A statistical model for in vivo neuronal dynamics

Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. Finally, we show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.Comment: 31 pages, 10 figure

Statistical Methods for High Dimensional Networked Data Analysis.

Networked data are frequently encountered in many scientific disciplines. One major challenges in the analysis of such data are its high dimensionality and complex dependence. My dissertation consists of three projects. The first project focuses on the development of sparse multivariate factor analysis regression model to construct the underlying sparse association map between gene expressions and biomarkers. This is motivated by the fact that some associations may be obscured by unknown confounding factors that are not collected in the data. I have shown that accounting for such unobserved confounding factors can increase both sensitivity and specificity for detecting important gene-biomarker associations and thus lead to more interpretable association maps. The second project concerns the reconstruction of the underlying gene regulatory network using directed acyclic graphical models. My project aims to reduce false discoveries by identifying and removing edges resulted from shared confounding factors. I propose sparse structural factor equation models, in which structural equation models are used to capture directed graphs while factor analysis models are used to account for potential latent factors. I have shown that the proposed method enables me to obtain a simpler and more interpretable topology of a gene regulatory network. The third project is devoted to the development of a new regression analysis methodology to analyze electroencephalogram (EEG) neuroimaging data that are correlated among electrodes within an EEG-net. To address analytic challenges pertaining to the integration of network topology into the analysis, I propose hybrid quadratic inference functions that utilize both prior and data-driven correlations among network nodes into statistical estimation and inference. The proposed method is conceptually simple and computationally fast and more importantly has appealing large-sample properties. In a real EEG data analysis I applied the proposed method to detect significant association of iron deficiency on event-related potential measured in two subregions, which was not found using the classical spatial ANOVA random-effects models.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111595/1/zhouyan_1.pd

Integrering av multivariate data i systembiologi

Owing to the rapid rate of development in the field of systems biology researchers have faced many new challenges with regard to handling the large amount of generated data sets originating from different –omics techniques, integrating and analyzing them and finally interpreting the results in a meaningful way. Different statistical methods have been implemented in the field of systems biology. The use of chemometrics approaches for the integration and analysis of systems biology data has recently increased. Different chemometrics methods are potentially available for integrating –omics data and detecting variable and sample patterns. An important challenge is to decide which method to use for the analysis of –omics data sets and how to pre-process the data sets for this purpose. Special attention needs to be given to the validity of the detected patterns. In this study we have been working on developing multi-block methods for integrating different types of systems biology data and investigating the co-variation patterns among the measured variables. A special focus was given to the validation of the results of the multi-block methods CPCA and MBPLSR. Different types of graphical tools were introduced for the purpose of validation. We have also developed pre-processing techniques that could explicitly be used for lipidomics data sets. A framework was built for pre-processing, integrating, analyzing and interpreting the lipidomics data sets. The framework was then used for the analysis of a lipidomics data set from a human intervention study. Working on the development of the validation tools required an understanding of the concept of DFs consumption during the multi-block modeling. Therefore, we ran simulation studies where we investigated the number of DFs that were consumed during the modeling processes of PCA and CPCA. Another important issue for applying multi-block methods is the choice of the deflation method. Hence, we studied different deflation strategies available for Multi-block PCA and investigated their interpretational aspects.På grunn av rask utvikling innen systembiologi har forskere møtt mange nye utfordringer med hensyn til håndtering av store datamengder, som genereres med forskjellige -omics teknikker. Det er en stor utfordring både å integrere, analysere og til slutt tolke resultatene på en meningsfull måte. Ulike statistiske metoder har blitt implementert for analyse av systembiologi data. Bruk av kjemometri for integrering og analyse av biologiske data har økt mye den siste tiden. I utgangspunktet finnes det flere metoder fra kjemometri som kan brukes for å integrere data fra forskjellige –omics teknikker og for å oppdage grupperinger av objekter og variabler. En stor utfordring er å bestemme hvilken metode som skal brukes til analyse av -omics datasett og hvordan pre-prosessere datasettene. Det er også viktig å validere de grupperingene som har blitt oppdaget. I denne studien har vi jobbet med å utvikle multiblokk metoder for å integrere ulike typer data fra systembiologi og å undersøke samvariasjon blant de målte variablene. Det har spesielt vært fokus på validering av resultatene av multiblokkmetoder som CPCA og MBPLSR. Ulike typer verktøy ble innført for å sikre valideringen. Vi har utviklet pre-prosessering teknikker som kan brukes spesielt til lipidomics datasett. Vi har bygget et rammeverk for pre-prosessering, integrering, analysering og tolkning av lipidomics datasett. Metoden er blitt brukt til å analysere et lipidomics datasett fra et human intervensjonsstudie. Utvikling av validerings metoder krever en forståelse av bruk av antall frihetsgrader under modelleringen. Det har derfor blitt gjennomført simuleringsstudier hvor vi undersøkte antallet frihetsgrader som ble brukt under modellering med PCA og CPCA. Et annet viktig tema når man bruker multiblokk metoder er valget av deflasjonsmetoden. Det er blitt studert ulike deflasjonsstrategier som er tilgjengelige for multiblokk PCA og undersøkt deres tolkningsaspekter.NordForsk ; Foundation for Research Levy on Agricultural Products in Norwa

Data-Based Modeling Methods for Fault Detection and Isolation in Heat Exchangers

A multivariate analysis method is developed for processing measurements, and for detecting and isolating faults and monitoring performance degradation in heat exchanger control loops. A heat exchanger in a typical temperature-to-flow cascade loop s considered. A mechanistic thermal-fluid model for the components in the system is developed and compared to an installed laboratory heat exchanger control loop. A supplemental model for condenser hear transfer is included. The mechanistic model generate data to develop a data driven model using the Group Method of Data Handling (GMDH) approach. The GMDH model matches the mechanistic model well. A Fault Detection and Isolation (FDI) rule-base is formulated from results of simulations performed using these models. The rule base allows the identification of faults in a heat exchanger control loop given suitable process measurements. The mechanistic model matches the physical system performance well and is used to create a Fault Detection and Isolation (FDI) algorithm for the system

Doctor of Philosophy

dissertationThe explosion in computing power and its application to complex multiphysics problems has led to the emergence of computer simulation as a new way of extending the inductive methods of science. Many fields, particularly combustion, have been greatly changed by the ability of simulation to explore in great detail the implications of theories. But problems have also arisen; a philosophical foundation for establishing belief in simulation predictions, particularly important for complex multiphysics systems where experimental data are sparse, is sorely lacking. Toward the end of establishing such a foundation, a comprehensive philosophical approach to model validation, called instrumentalism, is proposed. A framework for verification and validation/uncertainty quantification (V&V/UQ) of codes is presented in detail, and is applied to a novel entrained flow coal gasification model implemented in the massively parallel simulation tool Arches. The V&V/UQ process begins at the mathematical model. The novel coal gasification model, which utilizes the direct quadrature method of moments (DQMOM) for the solid phase and large eddy simulation (LES) for the gas phase and accounts for coupling between the gas and solid phases, is described in detail. A verification methodology is presented in the larger context of validation and uncertainty quantification, and applied to the Arches coal gasification model. A six-step validation framework is adopted from the literature and applied to the validation of the Arches gasification model. One important aspect of this framework is model reduction, creating surrogate models for complex and expensive multiphysics simulators. A procedure for constructing surrogate response surface models is applied to the Arches gasification model, with several statistical analysis techniques used to determine the goodness of fit of the coal gasification response surface. This response surface is then analyzed using two methods: the Data Collaboration methodology, an approach from the literature; and a Monte Carlo analysis of the response surface. These analyses elucidate regions of parameter space where the simulation tool makes valid predictions. The Monte Carlo analysis also yields probabilities of simulation validity, given input parameter values. These probabilities are used to construct a prediction interval, which can then be used to compute the probability of a consistent simulation prediction