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      pyFOOMB: Python framework for object oriented modeling of bioprocesses


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          Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black‐box bioprocess modeling with non‐linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi‐purpose programing language, ODEs can be formulated in an object‐oriented manner, which facilitates their modular design, reusability, and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi‐stage processes, discontinuities in the right‐hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programing language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Array programming with NumPy

            Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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              COPASI--a COmplex PAthway SImulator.

              Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

                Author and article information

                Eng Life Sci
                Eng Life Sci
                Engineering in Life Sciences
                John Wiley and Sons Inc. (Hoboken )
                06 January 2021
                March 2021
                : 21
                : 3-4 , Special Issue dedicated to Prof. Thomas Bley on the occasion of his 70th birthday ( doiID: 10.1002/elsc.v21.3-4 )
                : 242-257
                [ 1 ] Institute of Bio‐ and Geosciences ‐ IBG‐1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany
                [ 2 ] Computational Systems Biotechnology (AVT.CSB) RWTH Aachen University Aachen Germany
                [ 3 ] Bioeconomy Science Center (BioSC) Forschungszentrum Jülich Jülich Germany
                Author notes
                [*] [* ] Correspondence

                Stephan Noack, Institute of Bio– and Geosciences IBG‐1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.

                Email: s.noack@ 123456fz-juelich.de

                Author information
                © 2021 The Authors. Engineering in Life Sciences published by Wiley‐VCH GmbH

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                : 20 November 2020
                : 09 December 2020
                : 09 December 2020
                Page count
                Figures: 7, Tables: 3, Pages: 16, Words: 5943
                Funded by: Bioeconomy Science Center
                Award ID: 313/323‐400‐00213
                Funded by: Bundesministerium für Bildung und Forschung , open-funder-registry 10.13039/501100002347;
                Award ID: 031B0463
                Award ID: 031B0918A
                Research Article
                Research Articles
                Custom metadata
                March 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.9 mode:remove_FC converted:02.03.2021

                bioprocess modeling,object oriented modeling,odes,python


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