After years of sample-prep data and assay management data being collected in a variety of spreadsheets, a Life Sciences company realized they had a significant problem. They couldn’t always reproduce promising sample results.
Eventually, the culprit was determined to be slight variations in sample growth conditions that were missed. The different teams involved in the various projects generally kept their data in spreadsheets because of the flexibility, but over the years the inconsistency that resulted (such as different column labels or the occasional empty cell) had led to periodic variations in growth conditions.
The initial Snthesis Bio implementation aggregated, connected and harmonized several years’ worth of sample prep data. With invalid and anomalous data reviewed and corrected or dismissed, a firm and years-long baseline was established.
Today, data about growth conditions is collected by the teams that prepare samples for assays, including temperature, growth time, whether samples were shaken during growth, any modifications made to media, and other relevant growth parameters. This data is collected in spreadsheets or Electronic Lab Notebooks and fed into the Snthesis platform where it is aggregated and connected to previous data. Anomalies are identified immediately, before time is wasted on a sample with incomplete or inaccurate provenance.
Confirming consistency of this data is critical to ensuring predictability. When a promising sample is found, the growth conditions are now available in high fidelity. Being able to link how a sample was grown with its experimental results is critical in part because it enables scientists to analyze the effectiveness of particular growth methods and makes experiment results reproducible in later rounds of assays.
In this case, Snthesis Bio enables reuse of years of older data, ensures conformity in sample growth conditions going forward, and fosters continuous improvement in the sample growth process.
After years of sample-prep data and assay management data being collected in a variety of spreadsheets, a Life Sciences company realized they had a significant problem. They couldn’t always reproduce promising sample results.
Eventually, the culprit was determined to be slight variations in sample growth conditions that were missed. The different teams involved in the various projects generally kept their data in spreadsheets because of the flexibility, but over the years the inconsistency that resulted (such as different column labels or the occasional empty cell) had led to periodic variations in growth conditions.
The initial Snthesis Bio implementation aggregated, connected and harmonized several years’ worth of sample prep data. With invalid and anomalous data reviewed and corrected or dismissed, a firm and years-long baseline was established.
Today, data about growth conditions is collected by the teams that prepare samples for assays, including temperature, growth time, whether samples were shaken during growth, any modifications made to media, and other relevant growth parameters. This data is collected in spreadsheets or Electronic Lab Notebooks and fed into the Snthesis platform where it is aggregated and connected to previous data. Anomalies are identified immediately, before time is wasted on a sample with incomplete or inaccurate provenance.
Confirming consistency of this data is critical to ensuring predictability. When a promising sample is found, the growth conditions are now available in high fidelity. Being able to link how a sample was grown with its experimental results is critical in part because it enables scientists to analyze the effectiveness of particular growth methods and makes experiment results reproducible in later rounds of assays.
In this case, Snthesis Bio enables reuse of years of older data, ensures conformity in sample growth conditions going forward, and fosters continuous improvement in the sample growth process.
