Undergraduate Research Projects in Mathematical Biology

This folder contains descriptions for projects that are available for Truman students who are interested in working at the intersection of the mathematical and life sciences. The folder also contains support material for those projects (e.g., documents, images).

Gene Expression and Visualization Application (GENEVA)

This team of computer scientists, molecular geneticists, and cell biologists are collaborating in an effort to annotate genes in the corn (maize) shoot apical meristem.

Quantitative Identification of Missouri Bat via Acoustic Surveys

Identifying bats to species often requires close inspection of the bat, but this team is working on improving a method for identifying bat species in the field that uses acoustic signatures of the bat's search phase echolocation calls.

The effects of prescribed burning in grasslands on the population structure of predatory beetles: a spatial modeling approach

The spatial structure of populations and communities has become increasingly important to ecologists because it provides insights into ecological patterns and processes. For example, spatial structure influences parasitism rates in forests, the occurrence of generalist and specialist insect herbivores in agricultural landscapes, and the impact of grazing in desert grasslands. From a practical standpoint, understanding the spatial structure of a population can indicate the proper sampling scheme and statistical protocol.

Plastron respiration in ticks

Equipped with the physiological equivalent of SCUBA gear, tick's are able to survive being submerged in water for long periods of times. This group aims at creating mathematical models that relate the geometric structure of this apparatus to tick survival time.

Development of habitat suitability models to test how spatial scale influences predictions of occurrence patterns of the federally threatened, rare plant species, Missouri bladder-pod Lesquerella filiformis

Statistics, geographic information systems, and a rich data set (made richer by annual field work) help this group to shed light on issues surrounding the conservation of the Missouri bladder-pod.

Aerodynamic features of saccate pollen: Evolutionary implications for wind-pollinated plants

How do the relative size, density, shape, and suface texture of a pollen grain affect how fast it falls through the air? Using electron microscopy, fluid dynamics, and computer simulation, this group hopes to find some answers.

Quantitative Shape Description of two dimensional vascular networks

We study mathematical methods for describing the shape of a two-dimensional vascular network of endothelial cells using computer-assisted image analytic methods. We employ light and confocal microscopy to acquire images and ImageJ to analyze them.

Determining gravitropic sensitivity: a mathematical approach

A botanist and a computationally inclined mathematician are investigating how best to use digital images to measure the response of a growing root to the effects of gravity.

A GGH Model of the Four-Cell Stage Caenorhabditis elegans Embryo

 

A 4-cell C. elegans embryo

 

Unveiling the Past: Analysis of Evolutionary and Demographic History

A computational biologist and a mathematician use phylogenetics to study the influence geography exerts on way organism travel.

Statistics and Phylogenetic Community Ecology

An ecologist and a statistican aim to improve the statistical tools available in phylogenetic community ecology. They will start their work by focusing on katydids (Orthoptera: Tettigoniidae).

Graph theoretic modeling of the population dynamics of Missouri bladderpod (Lesquerella filiformis)

An ecologist and a mathematician aim to create models for population for the engandered Missouri bladder pod that take a graph theoretic approach.

Community Dynamics of Micromammals and their Resident Ectoparasites

 

Optimal Foraging in a Generalist Snake Predator, Nerodia sipedon

Optimal foraging theory predicts that an organism should prey upon that food source that maximizes the net energy gained and minimizes the fitness costs associated with each foraging bout. We will model optimal foraging using Agent-Based Modeling (ABM).

MathBio Projects with ATSU