SummaryDuring sporulation in Bacillus subtilis, the committedcell undergoes substantial membrane rearrangements to generate two cells of different sizes and fates: the mother cell and the forespore. Here, we demonstrate that the master transcription factor Spo0A reactivates lipid synthesis during development. Maximal Spo0A-dependent lipid synthesis occurs during the key stages of asymmetric division and forespore engulfment. Spo0A reactivates the accDA operon that encodes the carboxylase component of the acetylCoA carboxylase enzyme, which catalyses the first and rate-limiting step in de novo lipid biosynthesis, malonyl-CoA formation. The disruption of the Spo0A-binding box in the promoter region of accDA impairs its transcriptional reactivation and blocks lipid synthesis. The Spo0A-insensitive accDA 0A cells were proficient in planktonic growth but defective in sporulation (s E activation) and biofilm development (cell cluster formation and water repellency). Exogenous fatty acid supplementation to accDA 0A cells overcomes their inability to synthesize lipids during development and restores sporulation and biofilm proficiencies. The transient exclusion of the lipid synthesis regulon from the forespore and the known compartmentalization of Spo0A and ACP in the mother cell suggest that de novo lipid synthesis is confined to the mother cell. The significance of the Spo0A-controlled de novo lipid synthesis during B. subtilis development is discussed.

The optimal control policy for the maximization of the secreted heterologous protein in a fed-batch bioreactor was obtained using SEY2102 as a model host yeast and SUC2-s2 as a model secretory protein. A dynamic model for host cell growth, gene expression, and the secretion of expressed polypeptides was formulated. The optimal system trajectory contains multiple singular arcs that are distinct from one another. Optimal control requires transitions between these singular arcs. Optimal transitions between multiple singular arcs with bounded controls are uniquely located in observance with the Minimum Principle of Pontryagin. An iterative numerical search strategy for determining the optimal control showed successful convergence properties.

Optimal control strategies for maximizing the production of induced foreign protein by recombinant bacteria were sought by the optimal control theory. Nutrient and inducer feeding rates were selected as key control variables. Since the problem is linear in the control variables, the optimal control is bang-bang or singular. Singular solutions are shown to exist. The optimal control theory showed that the specific growth rate with respect to nutrient concentration must be kept in its maximum phase and that there exist both a cell growth period and a protein production period. The optimal control theory calculates exactly the growth and production periods. The glucose concentration is controlled along a singular arc to give a maximum specific growth rate. The inducer level is controlled along a separate singular arc.

This paper describes a neural network based modeling scheme of parameter function modeling that captures the inherently nonlinear dynamics of fed-batch bioreactor systems. A neural network model in conjunction with basic material and energy balances provides a better representation of the system dynamics than a purely empirical model. The combined balance relation and neural network parameter function model can be used with dynamic programming optimization to generate optimal operational policies. The results of applying this method to two fed-batch bioreactor systems are presented and shown to agree well with exact optimal strategies.