Research Overview

The broad goal in our laboratory is to better understand, and then beneficially manipulate, microbial expression systems. While in the past we have carried out projects involving bacteria, all our current work is with filamentous fungi.  Specifically, we use fungi to better understand eukaryotic gene-regulatory networks. Our primary focus is on filamentous fungal morphogenesis and cell wall biosynthesis. We use a multi-omics approach to understand systems behaviors. Phosphoproteomic analysis informs us regarding kinase-mediated signal transduction, and transcriptomics/proteomics informs us regarding cellular outcomes. These approaches allow us to develop hypotheses regarding cell function, which are tested using both a genetic approach and a sophisticated set of analytical tools (electron microscopy, digital image analysis, atomic force microscopy) to asses fungal morphology and the physical properties of fungal cell walls.

Systems Biology

NSF-MCB Schematic

The goal of this project is to develop a new approach for modeling gene regulatory networks. We are testing the hypothesis that initial experimental characterization of a network subset will permit identification of the biomolecular constituents and their connectivity, thus establishing network topology. System wide time-course measurements can then be used to refine this network into a reaction kinetic model capable of making accurate system predictions. The cell wall integrity signaling pathway of the experimentally tractable model fungus Aspergillus nidulans is serving as a model. This pathway responds to cell wall damage by activating repair mechanisms that restore cell integrity. Because protein kinases play a pivotal role in mediating cellular regulatory activities, we are  focusing on a subset of kinases and the discovery of their associated substrates to initially assemble a rudimentary network. Subsequently the system will be experimentally perturbed for measuring its dynamic response using a robust transcriptomic, proteomic and phosphoproteomic platform. Using this data, we will take a two-step approach to developing the dynamic system of coupled ordinary differential equations able to describe dynamic behavior of a model gene regulatory network. First, an ensemble approach of approximate models will be tested and refined. In the second step, the ensemble will act as the seed population for use in an evolutionary algorithm to generate a more refined and accurate model. We will then validate the model by iterative comparisons of in silico predictions with experimental results. This project involves collaboration with both Iowa State University and the University of Connecticut and is sponsored by the National Science Foundation.



One of Dr. Marten’s favorite articles…

The Student, The Fish, and Agassiz