Georgia Institute of Technology College of Sciences

Wild-type zebrafish are named for their dark and light stripes, but mutant zebrafish feature variable skin patterns, including spots and labyrinth curves. All of these patterns form as the fish grow due to the interactions of tens of thousands of pigment cells in the skin. This leads to the question: how do cell interactions change to create mutant patterns? The longterm biological motivation for my work is to shed light on this question — I strive to help link genes, cell behavior, and visible animal characteristics. Toward this goal, I build agent-based models to describe cell behavior in growing fish body and fin-shaped domains. However, my models are stochastic and have many parameters, and comparing simulated patterns, alternative models, and fish images is often a qualitative process. This, in turn, drives my mathematical goal: I am interested in developing methods for quantifying variable cell-based patterns and linking computational and analytically tractable models. In this talk, I will overview our agent-based models for body and fin pattern formation, share how topological data analysis can be used to quantify cell-based patterns and models, and discuss ongoing work on relating agent-based and continuum models for zebrafish patterns.

The type VI secretion system (T6SS) is a bacterial subcellular structure that has been likened to a molecular syringe, capable of directly injecting toxins into neighboring cells. Bacteria use T6SS to kill competitor cells, gaining limited space and resources, such as a niche in a host. T6SS has been found in about 25% of Gram negative bacteria, including some human pathogens. Thus, understanding regulation, control, and function of T6SS, as well as the role of T6SS in interbacterial competition, has far-reaching ramifications. However, there are many open questions in this active research area, especially since bacteria have evolved diverse ways in producing and engaging this lethal weapon.

In a multidisciplinary collaboration, we combine experiments and applied mathematics to address a central question about T6SS’s role in interbacterial competition: what is the connection between the subcellular dynamics of T6SS and the competitive strength of the population as a whole? Based on detailed microscopy data, we develop a model on the scale of individual T6SS structures, which is then integrated with an agent-based model (ABM) to enable multi-scale simulations. In this talk, we present the experimental data, the subcellular T6SS model, and findings about T6SS-dependent competitions obtained by simulating the ABM.

Recording link: https://bluejeans.com/s/6fzcqvzTQ5m

Cell cycle duration changes dramatically during development, starting out fast to generate cells quickly and slowing down over time as the organism matures. The cell cycle can also act as a transcriptional filter to control the expression of long gene transcripts which are partially transcribed in short cycles. Using mathematical simulations of cell proliferation, we identify an emergent property, that this filter can act as a tuning knob to control gene transcript expression, cell diversity and the number and proportion of different cell types in a tissue. Our predictions are supported by comparison to single-cell RNA-seq data captured over embryonic development. Additionally, evolutionary genome analysis shows that fast developing organisms have a narrow genomic distribution of gene lengths while slower developers have an expanded number of long genes. Our results support the idea that cell cycle dynamics may be important across multicellular animals for controlling gene transcript expression and cell fate.

Recording link: https://bluejeans.com/s/QhCWmELH6AC

Two years after the beginning of the pandemic, we are still working to understand the mechanisms of immunopathology in COVID-19. Immune responses following SARS-CoV-2 infections are heterogeneous, and biomarkers of this variability remain to be elucidated. In collaboration with experimentalists and clinicians, we have deployed various mathematical and computational approaches to understand longitudinal immunological data from patients, and to generate new hypotheses about the factors determining COVID-19 severity and disease dynamics.
To answer foundational questions about immunopathology and heterogeneity in COVID-19, we have developed a multi-scale, mechanistic mathematical model of the immune response to SARS-CoV-2 that includes several innate and adaptive immune cells and their communication via signalling networks. By generating a population of virtual patients, we identified dysregulated rates of monocyte-to-macrophage differentiation that distinguishes disease severity in these in silico patients. Further, our results suggest that maximal IL-6 concentrations can be used as a predictive biomarker of CD8+ T cell lymphopenia. Using the same cohort of virtual patients, we have also studied the influence of variant on immunopathology by combining our model with data of intra-host viral evolution. We predicted that the combined effects of mutations affecting the spike proteins and interferon evasion on the severity of COVID-19 are mostly determined by the innate host immune response. Our approaches can be used to study the factors regulated immunopathology during SARS-CoV-2 infections, and represent a quantitative framework for the study of COVID-19 and other viral diseases.

Recording link: https://bluejeans.com/s/6CmKwHWWc2O