Bioprocess Systems Engineering: Transferring Traditional Process Engineering Principles to Industrial Biotechnology

Abstract

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals.

Economic analysis of uricase production under uncertainty: Contrast of chromatographic purification and aqueous two-phase extraction (with and without PEG recycle)

“…Using bioprocess modelling it is possible to create virtual bioprocesses, allowing input parameters, obtained through research, to be varied so as to obtain estimates of the cost of production per gram (Cost of Goods per gram, CoG/g). Use of such model‐based tools can also help to decrease the costs of research by saving time and resources, reducing the number of experiments and focusing efforts where it is needed . Furthermore, it is possible to incorporate uncertainties that are inherent of any bioprocess .…”

Model‐based cell number quantification using online single‐oxygen sensor data for tissue engineering perfusion bioreactors

“…Therefore, functional bioprocess control in TE bioreactors, in the sense that it allows direct control over the critical quality attributes (CQAs) of the TE construct, such as proliferation rate, extra-cellular matrix production, differentiation stage, construct permeability, etc., remains a challenge due to inadequate online, non-invasive, and cost-effective monitoring tools (Glassey et al, 2011;Koutinas et al, 2012;Placzek et al, 2009;Read et al, 2010;Vojinović et al, 2006). Depending on the bioreactor setup and cell carrier or scaffold design under consideration, this can be attributed to the high technicality and/or cost of direct, online and non-destructive measurement of TE construct CQAs.…”

“…Although, more frequently used in other scientific domains, such as modeling and controlling heart rate responses of athletes (Lefever et al, 2012), energy transfer in buildings (Price et al, 1999), precision life stock farming (Silva et al, 2009), and other environmental and economic phenomena (Haredasht et al, 2011;Young, 1998), the DBM modeling technique is, as far as known by the author, unexplored in the context of bioprocess control for TE bioreactors. Nevertheless this data-based approach holds promise for the TE domain since high quality bioprocess data are often available from the bioreactor system and purely mechanistic models for complicated biological processes are often non-existent or have a significant cost of development (Koutinas et al, 2012). In addition, since the DBM approach results in low order, parametrically efficient models that describe only the dominant behavior of the system, it forms an ideal basis for controlling processes Taylor et al, 2007).…”

Controlled gas exchange in whole lung bioreactors

“…The pharmaceutical industry has spearheaded development of cellular bioreactors, enabling large‐scale production of proteins and antibodies from individual cells (Ozturk and Hu, ). These bioreactors are highly controlled with regard to temperature, fluid flow, pH, nutrients and dissolved oxygen, and computational models have been developed to understand, monitor and control these factors (Almquist et al , ; Cvijovic et al , ; Koutinas et al , ; Mata‐Alvarez and Mitchell, ). In contrast, systems for culturing intact, functional organs, developed in both academic (Hogan et al , ; Niklason et al , ; Petersen et al , , 2010; Song et al , ) and commercial settings (Asnaghi et al , ; Clause et al , ; Macchiarini et al , ; Ott et al , ; Price et al , , ), are crude by comparison.…”