Branch and bound ({BB}) is the primary deterministic approach that has been applied successfully to solve mixed-integer nonlinear programming ({MINLPs}) problems in which the participating functions are nonconvex. Recently, a decomposition algorithm was proposed to solve nonconvex MINLPs. In this work, a generalized branch and cut ({GBC}) algorithm is proposed and it is shown that both decomposition and BB algorithms are specific instances of the {GBC} algorithm with a certain set of heuristics. This provides a unified framework for comparing {BB} and decomposition algorithms. Finally, a set of heuristics which may be potentially more efficient computationally compared to all currently available deterministic algorithms is presented.