SPOTLIGHT ON MCB FUNDED SCIENCE

A spotlight illuminates the words 'Spotlight on MCB-funded Science.'

Photo Credit: Matusciac Alexandru/Shutterstock.com

Sharing MCB Science is one of our six blog themes where you can learn about exciting MCB-funded research submitted by our investigators (via this webform). We greatly appreciate the overwhelmingly positive response of the MCB scientific community and have received many more submissions than can be featured in long form on the blog. Enjoy this shorter spotlight of submissions we have received!

a closeup of a cell, it is shapes like a lollypop, a long thin stalk with a rounded top which is darker in color

Joseph K. E. Ortega – Photograph of a stage IV sporangiophore of Phycomyces blakesleeanus with the micro-capillary tip of a pressure probe.

Algal, fungal, and plant cells interact with their environment by regulating their size and shape through expansive growth, an increase in cell volume due predominately to an increase in water uptake. This process presents a special challenge for algae, fungi, and plants because cells in these organisms have an exoskeleton-like cell wall that provides support, protection, and shape.  When water enters these cells, turgor pressure builds up, which stretches (deforms) the cell wall; at the same time, new material is added to the cell wall to fill in the expanded regions and thereby control the size and shape of the enlarging cell. The interconnected processes of water uptake, wall deformation, and control of cell size and shape are crucial for algal, fungal, and plant survival.

Previous research has provided a good description of the molecular and mechanical changes accompanying expansive growth.  Taking advantage of this foundation, Dr. Joseph K. E. Ortega, Professor of Mechanical Engineering at the University of Colorado, Denver, is bringing a new dimension to quantify cellular changes during expansive growth.  He has developed a mathematical model of the interconnected processes, called the Augmented Growth Equations (AGE). As described in his new publication, the model organizes multiple equations to represent the relationships between variables and uses dimensional analysis to produce dimensionless coefficients. The dimensionless coefficients enable researchers to more easily quantify the biophysical processes and better predict how changes in water absorption and cell wall deformation regulate expansive growth. While the model does not address the shape of the cells, the mathematical framework provides insight as to how water uptake and wall deformation are regulated in algal, fungal, and plant cells to control expansive growth during normal conditions and in response to changes in the environment.

This work is partially funded by the Molecular Biophysics Cluster of the Division of Molecular and Cellular Biosciences, Awards #MCB-0948921.

on the left is a still image from an MRI of a chest with the heart and lungs visible. On the right is a graph with the quick heartbead and slower respiration plotted as curves.

Dr. Steven Van Doren – Example of how the TREND software can distinguish the patterns of a heart beating and lungs breathing from an MRI movie

Visual outputs, such as photographs or movies, contain important data from scientific experiments. For example, identifying biologically relevant signals over time (trends) can be challenging, as they may be subtle or mixed into background movements or noise. To identify trends, researchers scour the data looking for specific features, such as peaks, and either mark them by hand, which is time consuming and subjective, or set specific background thresholds in instrumentation, which can result in mistaking signal for noise. In a new publication, Dr. Steven Van Doren, Professor of Biochemistry at the University of Missouri, and his post-doctoral researcher, Dr. Jia Xu, describe a new software program called TREND (Tracking and Resolving Equilibrium and Nonequilibrium population shifts in Data). The software allows researchers to objectively extract information from two-dimensional images or videos, and in another recent publication, they describe extending the analysis to several different types of data.

TREND uses a statistical approach, called principal component analysis (PCA), on a series of individual measurements to compress and organize multivariate data so that researchers can select for and detect changes in a variety of factors over time. The factors can be anything: characteristics of a stream, grades in a class, or gene variants. When applied to movies, such as this sample video of a sunset, a trend in the data, such as the movement of the sun across the sky, can be plotted after removing background noise, such as motion from clouds. Another example is separating the pattern of a heart beating from lungs breathing using TREND (a photo of this video is shown above). TREND is available for licensing and download at http://biochem.missouri.edu/trend and https://nmrbox.org/.

 

This work is partially funded by the Molecular Biophysics Cluster of the Division of Molecular and Cellular Biosciences, Awards #MCB-1409898.

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