Integrated time sampling design and measurement set selection for biochemical systems

The optimal experimental design (OED) for observation strategy is investigated in this paper to collect the most informative experimental data for parameter estimation. The aim is to determine the best sampling time points and also select the most valuable measurement state variables through OED. The two design objectives are integrated together as a single-objective optimisation problem in which the variables and their sampling times are weighted in an expanded time sampling framework. Three optimisation methods, i.e., the Powell’s method, the sequen- tial selection method, and the sequential quadratic programming method, are employed to solve the optimisation problem. Their computation efficiencies are compared using a biodiesel case study system simulation. Simulation results demonstrate the effectiveness of the proposed method in reducing parameter estimation uncertainties as well as reducing parameter correlations. It can also be observed that the integrated OED doesn’t cost extra computation efforts when variable selection is considered together with the time sampling task

Comprehensive experimental design for chemical engineering processes : a two-layer iterative design approach

A systematic framework for optimal experimental design (OED) of multiple experimental factors is proposed to support data collection in chemical engineering systems with the purpose to obtain the most informative data for modeling. The structural identifiability is firstly investigated through a combined procedure with the generating series method and the identifiability tableau. Next the parameter estimability is analyzed via the orthogonalized sensitivity analysis in order to identify crucial and identifiable model parameters. Traditionally OED treats separate problems such as the choice of input conditions, the selection of variables to measure, and the design of sampling time profile. A new OED strategy is proposed that optimizes these interdependent factors in one framework. An iterative two-layer design structure is developed. In the lower layer for observation design, the sampling profile and the measurement set selection are combined and formulated as a single integrated observation design problem, which is relaxed to a convex optimization problem that can be solved with a local method. Thus the measurement set selection and the sampling profile can be determined simultaneously. In the upper layer for input design, the optimization of input intensities is obtained through stochastic global searching. In this way, the multi-factor optimization problem is solved through the integration of a stochastic method, for the upper layer, and a deterministic method, for the lower layer. Case studies are conducted on two biochemical systems with different complexities, one is an enzyme kinetically controlled synthesis system and the other one is a lab-scale enzymatic biodiesel production system. Numerical results demonstrate the effectiveness of this double-layer OED optimization strategy in reducing parameter estimation uncertainties compared with conventional approaches

A two-loop optimization strategy for multi-objective optimal experimental design

A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both observation design and input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of input intensity is calculated in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain maximum production rate by taking into account of the experimental cost

Optimal experimental design and its applications to biochemical engineering systems

This work is motivated by challenges in data-based modelling of complex systems due to limited information of sparse and noisy experimental data. Optimal experimental design (OED) techniques, which aim at devising necessary experiments to generate informative measurement data to facilitate model identification, have been investigated comprehensively.The limitations of existing experimental design approaches have been extensively discussed, based on which advanced experimental design methods and efficient numerical strategies have been developed for improved solutions.;Two case study biochemical systems have been used through the research investigation, one is an enzyme reaction system, the other one is a lab-scale enzymatic biodiesel production system. The main contributions of this PhD work can be summarised as follows:;Single objective experimental designs by considering one type of design factors, i.e. input intensity, measurement set selection, sampling profile design, respectively, has been formulated and numerical strategies to solve these optimisation problems have been described in detail. Implementations of these design methods to biochemical systems have demonstrated its efficiency in reducing parameter estimation errors.;A new OED strategy has been proposed to cope with OED problems including multiple design factors in one optimisation framework. An iterative two-layer design structure is developed. In the lower layer for observation design, the sampling profile and the measurement set selection are combined and formulated as a single integrated observation design problem, which is relaxed to a convex optimization problem that can be solved with a local method.;Thus the measurement set selection and the sampling profile can be determined simultaneously. In the upper layer for input design, the optimisation of input intensities is obtained through stochastic global searching. In this way, the multi-factor optimisation problem is solved through the integration of a stochastic method, for the upper layer, and a deterministic method, for the lower layer.;A new enzyme reaction model has been established which represents a typical class of enzymatic kinetically controlled synthesis process. This model contains important kinetic reaction features, moderate complexity, and complete model information. It can be used as a benchmark problem for development and comparison of OED algorithms. Systematic analysis has been performed in order to examine the system behaviours, and the dependence on model parameters, initial operation conditions.;Structural identifiability and practical identifiability of this system have been analysed and identifiable parameters determined. The design of experiment for the enzyme reactionsystem by considering different types of design variables have been investigated. The parameter estimation precision can be improved significantly by using the proposed OED techniques, compared to the non-designed condition.;The OED techniques are numerically investigated based on a lab-scale biodiesel production process with real experimental data through research collaboration with DTU in Denmark. The OED applications on this real system model allow to examine the effectiveness and efficiency of those new proposed OED methods. The measurement set selection and the sampling design of this system are developed which provide detailed instructions on how to improve experiments through OED.;Also, the sensitivity analysis and parameter identifiability analysis are conducted; and their impacts to experimental design are clearly identified.This work is motivated by challenges in data-based modelling of complex systems due to limited information of sparse and noisy experimental data. Optimal experimental design (OED) techniques, which aim at devising necessary experiments to generate informative measurement data to facilitate model identification, have been investigated comprehensively.The limitations of existing experimental design approaches have been extensively discussed, based on which advanced experimental design methods and efficient numerical strategies have been developed for improved solutions.;Two case study biochemical systems have been used through the research investigation, one is an enzyme reaction system, the other one is a lab-scale enzymatic biodiesel production system. The main contributions of this PhD work can be summarised as follows:;Single objective experimental designs by considering one type of design factors, i.e. input intensity, measurement set selection, sampling profile design, respectively, has been formulated and numerical strategies to solve these optimisation problems have been described in detail. Implementations of these design methods to biochemical systems have demonstrated its efficiency in reducing parameter estimation errors.;A new OED strategy has been proposed to cope with OED problems including multiple design factors in one optimisation framework. An iterative two-layer design structure is developed. In the lower layer for observation design, the sampling profile and the measurement set selection are combined and formulated as a single integrated observation design problem, which is relaxed to a convex optimization problem that can be solved with a local method.;Thus the measurement set selection and the sampling profile can be determined simultaneously. In the upper layer for input design, the optimisation of input intensities is obtained through stochastic global searching. In this way, the multi-factor optimisation problem is solved through the integration of a stochastic method, for the upper layer, and a deterministic method, for the lower layer.;A new enzyme reaction model has been established which represents a typical class of enzymatic kinetically controlled synthesis process. This model contains important kinetic reaction features, moderate complexity, and complete model information. It can be used as a benchmark problem for development and comparison of OED algorithms. Systematic analysis has been performed in order to examine the system behaviours, and the dependence on model parameters, initial operation conditions.;Structural identifiability and practical identifiability of this system have been analysed and identifiable parameters determined. The design of experiment for the enzyme reactionsystem by considering different types of design variables have been investigated. The parameter estimation precision can be improved significantly by using the proposed OED techniques, compared to the non-designed condition.;The OED techniques are numerically investigated based on a lab-scale biodiesel production process with real experimental data through research collaboration with DTU in Denmark. The OED applications on this real system model allow to examine the effectiveness and efficiency of those new proposed OED methods. The measurement set selection and the sampling design of this system are developed which provide detailed instructions on how to improve experiments through OED.;Also, the sensitivity analysis and parameter identifiability analysis are conducted; and their impacts to experimental design are clearly identified

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterising stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealisations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with MATLAB implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community

Sampling of planetary surface solids for unmanned in situ geological and biological analysis - Strategy, principles, and instrument requirements

Sampling requirements for in situ geological and biological analysis of planetary surface rocks by unmanned spacecraf

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed