The Influence of Copper Binding on the Stability of the SCO Protein from Bacillus subtilis

Every aerobic organism expresses cytochrome c oxidase to catalyze reduction of molecular oxygen to water, and takes advantage of this energy releasing reaction to produce an electrochemical gradient used in cellular energy production. The protein SCO (Synthesis of cytochrome c oxidase) is a required assembly factor for the oxidase, conserved across many species. SCO is implicated in the assembly of one of two copper centres (ie., CuA) of cytochrome oxidase. The exact mechanism of SCO’s participation in CuA assembly is not known. SCO has been proposed to bind and deliver copper, or alternatively to act in reductive preparation of the CuA site within the oxidase. In this body of work, the strength and stability of Cu(II) binding to Bacillus subtilis SCO is explored via electronic absorption and fluorescence spectroscopies and by calorimetric methods. An equilibrium dissociation constant (Kd) of 3.5x10-12 M was determined as an upper limit for the BsSCO-Cu(II) interaction, via differential scanning calorimetry. In the first reported case for a SCO homolog, dissociation kinetics of Cu(II) from BsSCO were characterized, and found to be dependent on both ionic strength and the presence of free Cu(II) in solution. Further differential scanning calorimetry experiments performed at high ionic strength support a two-step model of BsSCO and Cu(II) binding. The implications of this model for the BsSCO-Cu(II) interaction are presented in relation to the mechanism of interaction between SCO and the CuA site of cytochrome c oxidase.

Robust scenario formulations for strategic supply chain optimization under uncertainty

Strategic supply chain optimization (SCO) problems are often modelled as a two-stage optimization problem, in which the first-stage variables represent decisions on the development of the supply chain and the second-stage variables represent decisions on the operations of the supply chain. When uncertainty is explicitly considered, the problem becomes an intractable infinite-dimensional optimization problem, which is usually solved approximately via a scenario or a robust approach. This paper proposes a novel synergy of the scenario and robust approaches for strategic SCO under uncertainty. Two formulations are developed, namely, naïve robust scenario formulation and affinely adjustable robust scenario formulation. It is shown that both formulations can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded with the infinity-norm, and the uncertain equality constraints can be reformulated into deterministic constraints without assumption of the uncertainty region. Case studies of a classical farm planning problem and an energy and bioproduct SCO problem demonstrate the advantages of the proposed formulations over the classical scenario formulation. The proposed formulations not only can generate solutions with guaranteed feasibility or indicate infeasibility of a problem, but also can achieve optimal expected economic performance with smaller numbers of scenarios.