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# So what can we glean from this illuminative

So what can we glean from this illuminative foray into how Chk1 and MK2 participate in checkpoint control? First and foremost, this work reveals that 69 2 deficient in the tumor suppressor p53 contain two spatially distinct G2/M phase checkpoint control kinase networks. This provides compelling evidence to support the notion that kinase targeting creates order out of chaos by clustering enzymes with their preferred substrates. Second, a combination of biochemical and genetic techniques show that local MK2-mediated phosphorylation of the targets involved in RNA regulation potentiates the response by stabilizing the Gadd45α mRNA. This not only uncovers a vital link between checkpoint control and gene expression but emphasizes how localized protein kinase activity can definitively act at the posttranscriptional level. Finally, this study illustrates how p53-deficient cells, or cells that lack a functional p53/p21 pathway, rewire their G2/M checkpoint mechanisms to depend on Gadd45α/p38/MK2. This finding could have significant implications for understanding cell-cycle changes in cancer cells. The next chapter in this intriguing story could assess the differential impact that local and dynamic changes in Chk1 and MK2 activity exert on the posttranscriptional control and protein stability of addition target molecules. This may reveal further consequences of “Chk-ing in” and “Chk-ing out” of the nucleus.

Introduction
A bilevel programming (BLP) problem is characterized by a nested optimization problem with two levels, namely, an upper and a lower level, in a hierarchy where the constraint region of the upper level problem is implicitly determined by the lower level optimization problem. Such problems arise in game-theoretic and decomposition related applications in engineering design. Recently, these problems have received increasing attention and have been applied in various aspects of resource planning, management, and policy making, including water resource management, and financial, land-use, production, and transportation planning [1].
After a real-world problem has been modeled as a bilevel decision problem, how to find the optimal solution for this decision problem becomes very important. However, the nested optimization model described above is quite difficult to solve. It has been proved that solving a linear bilevel program is NP-hard [2], [3]. In fact, even searching for a locally optimal solution of a linear bilevel program is strongly NP-hard [4]. Herminia and Carmen [5] pointed out that the existence of multiple optima for the lower-level problem can result in an inadequate formulation of the bilevel problem. Moreover, the feasible region of the problem is usually non-convex, without necessarily being a connected set, and empty, making diploid difficult to solve BLP problems.
Existing methods for solving BLP problems can be classified into the following categories [6]: extreme-point search, transformation, descent and heuristic, interior point, and intelligent computation approaches. Recently, the latter approach has become an important computational method for solving BLP problems. Genetic algorithm [7], [8], [9], Neural networks [10], Tabu search [11], [12], [13], ant colony optimization [14], and particle swarm optimization [15], [16], [17] are typical intelligent algorithms for solving BLP problems. In all these algorithms, the BLP problem is first reduced to a one-level problem with complementary constraints by replacing the lower level problem with its Karush–Kuhn–Tucker (KKT) optimality condition. However, the one-level complementary slackness problem is non-convex and non-differential. The major difficulty in solving this type of problem is that its constraints fail to satisfy the standard Mangasarian–Fromovitz constraint qualification at any feasible point, and that general intelligent algorithms may fail to uncover such local minima or even mere stationary points.