(361k) Prototype Development to Guide the Systemic Description of Biotechnological Processes

In recent decades, there has been a focus on replacing conventional chemical processes with bioprocesses, aiming to obtain more elaborate and complex products in an efficient and sustainable way. However, for the production of compounds via biological routes to be economically viable, bioprocesses need to be studied and optimized an engineered microbial producer strain (HEMMERICH et al., 2021). Besides, data acquisition techniques have improved over the years, generating an expressive amount of data from bioprocesses, which need to be processed and interpreted through the development of quantitative methods (MEYER et al., 2021). The integration of biology and modeling fields allows for the development of tools to quantify and characterize bioproducts and bioprocesses at different scales, from genomic to metabolomics. Therefore, computing becomes a key resource that gives rise to a field known as systems biology, which is a multidisciplinary area, requiring different expertise from researchers, such as biology and mathematical modeling (BASSALO et al., 2016; ZUO et al., 2018; MEYER et al., 2021). Concurrently, scientific research increasingly depends on collaborations to solve real-world problems. Given the different nature of the areas involved, communication and competence sharing can be a challenge that is often overlooked (ZUO et al., 2018).

In this context, computational frameworks for simulation and optimization of bioprocesses are fundamental; however, it is also important that they are easy to use and can be understood by researchers with different backgrounds, and that they are comprehensive within the subfield of application. For this reason, the structure of such frameworks must be carefully designed by the programmer. In this work, a software prototype is proposed to be used for quantitative analysis of biotechnological processes. Its conception is initially based on the ontological construction of bioprocesses, which can be conceived from the use of Unified Modeling Language (UML) diagrams, and provides the basis for developing the software in an organized manner, aiming to use and reuse blocks of code, without compromising the structure and functioning of the software (GUEDES, 2018; YURIN et al., 2020). UML diagrams are created for the representation of the abstraction and classification of the bioprocess components, where each bioprocess component abstraction is a class. Each class can be subdivided into others that contain the same characteristic as the parent class. For example, a class can represent the mass balance of a general bioreactor reaction system and corresponding subclasses represent types of operation modes – batch, fed-batch and continuous. In these cases, it is possible to adapt the balance based on the set of experiments. Thus, users can configure the bioprocess and choose the calculation they wish to perform (simulate, estimate parameters or optimization). A case of simulation of the bioprocess in batch is performed, and parameters, such specific growth, uptake and production rates, are considered to describe cellular behavior. It is noticed the abstraction of concepts involving bioprocess must be done with care, in order to generate several options, and should allow for increased complexity and flexibility of the analysis. Structuring the software allows for more and more complex models to be inserted into it, keeping its functionalities and providing flexibility for the experimental user to choose several procedures to be performed in bioprocesses. The attention to the software architecture will allow that more detailed kinetic metabolic models could be used in the future. This will bring new perspectives for bioprocesses modeling, as nowadays the main softwares treat separately and punctually for a given set of models, with an interface that does not allow new possibilities of models to be added. In summary, this prototype is an alternative tool for model based integration and analysis of data, which can be the germ of a new, robust and sophisticated tool for systems biology.

Acknowledgements

This research was financially supported by the Coordination of Superior Level Staff Improvement (CAPES) grant no. 88887.464619/2019-00, PROEX program, National Council for Scientific and Technological Development (CNPQ) and RCGI/FAPESP (2020/15230-5). Galo A. C. Le Roux acknowledges his productivity fellowships from CNPq (312049/2018-8) and Priscila M. da Paz for her scholarship from CNPq (142147/2019-2).

References

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