Rajis Gunawan - Academia.edu (original) (raw)
Papers by Rajis Gunawan
Biological robustness has been recognized as a fundamental organizational principle in cellular b... more Biological robustness has been recognized as a fundamental organizational principle in cellular behavior. The understanding of robustness trade-off in biology has significant implications in the drug discovery research. Some diseases such as cancer can hijack cellular robustness complicating their treatment. Most of the published robustness analyses in systems biology relate this property to the output parametric sensitivity. A new analysis is proposed in which the sensitivities are evaluated for perturbations on the system states rather than on the model parameters. The result of this analysis can be directly validated in experiments, and further used in the drug discovery research to understand drug effects, to optimize drug dosing and timing, and to identify potential molecules as drug targets. The application to a model of cell death regulation shows the biological insights offered by this analysis.
1010 Systems Analysis for Systems Biology Scott Hildebrandt, Neda Bagheri, Rudiyanto Gunawan, Hen... more 1010 Systems Analysis for Systems Biology Scott Hildebrandt, Neda Bagheri, Rudiyanto Gunawan, Henry Mirsky, Jason Shoemaker, Stephanie Taylor, Linda Petzold and Francis J. Doyle III University of California, Santa Barbara OUTLINE Summary Definitions Introduction ...
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but s... more Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional "best-fit" models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics.
Synthesizing optimal controllers for large scale uncertain systems is a challenging computational... more Synthesizing optimal controllers for large scale uncertain systems is a challenging computational problem. This has motivated the recent interest in developing polynomial-time algorithms for computing reduced dimension models for uncertain systems. Here we present algorithms that compute lower dimensional realizations of an uncertain system, and compare their theoretical and computational characteristics. Three polynomial-time dimensionality reduction algorithms are applied to the
This study focuses on the optimal control of rapid thermal annealing (RTA) used in the formation ... more This study focuses on the optimal control of rapid thermal annealing (RTA) used in the formation of ultrashallow junctions needed in next-generation microelectronic devices. Comparison of different parameterizations of the optimal trajectories shows that linear profiles give the best combination of minimizing junction depth and sheet resistance. Worst-case robustness analysis of the optimal control trajectory motivates improvements in feedback control implementations for these processes. This is the first time that the effects of model uncertainties and control implementation inaccuracies are rigorously quantified for RTA.
Background: The importance of stochasticity in cellular processes having low number of molecules ... more Background: The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data. Results: Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality. Conclusions: The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.
Circadian rhythm synchronizes the daily activities of living organisms with the 24-hour earth rot... more Circadian rhythm synchronizes the daily activities of living organisms with the 24-hour earth rotation. The core of this clock is a transcriptional regulation of genes which produces an autonomous oscillation of 24-hour period. Despite the prevalence of oscillatory systems, sensitivity analysis of the key characteristics, such as period and phase, does not directly fit into the traditional framework for differential equations, which motivates the development of analyses aimed for these systems. This work focuses on the phase sensitivity analysis of circadian rhythm. Many of the concepts however can apply to oscillatory systems in general. Two examples of Drosophila circadian models illustrate the utilities of the developed analysis. I. INTRODUCTION Circadian rhythm governs the day and night living cycle of many different species, from unimolecular Neurospora to highly multicellular mammals, as an adaptation to the 24-hour earth rotation. The structure of the circadian gene network is remarkably preserved across different species suggesting an evolutionary convergence [1]. The rhythm manifests itself in overt behavior such as the rest-activity cycle (sleep-wake), and controls many hormonal, physiological, and psychomotor performance functions [2]. The core of this rhythm consists of an autonomous genetic oscillator with multiple feedback loops forming a limit cycle which can be entrained by environmental cues, e.g., sunlight. Disruptions to the circadian mechanism can lead to sleep disorders and possibly seasonal affective disorders [3]. Biological systems, including circadian rhythm, are known to exhibit robustness to external and internal disturbances, such as temperature fluctuations (external) [4] and inherent stochastic noise in gene regulation (internal) [5]. Here, robustness constitutes the ability of biological organisms to maintain certain phenotype under uncertainties in the environment. In circadian rhythm, the robustness of the period as a phenotype has been investigated using sensitivity analysis to elucidate the dependence of the states and period on the system parameters [6], [7]. Another key phenotype in a circadian rhythm is the (relative) phase of the circadian clock which describes the relative position in the limit cycle. Many circadian disorders arise because of the inability of certain organism to entrain its internal circadian clock to
New applications in materials, medicine, and computers are being discovered where the control of ... more New applications in materials, medicine, and computers are being discovered where the control of events at the molecular and nanoscopic scales is critical to product quality, although the primary manipulation of these events during processing occurs at macroscopic length scales. This motivates the creation of tools for the design and control of multiscale systems that have length scales ranging from the atomistic to the macroscopic. This paper describes a systematic approach that consists of stochastic parameter sensitivity analysis, Bayesian parameter estimation applied to ab initio calculations and experimental data, model-based experimental design, hypothesis mechanism selection, and multistep optimization.
This paper proposes a strategy for reducing the estimation error due to the digitizing problem in... more This paper proposes a strategy for reducing the estimation error due to the digitizing problem in a speed sensorless induction motor control using the speed adaptive observer. An estimated current error compensation is introduced into the rotor magnetizing current's and synchronous speed's calculations in the flux model. A modification of the full-order observer is made in order to affect the compensation strategy into the observer. This modified observer uses the rotor flux calculated by the flux model instead of estimating the flux by itself. The proposed strategy can reduce effectively the estimation error. Simulation results confirm the effectiveness of the proposed strategy.
Journal of applied …, 2004
Recent experimental work has demonstrated the existence of band bending at the interface after io... more Recent experimental work has demonstrated the existence of band bending at the interface after ion implantation. The present work employs FLOOPS-based numerical simulations to investigate the effects this bending can have upon dopant profiles that evolve during transient ...
Physical Review B, 2003
A mechanism is described by which interface electronic properties can affect bulk semiconductor b... more A mechanism is described by which interface electronic properties can affect bulk semiconductor behavior. In particular, experimental measurements by photoreflectance of Si(100)-SiO 2 interfaces show how a controllable degree of band bending can be introduced near the interface by ...
Biological robustness has been recognized as a fundamental organizational principle in cellular b... more Biological robustness has been recognized as a fundamental organizational principle in cellular behavior. The understanding of robustness trade-off in biology has significant implications in the drug discovery research. Some diseases such as cancer can hijack cellular robustness complicating their treatment. Most of the published robustness analyses in systems biology relate this property to the output parametric sensitivity. A new analysis is proposed in which the sensitivities are evaluated for perturbations on the system states rather than on the model parameters. The result of this analysis can be directly validated in experiments, and further used in the drug discovery research to understand drug effects, to optimize drug dosing and timing, and to identify potential molecules as drug targets. The application to a model of cell death regulation shows the biological insights offered by this analysis.
1010 Systems Analysis for Systems Biology Scott Hildebrandt, Neda Bagheri, Rudiyanto Gunawan, Hen... more 1010 Systems Analysis for Systems Biology Scott Hildebrandt, Neda Bagheri, Rudiyanto Gunawan, Henry Mirsky, Jason Shoemaker, Stephanie Taylor, Linda Petzold and Francis J. Doyle III University of California, Santa Barbara OUTLINE Summary Definitions Introduction ...
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but s... more Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional "best-fit" models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics.
Synthesizing optimal controllers for large scale uncertain systems is a challenging computational... more Synthesizing optimal controllers for large scale uncertain systems is a challenging computational problem. This has motivated the recent interest in developing polynomial-time algorithms for computing reduced dimension models for uncertain systems. Here we present algorithms that compute lower dimensional realizations of an uncertain system, and compare their theoretical and computational characteristics. Three polynomial-time dimensionality reduction algorithms are applied to the
This study focuses on the optimal control of rapid thermal annealing (RTA) used in the formation ... more This study focuses on the optimal control of rapid thermal annealing (RTA) used in the formation of ultrashallow junctions needed in next-generation microelectronic devices. Comparison of different parameterizations of the optimal trajectories shows that linear profiles give the best combination of minimizing junction depth and sheet resistance. Worst-case robustness analysis of the optimal control trajectory motivates improvements in feedback control implementations for these processes. This is the first time that the effects of model uncertainties and control implementation inaccuracies are rigorously quantified for RTA.
Background: The importance of stochasticity in cellular processes having low number of molecules ... more Background: The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data. Results: Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality. Conclusions: The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.
Circadian rhythm synchronizes the daily activities of living organisms with the 24-hour earth rot... more Circadian rhythm synchronizes the daily activities of living organisms with the 24-hour earth rotation. The core of this clock is a transcriptional regulation of genes which produces an autonomous oscillation of 24-hour period. Despite the prevalence of oscillatory systems, sensitivity analysis of the key characteristics, such as period and phase, does not directly fit into the traditional framework for differential equations, which motivates the development of analyses aimed for these systems. This work focuses on the phase sensitivity analysis of circadian rhythm. Many of the concepts however can apply to oscillatory systems in general. Two examples of Drosophila circadian models illustrate the utilities of the developed analysis. I. INTRODUCTION Circadian rhythm governs the day and night living cycle of many different species, from unimolecular Neurospora to highly multicellular mammals, as an adaptation to the 24-hour earth rotation. The structure of the circadian gene network is remarkably preserved across different species suggesting an evolutionary convergence [1]. The rhythm manifests itself in overt behavior such as the rest-activity cycle (sleep-wake), and controls many hormonal, physiological, and psychomotor performance functions [2]. The core of this rhythm consists of an autonomous genetic oscillator with multiple feedback loops forming a limit cycle which can be entrained by environmental cues, e.g., sunlight. Disruptions to the circadian mechanism can lead to sleep disorders and possibly seasonal affective disorders [3]. Biological systems, including circadian rhythm, are known to exhibit robustness to external and internal disturbances, such as temperature fluctuations (external) [4] and inherent stochastic noise in gene regulation (internal) [5]. Here, robustness constitutes the ability of biological organisms to maintain certain phenotype under uncertainties in the environment. In circadian rhythm, the robustness of the period as a phenotype has been investigated using sensitivity analysis to elucidate the dependence of the states and period on the system parameters [6], [7]. Another key phenotype in a circadian rhythm is the (relative) phase of the circadian clock which describes the relative position in the limit cycle. Many circadian disorders arise because of the inability of certain organism to entrain its internal circadian clock to
New applications in materials, medicine, and computers are being discovered where the control of ... more New applications in materials, medicine, and computers are being discovered where the control of events at the molecular and nanoscopic scales is critical to product quality, although the primary manipulation of these events during processing occurs at macroscopic length scales. This motivates the creation of tools for the design and control of multiscale systems that have length scales ranging from the atomistic to the macroscopic. This paper describes a systematic approach that consists of stochastic parameter sensitivity analysis, Bayesian parameter estimation applied to ab initio calculations and experimental data, model-based experimental design, hypothesis mechanism selection, and multistep optimization.
This paper proposes a strategy for reducing the estimation error due to the digitizing problem in... more This paper proposes a strategy for reducing the estimation error due to the digitizing problem in a speed sensorless induction motor control using the speed adaptive observer. An estimated current error compensation is introduced into the rotor magnetizing current's and synchronous speed's calculations in the flux model. A modification of the full-order observer is made in order to affect the compensation strategy into the observer. This modified observer uses the rotor flux calculated by the flux model instead of estimating the flux by itself. The proposed strategy can reduce effectively the estimation error. Simulation results confirm the effectiveness of the proposed strategy.
Journal of applied …, 2004
Recent experimental work has demonstrated the existence of band bending at the interface after io... more Recent experimental work has demonstrated the existence of band bending at the interface after ion implantation. The present work employs FLOOPS-based numerical simulations to investigate the effects this bending can have upon dopant profiles that evolve during transient ...
Physical Review B, 2003
A mechanism is described by which interface electronic properties can affect bulk semiconductor b... more A mechanism is described by which interface electronic properties can affect bulk semiconductor behavior. In particular, experimental measurements by photoreflectance of Si(100)-SiO 2 interfaces show how a controllable degree of band bending can be introduced near the interface by ...