Abstract: The biological signaling network in cells is a highly complex system composed of various local signaling modules made up of components such as receptors, enzymes, transducers, diffusible secondary messengers, and DNA. Many of these components have isoforms with overlapping functions that arise from component connections and also their spatial relationships [30]. High throughput bioinformatics, genomic/proteomic, and systems investigative techniques have made major advances in studying complex biological systems. However, these techniques are tedious and labor intensive. By using the closed-loop optimization scheme, we introduce an optimization approach that bypasses one-by-one high throughput techniques to analysis biological signaling networks and manipulate their behavior. The advantage of this approach is that it is well-suited to study complex systems, it circumvents the need for detailed information of individual signaling components, and it investigates the network as a whole by utilizing key systemic outputs as indicators. Recent development in nanotechnology introduces various advantages over traditional cellular studies. The ability for experimental tools to match the length scale of biological cells facilitates the study of cell-surface, cell-to-cell relationships, and cell responses to its environmental stimulation. We propose to use control principles and technique developed in field of Engineering and Mathematics to drive cellular systems towards certain behavioral or response outcomes. Cellular stimulation and feedback systems are highly nonlinear and yield the possibility of a large distributed system. Direct and parallel search algorithms have been used successfully used to solve many large-scale optimization problems in the field of Engineering and Science [3, 29]. Therefore, stochastic search algorithms that have inherent random search properties may be suitable in determining the optimal combination of stimulations needed to elicit certain cellular responses. In this study, we aimed to maximize the reactivation of Herpesvirus 8 or Kaposi's Sarcoma-associated Herpesvirus (KSHV) in a body cavity-based lymphoma cell line (BC-3). In the absence of a proper search algorithm, a study with 6 reactivation drugs and 10 concentrations for each drug can easily exceed 1 million test cases. To implement this study, a microfluidic platform was developed that has the capability of live cell imaging and precise cellular environment and stimulation control. At the heart of the closed-loop feedback control scheme is the stochastic search algorithm that analyzes the system information and establishes the driving inputs. Two algorithms, Gur game and Differential Evolution, were considered for the feedback control scheme. In this study, the feedback scheme was able to locate the optimal combination of drugs to reactivate KSHV is less than 30 test iterations. The optimal cocktail was able to perform 49% better than the best performing single drug. For the KSHV system, the results gave us a deeper understanding of the dynamics of viral reactivation that have potential therapeutic applications. We offered a novel systems approach that is generic in nature and can be applied to the study of different biological and cellular systems.