Paul Lindahl Lab at Texas A&M University

Welcome to Lindahl Lab!

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Iron Metabolism

One of our research areas involves the study of iron metabolism in cells. Iron is an essential transition metal in cell biology because of its involvement in a number of enzymes and proteins required for cell survival, such as respiratory complexes and hemoglobin. Biological iron is found mainly in the form of iron-sulfur clusters and heme prosthetic groups. The uptake and efficient incorporation of iron into these enzymes and proteins are tightly regulated in normal cells. However, a number of diseases exist as a consequence of mutations in proteins involved in iron uptake, utilization, trafficking or export, resulting in defective iron metabolism.

We are particularly interested in studying iron uptake into the cell and its trafficking from the cytosol to other organelles such as the mitochondria and vacuoles. We study iron metabolism mainly from a systems biology perspective. Hence, we have developed an integrative biophysical approach to study iron distribution in whole cells and isolated organelles, using Mössbauer spectroscopy, Electron Paramagnetic Resonance (EPR), Electron Absorption Spectroscopy and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). We are also currently developing a liquid chromatography method to isolate and characterize low-molecular-weight iron complexes of interest from cells and isolated organelles.

Whole Cell Mathematical Modeling

We have a second project to mathematically model the growth and division of whole cells on the biochemical level. This is an NSF-funded collaboration with Professor Jeffery J. Morgan in the Math department at the University of Houston. We first developed a mathematical framework for modeling a growing and dividing cell, and then designed a minimal chemical cell model. This symbolic model involves only 5 components and reactions. Now we are adding additional, more realistic, "modules". Our first module controls the advancement of mitosis from one stage to the next. We also added a module to determine the shape of the cell by minimizing membrane bending energy. Unlike real cells, our in silico cell models did not "pinch" around their middles, undoubtedly because these cells lacked contractile rings. So we developed a chemical model for the assembly and contraction of the FtsZ ring used by prokaryotes in cytokinesis. We installed it into our whole-cell model and redetermined cell geometry. With this module installed, the cell pinches to form two daughter cells . Another problem is that our cells can only grow properly at a single nutrient concentration. Real cells can grow and divide using a wide range of nutrient concentrations, because they have size checkpoints that delay division until they reach a critical size. We recently developed a checkpoint module, and are currently attempting to install it into a whole cell model. Another challenge is to center the contractile ring in the middle of the cell. To do this we have modeled the oscillatory Min system which functions in E coli to center the FtsZ ring (submitted). Finally, we are developing a model for the assembly and contraction of the Actomyosin ring found in Eukaryotes. Once developed, we will substitute this for the FtsZ ring in a whole cell model to generate a cell composed exclusively of eukaryotic parts. A cell with uniformly bacterial parts will be developed in parallel.

Through these developments, we hope to illustrate the advantages of whole-cell mathematical modeling to others in the field of computational cell biology. As whole-cell models increase in complexity, they should develop some predictive power, along with new mechanistic insights regarding the interactions of biochemical processes in cells. We anticipate that there will be great advantages to modeling processes within a whole-cell context as such models will include large numbers of component interactions. Students and post-docs interested in this project require backgrounds in physical chemical kinetics, biochemistry, applied mathematics, and computer programming.