Traditionally, computational and experimental approaches in biotechnological research are separate fields. EV Biotech integrates these fields to enhance the output of biotechnological research by combining well-designed experiments and models.

Our synergy

Developing and optimising biological systems as complex as microorganisms requires extensive trial-and-error testing, which is a time consuming, cost intensive and risk sensitive process. Mechanistic modelling greatly streamlines strain development. Models are useful to uncover interdependencies between mechanisms and to narrow down lab work. In turn, well-designed experiments can greatly enhance mechanistic models, increasing information-providing variables and even adding new mechanisms. 

At EV Biotech, we integrate multiple mechanistic models and their predictions with our strain engineering and laboratory experiments. In this way we can uncover interdependencies between mechanisms to narrow down lab work.

Mechanistic models employ mathematical relations to describe organism behaviour. These models support strain development by: 

  • Generating and testing hypotheses in silico.

  • Replicating and interpreting behaviour over different scales.

  • Improving understanding by adding and refining mechanisms and parameters within the model.

How our integrated feedback loop can make a difference

Example: Pathway modification for the production of biomolecules

Traditional fermentation utilises the ability of certain microorganisms to produce compounds of interest. Genetic modification of these pathways can further increase natural production and decrease the accumulation of unwanted byproducts. Genome scale modelling can be used to test potential targets, reducing the number of modifications that are created in the lab.

Example: Modelling over different scales greatly speeds up strain development

Microtitre plates and shake flasks enable high-throughput strain screening due to their simplicity and size. Substrate release systems replicate lower growth rates in fed-batch mode, as opposed to batch growth. Robust fermentation models on different scales and feed modes determine set-ups with consistent growth and production rates.

Example: Synthetic secretion signals for optimised downstream processing

Downstream processing to collect compounds produced in cells can contribute significantly to costs. Secretion signals are often used to simplify downstream processing, but the efficiency of these signals can be limited by both the organism used and the specific desired products. AI assisted predictions enabled the development of a library of synthetic secretion signals that can be tested by our strain developers to identify the most promising sequences for a desired organism and product.

Example: Scale down fermentation simulation

Comprehensive data collection in large-scale reactors is extremely challenging. Computational fluid dynamic models, however, simulate heterogeneous conditions, aiding experimental design. Simulations can be used to replicate glucose concentration gradients in bench-top reactors to study transcriptomic responses.

A: A coupled spatial model of a reactor with a pooled metabolic model is used to simulate the B: conditions a cell encounters throughout the reactor. C: These conditions are replicated in bench-top reactors to collect D: data on large scale conditions. E: After data analysis, the metabolic model can be improved and used in a new experimental cycle.

Example: Strain engineering to enable low-cost carbon alternatives

While fermentation can be a green alternative to petroleum-based production, the need for high-quality carbon sources can contribute significantly to cost and increase demand on food-based crops. Strain engineering can enable the use of low-cost carbon sources derived from waste products. For example, the expression of amylase enzymes in yeast can allow for growth on starch derived from plant waste. Alternatively, with our media optimisation tool we can predict how to utilise a side stream as carbon source for the microorganism.

Why we stand out

Agile & Transparent

Working agile enables us to deliver the product faster with enhanced quality, predictability, and flexibility to respond to change. And transparency, one of the key pillars of agile framework, allows our clients to learn how we are doing the work, fosters trust, and leads to making good decisions.


At EV Biotech we undertake a systemic and holistic risk assessment across the key success factors, and catalogue all the ways a project could fail, even at the earliest stages of development. This allows us to terminate project early, or deploy risk mitigation strategies throughout the full client journey.

Fair IP Ownership

We value and protect client's intellectual property and work with a clear distinctions between the foreground and background IP.


Interested to learn more about our approach?

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