Keywords: Systems Biology, Bioinformatics, Metabolic Engineering
Abstract: Metabolomic analysis can indeed enhance the prime variable dataset for the monitoring of perfusion cultures by providing a higher resolution view of the metabolic state. Metabolic profiles can capture physiological state shifts over the course of the perfusion cultures and indicate a metabolic “signature” of the phase transitions, which is not observable from prime variable data. Notably, metabolomics provides orthogonal (to prime variables) evidence that all cultures follow this same metabolic state shift with cell age, independently of bioreactor scale. Additionally, this analysis can increase the information content of process development experiments by helping better understand the impact of changes in bioreactor operating conditions on cell physiology. In this context, metabolic profiling could be integrated into the monitoring of cell physiology in perfusion cultures.

Keywords: Modelling and Identification, Dynamics and Control, Environmental Processes (Wastewater, Bioremediation)
Abstract: This study describes on-line monitoring and parameter estimation during Microbial Fuel Cell operation with a pulse-width modulated connection of the external resistor (R-PWM mode) at low and high frequencies. Analysis of the output voltage profiles acquired during R-PWM tests showed the presence of slow and fast dynamic components, which can be described by an equivalent circuit model suitable for process monitoring and control applications. To demonstrate the proposed monitoring and parameter estimation procedure, the MFC was operated at several influent concentrations of acetate (carbon source) and an on-line parameter estimation procedure was used for estimating internal resistance and internal capacitance. Furthermore, these parameters were re-estimated at the end of each test yielding similar results. The proposed on-line procedure can be used for real-time process optimization.

Keywords: Systems Biology, Metabolic Engineering, Microbial Technology
Abstract: Regulatory on off minimization (ROOM) is a popular metabolic modeling strategy for obtaining the fluxes of various metabolic reactions in a mutant. It is based on minimization of the number of flux changes with respect to the wild-type. The ROOM approach involves solving an integer programming problem. In ROOM, the number of integer decision variables is equal to the number of reactions in the metabolic network under consideration. Typically, metabolic networks of interest are genome scale implying that the number of reactions in the network and hence the number of integer decision variables is large. The ROOM approach thus has inherent difficulties associated with large scale integer programming problems. In the current work, motivated by the emerging area of compressed sensing, we propose a reformulation, known as basis-pursuit, of the ROOM algorithm. The proposed formulation is an L1 norm minimization problem and is thus convex in nature. The proposed approach is used to obtain the flux profiles for various mutants of the Synechocystis species strain PCC 6803. The results are compared with the existing ROOM approach. It is observed that the proposed algorithm performs better in most cases. Use of compressed sensing based formulation creates exciting possibilities of efficiently reformulating various other metabolic network analysis problems.

Keywords: Systems Biology, Fault Diagnosis and Monitoring, Modelling and Identification
Abstract: A fuzzy inference system (FIS) that aids in atherogenesis risk diagnosis is described in this document, from data of human plasma analysis. This FIS uses Total Cholesterol, Low-Density Lipoproteins, Atherogenic Index and Triglycerides in plasma levels as variables in order to propose a non-invasive diagnosis method to help in low-cost early detection of atherogenesis risk.

Keywords: Fault Diagnosis and Monitoring, Sensors and Soft Sensors, Modelling and Identification
Abstract: Although refractory epileptic patients suffer from uncontrolled seizures, their quality of life (QoL) may be improved if the seizure can be predicted in advance. In the preictal period, the excessive neuronal activity of epilepsy affects the autonomic nervous system. Since the fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), reflects the autonomic nervous function, an epileptic seizure may be predicted through monitoring RRI data. The present work proposes an HRV-based epileptic seizure monitoring method by utilizing multivariate statistical process control (MSPC) technology. Various HRV features are derived from the RRI data in both the interictal period and the preictal period recorded from epileptic patients, and an MSPC-based seizure prediction model is built from the interictal HRV features. The result of applying the proposed monitoring method to a clinical data demonstrates that seizures can be detected at least one minutes prior to the seizure onset. The possibility of realizing an HRV-based seizure monitoring system is shown.

Keywords: Sensors and Soft Sensors, Parameter and State Estimation, Dynamics and Control
Abstract: The cultivation of animal cells in perfusion allows the production of various biopharmaceutical products. In this work, the observability properties of a nonlinear dynamic model of these animal cell cultures is assessed using a method based on a natural dynamical interpretation of the observability/detectability concepts, leading to the description of the indistinguishable dynamics of the system. Following this analysis, a Kalman filter is designed to reconstruct on-line variables which are difficult or expensive to measure directly with a hardware sensor.

Keywords: Parameter and State Estimation, Environmental Processes (Wastewater, Bioremediation), Sensors and Soft Sensors
Abstract: In order to design an observer for a dynamical system it is usually required that the model of the process is observable, or at least detectable. However, in some cases, in particular when unknown uncertainties are present, none of these properties is available. We would be tempted to give up the possibility of constructing an observer. However, in certain situations a multivalued observer, an observer giving multiple possible values of the state can be a reasonable alternative. In this paper we will analyze a realistic reactor model for which this situation is met: the process is unobservable and undetectable but a bivalued observer can be designed that provides a very satisfactory solution to the estimation problem.

Keywords: Dynamics and Control, Mammalian, Insect and Plant Cell Technology, Modelling and Identification
Abstract: The maximization of biomass productivity in fed-batch cultures of hybridoma cells is analyzed based on the overflow metabolism model. Due to overflow metabolism, often attributed to limited oxygen capacity, lactate and ammonia are formed when the substrate concentrations (glucose and glutamine) are above a critical value, which results in a decrease in biomass productivity. Optimal feeding rate, on the one hand, for a single feed stream containing both glucose and glutamine and, on the other hand, for two separate feed streams of glucose and glutamine are determined using a Nelder-Mead simplex optimization algorithm. The optimal multi exponential feed rate trajectory improves the biomass productivity by 10% as compared to the optimal single exponential feed rate. Moreover, this result is validated by the one obtained with the analytical approach in which glucose and glutamine are fed to the culture such as to control the hybridoma cells at the critical metabolism state, which allows maximizing the biomass productivity.

Keywords: Dynamics and Control
Abstract: This paper presents an effective and robust structure for the estimation and control of perfusion cell cultures, in which the cells and glucose concentrations are simultaneously controlled by manipulating the dilution and bleed rates. Firstly, a partially linearizing feedback controller is designed to ensure an approximately linear decoupled dynamics between the controlled outputs and the manipulated inputs. Then the model of the inner loop is used to design an extended Kalman filter, which estimates all the system states used in the implementation of the linearizing feedback control law from the (possibly noisy) measurements of cells and glucose concentrations. Two PI controllers are used in the outer loop for a good tracking performance, which are tuned using a receding horizon optimization procedure. The proposed structure shows good performance and robustness with respect to parameter uncertainties, non-cancelled nonlinearities and measurement noise.

Keywords: Dynamics and Control, Sensors and Soft Sensors, Parameter and State Estimation
Abstract: Microalgae are microscopic plants existing in aquatic environment. They can be used in the production of high value compounds and they have promising opportunities in energy production, wastewater treatment and fixation of carbon dioxide. In this context, the control of the cultivation requires a special attention in order to obtain high amounts of biomass.The control of microalgae cultivation is approached from passivity-based control perspectives. The proposed controller solves set point tracking and stabilizes the control loop by the passivity properties. Moreover, in order to obtain the process state variables from the measurement output, which are used in the control law, a nonlinear observer with guaranteed stability of estimation error dynamics is proposed.The design of the nonlinear controller and the observer is based on the Droop model, describing the dynamic behaviour of the microalgae process in a practical way. Finally,the performance of the nonlinear observer-based controller is shown by simulation.

Keywords: Dynamics and Control
Abstract: This work gives mathematical conditions that guarantee the local stability of the equilibrium regimen of two classes of cell metabolism. In fact, we have analyzed reversible and irreversible linear bacterial metabolic pathways that integrate both genetic and enzymatic control. Moreover, due to these conditions, we can state that: regardless the size of a doubly controlled linear metabolic pathway, the local stability of its steady state depends only on the dynamics of its input and output flux with respect to the concentration of its end product. These results are proved theoretically using some properties of the cooperative matrices.

Keywords: Dynamics and Control, Modelling and Identification, Systems Biology
Abstract: A Glucose- Insulin steady state static map is obtained from the Hovorka’s 8th order virtual patient model. Three First Order Plus Time Delay (FOPTD) models are derived for the three piecewise linear regions in it. Through polyhedral vector space partitioning based on constraint violation, critical regions in state vector space are identified. A state feedback gain based controller is designed for each such critical region. The controller design prevents constraint violations and ensures convexity while regulating the state vector to origin. The solution is also globally minimal. The state vector space of each empirical model is subjected to such analysis, resulting in three different set of critical regions and corresponding controllers. Gain Scheduling (GS) based on the Blood Glucose Concentration (BGC) measurement ensured proper profile selection. Through delay time compensation techniques, the multi model multi-parametric Model Predictive Control (mp-MPC) is designed for pure dynamics of each linear region. It is observed that the gain scheduled controller regulates the BGC within the nominal range (80mg/dL to 160 mg/dL) during multiple meal disturbances. The explicit state feedback gain nature of the controller implies ease of deployment on memory constrained embedded devices.

Keywords: Dynamics and Control, Environmental Processes (Wastewater, Bioremediation)
Abstract: In this paper, we propose a nonlinear adaptive controller for light-limited microalgae culture. This controller regulates the light absorption factor, defined by the ratio between the incident light and the light at the bottom of the reactor. Then, we propose a set-point for the light absorption factor which allows to optimize biomass productivity under constant illumination. Finally, we show by numerical simulation that the adaptive controller can be used to obtain near optimal productivity under day-night cycles.

Keywords: Parameter and State Estimation, Sensors and Soft Sensors, Microbial Technology
Abstract: This paper presents the application of a generalized super-twisting algorithm to estimate a critical variable needed for implementing real time optimization feedback controllers in a certain class of bioreactors. A bank of redundant super-twisting observers designed for equivalent systems under a state transformation is used. The procedure is motivated by the fed-batch cultivation of a strain of E. coli that has overflow metabolism, where a useful variable to estimate is the difference between two reaction rates. The results show the applicability of the procedure and illustrates some compromises between the accuracy and the measurement noise.

Keywords: Parameter and State Estimation, Modelling and Identification
Abstract: Mathematical models are expected to play a pivotal role for driving microalgal production towards a profitable process of renewable energy generation. To render models of microalgae growth useful tools for prediction and process optimization, reliable parameters need to be provided. This reliability implies a careful design of experiments that can be exploited for parameter estimation. In this paper, we provide guidelines for the design of experiments with high informative content that allows an accurate parameter estimation. We study a real experimental device devoted to evaluate the effect of temperature and light on microalgae growth. On the basis of a mathematical model of the experimental system, the optimal experiment design problem was solved as an optimal control problem. E-optimal experiments were obtained by using two discretization approaches namely sequential and simultaneous. The results showed that an adequate parameterization of the experimental inputs provided optimal solutions very close to those provided by the simultaneous discretization. Simulation results showed the relevance of determining optimal experimental inputs for achieving an accurate parameter estimation.

Keywords: Parameter and State Estimation, Modelling and Identification
Abstract: Mathematical modeling has become an indispensable tool in the analysis, prediction and control of chemical and biological systems. However, the estimation of consistent model parametrizations and model invalidation are challenging tasks, but crucial for reliable model-based analysis and prediction. Set-based estimation methods are useful to determine guaranteed outer approximations of consistent parameter sets, i.e. consistent parametrizations are never excluded. However, these conservative outer approximating sets often include inconsistent parametrizations which lead to inconsistent models and hence possibly wrong model-based predictions. This paper proposes a set-based framework to determine inner approximations, i.e. the model is guaranteed consistent with measurement data for all parametrizations from this set. Our approach is based on the reformulation and inversion of measurement data constraints and by imposing nonlinear constraints on binary variables. The relaxation of the mixed-integer nonlinear feasibility problem into a mixed-integer linear feasibility problem allows the inner approximations to be determined efficiently. The applicability of this approach is demonstrated considering a nonlinear biochemical reaction network.

Keywords: Modelling and Identification, Systems Biology, Pharmaceutical Processes
Abstract: A mathematical model has been developed for an enzymatic process with kinetically controlled synthesis. Model reduction and detailed system analysis have been undertaken to examine the main properties of this enzyme reaction system. Optimal experimental design (OED) is developed to obtain the experimental conditions that will generate the most informative measurement data for parameter estimation. Both single-input and multiple-inputs optimisation strategies have been investigated to determine the best intensity levels of control inputs. The results demonstrate that parameter estimation quality can be improved through proper model-based experimental design.

Keywords: Parameter and State Estimation, Modelling and Identification, Systems Biology
Abstract: Despite the rapid increase in quantity and quality of experimental data in many fields of engineering and science, quantitative measurements of many cellular components are still relatively scarce. This work deals with estimating the parameters of a double feedback gene-switching model. To achieve the goal, a model-based design of experiment (MBDOE) approach for parameter estimation is employed. To overcome the problem of convergence in parameter estimation step (due to correlation among the parameters), a non-dominated sorting genetic algorithm (NSGA-II) based, multi-objective optimization (MOO) based MBDOE has been used. The parameter estimates obtained through the MOO based DOE as well as a standard alphabetical DOE technique are then compared with the known true values from the literature to highlight the efficacy of the MOO-MBDOE technique.

Keywords: Parameter and State Estimation, Modelling and Identification
Abstract: The objective of this paper is to study the effect of intra-patient variations on the personalized parameters of the modified exercise minimal model by re-estimating them from the clinical data measured after several days of first estimation. Clinical data of eight type 1 diabetic children and adolescents have been used for this purpose. For the first estimation of 6 estimable parameters, clinical data collected during one of the visits have been employed. Subsequent re-estimation of parameters is accomplished using the second clinical visit data, which was collected after 7-35 days from the first visit. The results of re-estimation indicate that the estimable parameters corresponding to glucose-insulin compartments and meal absorption model are greatly affected by intra-patient variability in most of the patients, while the 2 estimable parameters corresponding to exercise compartments are least affected by intra-patient variations in 6 out of 8 patients.