Core modular blood and brain biomarkers in social defeat mouse model for post traumatic stress disorder

Abstract Background Post-traumatic stress disorder (PTSD) is a severe anxiety disorder that affects a substantial portion of combat veterans and poses serious consequences to long-term health. Consequently, the identification of diagnostic and prognostic blood biomarkers for PTSD is of great interest. Previously, we assessed genome-wide gene expression of seven brain regions and whole blood in a social defeat mouse model subjected to various stress conditions. Results To extract biological insights from these data, we have applied a new computational framework for identifying gene modules that are activated in common across blood and various brain regions. Our results, in the form of modular gene networks that highlight spatial and temporal biological functions, provide a systems-level molecular description of response to social stress. Specifically, the common modules discovered between the brain and blood emphasizes molecular transporters in the blood-brain barrier, and the associated genes have significant overlaps with known blood signatures for PTSD, major depression, and bipolar disease. Similarly, the common modules specific to the brain highlight the components of the social defeat stress response (e.g., fear conditioning pathways) in each brain sub-region. Conclusions Many of the brain-specific genes discovered are consistent with previous independent studies of PTSD or other mental illnesses. The results from this study further our understanding of the mechanism of stress response and contribute to a growing list of diagnostic biomarkers for PTSD

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs). MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence.</p> <p>Results</p> <p>We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM²): an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM) algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM² substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods.</p> <p>Conclusions</p> <p>This work provides a novel, accelerated version of a likelihood-based parameter estimation method that can be readily applied to stochastic biochemical systems. In addition, our results suggest opportunities for added efficiency improvements that will further enhance our ability to mechanistically simulate biological processes.</p

Screenshots of the StochSS model editor.

<p>Choose the model to edit from the selection list and view and edit your domain with the mesh editor (left), and define the biochemical species, initial conditions, parameters, and reactions (right).</p

Illustration of simulation times and data output sizes for the different modeling levels supported by StochSS.

<p>There is a steep increase in computational cost as the model is refined.</p

Process flow and component diagram for a modeling and simulation workflow with StochSS.

<p>The biochemical model and domain is defined as part of the problem specification. The ODE, spatial stochastic, and well-mixed simulation tools generate realizations of these models. The parameter estimation and parameter sensitivity tools allow for analysis of models. The output and visualization tools present the data.</p

2002 Research Honors Program Abstracts

Faculty in the College of Agriculture and Life Sciences at Cornell University mentor and guide undergraduate students who have chosen to pursue a research project and graduate with honors. These abstracts reflect the depth of their scholarship and intellectual ability. The research projects encompass work in animal science, biological science, entomology, natural resources, physical science, plant science, and social science