ReviewComputational and structural biotechnology journal2020
History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 59 citations in OpenAlex.
- Bioreactor Design and Engineering for Cultivated Meat Manufacturing.Advances in biochemical engineering/biotechnology · 2026Review
- Efficient data collection for establishing practical identifiability via active learning.Computational and structural biotechnology journal · 2025Article
- Advances in machine learning-enhanced nanozymes.Frontiers in chemistry · 2024Review
- Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico.Development, growth & differentiation · 2023Review
- Standards, dissemination, and best practices in systems biology.Current opinion in biotechnology · 2023Review
- Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing.Scientific reports · 2023Article
- Towards advanced bioprocess optimization: A multiscale modelling approach.Computational and structural biotechnology journal · 2023Article
- A Simplified Algorithm for Setting the Observer Parameters for Second-Order Systems with Persistent Disturbances Using a Robust Observer.Sensors (Basel, Switzerland) · 2022Article
- Modelling and mathematical optimisation of wastewater treatment in food industries.Open research Europe · 2022Article
- Bioreactor control systems in the biopharmaceutical industry: a critical perspective.Systems microbiology and biomanufacturing · 2022Review
- Modeling the time course of ComX: towards molecular process control for Bacillus wild-type cultivations.AMB Express · 2021Article
- Bioprocess Control: Current Progress and Future Perspectives.Life (Basel, Switzerland) · 2021Review
- Evaluation of an oxygen-dependent self-inducible surfactin synthesis in B. subtilis by substitution of native promoter PAMB Express · 2021Article
- Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.