(6iv) Stochasticity, Complexity, and Multiscale Dynamics in Cancer 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 characteristics are especially prevalent in cancer, which is a complex, adaptive disease characterized by genetic mutation, phenotypic plasticity, and cell-cell interactions among tumor cells and with the tumor microenvironment. Tumors are highly heterogeneous and exhibit complex responses to drugs and other therapeutic interventions. Patient outcomes to therapy can vary widely, but it is not uncommon for an initial favorable response to be followed by tumor recurrence and metastasis, which is very often fatal. The mechanisms that govern drug evasion and tumor reestablishment remain poorly understood.

It is becoming increasingly clear that to unravel the complexities of cancer a systems engineering approach is necessary. Cancer can be seen as a reverse engineering problem in that it is fundamentally a result of the breakdown of the cell’s natural tumor suppressing machinery. Reestablishing or rewiring this apparatus requires a deep understanding of the mechanisms that underlie normal and cancer cell function. Computational models provide a means by which current understanding of biological mechanisms can be formalized mathematically. In silico predictions can be made, tested experimentally, and experimental results used to update and refine the model. This iterative approach of model building and refinement is a powerful tool for constructing a detailed and comprehensive understanding of a biological system.

Here, I present results of combined computational and experimental investigations of in vitro responses of isoclonal non-small cell lung cancer and melanoma cell populations to targeted drugs. Our results point to cancer cell populations employing a “bet hedging” strategy whereby cells diversify across multiple phenotypes in the absence of drug to increase the chances of population survival to a future drug onslaught. We hypothesize that the surviving subpopulation can persist for an extended period of time and is susceptible to genetic mutation, acting as a reservoir from which tumor recurrence may emerge. However, the subpopulation may also constitute a phenotypic bottleneck vulnerable to secondary treatment. Characterizing the molecular nature of the surviving subpopulation is thus of critical importance for improving patient outcomes. Work is currently underway towards this end, including the recent submission of a K22 grant application aimed at building a comprehensive, mechanistic model of the biochemical pathways underlying cell fate decisions in individual cancer cells.

Teaching Interests:

My expertise lies in chemical kinetics, thermodynamics, and numerical methods and I have experience in 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 something is to do it yourself. Therefore, I aim to guide students in the right direction but ultimately leave it to them to solve a problem. It is a feeling of great satisfaction when something “clicks” for a student and they have that moment of deep understanding. I am prepared and enthusiastic about teaching full semester courses at both the undergraduate and graduate levels and look forward to mentoring graduate students and postdocs in their research endeavors. I am also strongly committed to minority outreach and increasing recruitment of underrepresented groups to higher education.

Select Publications:

Z.W. Jones, R. Leander, V. Quaranta, L.A. Harris*, 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," Nat. 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 Comput. Biol. 10, e1003544 (2014). (*equal authors)

L.A. Harris and P. Clancy, "A 'partitioned leaping' approach for multiscale modeling of chemical reaction dynamics," J. Chem. Phys. 125, 144107 (2006).

L.A. Harris and A.A. Quong, "Molecular chemisorption as the theoretically preferred pathway for water adsorption on ideal rutile TiO2(110)," Phys. Rev. Lett. 93, 086105 (2004).