Skip to content
#protein folding Open access

Bioprocess Intensification Using Perfusion Culture for Recombinant Protein Production from Lactococcus lactis

Oct 2026 · Microorganisms · 0 citations · 52 references

Abstract

Secretion of recombinant proteins from microbial cell factories is a promising strategy for recombinant protein biomanufacturing as it simplifies downstream processing. In this study, we sought to intensify the Lactococcus lactis high-cell-density culture, using basic fibroblast growth factor 2 (FGF2) as a model recombinant protein. We implemented a perfusion strategy using tangential flow filtration for cell retention, allowing the continuous removal of inhibitory metabolites while replenishing fresh nutrients. When conventional 2 × GM17 medium was used, the approach outperformed batch cultivation, achieving a 4.7-fold increase in biomass and a 3-fold increase in secreted FGF2, reaching a titer of 10,416 ± 892 µg·L−1. Concurrently, we developed a bioprocess model for L. lactis grown in a fortified spent cell culture medium, enabling systematic exploration of operating conditions. A Pareto front was generated for FGF2 titer against media usage, and perfusion profiles balancing both competing objectives were identified. An experimentally selected operating point validated the model predictions, yielding a final OD600 of 49.2 ± 0.3 and FGF2 titer of 2166 ± 161 µg·L−1 FGF2, which were 7.9-fold and 5-fold higher than batch process respectively. Overall, this work demonstrates a perfusion-based intensification strategy for L. lactis and highlights the utility of model-guided process decision-making to enhance productivity while reducing waste.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.