Lattice Models and Agent-Based Models in Biology: Linking Individual Properties to Population Properties

Wednesday, June 16 at 09:30am (PDT)
Wednesday, June 16 at 05:30pm (BST)
Thursday, June 17 01:30am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS13" time block.
Note: this minisymposia has multiple sessions. The second session is MS12-CBBS (click here).

Share this


Bhargav Karamched (Florida State University, United States of America)


Lattice models have a rich history in biological modeling. They provide a valuable framework for modeling complex spatiotemporal dynamics in biological tissues. Agent-based models provide realistic models of biological tissues and populations. Both have been used to model protein folding, cancer initiation and progression, and motor protein transport through a cell, amongst numerous other applications. Both frameworks have the important property of linking individual properties to population-level structure. Spatiotemporal dynamics in biological systems are often modeled by partial differential equations. Partial differential equation models offer scope for analysis, but they often coarse grain dynamics so that an individual's impact on the population is unclear. Lattice models are a viable alternative to the aforementioned. They capture individual properties at a high level but nevertheless sacrifice some fidelity to reality for the sake of analytical tractability. While agent-based models are less tractable, they are able to make concrete predictions about how systems behave in the lab--their results are easier to interpret for the non-theorist. This mini symposium will feature talks discussing lattice and agent-based models about specific biological systems, showing the relevance of such models today. How results therein can be interpreted and mapped to reality will also be discussed.

Tom Chou

(University of California - Los Angeles, United States of America)
"RNA polymerase and ribosome interactions in transcriptional error correction and translation-transcription coupling"
Backtracking of RNA polymerase (RNAP) is an important pausing mechanism during DNA transcription that is part of the error correction process. We model the backtracking mechanism of RNA polymerase which usually happens when the polymerase tries to incorporate a mismatched nucleotide. Previous models have made simplifying assumptions such neglecting the trailing polymerase behind the backtracking polymerase or assuming that the trailing polymerase is stationary. We derive exact analytic solutions of a discrete stochastic model that includes interacting (exclusionary) RNAPs by explicitly showing how a trailing RNAP influences the probability that an error is corrected or incorporated by the leading backtracking RNAP. Moments of conditional first passage times to error correction or error incorporation are also computed. We also develop a very similar model for describing translation-transcriptional coupling during which a ribosome simultaneously elongates (and produces a polypeptide) while interacting with the leading RNAP.

Claudia Neuhauser

(University of Houston, United States of America)
"Fighting Cancer with Viruses--Mathematical Models to Guide Therapy"
Virotherapy of cancer relies on engineered viruses that selectively attack and kill cancer cells but leave healthy cells unaffected. The success of this therapy relies on the successful establishment of an infection that results in the death of cancer cells. To gain a better understanding of the dynamics, we developed spatially explicit, stochastic models of multi-species interactions to map out under what conditions the symbiont (virus) effectively eliminates the host (cancer cells). I will present rigorous results and conjectures based on simulations. I will report on an experimental system (in vitro and in vivo) that was developed by Dr. David Dingli (Mayo Clinic) and uses this mathematical framework to predict the effectiveness of virotherapy in cancer.

James Glazier

(Indiana University, United States of America)
"Multicellular modeling of viral infection and immune response in epithelial tissues and response to drug therapy"
Simulations of tissue-specific effects of viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding, and parallel development by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage. We studied the effects on progression of treatment potency and time of first treatment for an antiviral. We also show an extended version of the simulation with additional immune cell types calibrated to match extensive existing data on the progression of murine influenza infection. Simulations suggest that the microenvironment in which a virus spreads plays a dominant role in disease onset and progression, and that spatially-resolved models may be important to better understand and more reliably predict future health states based on susceptibility of potential lesion sites using spatially resolved patient data on the state of an infection.

Joanna Wares

(University of Richmond, United States of America)
"Developing a computational modeling course in the time of COVID-19"
What happens when you decide, in January, that you will teach Computational Modeling in Public Health in the coming fall (2020), and then COVID-19 breaks out? You turn your class into a COVID-19 modeling class! Here, I describe efforts to teach upper-level undergraduate mathematics students how to create and analyze their own mathematical models, both differential-equations and agent-based types. I will explain my first attempt at the course design, and how I utilized the wealth of papers and examples provided by the scientific community in 2020 to answer questions about COVID-19. I will also describe some of the research my students developed in the course and then continued in the following semester. In their research, one focus was on understanding the underlying socio-economic inequities in COVID-19 outcomes.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.