Keywords: Mammalian, Insect and Plant Cell Technology, Gene Therapy, Pharmaceutical Processes
Abstract: We will recapitulate here a narrative in which we have just now been able to solve an important, frustrating and long standing puzzle as to how a brain control function of the heart is performed. The key to solving this puzzle has proved to be the acquisition of transcriptional profile data from single neurons. Analysis of this data forced a reconsideration of the fundamental structure of the brain control system. In turn, this reconsideration has led to subsequent re-ordering of a great deal of neuroanatomical and physiological data into a pattern that finally makes a great deal of sense.

Keywords: Integrated Bioprocessing, Downstream Processing, Scheduling, Coordination, Optimization
Abstract: Pichia pastoris was used for the expression of an artificial fusion protein D1M1 – a potential Malaria vaccine. A fully automated integrated bioprocess was set up which combined upstream and downstream operations and has been performed in alternating fed batch cycles with parallel protein capture by Expanded Bed Adsorption Chromatography. Design of Experiments was used to study the influence of procedural factors on protein recovery and purity. As a result it was achieved to purify the protein to a purity of up to 87 % and a recovery of 51 % was reached in a single downstream operation.

Keywords: Environmental Processes (Wastewater, Bioremediation), Modelling and Identification, Integrated Bioprocessing
Abstract: This paper presents an integrated mathematical model that is capable of predicting and assessing the impact of ultrasonic (US) treatment on the excess activated sludge production in a sequencing batch reactor (SBR) system. Such a model can be exploited to maximize the excess sludge reduction while minimizing the US operational costs in biological wastewater treatment systems. Biological processes in the reactor are simulated in Matlab/Simulink by the ASM1 model into which two algebraic equations, which capture the US treatment, are integrated. Validation data series come from a pilot plant installed at two locations, i.e., at a communal wastewater treatment plant and at an industrial production site of a food flavor producing factory in Haasrode both in the Flanders region in Belgium. The results show that the excess sludge reduction from the SBR can be correctly predicted. A reduction of nearly 42% for the communal case study can be reported, while preliminary results for the industrial case study, characterized by a very high organic loading, are even more promising.

Keywords: Bioinformatics, Environmental Processes (Wastewater, Bioremediation)
Abstract: Conventional tests in environmental monitoring have been performed by quantifying levels of toxicity of specific substances such as pesticides or drugs, and then comparing with known toxicity thresholds. These tests are conducted using analytical chemistry methods and can only be used for targeted substances, often missing unexpected toxicants. This shortcoming underlines the need for novel tests for rapid assessment of toxicity of environmental sample mixtures, followed by more detailed and expensive laboratory analysis as required. In order to evaluate the level of toxicity in water contamination, a mathematical model to predict the toxicity index is developed based on time-dependent cellular response curves (TCRCs). First, the water sample is diluted to a series of strength (80%, 60%, 40%, 30% 20% and 10%) to get the multiple concentrations. Then, the living cells are exposed to those water samples and the corresponding dynamic cytotoxicity response curves are collected via xCELLigence real-time cellular analyzer for high throughput (RTCA HT) system. A synthetical index, based on the calculation of area under curve (AUC) of the negative control, is proposed to evaluate the level of toxicity in water contamination. The proposed index also takes the variation of biological experiment into consideration. The biological experiment demonstrates the effectiveness of the proposed toxicity index to measure the level of toxicity in water contamination.

Keywords: Environmental Processes (Wastewater, Bioremediation), Dynamics and Control, Modelling and Identification
Abstract: This paper deals with the development of a multivariable predictive control structure for improving the nitrogen removal of a biological wastewater treatment plant while reducing the operational costs. A simple dynamic matrix control algorithm is utilised as predictive controller and applied to a full-scale municipal wastewater treatment plant for controlling nitrogen concentrations at the end of the biological process. The complex calibrated model of the process is implemented in a commercial simulator that acts as a real-time testing platform for the proposed control structure, and allows the identification of the multivariable input-output model for the predictive control. Simulation results show the potentialities of the chosen predictive control, which allows the reduction of ammonia peaks in the effluent and at the same time permits a reduction of the energy consumption costs.

Keywords: Integrated Bioprocessing, Dynamics and Control
Abstract: This paper presents a simulation of an industrial scale filamentous fermentation; the simulation focuses on modeling a 120,000 litre Penicillium chrysogenum batch process. The simulation attempts to address many of the challenges that that are faced by industrial scale filamentous fermentations; these include the control of dissolved oxygen concentration above its critical value and also controlling substrate feed to an optimum trajectory. Previous unstructured models, that didn’t consider the changing morphology of Penicillin fermentations, failed to adequately model the historical Penicillin production batch data presented here. This simulation extends previous structured models by including extra process variables such as gas inlet pressure and viscosity, which are shown to have a significant effect on the control strategy of these large-scale fermentations. The accuracy of the model is verified by successfully predicting both the Penicillin and dissolved oxygen concentration using the input data from two industrial 120,000 litre Penicillium chrysogenum batch fermentations. The overall aim of the simulation is to provide an improved test bed for fed-batch Penicillin fermentations that can be used for process monitoring, control and optimization studies.

Keywords: Fermentation processes, Batch control, simulation, Bio-control

Keywords: Systems Biology, Pharmaceutical Processes, Modelling and Identification
Abstract: Glycosylation is a critical protein post-translational modification with a profound impact on the therapeutic properties of Mab and research indicates that it depends on extracellular culture conditions. A novel dynamic model was developed to relate extracellular metabolites’ concentrations to a cumulative glycoprofile. The model has three components: dynamic evolution of extracellular metabolites, production of nucleotide sugars in the cytosol, and glycosylation inside the Golgi apparatus. Following comparisons with experimental data obtained from batch CHO cell cultures, the model was found capable of predicting the glycoform profile of Mab temporally, as well as the extent of galactosylation given in the form of galactosylation index. The model has the potential for use in controlling the glycoform profile by manipulating culture conditions.

Keywords: Modelling and Identification, Systems Biology, Dynamics and Control
Abstract: The aim of this paper is to analyze and to reduce coupled transcription-translation models developed in (Kremling 2007), which are a detailed representation of the process describing how the information in DNA turns into proteins in a cell, with polymerase and ribosomes. The full model is of high dimensions, and may be complex to deal with. Under assumptions on the parameters, we can write the full system in the form of a slow fast system. The fast part is a monotone system and we prove its global stability. We put this fast part to its quasi steady state, and show that it is possible to obtain a small reduced model (with three variables) having essentially the same dynamical behavior.

Keywords: Dynamics and Control, Parameter and State Estimation
Abstract: The regulation of the biomass specific growth rate is an important goal in many biotechnological applications. To achieve this goal in fed-batch processes, several control strategies have been developed employing a closed loop version of the exponential feeding law, an estimation of the controlled variable and some error feedback term. Moreover, in some bioprocesses there is more than one feeding flow entering the bioreactor and supplying different nutrients or substrates. Hence, the problem of estimating multiple substrate consumption rates together with the specific growth rate of the microorganism becomes relevant. In this context, the dynamic behavior of fed-batch processes with multiple substrates and Haldane kinetics is further investigated. In particular, a nonlinear PI control law based on a partial state feedback with gain dependent on the output error is used. Then, with a recent developed algorithm for several kinetic rates estimation based on second-order SM ideas, we extend the mentioned control strategy to a multi-substrate fed-batch bioprocesses. The observer provides smooth estimates that converges in finite time to the time-varying parameters and allows independent design of the observer and controller dynamics. The features of the proposed estimation and control strategies are assessed by simulation in different scenarios.

Keywords: Systems Biology, Modelling and Identification, Metabolic Engineering
Abstract: Glutaminolysis and citric acid cycle generate carbon sources and cellular energy in dependence of the biosynthesis needs. We here introduce a concept which combines a simple and practicable kinetic description of both pathways with a cell growth model to elucidate the influence of extracellular substrates, transport mechanisms and enzyme regulation on the metabolic activity. The derived model focuses on key reactions and replaces complex cellular mechanisms with growth-dependent functions. The uptake of glutamine and glutamate explains, in combination with cell size variations, a peak-like increase in intracellular metabolite pools at day two after inoculation of cells. Additional regulation of citric acid cycle enzymes prevents the degradation of metabolites during stationary growth phase even under substrate limitation. This work reveals possible ways to improve the biotechnological process of cell cultivation and provides a deeper understanding of glutaminolysis and citric acid cycle activity regulation in mammalian cells.

Keywords: Integrated Bioprocessing, Modelling and Identification, Environmental Processes (Wastewater, Bioremediation)
Abstract: The present paper describes the design and the validation of a lumostatic controller for artificially lighted photobioreactors operated in discontinuous mode. The ratio between the incident light intensity and the biomass concentration, termed light-to-microalgae ratio, was selected as output variable, while its control was provided by manipulating the power supply of a light source and consequently the incident light intensity. The biomass yield on light energy was introduced in order to properly compare the proposed lumostatic batch operation with a batch operation under constant incident light intensity. The results obtained in simulation show that a lumostatic batch can yield at least 10% more biomass per mole of supplied photons. The nonlinear controller, synthesized on the feedback linearizing technique, was implemented and validated on a laboratory torus photobioreactor inoculated with Chlamydomonas reinhardtii cells.

Keywords: Scheduling, Coordination, Optimization, Sensors and Soft Sensors, Pharmaceutical Processes
Abstract: A designed multi-bioreactor plant, comprising a 5 l cell breeding BIOSTAT^® Bplus system and a sixfold 1 l BIOSTAT^® Qplus system, was equipped with different, interlinked automation structures for systemically process controlling and reliable optimization procedures. Benefits obtained were time saving, assurance and improvement of quality and cost reduction in bioprocess production. In this report the concept for moving beyond the state-of-the-art by creating a fully automated multi-bioreactor plant is explained. The efficiency of the approach is demonstrated by common application fields with Pichia pastoris in bioprocessing.

Keywords: Dynamics and Control, Pharmaceutical Processes, Systems Biology
Abstract: This work addresses the application of control systems to the optimization of a monoclonal antibodies (MAb) production chain. The attention is focused on the maximization of hybridoma fedbatch culture productivity. The proposed model presents kinetics showing strong nonlinearities through min-max functions expressing overflow metabolism. A nonlinear model predictive control (NMPC) algorithm, choosing the best trajectory over a moving finite horizon among different sequences of inputs, is suggested in order to optimize productivity. Sensitivities of selected objective functions are considered in a minimax robust version of the NMPC in order to choose the best configuration with respect to practical operating conditions.

Keywords: Fault Diagnosis and Monitoring, Data Mining Tools, Pharmaceutical Processes
Abstract: Online batch process monitoring has been a challenging task, as batch processes do not operate around a nominal steady state operating point. Various monitoring approaches, where future batch trajectory is filled with average (nominal) batch trajectory have been proposed. Predicting future trajectory for a batch process is a difficult task. Recently a multiway principal component analysis (MPCA) based approach that does not involve future trajectory prediction was proposed. In this paper a new technique based on function space principal component analysis (FSPCA) is proposed for online batch process monitoring. The main advantage of the proposed FSPCA based methodology is its ability to detect incipient and small to medium magnitude faults and its relevance for uneven length batch processes. Efficiency and effectiveness of the proposed algorithm is demonstrated via a fed-batch penicillin cultivation process simulation. The diagnostic performance of the proposed approach is significantly better compared to MPCA based approaches.

Keywords: Dynamics and Control, Modelling and Identification, Systems Biology
Abstract: Living cells are continuously monitoring their surroundings and making appropriate decisions to enhance chances of survival. Information regarding environmental conditions is processed in biochemical and genetic networks, eventually leading to a cellular phenotype. However, for all transfer of information, a large number of possible network topologies are available to the cell to choose from. In the common bacterium Escherichia coli, frequency of certain topologies far outnumbers others. What decides the choice of a particular network over the others? In this work, we work with the simplest transcriptional network - an interaction between a regulator, R and its target gene, T - and develop a computational and experimental framework to characterize performance of different possible networks in terms of a list of objective functions. Our results suggest and we speculate that a group of proteins evolve into a particular network topology so as to optimize the most crucial objective function for their particular role in cellular physiology.

Keywords: Systems Biology, Dynamics and Control, Modelling and Identification
Abstract: Bistable switches are frequently encountered in biological systems. Typically, a bistable switch models a binary decision where each decision corresponds to one of the two stable equilibria. Recently, we showed that the global decision-making process in bistable switches strongly depends on a particular equilibrium point of these systems, their saddle point. In particular, we showed that a saddle point with a time-scale separation between its attractive and repulsive directions can delay the decision-making process. In this paper, we study the effects of white Gaussian noise on this mechanism of delayed decision-making induced by the saddle point. Results show that the mean decision-time strongly depends on the balance between the initial distance to the separatrix and the noise strength.

Keywords: Mammalian, Insect and Plant Cell Technology, Gene Therapy
Abstract: Series enzymatic cascades are ubiquitously found in signaling networks and act as key signal amplifiers. During signal transduction process, cell-to-cell variability or noise travels along with the signal and strongly affects the fitting response that the cells exhibit. Modulation of noise propagation through the enzymatic cascades can play a strong role in regulating cellular response. We find the conditions under which the noise propagation through the cascades is bounded. Using global sensitivity analysis, we quantified the dependence of these conditions on the system parameters and estimated the parameter range in which the system when operated will result in attenuation of noise propagation through the cascade.

Keywords: Systems Biology, Bioinformatics, Modelling and Identification
Abstract: miRNAs play important roles in plant post-transcriptional gene regulation by targeting mRNAs through cleavage or repressing translation. Here, we have reconstructed genome scale level miRNA-miRNA co-targeting network of rice plant and identified several significant modules (highly-dense sub-graphs). Some of the modules are involved in multiple biological processes, while some are involved in single biological process. The distribution of the out-going connectivities of co-target network can be approximated best by a power-law equation. We have also observed a wide variation in inter chromosomal regulation. The genes of chromosome 3 are highly targeted by the miRNAs synthesizing from other chromosomes. The results presented here might provide a platform for testing the hypothetical role of co-targeting associations in rice post-transcriptional control.

Keywords: Bioinformatics
Abstract: The genus mycobacterium encompasses both pathogens and non-pathogens, alternatively both slow and rapid growers. They are the source of a variety of infectious diseases in a range of hosts. Comparative genome analyses provide useful information to understand the genome feature of each pathogenic species to its unique niche. In this work, we report the phylogenetic analysis of 47 mycobacterium species, whose genome sequences are complete and available. Trees were constructed using two approaches namely single sequence and genome feature based methods. While single sequence based tree cannot distinguish between MTB complex genomes, trees based on genome features were able to resolve them better. Gene order based phylogeny highlights distinct evolutionary characteristics as illustrated by the shift in the relative position of drug susceptible and resistant M. tuberculosis complex species. Thus, phylogenetic relationship between closely related organisms can be resolved by genome feature based tree methods.