Applying Statistical Design of Experiments To Understanding the Effect of Growth Medium Components on Cupriavidus necator H16 Growth

Data-driven learning of structure augments quantitative prediction of biological responses

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

Employing active learning in the optimization of culture medium for mammalian cells

Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems. Here, we addressed this issue by employing active learning. Specifically, we introduced machine learning to cell culture experiments to optimize culture medium. The cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used to find optimized media as pilot studies. To acquire the training data, cell culture was performed in a large variety of medium combinations. The cellular NAD(P)H abundance, represented as A450, was used to indicate the goodness of culture media. In active learning, regular and time-saving modes were developed using culture data at 168 h and 96 h, respectively. Both modes successfully fine-tuned 29 components to generate a medium for improved cell culture. Intriguingly, the two modes provided different predictions for the concentrations of vitamins and amino acids, and a significant decrease was commonly predicted for fetal bovine serum (FBS) compared to the commercial medium. In addition, active learning-assisted medium optimization significantly increased the cellular concentration of NAD(P)H, an active chemical with a constant abundance in living cells. Our study demonstrated the efficiency and practicality of active learning for medium optimization and provided valuable information for employing machine learning technology in cell biology experiments.

Polyhydroxyalkanoate production from animal by-products: Development of a pneumatic feeding system for solid fat/protein-emulsions

Fat-containing animal by-product streams are locally available in large quantities. Depending on their quality, they can be inexpensive substrates for biotechnological processes. To accelerate industrial polyhydroxyalkanoate (PHA) bioplastic production, the development of efficient bioprocesses that are based on animal by-product streams is a promising approach to reduce overall production costs. However, the solid nature of animal by-product streams requires a tailor-made process development. In this study, a fat/protein-emulsion (FPE), which is a by-product stream from industrial-scale pharmaceutical heparin production and of which several hundred tons are available annually, was evaluated for PHA production with Ralstonia eutropha. The FPE was used as the sole source of carbon and nitrogen in shake flask and bioreactor cultivations. A tailored pneumatic feeding system was built for laboratory bioreactors to facilitate fed-batch cultivations with the solid FPE. The process yielded up to 51 g L^-1 cell dry weight containing 71 wt% PHA with a space-time yield of 0.6 g_PHA L^-1  h^-1 without using any carbon or nitrogen sources other than FPE. The presented approach highlights the potential of animal by-product stream valorization into PHA and contributes to a transition towards a circular bioeconomy.

PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies

We present here a newly developed workflow—which we have called PASIV—designed to provide a solution to a practical problem with design of experiments (DoE) methodology: i.e., what can be done if the scoping phase of the DoE cycle is severely hampered by burden and toxicity issues (caused by either the metabolite or an intermediary), making it unreliable or impossible to proceed to the screening phase? PASIV—standing for pooled approach, screening, identification, and visualization—was designed so the (viable) region of interest can be made to appear through an interplay between biology and software. This was achieved by combining multiplex construction in a pooled approach (one-pot reaction) with a viability assay and with a range of bioinformatics tools (including a novel construct matching tool). PASIV was tested on the exemplar of the lycopene pathway—under stressful constitutive expression—yielding a region of interest with comparatively stronger producers.

Pure cultures for synthetic culture development: Next level municipal waste treatment for polyhydroxyalkanoates production

Polyhydroxyalkanoates (PHAs), as bio-based plastics, promise a transition from petroleum products to green and sustainable alternatives. However, their commercial production is yet impeded by high production costs. In this study, we assessed synthetic culture in mono and co-culture modes for bacterial PHA production. It was demonstrated that volatile fatty acids (VFAs) derived from food waste and primary sludge are cheap carbon sources for maintaining high production yields in the synthetic cultures. The maximum obtained PHA was 77.54 ± 5.67% of cell dried weight (CDW) (1.723 g/L) from Cupriavidus necator and 54.9 ± 3.66% of CDW (1.088 g/L) from Burkholderia cepacia. The acquired results are comparable to those in literature using sugar substrates. Comparatively, lower PHA productions were obtained from the co-cultivations ranging between 36-45 CDW% (0.39-0.48 g/L). Meanwhile, the 3-hydroxyvalerate content in the biopolymers were increased up to 21%.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

In situ quantification of poly(3-hydroxybutyrate) and biomass in Cupriavidus necator by a fluorescence spectroscopic assay

Abstract

Fluorescence spectroscopy offers a cheap, simple, and fast approach to monitor poly(3-hydroxybutyrate) (PHB) formation, a biodegradable polymer belonging to the biodegradable polyester class polyhydroxyalkanoates. In the present study, a fluorescence and side scatter-based spectroscopic setup was developed to monitor in situ biomass, and PHB formation of biotechnological applied Cupriavidus necator strain. To establish PHB quantification of C. necator, the dyes 2,2-difluoro-4,6,8,10,12-pentamethyl-3-aza-1-azonia-2-boranuidatricyclo[7.3.0.03,7]dodeca-1(12),4,6,8,10-pentaene (BODIPY^493/503), ethyl 5-methoxy-1,2-bis(3-methylbut-2-enyl)-3-oxoindole-2-carboxylate (LipidGreen2), and 9-(diethylamino)benzo[a]phenoxazin-5-one (Nile red) were compared with each other. Fluorescence staining efficacy was obtained through 3D-excitation-emission matrix and design of experiments. The coefficients of determination were ≥ 0.98 for all three dyes and linear to the high-pressure liquid chromatography obtained PHB content, and the side scatter to the biomass concentration. The fluorescence correlation models were further improved by the incorporation of the biomass-related side scatter. Afterward, the resulting regression fluorescence models were successfully applied to nitrogen-deficit, phosphor-deficit, and NaCl-stressed C. necator cultures. The highest transferability of the regression models was shown by using LipidGreen2. The novel approach opens a tailor-made way for a fast and simultaneous detection of the crucial biotechnological parameters biomass and PHB content during fermentation.

Key points

• Intracellular quantification of PHB and biomass using fluorescence spectroscopy.

• Optimizing fluorescence staining conditions and 3D-excitation-emission matrix.

• PHB was best obtained by LipidGreen2, followed by BODIPDY^493/503 and Nile red.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11670-8.

Microbial assimilation of lignin-derived aromatic compounds and conversion to value-added products

Lignin is an abundant and sustainable source of aromatic compounds that can be converted to value-added products. However, lignin is underutilized, since depolymerization produces a complex mixture of aromatic compounds that is difficult to convert to a single product. Microbial conversion of mixed aromatic substrates provides a potential solution to this conversion challenge. Recent advances have expanded the range of lignin-derived aromatic substrates that can be assimilated and demonstrated efficient conversion via central metabolism to new potential products. The development of additional non-model microbial hosts and genetic tools for these hosts have accelerated engineering efforts. However, yields with real depolymerized lignin are still low, and additional work will be required to achieve viable conversion processes.

The pMTL70000 modular, plasmid vector series for strain engineering in Cupriavidus necator H16

Cupriavidus necator H16 can convert CO₂ into industrial chemicals and fuels. To facilitate its engineering, we designed, built and tested the pMTL70000 modular plasmids comprising standardised Cupriavidus and E. coli replicons, selectable markers and application specific modules. Plasmids were characterised in terms of transmissibility, stability, copy number and compatibility.

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A standardised, modular vector system for engineering Cupriavidus necator H16.

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An improved procedure for DNA transfer by electroporation.

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Vectors characterised in terms of segregational stability, copy number and compatibility.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.