strainbytreatment

Strainbytreatment is a term used in experimental design and data analysis to describe the interaction between biological strains and applied treatments. It refers to situations where the effect of a treatment on a measured response varies across strains. In a two-factor design with factors strain and treatment, the response can be modeled as Y = μ + α_strain + β_treatment + (αβ)_strainxtreatment + ε. The term (αβ)_strainxtreatment represents the strain-by-treatment interaction.

Applications include microbiology, pharmacology, and plant pathology, where researchers compare multiple strains or isolates of a species under different treatments such as antibiotics, environmental conditions, or chemical stimuli. This approach helps identify strain-specific susceptibilities or resistances and can inform personalized or strain-targeted interventions.

Methodology: Designs are typically factorial with replication. Analysis commonly uses two-way ANOVA or linear mixed models that include an interaction term. Significant interaction indicates that treatment effects differ by strain; non-significant interaction suggests a common treatment effect across strains, with possible main effects.

Examples: Testing antibiotic susceptibility across several bacterial strains under varying drug concentrations; evaluating fungicide efficacy on multiple fungal isolates; assessing production yield of microbial strains under different nutrient or stress conditions.

Limitations: Detecting interactions requires adequate sample size and power; multiple testing can inflate false positives; misinterpretation can occur if main effects are read without considering interactions; biological relevance must be established.

See also: genotype-by-environment interaction, interaction effect, two-way ANOVA, factorial design, linear mixed model.