List of questions

What are some good practises when communicating between biologists, data scientists and engineers? In particular around building out tooling, data management, version management of protocols and models?

At LabGenius we apply empirical computation methods to accelerate evolution with the mission to find radically new proteins for therapeutics. To this end we have a broad interdisciplinary team of biologists and tech, and would like to learn best practises of communication of data and processes.

What are the advantages of using AI models over regression models to optimize and model highly nonlinear and complex biological processes in fermentation?.

Puratos is an international group offering a full range of innovative products, raw materials and application expertise to the bakery, patisserie and chocolate sectors.
Puratos services are available in over 100 countries around the world with more than four hundred people dedicated to research and development.
Bread is one of the most common, relatively low cost, traditional foods around the world. Yet bread actually has close links with biotechnology, being enzymes the common denominator.
Due to the changes in the industry and the increasing demand for more natural products, enzymes have gained real importance in bread formulations. New and rapid advances in biotechnology tools have made new enzymes available to the baking industry.
The Enzymes Research team in Puratos is dedicated to innovating and differentiating the products though enzymes research. In collaboration with internal and external partners, the team covers the entire biotechnological process of obtaining industrial enzymes from early stage to industrial production.
Data analysis, statistics methods and bioinformatics are also key tools to continue our innovation in the enzymatic field. Incorporating systems associated with data management, analytics, and predictive allow us to accelerate the entire life cycle of enzymes research and discovery.

As a Biotech unit we manage several diversity of data from biological and metabolic response to yield after downstream process . Then a diversity of biological data depends from each other in a living model.

How in an R&D biotechnology department (enzymes production) using AI and data models could reduce timing of experimental design and time to market?

Adaption of data and AI technologies needs to be translated in business impact. Time to market reduction is one of them

Could the use of Genetic Algorithm and AI speed the discovery process for new enzymes? How?

Time to market in a company is always a pressure for R&D teams. Could the use of AI really decrease the number of experimental designs

Starting from scratch in a private R&D biotech center with genetics, upstream and Downstream process. how will you start implementing a data governance and predictive models strategy?

Setting up in a company a Data process flow and analytics, predictive models Is needed but . How to start where do they add the most value in the process?