Simulation of Drosophila circadian oscillations, mutations, and light responses by a model with VRI, PDP-1, and CLK.

A model of Drosophila circadian rhythm generation was developed to represent feedback loops based on transcriptional regulation of per, Clk (dclock), Pdp-1, and vri (vrille). The model postulates that histone acetylation kinetics make transcriptional activation a nonlinear function of [CLK]. Such a nonlinearity is essential to simulate robust circadian oscillations of transcription in our model and in previous models. Simulations suggest that two positive feedback loops involving Clk are not essential for oscillations, because oscillations of [PER] were preserved when Clk, vri, or Pdp-1 expression was fixed. However, eliminating positive feedback by fixing vri expression altered the oscillation period. Eliminating the negative feedback loop in which PER represses per expression abolished oscillations. Simulations of per or Clk null mutations, of per overexpression, and of vri, Clk, or Pdp-1 heterozygous null mutations altered model behavior in ways similar to experimental data. The model simulated a photic phase-response curve resembling experimental curves, and oscillations entrained to simulated light-dark cycles. Temperature compensation of oscillation period could be simulated if temperature elevation slowed PER nuclear entry or PER phosphorylation. The model makes experimental predictions, some of which could be tested in transgenic Drosophila.

Respiratory rhythm generation: A modeling study of the respiratory central pattern generator and the phrenic motor neuron

The respiratory central pattern generator is a collection of medullary neurons that generates the rhythm of respiration. The respiratory central pattern generator feeds phrenic motor neurons, which, in turn, drive the main muscle of respiration, the diaphragm. The purpose of this thesis is to understand the neural control of respiration through mathematical models of the respiratory central pattern generator and phrenic motor neurons. ^ We first designed and validated a Hodgkin-Huxley type model that mimics the behavior of phrenic motor neurons under a wide range of electrical and pharmacological perturbations. This model was constrained physiological data from the literature. Next, we designed and validated a model of the respiratory central pattern generator by connecting four Hodgkin-Huxley type models of medullary respiratory neurons in a mutually inhibitory network. This network was in turn driven by a simple model of an endogenously bursting neuron, which acted as the pacemaker for the respiratory central pattern generator. Finally, the respiratory central pattern generator and phrenic motor neuron models were connected and their interactions studied. ^ Our study of the models has provided a number of insights into the behavior of the respiratory central pattern generator and phrenic motor neurons. These include the suggestion of a role for the T-type and N-type calcium channels during single spikes and repetitive firing in phrenic motor neurons, as well as a better understanding of network properties underlying respiratory rhythm generation. We also utilized an existing model of lung mechanics to study the interactions between the respiratory central pattern generator and ventilation. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

Neocortical hyperexcitability defect in a mutant mouse model of spike-wave epilepsy, {\it stargazer}

Single-locus mutations in mice can express epileptic phenotypes and provide critical insights into the naturally occurring defects that alter excitability and mediate synchronization in the central nervous system (CNS). One such recessive mutation (on chromosome (Chr) 15), stargazer(stg/stg) expresses frequent bilateral 6-7 cycles per second (c/sec) spike-wave seizures associated with behavioral arrest, and provides a valuable opportunity to examine the inherited lesion associated with spike-wave synchronization.^ The existence of distinct and heterogeneous defects mediating spike-wave discharge (SWD) generation has been demonstrated by the presence of multiple genetic loci expressing generalized spike-wave activity and the differential effects of pharmacological agents on SWDs in different spike-wave epilepsy models. Attempts at understanding the different basic mechanisms underlying spike-wave synchronization have focused on $\gamma$-aminobutyric acid (GABA) receptor-, low threshold T-type Ca$\sp{2+}$ channel-, and N-methyl-D-aspartate receptor (NMDA-R)-mediated transmission. It is believed that defects in these modes of transmission can mediate the conversion of normal oscillations in a trisynaptic circuit, which includes the neocortex, reticular nucleus and thalamus, into spike-wave activity. However, the underlying lesions involved in spike-wave synchronization have not been clearly identified.^ The purpose of this research project was to locate and characterize a distinct neuronal hyperexcitability defect favoring spike-wave synchronization in the stargazer brain. One experimental approach for anatomically locating areas of synchronization and hyperexcitability involved an attempt to map patterns of hypersynchronous activity with antibodies to activity-induced proteins.^ A second approach to characterizing the neuronal defect involved examining the neuronal responses in the mutant following application of pharmacological agents with well known sites of action.^ In order to test the hypothesis that an NMDA receptor mediated hyperexcitability defect exists in stargazer neocortex, extracellular field recordings were used to examine the effects of CPP and MK-801 on coronal neocortical brain slices of stargazer and wild type perfused with 0 Mg$\sp{2+}$ artificial cerebral spinal fluid (aCSF).^ To study how NMDA receptor antagonists might promote increased excitability in stargazer neocortex, two basic hypotheses were tested: (1) NMDA receptor antagonists directly activate deep layer principal pyramidal cells in the neocortex of stargazer, presumably by opening NMDA receptor channels altered by the stg mutation; and (2) NMDA receptor antagonists disinhibit the neocortical network by blocking recurrent excitatory synaptic inputs onto inhibitory interneurons in the deep layers of stargazer neocortex.^ In order to test whether CPP might disinhibit the 0 Mg$\sp{2+}$ bursting network in the mutant by acting on inhibitory interneurons, the inhibitory inputs were pharmacologically removed by application of GABA receptor antagonists to the cortical network, and the effects of CPP under 0 Mg$\sp{2+}$aCSF perfusion in layer V of stg/stg were then compared with those found in +/+ neocortex using in vitro extracellular field recordings. (Abstract shortened by UMI.) ^