Experimental design methods for bioengineering applications

Formula Optimization of a Perilla-canola Oil (O/W) Emulsion and Its Potential Application as an Animal Fat Replacer in Meat Emulsion

The formulation of an oil/water (o/w) emulsion made up of a mixture of perilla oil and canola oil (30/70 w/w) was optimized using a response surface methodology to find a replacement for animal fat in an emulsion-type meat product. A 12 run Plackett-Burman design (PBD) was applied to screen the effect of potential ingredients in the (o/w) emulsion, including polyglycerol polyricinoleate (PGPR), fish gelatin, soy protein isolate (SPI), sodium caseinate, carrageenan (CR), inulin (IN) and sodium tripolyphosphate. The PBD showed that SPI, CR and IN showed promise but required further optimization, and other ingredients did not affect the technological properties of the (o/w) emulsion. The PBD also showed that PGPR played a critical role in inhibiting an emulsion break. The level of PGPR was then fixed at 3.2% (w/w total emulsion) for an optimization study. A central composite design (CCD) was applied to optimize the addition levels of SPI, CR or IN in an (o/w) emulsion and to observe their effects on emulsion stability, cooking loss and the textural properties of a cooked meat emulsion. Significant interactions between SPI and CR increased the cooking loss in the meat emulsion. In contrast, IN showed interactions with SPI leading to a reduction in cooking loss. Thus, CR was also removed from the formulation. After optimization, the level of SPI (4.48% w/w) and IN (14% w/w) was validated, leading to a perilla-canola oil (o/w) emulsion with the ability to replace animal fat in an emulsion-type meat products.

Leveraging advances in biology to design biomaterials

Biomaterials have dramatically increased in functionality and complexity, allowing unprecedented control over the cells that interact with them. From these engineering advances arises the prospect of improved biomaterial-based therapies, yet practical constraints favour simplicity. Tools from the biology community are enabling high-resolution and high-throughput bioassays that, if incorporated into a biomaterial design framework, could help achieve unprecedented functionality while minimizing the complexity of designs by identifying the most important material parameters and biological outputs. However, to avoid data explosions and to effectively match the information content of an assay with the goal of the experiment, material screens and bioassays must be arranged in specific ways. By borrowing methods to design experiments and workflows from the bioprocess engineering community, we outline a framework for the incorporation of next-generation bioassays into biomaterials design to effectively optimize function while minimizing complexity. This framework can inspire biomaterials designs that maximize functionality and translatability.

A two-step optimization strategy for 2nd generation ethanol production using softwood hemicellulosic hydrolysate as fermentation substrate

Ethanol production using waste biomass represents a very attractive approach. However, there are considerable challenges preventing a wide distribution of these novel technologies. Thus, a fractional-factorial screening of process variables and Saccharomyces cerevisiae yeast inoculum conditions was performed using a synthetic fermentation media. Subsequently, a response-surface methodology was developed for maximizing ethanol yields using a hemicellulosic solution generated through the chemical hydrolysis of steam treatment broth obtained from residual softwood biomass. In addition, nutrient supplementation using starch-based ethanol production by-products was investigated. An ethanol yield of 74.27% of the theoretical maximum was observed for an initial concentration of 65.17g/L total monomeric sugars. The two-step experimental strategy used in this work represents the first successful attempt to developed and use a model to make predictions regarding the optimal ethanol production using both softwood feedstock residues as well as 1st generation ethanol production by-products.

Strategies for Fermentation Medium Optimization: An In-Depth Review

Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical “one-factor-at-a-time” to modern statistical and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.