One important goal of systems pharmacology is to develop predictive mathematical models of signaling and regulatory networks that can be used to understand how these systems become dysregulated by disease. Such as systems level understanding should enable the development of novel therapeutic strategies for treating human disease. We have chosen to study the model organism Saccharomyces cerevisiae (budding yeast) because its experimental tractability allows rigorous testing of the models. However, we are currently translating the computational tools and insights gained from these investigations to mammalian systems.

Signal Transduction Gradient Sensing NAV AirwaySLReg
Signal transduction Polarity establishment and gradient sensing Airway surface liquid regulation


Gomez Computational modeling Tsygankov Comp Mod NAV
Stochastic models Metabolic networks Modeling multicellular formations


Signaling through MAPK pathways

Our goal is to dissect the feedback and feed-forward loops that regulate signaling through the pheromone response pathway by combining mathematical modeling with microfluidic devices to expose yeast to time-dependent pheromone concentrations. Such studies hold the promise for truly predictive models of signaling pathways and a deeper understanding of the logical structure and dynamic behavior of these systems. (Investigators: John Houser, Matt Pena, Gauri Dixit, Henrik Dohlman, Beverly Errede, Tim Elston)

Signaling through MAPK pathways

Timothy Elston, PhD | Webpage | EmailPublications


Polarity establishment and Gradient sensing

MAPK-mediated alterations in gene expression are not sufficient for efficient mating. Cells must also orient growth toward a mating partner. Because the environmental cue that determines the direction of growth is a pheromone gradient, this process is referred to as chemotropism. Chemotropism requires cells to establish a front (i.e., polarize) and this front is maintained during directed growth. Polarity establishment and maintenance are required for migration and differentiation in all eukaryotes, and often become dysregulated in diseases, such as cancer. Using microfluidic devices, we expose yeast to spatially varying pheromone concentrations to study the mechanism that underlie polarity establishment and gradient sensing . A mathematical model for gradient sensing suggests that the fusion of exocytic vesicles with the cell membrane drives movement of the polarity patch and provides a mechanism for gradient sensing .  (Investigators: Maria Minakova, Daniel Lew, Tim Elston)

Patch wandering.

Timothy Elston, PhD | Webpage | EmailPublications

Airway Surface Liquid Regulation

The goal of this project is to understand how airway surface liquid (ASL) levels are regulated in the lung to ensure proper mucociliary clearance. The importance of this clearance process is illustrated by the disease Cystic Fibrosis (CF), where absence of a single gene (CFTR) causes mucus dehydration, reduced clearance and persistent lung infection. We are developing models that couple extracellular nucleotide concentrations with the regulation of ion conducting channels. Both ATP and ADO bind to and activate G-protein coupled receptors that in turn initiate signaling pathways that regulate ion channels. The ultimate goal of this project is to uses the mathematical models to suggest novel treatments for lung diseases, such as CF.


Timothy Elston, PhD | Webpage | EmailPublications


Stochastic Models of Cell Migration

Cell migration requires precise spatiotemporal regulation of the actin cytoskeleton. Key regulators of cell movement are the Rho family of GTPases. To investigate the role of Rho GTPases in cell migration, we developing stochastic models of cell movement to analyze time series data for the position of migrating cells. Our approach allows parameters that quantitatively characterize cell movement to be efficiently estimated from experimental data.  Our preliminary results indicate that randomly migrating cells stochastically transition between distinct states of migration characterized by differences in cell speed and persistence. (Investigators: Richard Allen, Chris Welch, Klaus Hahn and Tim Elston).

Stochastic Graph

Timothy Elston, PhD | Webpage | EmailPublications


Metabolic Networks

Mathematical models are powerful tools for improving our understanding of biological function, the creation of new hypotheses and the generation of testable predictions. Complimentary to the high-level network representation of biological systems are detailed “mechanistic” models that attempt to provide greater detail as to the role of particular molecular players. In collaboration with Tim Elston and others, we have been developing large-scale mechanistic models of metabolism that span multiple tissues. Models have dealt with energy harvesting/storage pathways as well as redox and detoxification pathways (e.g., glutathione metabolism). We are linking these models to cancer growth processes as well as pursuing collaborations with geneticists (e.g., Daniel Pomp and Fernando Pardo Manuel de Villena), where the goal is to understand the effect genetic variation has on metabolic function.

Gomez Computational modeling


Shawn Gomez, PhD | Webpage | Email | Publications



Computational Modeling of Cerebral Cavernous Malformations

Defects in the proper formation of blood vessels and capillaries lead to many diseases. In particular, cerebral cavernous malformations (CCMs) are clusters of leaky, dilated capillaries causing seizures, stroke, and neurological deficits. CCMs occur in 0.5-1.5% of the population, and are only treatable through complex and often risky surgical intervention. A deficiency in any of three CCM proteins (CCM1, CCM2, or CCM3) is sufficient to cause the disease. The Johnson lab has demonstrated using genetic approaches that CCM1, 2 and 3 are required for initiating proper tube formation. The CCM proteins are known to regulate the mechanical properties of the cell by controlling the activity of RhoA, a small GTPase that activates actomyosin based contractility and regulates the actin cytoskeleton. We use mathematical modeling to gain a mechanistic understanding of how the CCM proteins regulate the intracellular signaling system that in turn controls the mechanical properties of the cell. Importantly, the computational tools developed for these investigations will not only help to predict therapeutic targets for CCM treatment but also should significantly impact other fields where multicellular formation is a crucial aspect of the physiological process.

Tsygankov Computational Modeling

Timothy Elston|Webpage|Email      Gary Johnson|Webpage | Email





Fu, y., S. Lim, D. Urano, N. Phan, T.C. Elston and A. Jones. 2013. Reciprocal encoding of signal intensity and duration in the glucose sensing circuit in Arabidopsis thalianaCell (Theory), (accepted for publication).

Hao, N., N. Yildirim, S. Parnell, B. Errede, H. G. Dohlman and T. C. Elston. 2012. A computational-biology analysis of feedback regulation of pathway signaling specificity in yeastMolecular Biology of the Cell 23: 3899–3910.

Houser, J., E. Ford, B. Errede, and T.C. Elston2012. Positive roles for negative regulators in the mating response of yeastMolecular Systems Biology 8:586.

Howell, S., M. Jin, C. Wu, T. Zyla, T. C. Elston, and D. Lew. 2012. Negative feedback enhances robustness in the yeast polarity circuitCell 149: 322-333

Dyer, J., N. Savage, M. Jin, T. Zyla, T.C. Elston and D. Lew. 2013. Tracking shallow gradients by actin-driven wandering of the polarization siteCurrent Biology 23:32-41.

Jin, M., B. Errede, M. Behar, W. Mather, S. Nayak, J. Hasty, H.G. Dohlman and T.C. Elston. 2011. Yeast dynamically modify their environment to achieve better mating efficiency.  Science Signaling 4(186):ra54

Garcia, G., R. Boucher and T.C. Elston. 2013. Biophysical model of ion transport across human respiratory epithelia allows quantification of ion permeabilitiesBiophys. J. 104:716-26.

Xu K, Morgan KT, Todd A, Elston TC and Gomez SM. A whole-body model for glycogen regulation reveals a critical role for substrate cycling in maintaining blood glucose homeostasisPLoS Computational Biology. 2011. Dec;7(12):e1002272.

Yang W, Johnson GL, Gomez SM. Data-driven modeling of cellular stimulation, signaling and output response in RAW 263.7 cellsJ Mol Signal. 2008 May 22;3:11.