Too often, physiological constraints preclude the ability to perturb the in vivo system enough to identify key input/output relationships and their linkage mechanisms.
However, we can move to experimental design space and push the system farther outside its operating regime to quantitatively resolve these relationships. Furthermore, while translating to a constraint-based theoretical space, we can make accurate predictions of model-derived therapeutic strategies and project them back into in vivo space to foresee how various perturbations and stressors affect organ function. This overall strategy motivates our thought processes, weaves through our project aims, and aids mitochondrial physiology studies.
When we develop computational models, we emphasize parsimony and quantitative accuracy. This avoids unnecessarily complicated models and ensures tractable analyses. We use the fundamental modeling equation: Accumulation = In – Out + Production – Consumption to derive differential equations that govern system behavior. Enzyme rate equations and biophysical mechanisms are mass and charge balanced, consistent with the laws of thermodynamics, and obey all conservation laws. We construct these models with biophysical details that range in complexity from the placement of redox centers embedded in protein complexes to more macroscopic hierarchical organization. Moreover, we model the experiment in addition to the physiology.
For example, we ensure accurate representation of compartment volumes, metabolite contents, and biological sample activities. While it is impossible to simulate everything occurring in physiological and experimental domains, we include all major phenomena in the model descriptors. Thus, they are ideal when studying complex biological phenomena.
