Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics
Erika I. Martinez - Texas Southern University
The advent of high thorough-put technology enables us to gather a huge amount of data pertaining to interactions among biological components. However, data accuracy due to missing and/or false interactions is often a concern. In this work, we aim to address the potential effects due to missing and/or false interactions. In other words, we compare differences in dynamics between networks with and without certain arrows. In particular, we consider possible effects due to an extra inhibitory/activating interaction on the dynamics of negative feedback loops and positive feedback loops. Our analysis is based on two commonly used frameworks: Boolean networks and coupled ordinary differential equations. We found that adding an extra activating or inhibiting interaction to a negative or positive feedback loop will have several possible results: (I) no change in dynamics; (II) disappearance/appearance of cycles; (III) change in the length of cycles; (IIII) fixed points. Therefore, missing interactions may sometimes have no effect or play a critical role in its dynamics. This study will help provide general ideas of the possible effects due to missing interactions.
Not SubmittedFunder Acknowledgement(s): This research was supported by the Summer Undergraduate Research Program in the College of Science Engineering and Technology at Texas Southern University.
Faculty Advisor: Yunjiao Wang, wangyx@tsu.edu
Role: I configured a C++ program to display the possible outcomes that the extra inhibitory/activating interactions have on the dynamics of negative feedback loops and positive feedback loops. Then I gathered all the data and found all possible results that could occur.