An Investigation Of Gene Regulatory Network State Space Variability

Genes are segments of DNA that provide a blueprint for cells and organisms to effectively control processes and regulations within individuals. There have been many attempts to quantify these processes, as a greater understanding of how genes operate could have large impacts on both personalized and precision medicine. Gene interactions are of particular interest, however, current biological methods can not easily reveal the details of these interactions. Therefore, we infer networks of interactions from gene expression data which we call a gene regulatory network, or GRN. Due to the robust behavior of genes and the inherent variability within interactions, models incorporating stochasticity are more realistic than those that are only deterministic. These methods are designed to bypass the need for large amounts of data and extensive knowledge about a network. In this work, we extend previous work investigating additional ways to incorporate stochasticity into gene regulatory networks. First, we use a transition function and investigate its inherent variation, then we use a statistical distribution for activating and degrading the states of genes, and finally, we use a new method incorporating spectral density to incorporate stochasticity within a GRN

Impacts of a Cross-Institutional Research Experience Workshop on Student Understanding of and Self-Efficacy for Research

There are many perceived benefits to undergraduate student research; however, students may not have a full understanding of the research process prior to engaging in a project. To help students gain an understanding of the research process, the Intercollegiate Biomathematics Alliance organizes a Cross-Institutional Research Experience (IBA-CURE) that brings students together to work on research skills and problems. In this presentation, we analyze the impact of an undergraduate research workshop on students’ understanding of academic research as well as the impact on their self-efficacy for conducting research through an analysis of the 2018 and 2019 IBA-CURE workshops. Students completed before and after surveys addressing their understanding of research and effective collaboration in conducting research, their perceived role in conducting research, and their perception of their own skills specific to biomathematics research. Here we discuss improvements in self-efficacy and shifts in perception of research.https://ir.library.illinoisstate.edu/ursmat/1004/thumbnail.jp

An Investigation of Gene Regulatory Network State Space Variability

Genes are segments of DNA that provide a blueprint for cells and organisms to effectively control processes and regulations within individuals. There have been many attempts to quantify these processes, as a greater understanding of how genes operate could have large impacts on both personalized and precision medicine. Current biological methods cannot easily reveal the details of gene interactions. Therefore, we use gene expression data to infer networks of interactions, which are called gene regulatory networks or GRNs. These methods are designed to bypass the need for large amounts of data and extensive knowledge about a network. In this work, we extend previous work by investigating additional ways to incorporate stochasticity into gene regulatory networks