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Academic research and industrial development in all major areas of biosystems and bioprocesses where computers are used to aid bioprocess design, supervision, diagnosis, operation, optimization and control, and in particular dedicated to the following topics:

  • Metabolic engineering
  • Modelling and identification
  • Parameter and state estimation
  • Fault diagnosis and monitoring
  • Data mining tools
  • Sensors
  • Bioinformatics
  • Dynamics and control
  • Downstream processing
  • Integrated bioprocessing: case studies
  • Scheduling, coordination, optimization
  • Life cycle analysis

with applications in the fields of:

  • microbial technology
  • mammalian, insect and plant cell technology
  • pharmaceutical processes
  • food engineering
  • wastewater treatment processes

as well as in any upcoming field (e.g. biomaterials).

1. Introduction

Biotechnology is considered to be one of the main sources of innovative business opportunities/products and creation of jobs in the coming century. The field of biotechnology is characterized by rapid changes in terms of novelty and by highly sophisticated processes that require careful design, operation and control in order to be run in safe and optimal conditions. Therefore operational excellence is the key to exploit these opportunities, facing a highly competitive global economic environment. This has to be added by the inherent complexity of the biological systems. This requires "innovative, reliable, smart and cost-effective manufacturing processes and systems". This is one of the essential issues of the activities of the present TC.

To enable the development of these bioprocesses, the quantitative understanding of the underlying biological systems is essential. Novel analytical approaches in the fields of e.g.genome, transcriptome, proteome and metabolome analysis offer the opportunity to get access to intensive measurement information characterizing the in vivo state of the living cell. For instance, data of in vivo intracellular metabolite concentration measurements, the analysis of cellular protein content or the investigation of gene expression levels are nowadays already available and will surely become even more accessible in the future. The understanding of the control laws that drive the cell behavior will help to design news drugs. Hence, modelling of biological processes should keep abreast with the ongoing analytical development by focusing the living cell as a modelling target. This will correspond to the 'biosystem" approach that aims at a detailed quantitative, mechanistic understanding of microbial systems to be used in bioprocesses.

Biological systems are known to be immensely complex. Thus, from the engineering point of view, production systems in biotechnology so long could not be designed and controlled as thoroughly as processes in many other branches of industry. In order to exploit the economical benefits of model supported process design and control already experienced in most other industries, considerable efforts must be made in modelling and control. The high complexity of the systems requires a joint effort of the leading World specialists. Particularly in biotechnology, well-optimized tightly controlled processes will not only increase productivity but particularly will significantly improve process and product safety and quality.

2. Brief description of the research activities related to the TC

TThe modelling, the monitoring and the control of bioprocesses pose a number of challenging scientific and industrial questions. These largely come from the presence of living organisms in bioprocesses, the high complexity of the interactions between the possibly different microorganisms present in the same process, as well as the high complexity of the metabolic reactions in which the microorganisms are involved. This typically results in models that may be difficult to handle for monitoring and control.

Roughly speaking, the dynamical models are either complex or simple. The apparent advantage of complex models may be most often largely balanced by their lack of extrapolation properties. In other words, complex models may turn out to represent poorly the experimental reality when used in conditions different than those for which it has been designed and calibrated. This may be the result of problems like overparametrization, structural identifiability, lack of sufficient, sufficiently informative or reliable data for parameter identification. More fundamentally, the necessary data are simply just not available because there exists no measurement device available. The latter question is of particular importance when using more and more sophisticated models, like those considered in metabolic engineering or those based on population balance considerations.

On the other extreme, models may appear very simple. The advantage of simplicity is that the models are easy to interpret and to handle either for monitoring or for control design. The identifiability problems mentioned hereabove are easier to handle (even if they may remain difficult for many practical reasons like the difficulty to generate informative data for nonlinear systems). If they cover the "dominant" dynamics of the process, they will be able to represent rather well the process dynamics over a sufficient range of operating conditions. However the limitations clearly appear: because of the (over)simplification of the experimental reality, the process model may be apparently non stationary. In other words, the parameters of the dynamical model that should be conceptually constant like stoichiometric and yield coefficients, transfer coefficients, or kinetic constants need to be changing with time in order to give a satisfactory prediction of the model dynamics.

Moreover, beside the modelling question, it appears that for monitoring and control applications, only a few measurements are available, either because the measuring devices do not exist on the market or are too expensive for the considered applications, or because the available devices do not give reliable measurements under the considered operating conditions or industrial environment.

From the above considerations, we can deduce that the main difficulties arising in modelling, monitoring and control of bioprocesses and biosystems come from two main sources:
A. the process complexity;
B. the difficulty to have reliable measurements of the necessary process variables.

If the scientific activity has resulted in a wide variety of results and industrial applications in the field of monitoring and control of bioprocesses, many challenging problems remain to be solved in order to maintain and possibly increase the competitiveness of the biotechnological process industry in the different fields of application : food industry, pharmaceutical industry, biopolymer industry,... as well as waste and wastewater treatment. The research trend is presently multiple and cover fields like metabolic engineering or population balance modelling (where complex models of bioprocesses are explicitly considered) as well as instrumentation (where techniques like image analysis appear for instance more and more attractive for future applications, especially to identify and quantify filamentous microorganisms). Another challenging topic is the design of bioprocesses that integrate the whole chain form the raw products to the final products (including downstream processing) and the wastes in a sustainable way in order to decrease the impact on the environment. The list of challenging topics is indeed very long and covers themes like the integration of control in the design of bioprocesses; the mathematical modelling, identification and analysis of biological phenomena; the design, analysis and application of appropriate techniques for the monitoring and fault diagnosis in bioprocesses, or for the optimization and control of "batch" processes (including fedbatch reactors and start-up of reactors) and of tubular reactors (fixed bed reactors, fluidized bed reactors,...). These (non-exhaustive) topics are listed here below:

  • Metabolic engineering (including cyber cells and cybernetic models) and its connection with systems theory including metabolic control analysis).
  • Quantification of in vivo enzyme kinetics for data driven whole-cell structured modelling.
  • Population balance modelling (PBM): model properties analysis and the potential role of PBM in process estimation and control.
  • Instrumentation, including image analysis:
    • on-line measurements for monitoring and control;
    • off-line measurements for identification (in particular PBM).
  • Integrated design of bioprocesses: Design of “clean” processes, Life Cycle Analysis (LCA), integration of control and design.
  • Mathematical modelling, identification and analysis of biological phenomena (e.g. microbial and non-microbial ecosystems).
  • Monitoring and fault detection in bioprocesses.
  • Control of “batch” processes (including fedbatch reactors and start-up of reactors).
  • Distributed parameter systems (population models, tubular reactors, settlers): model analysis, model identification, software sensors and control.
  • Software sensors, optimization and control for (nonlinear) bioprocesses.
  • Process monitoring (and automatic control) from the process validation point-of-view.
  • System theory applications for design, operation (monitoring, control, FDI) and optimization of downstream processes.
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