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Digital models in biotechnology

Industrial biotechnology covers a broad spectrum of research across multiple scales and disciplines. With the ongoing wave of digitalization transforming all sectors, the biotechnology industry is increasingly focused on creating, integrating, and applying digital simulations and models to enhance processes and efficiency.

Axel Knight

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In this context, we systematically review current modeling strategies used at different scales in industrial biotechnology, critically evaluating their advantages, limitations, and practical applications. We also examine contemporary approaches for linking cellular-level models with computational fluid dynamics (CFD) to better predict the behavior of industrial microorganisms in large-scale bioprocesses—a critical requirement for bio-based manufacturing. One of the main challenges in this area is acquiring intracellular data under industrially relevant conditions. We highlight how scale-down strategies combined with precise analytical methods can capture intracellular states at the single-cell level, offering the foundation for creating digital models suitable for both cell factory design and large-scale process optimization in the future.

Introduction

Industrial bioprocesses encompass everything from biofuels and platform chemicals to biopharmaceutical production. Industrial biotechnology, by nature, spans multiple scales and disciplines. The trend of digitalization has brought a new focus on developing digital models—including digital twins—both in academia (Udugama et al., 2022) and in industry (Udugama et al., 2021a, 2021b). At the heart of these models are mathematical frameworks, each designed to represent phenomena at a specific scale. For instance, a metagenomic model captures cellular-level processes, while CFD models describe fluid dynamics in industrial bioreactors exceeding 100 m³.

Even for well-studied organisms such as Escherichia coli, modeling life processes is complex (Fredens et al., 2019). Understanding the metabolic network alone, which governs enzymatic reactions for metabolism, growth, or maintenance (the biotic phase), is insufficient. Cellular performance is strongly influenced by environmental factors (abiotic phase), such as substrate availability, pH, temperature, oxygen, and toxic compounds. Consequently, both biotic and abiotic factors are modeled mathematically to predict system performance and inform rational cell and process design.

Types of Cell Models

Mathematical models of cells vary in complexity. In biotechnology, they are broadly categorized as unstructured or structured models (Bailey, 1998; Fredrickson et al., 1970).

Unstructured Models: These describe a cell using a single global parameter, such as growth rate, biomass, or product formation, ignoring intracellular processes. Here, the biotic phase acts as a “black box.”

Structured Models: These incorporate intracellular components, including the genome, transcriptome, proteome, metabolome, and regulatory networks.

Models can also be unsegregated (treating cells as identical) or segregated (accounting for variability among individual cells). Thus, structured/segregated models describe both intracellular states and differences across the cell population.

Traditional segregation focuses on biological heterogeneity without considering environmental variability. We propose distinguishing biological segregation ((un)segregatedB) from environmental segregation ((un)segregatedS) to account for heterogeneity in both the biotic and abiotic phases. This distinction is essential when scaling up from laboratory cultures to industrial bioprocesses, where environmental heterogeneity increases.

Current Modeling Approaches

Process models in industrial biotechnology can be classified into eight categories based on complexity. The simplest combines unstructured and unsegregated representations, while the most complex integrates structured, spatially-resolved, and biologically segregated models.

A critical component of model development is data acquisition. Accurate modeling relies on high-quality experimental data at both spatial and cellular levels. Scale-down bioreactors and advanced analytical tools are used to capture the intracellular and extracellular states required to validate these models.

Future Directions

Advances in modeling aim to create multi-scale, multi-disciplinary digital frameworks. Future efforts focus on improving integration between cell-level simulations and large-scale bioprocess models, enhancing predictive capabilities, and supporting rational design for industrial applications.

Conclusion

Mathematical models are indispensable in biotechnology, underpinning research in metabolic engineering, process control, and optimization. Traditionally, models are domain-specific and simplify or omit phenomena outside their focus. The current challenge is to develop multi-disciplinary digital models that unify these approaches, enabling better synergy across research fields and supporting advanced industrial bioprocess design.

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