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CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

Ayca Cankorur-Cetinkaya¹, Joao M L Dias^2,3, Jana Kludas⁴

¹Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK.

Microbiology (Reading, England)

|June 22, 2017

Summary

This summary is machine-generated.

This study introduces CamOptimus, a user-friendly tool enabling biologists to optimize synthetic biology experiments using genetic algorithms. It simplifies complex multiparametric optimization, making advanced methods accessible for improved system performance.

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Area of Science:

Synthetic biology
Biotechnology
Bioengineering

Background:

Engineered biological systems involve multiple interacting factors, necessitating multiparametric optimization for experimental design.
Current optimization methods are often impractical due to combinatorial complexity or inaccessible due to a lack of user-friendly software.
Evolutionary algorithms offer potential but are limited to specialists because of software accessibility issues.

Purpose of the Study:

To provide a freely available, graphical user interface (GUI) for optimizing experimental designs in synthetic biology.
To empower a broader range of experimental biologists to utilize complex evolutionary algorithms.
To address the limitations of existing methodologies in terms of practicality and accessibility.

Main Methods:

Development of CamOptimus, a GUI-based software tool.
Implementation of a Genetic Algorithm (GA) to identify optimal parameter combinations.
Application of Symbolic Regression (SR) to model parameter sensitivity.

Main Results:

CamOptimus successfully simplifies the application of evolutionary algorithms for experimental design optimization.
The GA efficiently discovers the subspace containing optimal experimental parameters.
SR provides a model for evaluating the sensitivity of experiments to individual parameters.

Conclusions:

CamOptimus democratizes the use of advanced optimization techniques for synthetic biology research.
The integrated GA and SR approach facilitates comprehensive analysis and exploitation of biotechnological systems.
The tool was validated by optimizing microbial culture conditions for bioactive protein production.