Insilico Cell Line Engineering and Process Optimisation

Biotechnological production processes can be improved by either optimising the producing organism or optimising process conditions. Insilico's high-end technology platform facilitates both and can be applied to microbial as well as mammalian systems. A thorough quantitative characterisation of the existing process provides the basis for Insilico’s optimisation strategies.

Aiming at improving the producing organism, Insilico Biotechnology offers an advanced cell line engineering strategy which can generate prokaryotic and eukaryotic production strains with enhanced product yield and productivity. Besides optimising existing production strains by identifying multiple gene targets for enhancement or attenuation of expression, Insilico also designs new cells from scratch which feature alternative biosynthesis pathways.

For the optimisation of the process conditions aiming at increasing product yield and product titre, boosting cell growth, and/or reducing byproduct formation, Insilico designs optimised media, feeding strategies and strategies for clone selection and upscaling.

Furthermore, Insilico helps its customers identify the most appropriate clone for large-scale fermentations.

Identification of Multiple Gene Targets Increase product yield and productivity

Significant improvements in bioprocesses as regards product yield or product titre can only be achieved with Metabolic Engineering if changes in the expression level of multiple target genes are accurately balanced. By automatic simulation of hundreds of thousands of different expression scenarios, Insilico is able to identify and prioritise the most promising gene target combinations, with customer data from previous fermentations being integrated into the model. For each target gene, Insilico provides recommendations stipulating whether it should be over-expressed, deleted or attenuated in expression and to which degree.

This approach has a proven track record for increased product yield and product titre within shorter time and with less experimental effort compared with traditional strain optimisation techniques. Incontrast to undirected mutagenesis, our rational computational strategy has the advantage that our customers know what has changed inside their production cells, so that they can transform this know-how into Intellectual Property. Moreover, Insilico’s strategy facilitates the optimisation of strains for which mutagenesis techniques fail to result in further strain improvement.

Design of Novel Metabolic Pathways Develop innovative bioprocesses

Insilico Biotechnology designs innovative synthesis routes for already existing biotechnological products as well as synthesis pathways for new bio-products using a wide range of production hosts including, but not limited to, Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae and CHO cells.

Host metabolism is augmented in silico using our proprietary reaction database containing more than 8,000 enzymatic and non-enzymatic reactions from over 60 different organisms. The resulting “supernetworks” are screened on high performance computer clusters for novel biochemical pathway solutions leading to the desired product. In many cases, these novel metabolic pathways are eligible for patenting.

Media Design and Feeding Strategies Speed up process development and increase product titre

Insilico Biotechnology improves process performance by aligning nutrient supply exactly with the true demand of your cells. Mismatches between cellular nutrient demand and available supply, due to either nutrient limitation or overfeeding, often result in poor process performance such as slow growth, reduced productivity or excessive byproduct formation. Insilico Biotechnology develops optimised feed media compositions and improved feeding strategies tailored to the actual nutrient demand in each process phase by simulating and evaluating millions of feeding scenarios on our high performance computing platform. These optimised media and feeding schemes increase process performance by yielding higher product titres, improving growth and reducing byproduct formation.

Clone Selection and Upscaling Shorten timelines and save experimental resources

Many of today's bioproduction processes are designed to produce similar products on the same facility and/or using a standardised production process. This is especially true for the production of recombinant proteins. Here, the early choice of a suitable production clone which performs robustly in a given process format is the key to accelerating process development and reducing time to market.

Insilico Biotechnology makes an early selection of production clones possible by providing an extensive range of indicators characterising clone performance and based on fermentation data collected in scale-down cultivation systems. These include time-resolved indicators not only for cell growth and product synthesis, but also for metabolic efficiency, intracellular fluxes and byproduct formation.

This form of comprehensive characterisation provides a sound basis for detecting promising robust candidate clones for large-scale production early during clone assessment. The method further reduces the risk of overlooking good producers at early stages and of propagating poor candidates for too long along the process development pipeline, ultimately saving costly time and resources.

Service Workflow Cell Line Engineering and Process Optimisation See which data is required and which results are obtained

To reach your objectives with predictive network models, Insilico follows a cost-efficient three-step workflow which addresses your needs in an optimum fashion.

I. Model customisation

A whole-cell network model for the selected organism is tailored to the specific genetic background which is of interest to our customer.

Input: Specific cell line properties, media and growth conditions

Result: Customised network model

II. Experimental design and model verification

The network model is verified by incorporating experimental data so that the status quo, e.g. the time course of metabolite concentrations, is reflected correctly. Insilico designs such verification experiments and suggests the most informative measurements, time points and number of replicates depending on the application area of the model (e.g. gene target identification or media optimisation).

Input: Experimental fermentation data (metabolites, gene expression) at typically 5-10 time points

Result: Verified network model

III. Hypothesis testing and model validation

To solve a customer's issue, changes in external process conditions or in the genetic background of the host cells are simulated systematically with the help of high performance computing. The customer receives a detailed report which includes (i) suggested changes and a strategy on how to implement them, (ii) a prediction of how these changes impact cell physiology and process outcome, and (iii) a recommendation for experiments to monitor these effects. Data from these validation experiments are fed back into the model, enabling the customer to obtain continuously refined model predictions by repeating steps II and III in a cyclic fashion.

Input: Experimental fermentation data (metabolites, gene expression) at typically 5-10 time points

Result: Optimised product titre, cell growth or product yield

If you wish to outsource the experimental analytics, Insilico can collect the data in cooperation with its business partners. Please contact us for details.