Cardiac contraction arises from coordinated sarcomeric protein interactions, yet their molecular dynamics remain difficult to quantify. We introduce a likelihood-based framework integrating maximum and profile likelihood methods to estimate and assess parameter identifiability in cardiac myocyte models. Applied to engineered tissues from filamin C-mutant and CRISPR-corrected lines, the approach identifies key kinetic parameters and enables uncertainty-aware model calibration.