Personnel

Michael Mayo, Ph.D.

Research Physicist

Research background/interestsCan the tools of complex network theory be used to understand how biology processes information at different scales in order to resist, attenuate, or compensate for disruptions? Leveraging microarray data, we identified regulatory interactions in the hypothalamus-pituitary-gonad (HPG) axis of the teleost fathead minnow (Pimephales promelas) involving a novel androgen receptor mechanism though which vertebrates first sense, adapt, and then respond to aqueous exposures to the aromatase inhibitor fadrozole. I used chemical kinetics modeling (ODEs) to model the data, numerical optimization to infer rate constants from data, and sensitivity analyses to analyze the model. I validated these predictions using exposure data from independent experiments.I have recently been investigating how the cellular environment can resolve stimuli encoded in the fluctuations of protein concentrations regulated at the transcriptional level. Specifically, my colleagues and I have used tools from nonequilibrium statistical mechanics to model sequential transcriptional interactions along regulatory daisy-chains to show that more information, not less, can be conveyed using longer chains of more transcriptional interactions, in seeming contradiction to the data processing inequality (DPI) of information theory. Roughly, the DPI states that more information than contained in the initial state cannot be gained from a transformative process rife with noise.I am broadly interested in how time-dependent nonequilibrium phenomena at smaller scales affects linked processes at higher scales. Although ongoing work leverages information flow, transfer entropy, and causation entropy concepts to model the behavior of coordinating animals, such as flocking birds or schooling fish, I am also investigating the extent to which populations can be effected by adverse individual response, such as through reproductive impacts or by the spread of gene-drives over generational time scales.

• Mathematical & Systems Biology
  • Metabolic Network Modeling; Information Theory; Statistical Mechanics
• Programming
  • MATLAB; c++; python
• Statistics
  • Significance Testing (t-test, KS-test, etc.); Distribution Fitting (parametric & non-parametric methods); Analysis (ANOVA, PCA, etc.)
• Mathematical Modeling
  • Analytical Complex Network modeling; Stochastic Modeling (e.g., Langevin equations); Chemical Kinetics Modeling (ODEs); Diffusion-Reaction Modeling (Fokker-Planck); Master Equations
• Computational Modeling
  • Curve Fitting & Numerical Optimization; Stochastic & Agent-Based Modeling (SSA); High-Performance & Cluster Computing (e.g., HPC)

Educational backgroundPhD Physics, University of Missouri, 2009PhD Thesis: Hierarchical Model of Gas Exchange within the Acinar Airways of the Human LungSelected publications

• Conolly RB, Ankley GT, Cheng W-Y, Mayo ML, Miller DH, Perkins EJ, Villeneuve DL, and Watanabe KH (2017). Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology, Environmental Science & Technology, 51(8) :4661-4672. (https://dx.doi.org/1021/acs.est.6b06230)
• Rowland MA, Perkins EJ, and Mayo ML. (2017). Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches. BMC Systems Biology, 11:35. (https://dx.doi.org/10.1186/s12918-017-0407-3)
• Watanabe KH, Mayo ML, Jensen KM, Villeneuve DL, Ankley GT, and Perkins EJ. (2016). Predicting fecundity of fathead minnows (Pimephales promelas) exposed to endocrine-disrupting chemicals using a MATLAB(R)-based model of oocyte growth dynamics. PLoS One, 11:1
• Pilkiewicz KR, and Mayo ML. (2016). Fluctuation Sensitivity in a Transcriptional Signaling Cascade. Physical Review E, 94(3):032412 (https://doi.org/10.1103/PhysRevE.94.032412)
• Mayo ML, Collier ZA, Winton C, and Chappell M. (2015). Data-Driven Method to Estimate Nonlinear Chemical Equivalence. PLoS One, 10(7):e0134652. DOI: 10.1371/journal.pone.0134652 (https://dx.doi.org/10.1371/journal.pone.0130494).
• Mayo ML, Georghiu S, and Pfeifer P. (2012). Diffusional Screening in Treelike Spaces: an Exactly Solvable Diffusion-Reaction Model. Physical Review E, 85 :011115. (https://doi.org/10.1103/PhysRevE.85.011115)
• Hou C, and Mayo ML. (2011). Pulmonary Diffusional Screening and the Scaling Laws of Mammalian Metabolic Rates. Physical Review E, 84:061915 (https://doi.org/10.1103/PhysRevE.84.061915)