A process systems engineering view of biochemical process operations

Bogle, David; Cockshott, A.R.; Bulmer, M.; Thornhill, Nina F.; Gregory, M. E.; Dehghani, Mohammad Amin

doi:10.1016/0098-1354(95)00189-1

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…Control of fermentations deals with complex dynamic behavior of microorganisms, significant model mismatch, nonlinear and even inherently unstable dynamics, scarce online measurements of a number of representative variables, etc. [31]. Hence, to date, the control systems applied to industrial fermenters aim mainly at the regulation of the process, that is they aim at keeping a certain control variable at a given set point or trajectory despite disturbances.…”

Section: Engineering Tools For Process Improvementsmentioning

confidence: 99%

Bis‐arylidene Oxindoles as Anti‐Breast‐Cancer Agents Acting via the Estrogen Receptor

et al. 2014

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We report a new family of bis-arylidene oxindole derivatives that show highly selective estrogen receptor (ER)-mediated anticancer activity at low-nanomolar concentrations in ER-positive (ER+) breast cancer cells. In terms of cell growth inhibition, IC50 values for these compounds in ER+ breast cancer cells are two to three orders of magnitude lower than in ER-negative (ER-) breast cancer cells and non-cancer cells. In comparison with known bis-arylidene drugs, these compounds are at least three orders of magnitude more toxic than tamoxifen and 1.5-4-fold more toxic than 4-hydroxytamoxifen in ER+ MCF-7 cancer cells. These oxindoles inhibit ER transactivation, and their anticancer activities are inhibited in ER-depleted MCF-7 cells. Some of these nonsteroidal molecules also exhibit essential properties of selective ER down-regulation. From the development of two series of bis-arylidene oxindole-based compounds, we report a new series of anticancer agents for estrogen-responsive breast cancer.

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Section: Engineering Tools For Process Improvementsmentioning

confidence: 99%

Bis‐arylidene Oxindoles as Anti‐Breast‐Cancer Agents Acting via the Estrogen Receptor

et al. 2014

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“…The main factors contributing to the dif®culty in control of fermenters are: (1) Their dynamic behaviour is inherently nonlinear, (2) Accurate process models are rarely available due to the complexity of the underlying biochemical processes, (3) Model parameters vary in an unpredictable manner and (4) Reliable biosensors to measure intercellular activities are rarely available, making the process states very dif®cult to characterise. Bogle et al [1] discussed the techniques of metabolic pathway engineering which help to identify how to modify the micro-organisms for improved process performance through better design leading to less sensitivity to load changes.…”

Section: Introductionmentioning

confidence: 99%

Control of fermenters – a review

Rani¹,

Rao²

1999

Bioprocess Engineering

134

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Fermenter control has been an active area of research and has attracted more attention in recent years. This is due to the new developments in other related areas which can be exploited to overcome the inherent dif®-culties in fermenter control. Beginning with conventional regulatory control of operating variables such as temperature, pH and dissolved oxygen concentration, research in fermenter control has undergone signi®cant changes including the recent neural network based approaches. The objective of the paper is to focus the attention of the researchers to the developments in the control of batch, fedbatch and continuous fermenters over the past few years.

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“…However, the application of computer simulation has been slower in the biopharmaceutical industry and the reasons for this have been highlighted by several authors (Gritsis, & Titchener-Hooker, 1989;Bogle et al, 1996;Petrides, Calandranis, & Cooney, 1995;Polakovic & Mandenius, 1996;Gosling, 1996). Despite these problems, the potential benefits of computer-aided process design tools have been gaining increasing recognition in the bioprocessing industry which is confronted with limited finances and pressing timelines.…”

Section: Process Simulationmentioning

confidence: 99%

Modelling biopharmaceutical manufacture: Design and implementation of SimBiopharma

Farid

Washbrook

Titchener‐Hooker

2007

Computers & Chemical Engineering

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This paper presents the implementation of a conceptual framework, for modelling a biopharmaceutical manufacturing plant, into a prototype decision-support tool, SimBiopharma. The tool's scope covers the ability to evaluate manufacturing alternatives in terms of cost, time, yield, resource utilisation and risk. Incorporating uncertainty means that investment appraisal can be based on both the expected outputs and the likelihood of achieving them. A hierarchical approach to represent the key activities in a manufacturing process is introduced. Emphasis is placed on how a closer integration of bioprocess and business process modelling can be achieved by capturing common information in an object-oriented environment, G2 (Gensym Corporation, Cambridge, MA). The key features of SimBiopharma are highlighted; these include interactive graphics, task-oriented representation and dynamic simulation which create a much more flexible environment for modelling processes. Examples of typical outputs generated by SimBiopharma, when addressing the impact of manufacturing options on strategic operational and financial indicators, are given.

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A process systems engineering view of biochemical process operations

Cited by 13 publications

References 11 publications

Bis‐arylidene Oxindoles as Anti‐Breast‐Cancer Agents Acting via the Estrogen Receptor

Bis‐arylidene Oxindoles as Anti‐Breast‐Cancer Agents Acting via the Estrogen Receptor

Control of fermenters – a review

Modelling biopharmaceutical manufacture: Design and implementation of SimBiopharma

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