We can sequence any DNA of interest, including full genome of an individual, but we are not making full use of this information yet. The ability to predict phenotype emerging from interactions between genotype and environmental conditions will revolutionise medicine and biotechnology. I am convinced that Molecular Biology knowledge will be used to reverse engineer molecular machinery of the cell as a computer model and use mechanistic simulation to predict cellular behaviour for particular set of genetic and environmental perturbations. The major limitation of mechanistic simulation of molecular cell biology is determination of quantitative parameters at whole-cell scale. This challenge has been addressed in the special case of metabolic networks at steady state. Constraint Based Methods enable qualitative prediction of metabolic capabilities of Genome Scale Metabolic Networks (GSMN) without knowledge of rate constants and molecular concentrations. I will present application of this approach to analysis of transcriptome data on individual breast cancer tumours in the context of Recon2 human GSMN revealing low prognosis cluster with active serotonin production. Moreover, Systems Biology has lead to legacy of stochastic kinetic and ODE models of small, quantitatively parameterised sub-networks. This motivates iterative integration of GSMNs, large-scale rule-based models of regulatory networks and legacy of small-scale dynamic models towards whole-cell mechanistic simulations. I will introduce Quasi Steady State Petri Net (QSSPN) – a hybrid simulation algorithm allowing multi-formalism simulation integrating i) qualitative rule based ii) stochastic kinetic iii) deterministic kinetic and iv) flux balance models. I will present dynamic simulation of molecular interaction network describing gene regulation, signalling and whole-cell metabolism in human hepatocyte. I will also introduce new version of SurreyFBA software supporting multi-scale, multi-formalism simulations with QSSPN as well as wide range of constraint-based methods.