show_simul

show_simul simulates hidden state sequences and corresponding DNA sequences under a specified Hidden Markov Model (HMM) framework for analysis of genomic sequence evolution and stochastic processes.

Key Features:

• Simulation of Hidden State Sequences: Simulates sequences of hidden biological states according to the transition structure of a specified HMM.
• DNA Sequence Generation: Generates DNA emission sequences that correspond to the simulated hidden states to model observable sequence outcomes.
• Customizable Hidden Markov Models: Allows specification of HMM parameters to tailor simulations to particular biological questions or organisms such as Lactobacillus sakei.

Scientific Applications:

• Genomics and Microbiology: Model stochastic processes and sequence evolution in genomic studies of bacteria and other organisms.
• Lactobacillus sakei Studies: Simulate genomic sequences to investigate metabolic pathways and adaptive strategies relevant to meat and fish processing.
• Bacterial Adaptation and Food Processing: Explore genetic bases for resilience to fluctuating redox and oxygen levels and biofilm formation on meat surfaces to inform biotechnological approaches for food safety and preservation.

Methodology:

Uses Hidden Markov Models to represent state transition probabilities and emission distributions and simulates hidden state sequences together with corresponding DNA emission sequences.