The two mechanistically distinct force-generating modes in confluent tissues (traction forces vs junctional tension fluctuations) map onto separable noise regimes in sampling-based mRNA optimisation, such that traction-force-like correlated perturbations improve global codon-usage exploration while junctional-tension-like uncorrelated fluctuations refine local secondary-structure stability, and combining both in a hybrid sampler will outperform either alone on multi-objective mRNA benchmarks.

When designing mRNA sequences via sampling-based optimization (e.g., simulated annealing, MCMC, or evolutionary search) over a multi-objective fitness landscape combining codon adaptation index (CAI), GC-content constraints, and minimum free energy (MFE) secondary structure stability, injecting two distinct noise types—(a) spatially/temporally correlated perturbations modeled on tissue traction-force statistics (long-range, persistent, low-frequency codon-block resampling) and (b) uncorrelated high-frequency perturbations modeled on junctional-tension fluctuation statistics (localized single-codon or single-nucleotide moves)—will produce measurably different and complementary optimization effects: correlated noise will improve global codon-usage metrics (CAI, tAI, codon-pair bias) by ≥10% relative to uncorrelated-noise-only sampling at matched compute budget, uncorrelated noise will improve local structural metrics (MFE, base-pairing probability, unpaired probability in UTR/start-codon windows) by ≥10% relative to correlated-noise-only sampling, and a hybrid sampler combining both (in a defined ratio or schedule) will Pareto-dominate both single-mode samplers on a joint CAI+MFE objective in ≥70% of benchmark sequences tested.

Minimum viable test: (1) Implement three samplers—correlated-noise-only, uncorrelated-noise-only, and hybrid—as variants of a simulated annealing / MCMC optimizer over a fixed multi-objective fitness function (weighted CAI + MFE + GC constraint). (2) Run all three on a benchmark set of 20 mRNA sequences (5 each: SARS-CoV-2 spike fragment, EGFP reporter, erythropoietin, firefly luciferase) with matched compute budget (fixed number of fitness evaluations, e.g., 50,000 per run). (3) Repeat each condition with 10 random seeds. (4) Compare final Pareto fronts and per-objective metrics using nonparametric statistical tests (Mann-Whitney U, Wilcoxon signed-rank) with multiple-comparison correction. (5) Run ablation replacing tissue-derived correlation kernel with matched-statistics random kernel to isolate mechanism-specificity.

def fitness(seq):
    return w1*CAI(seq) + w2*(-MFE(seq)) + w3*gc_penalty(seq)

def correlated_move(seq, k=10):
    i = random.randint(0, len(seq)-k)
    block = seq[i:i+k]
    new_block = resample_codons(block, bias='smooth_kernel')
    return seq[:i] + new_block + seq[i+k:]

def uncorrelated_move(seq):
    i = random.randint(0, len(seq)-1)
    return mutate_single_codon(seq, i)

def hybrid_sampler(seq0, budget, p_local=0.7):
    seq = seq0
    best = seq
    for t in range(budget):
        move = uncorrelated_move if random.random() < p_local else correlated_move
        candidate = move(seq)
        if metropolis_accept(fitness(candidate), fitness(seq), T(t)):
            seq = candidate
        if fitness(seq) > fitness(best):
            best = seq
    return best

# Run for each condition x sequence x seed; log trajectory; compute Pareto front over {CAI, -MFE}