History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance

Abstract

Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a "one-to-one" representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.

Keywords: Advanced process control; Bioprocess engineering; Biotechnology; Hardware development; Industry 4.0; Modeling & optimization; Soft sensor.

Graphical abstract

Fig. 1

Timeline of model concepts (red), computational infrastructure (brown), monitoring- (blue), control- (purple) and optimization concepts (green) as well as computationally intensive models and potential future trends (black) in biotechnology. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Fields of application of process models.

Fig. 3

(A) Development of microprocessor power, given in million floating point operations per second (mFLOPS) (filled circles) and average time required to solve (empty squares) or optimize (filled squares) a reference ODE (ordinary differential equation) system starting from the 1980s. The reference ODE system consists of 11 equations and 36 parameters 19 solved using MATLAB ode15s package and fminsearch 5 parameter optimization with initial deviations of 20% from the final values. (B) Price development in USD for local data storage equivalent to 100 E. coli genomes (filled circles) in comparison to the average annual budget of an NIH principal investigator (grey bars). Data and specifications are listed as additional section before the references.

Fig. 4

Trends of future and current model-based techniques with proposed assignment to bioreactor, process and molecular & metabolic layer of modeling.