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

Abstract

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple‐to‐use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

A heuristic approach to handling missing data in biologics manufacturing databases

“…Starting from a random population of individual models, the population evolves through a course of generations until an acceptable fit is achieved [10]. The evolutionary nature of the approach renders each analysis unique, and essentially unrepeatable.…”

Process development for the continuous production of heterologous proteins by the industrial yeast, Komagataella phaffii

“…These results indicate that, despite some recognizable trends within data sets, no general inference can be made concerning how cells expressing the same r‐protein using different promoters or different r‐proteins using the same promoter will perform across different conditions. It is evident that the development of a widely usable promoter/condition combination will require a large number of experiments (including directed mutation of promoter sequences; Hartner et al, ) and the application of a machine‐learning approach (Cankorur‐Cetinkaya, Dias et al, ) to this multiparametric optimization problem, if the number of trials involved is not to become unfeasibly large.…”

“…Accordingly, we tested all the strains under consideration (the HuLy-producing strains using either the GAP or oSPI1 promoters and the Fab-3H6-producing strain using either the GAP or TEF1-⍺ promoters) under 12 different conditions. These conditions were randomly generated using CamOptimus (Cankorur-Cetinkaya, Dias et al, 2017) by changing the concentrations of seven nutrients: ammonium, potassium, magnesium, iron, calcium, sorbitol, and glucose (for TEF1-⍺ and GAP promoters) or T A B L E 1 Comparison of the productivity levels of HuLy-producing strain using the oSPI1 promoter Note. HuLy: human lysozyme F I G U R E 4 R-protein production across different conditions.…”

“…A recent review on r‐protein production by K. phaffii using the GAP promoter summarized studies investigating the effect of different carbon sources, or amino‐acid supplementations, different bioreactor operation parameters (i.e., pH, temperature, oxygenation level) on the quality and quantity of the r‐protein production (Çalık et al, ). The fact that most of these studies investigated one process parameter at a time and the concentrations of other nutrients were generally kept constant across different studies implies that there remains a room for improving productivity levels by further media development using multiparametric optimization (Cankorur‐Cetinkaya, Dias et al, ).…”

Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

“…Standard protocols were followed for multiple linear regression (MLR) and the determination of data normality (26). Symbolic regression (SR) was employed as described by (27,28). The gene expression data (Gene Expression Omnibus accession number GSE41094) was adopted from Rodriguez-Lombardero et al (29).…”