systems differ from human engineered systems in three primary respects: 1) they are highly stochastic, 2) they are extraordinarily complex, and 3) they exhibit dynamics over a wide range of temporal and spatial scales. These factors are especially prevalent in cancer, which is a complex and adaptive disease characterized by genetic mutations, phenotypic plasticity, and interactions among tumor cells and with the tumor microenvironment. Tumors are highly heterogeneous and exhibit complex responses to drugs and other therapeutic interventions. Indeed, patient responses to therapy can vary widely and it is not uncommon for an initial positive response to be followed by tumor recurrence and metastasis, which is often fatal.

It is becoming increasingly apparent that to unravel the complexities of cancer a systems engineering approach is necessary. Cancer can be thought of as a reverse engineering problem: it is fundamentally the result of a breakdown of the cell’s natural tumor suppressing machinery. Thus, therapeutic approaches aimed at reestablishing or rewiring this apparatus are of great interest. However, the molecular mechanisms that underlie cancer cell function remain poorly understood. Computational models are powerful tools for addressing this challenge. They are a means by which current understanding of a biological system can be formalized in mathematical terms. Moreover, through an iterative process of model building, prediction, and experimentation, models can be systematically expanded over time to capture increasingly complex behaviors. This is the basis of the cancer systems biology approach.

In this poster, I present results of combined computational and experimental investigations of numerous cancer types treated with therapeutic drugs, including small cell and non-small cell lung cancers and melanoma. My results indicate that tumors employ a "bet hedging" strategy whereby cells diversify across multiple phenotypes in the absence of drug in order to increase the chances of population survival when faced with drug. I hypothesize that surviving subpopulations can persist in a patient for extended periods of time before eventually acquiring genetic resistance mutations that drive tumor recurrence. Therefore, developing a comprehensive understanding of the molecular networks underlying tumor heterogeneity may lead to novel therapies that can reduce or even eliminate these surviving cells before resistance mutations arise, improving patient outcomes. This work is the basis for a recent K22 grant application (Impact Score: 25) aimed at building a detailed, mechanistic model of the biochemical pathways underlying cell fate decisions in cancer cells.

Teaching Interests:

My expertise lies in chemical kinetics, thermodynamics, and numerical methods. I also have experience in physical chemistry, biochemistry, statistics, theoretical computer science, and software engineering. During my years as a postdoctoral researcher I have had the opportunity to mentor numerous undergraduate and graduate students and have given numerous lectures on systems biology. I enjoy teaching and make a concerted effort to engage students by providing analogies that convey complex concepts in an easy-to-understand manner. My philosophy of teaching is that nothing is ever as complicated as it seems and the best way to learn is through a hands-on approach. Thus, I aim to guide students in the right direction but ultimately leave it to them to find the solution to a problem. There is nothing more satisfying than when something "clicks" for a student and they have that moment of true understanding. I am prepared and enthusiastic about teaching full semester chemical engineering courses at both the undergraduate and graduate levels and look forward to mentoring graduate students and postdocs in their research endeavors. As a Hispanic-American, I am also strongly committed to minority outreach and recruiting underrepresented students into STEM programs.

Select Publications:

B.B. Paudel, L.A. Harris, K.N. Hardeman, A.A. Abugable, C.E. Hayford, D.R. Tyson and V. Quaranta, “A non-quiescent ‘idling’ population state in drug-treated, BRAF-mutated melanoma,” Biophysical Journal 114, 1499–1511 (2018).

Z.W. Jones, R. Leander, V. Quaranta, L.A. Harris* and D.R. Tyson*, “A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase,” PLoS One 13, e0192087 (2018). (*equal authors)

L.A. Harris*, M.S. Nobile*, J.C. Pino*, A.L.R. Lubbock, D. Besozzi, G. Mauri, P. Cazzaniga and C.F. Lopez, “GPU-powered model analysis with PySB/cupSODA,” Bioinformatics 33, 3492–3494 (2017). (*equal authors)

L.A. Harris*, P.L. Frick*, S.P. Garbett, K.N. Hardeman, B.B. Paudel, C.F. Lopez, V. Quaranta and D.R. Tyson, “An unbiased metric of antiproliferative drug effect in vitro,” Nature Methods 13, 497–500 (2016). (*equal authors)

L.A. Harris, J.S. Hogg, J.J. Tapia, J.A.P. Sekar, S. Gupta, I. Korsunsky, A. Arora, D. Barua, R.P. Sheehan and J.R. Faeder, “BioNetGen 2.2: Advances in rule-based modeling,” Bioinformatics 32, 3366–3368 (2016).

J.S. Hogg*, L.A. Harris*, L.J. Stover, N.S. Nair and J.R. Faeder, “Exact hybrid particle/population simulation of rule-based models of biochemical systems,” PLoS Computational Biology 10, e1003544 (2014). (*equal authors)