Tailor-Made Zinc-Finger Transcription Factors Activate FLO11 Gene Expression with Phenotypic Consequences in the Yeast Saccharomyces cerevisiae

Cys2His2 zinc fingers are eukaryotic DNA-binding motifs, capable of distinguishing different DNA sequences, and are suitable for engineering artificial transcription factors. In this work, we used the budding yeast Saccharomyces cerevisiae to study the ability of tailor-made zinc finger proteins to activate the expression of the FLO11 gene, with phenotypic consequences. Two three-finger peptides were identified, recognizing sites from the 5′ UTR of the FLO11 gene with nanomolar DNA-binding affinity. The three-finger domains and their combined six-finger motif, recognizing an 18-bp site, were fused to the activation domain of VP16 or VP64. These transcription factor constructs retained their DNA-binding ability, with the six-finger ones being the highest in affinity. However, when expressed in haploid yeast cells, only one three-finger recombinant transcription factor was able to activate the expression of FLO11 efficiently. Unlike in the wild-type, cells with such transcriptional activation displayed invasive growth and biofilm formation, without any requirement for glucose depletion. The VP16 and VP64 domains appeared to act equally well in the activation of FLO11 expression, with comparable effects in phenotypic alteration. We conclude that the functional activity of tailor-made transcription factors in cells is not easily predicted by the in vitro DNA-binding activity

Equivalent Formulations of the Stochastic Cash Balance Problem

This paper shows that the widely appearing "marginal cost" formulations of the dynamic, periodic review, stochastic cash balance problem are equivalent to a "total cost" formulation, as long as the holding, shortage cost, and "closing of account" functions are constructed correctly.

On the Optimality of Generalized (s, S) Policies

A standard inventory model is examined with a concave increasing ordering cost function rather than simply a linear one with a setup cost. A generalized (s, S) policy is shown to be optimal in the n-period problem. A generalization of k-convex and quasi-convex functions to quasi-k-convex functions is required in the process. The probability densities of demand must be one-sided Pólya densities.

On the Optimality of Structured Policies in Countable Stage Decision Processes

Multi-stage decision processes are considered, in notation which is an outgrowth of that introduced by Denardo [Denardo, E. 1967. Contraction mappings in the theory underlying dynamic programming. SIAM Rev. 9 165-177.]. Certain Markov decision processes, stochastic games, and risk-sensitive Markov decision processes can be formulated in this notation. We identify conditions sufficient to prove that, in infinite horizon nonstationary processes, the optimal infinite horizon (present) value exists, is uniquely defined, is what is called "structured," and can be found by solving Bellman's optimality equations: \epsilon -optimal strategies exist: an optimal strategy can be found by applying Bellman's optimality criterion; and a specially identified kind of policy, called a "structured" policy is optimal in each stage. A link is thus drawn between (i) studies such as those of Blackwell [Blackwell, D. 1965. Discounted dynamic programming. Ann. Math. Stat. 36 226-235.] and Strauch [Strauch, R. 1966. Negative dynamic programming. Ann. Math. Stat. 37 871-890.], where general policies for general processes are considered, and (ii) other studies, such as those of Scarf [Scarf, H. 1963. The optimality of (S, s) policies in the dynamic inventory problem. H. Scarf, D. Gilford, M. Shelly, eds. Mathematical Methods in the Social Sciences . Stanford University Press, Stanford.] and Derman [Derman, C. 1963. On optimal replacement rules when changes of state are Markovian. R. Bellman, ed. Mathematical Optimization Techniques. University of California Press. Berkeley.] where structured policies for special processes are considered. Those familiar with dynamic programming models (e.g., inventory, queueing optimization, replacement, optimal stopping) will be well acquainted with the use of what we call structured policies and value functions. The infinite stage results are built on finite stage results. Results for the stationary infinite horizon case are also included. For an application, we provide conditions sufficient to prove that an optimal stationary strategy exists in a discounted stationary risk sensitive Markov decision process with constant risk aversion. In Porteus [Porteus, E. On the optimality of structured policies in countable stage decision processes. Research Paper No. 141, Graduate School of Business, Stanford University, 71 pp., 1973, 1974, unabridged version of present paper.], of which this is a condensation, we also (i) show how known conditions under which a Borel measurable policy is optimal in an infinite horizon, nonstationary Markov decision process, fit into our framework, and (ii) provide conditions under which a generalized (s, S) policy [Porteus, E. 1971. On the optimality of generalized (s, S) policies. Management Sci. 17 411-426.] is optimal in an infinite horizon nonstationary inventory process.

Some Bounds for Discounted Sequential Decision Processes

New bounds are obtained on the optimal return function for what are called discounted sequential decision processes. Such processes are equivalent to ones satisfying the contraction and monotonicity properties (Denardo [Denardo, E. V., 1967. Contraction mappings in the theory underlying dynamic programming. SIAM Review. Vol. 9, pp. 165-177.]). The bounds are useful primarily in the infinite horizon case. Certain subprocesses are exploited, based on the simple notion of taking only those states which are relevant into consideration. Some existing algorithms and some of their obvious extensions are listed. The possibility of identifying nonoptimal decisions, as in MacQueen [MacQueen, J. B., 1966. A Modified dynamic programming method for markovian decision problems. Journal of Mathematical Analysis and Applications. Vol. 14, pp. 38-43.], is included.

Investing in Reduced Setups in the EOQ Model

This paper is motivated by the observation that the Japanese have devoted much time and energy to decreasing setup costs in their manufacturing processes and that there has been little in the way of a formal framework available to use to think about such efforts. The object of this paper is to begin to provide such a framework. The framework developed identifies only one aspect of the advantages of reducing setups, namely reduced inventory related operating costs. The other advantages, such as improved quality control, flexibility, and increased effective capacity, are not accounted for in this paper. Nevertheless, substantial reductions in setups may be warranted based solely on the benefits identified in this paper. The approach taken here introduces an investment cost associated with changing the (current) setup level and adds a per unit time amortization of this cost to the other costs identified in the standard EOQ model. The general problem becomes that of minimizing the sum of a convex and a concave function. In two special cases, the minimization can be carried out explicitly. In one of these cases, numerous interpretations of the results are made, including comparisons of Japanese and American practices. For example, holding other parameters constant, there is a critical sales level such that investment is made in reducing setups if and only if the sales rate is above that level. When such investment is made, the optimal lot size is independent of the sales rate. The paper also addresses the joint selection of the setup cost and the sales rate. Selection of the sales rate is seen as incorporating explicit production and holding costs into the classical monopolist's pricing problem. An explicit solution is obtained for the model postulated.production/inventory, EOQ, operating characteristics

Simultaneous Capacity and Production Management of Short-Life-Cycle, Produce-to-Stock Goods Under Stochastic Demand

This paper derives the optimal simultaneous capacity and production plan for a shortlife-cycle, produce-to-stock good under stochastic demand. Capacity can be reduced as well as added, at exogenously set unit prices. In both cases studied, with and without carryover of unsold units, a target interval policy is optimal: There is a (usually different) target interval for each period such that capacity should be changed as little as possible to bring the level available into that interval. Our contribution in the case of no carry-over, is a detailed characterization of the target intervals, assuming demands increase stochastically at the beginning of the life cycle and decrease thereafter. In the case of carry-over, we establish the general result and show that capacity and inventory are economic substitutes: The target intervals decrease in the initial stock level and the optimal unconstrained base stock level decreases in the capacity level. In both cases, optimal service rates are not necessarily constant over time. A numerical example illustrates the results.Capacity management, Production management, Capacity expansion, Capacity contraction, Finite lifetime, Stochastic demand, Nonstationary