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Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling

Taslim, Cenny
http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112

Abstract Details

2008, Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering.
This dissertation proposes methods for steady state linear system identification for both forward cases in which prediction of outputs for new inputs are desired and also inverse prediction of which inputs fostered measured outputs are needed. Special attention is given to genetic network modeling applications. Inverse prediction matters here because then one can predict the effective genetic perturbation associated with a new target drug compound or therapy. The primary application addressed in this dissertation is motivated by our on-going contributions related to Down syndrome which affects approximately 1 out of every 800 children. First, single shot experimentation and analysis to develop network models is considered. The discussion focuses on linear models because of the relevance of equilibrium conditions and the typical scarcity of perturbation data. Yet, deviations from linear systems modeling assumptions are also considered. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. In addition, topics for further research are suggested including the need to develop more numerically stable analysis methods, improved diagnostic procedures, sequential design and analysis procedures. Next, multi-stage design and analysis procedures are proposed for experimentation in which both forward and inverse predictions are relevant. Methods are proposed to derive desirable experimental plans for the next batch of tests based on both space filling and D-optimality. The space filling designs are intended to support both linear and nonlinear modeling while D-optimality methods are relatively model-dependent. Rigorous results related to linear optimality criteria are presented in relation to multi-criteria formulations of the forward-inverse problem. Computational results are presented based on the SOS pathway and inspired by an on-going study of the genetic network associated with Down syndrome. In the studied cases, the biologists added a multiple choice constraint to the formulation for their simplicity.
Theodore Allen, PhD (Committee Chair)
Mario Lauria, PhD (Committee Co-Chair)
Clark Mount-Campbell, PhD (Committee Member)
Hakan Ferhatosmanoglu, PhD (Committee Member)
Bioinformatics; Biostatistics; Engineering; Operations Research; Statistics
Optimal Design of Experiments; D-Optimality; Inverse Regression; Transcriptional Networks; Statistical Simulation; System Identification; Steady-State; Forward-Inverse Modeling; Bayesian regression

Recommended Citations

Citations

  • Taslim, C. (2008). Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112

    APA Style (7th edition)

  • Taslim, Cenny. Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling. 2008. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112.

    MLA Style (8th edition)

  • Taslim, Cenny. "Multi-Stage Experimental Planning and Analysis for Forward-Inverse Regression Applied to Genetic Network Modeling." Doctoral dissertation, Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1213286112

    Chicago Manual of Style (17th edition)

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© 2008, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.