The Graphical Representation of the Digital Astronaut Physiology Backbone

This report summarizes my internship project with the NASA Digital Astronaut Project to analyze the Digital Astronaut (DA) physiology backbone model. The Digital Astronaut Project (DAP) applies integrated physiology models to support space biomedical operations, and to assist NASA researchers in closing knowledge gaps related to human physiologic responses to space flight. The DA physiology backbone is a set of integrated physiological equations and functions that model the interacting systems of the human body. The current release of the model is HumMod (Human Model) version 1.5 and was developed over forty years at the University of Mississippi Medical Center (UMMC). The physiology equations and functions are scripted in an XML schema specifically designed for physiology modeling by Dr. Thomas G. Coleman at UMMC. Currently it is difficult to examine the physiology backbone without being knowledgeable of the XML schema. While investigating and documenting the tags and algorithms used in the XML schema, I proposed a standard methodology for a graphical representation. This standard methodology may be used to transcribe graphical representations from the DA physiology backbone. In turn, the graphical representations can allow examination of the physiological functions and equations without the need to be familiar with the computer programming languages or markup languages used by DA modeling software

Mathematical modeling for pattern design in networks of mammalian cells

During the early stages of embryonic development, mammalian cells communicate, undergo morphological changes, and self-assemble into highly organized tissues, and eventually organ systems. Recently, there have been several efforts to engineer the multicellular patterning in mammalian cells to better understand early development and create organoid systems to better understand human disease and drug interactions. However, existing approaches to engineer large scale multicellular patterning in mammalian cells are limited to reproducing well known behaviors or trail-and-error based design. In this thesis, I developed mathematical models to predictively design and quantitatively validate de novo multicellular patterning in mammalian cells. First, I have developed a computational to automate self-organized multicellular organization in human pluripotent stem cells that quantitatively matches the in vitro velocity distribution, temporal dynamics of CRISPR induced perturbations to protein expression, and the resulting changes in spatial organization in human pluripotent stem cell colonies. I have also developed a mathematical model to predict the programmable self-assembly from a single cell into 3D shapes. Overall, this work offers insights into how mathematical modeling can be integrated with pattern recognition and optimization algorithms to efficiently discover and direct self-organized multicellular patterning in cell aggregates and tissues.2021-02-20T00:00:00

Automated Design of Pluripotent Stem Cell Self-Organization

Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information