FSC for Optimization of Biofuel Production from Microalgae (Leyla Sabet, Gauvain Haulot)
Mouse Stem Cell (Zhicao Yue)
Cell pattern formation (Cecil Chen)
FSC for hES cell culture (Hideaki Tsutsui)
A major challenge in stem cell-mediated regenerative medicine is the development of defined culture systems for the maintenance of clinical-grade human embryonic stem (hES) cells. In this project, we identified, using a feedback system control scheme, a unique combination of three small molecule inhibitors that enables the maintenance of hES cells on a fibronectin-coated surface through single cell passaging. After 20 passages, the undifferentiated state of the hES cells was confirmed by OCT4, SSEA4 and NANOG expressions, whereas their pluripotent potential and genetic integrity were demonstrated by teratoma formation and normal karyotype, respectively. Our study attests to the power of the feedback system control scheme to quickly pinpoint optimal conditions for desired biological activities, and provides a chemically defined, scalable and single cell passaging culture system for hES cells.
Tsutsui, H., Valamehr, B., Hindoyan, A., Qiao, R., Ding, X., Guo, S., Witte, O.N., Liu, X., Ho, C.M., and Wu, H., “An Optimized Small Molecule Inhibitor Cocktail Supports Long-term Maintenance of Human Embryonic Stem Cells,” Nature Communications, 2:167, DOI:10.1038/ncomms1165, 2011.
Feedback System Control (FSC) on Cancer Eradication (Chien Sun)
Figure. Average cell viability of WEHIs and MEFs normalized for each iteration through the DE algorithm. A total of 14 iterations were performed. Each iteration has 22 test vectors for WEHIs and 22 test vectors for MEFs. Each cell viability data point is an average of 22 test vectors in its corresponding iteration. Cell viability for each test vector was normalized to the DMSO control for each iteration.
We are using systems control algorithms to identify an anti-cancer agent combination that is toxic to leukemia cells while sparing non-malignant cells. Clinically, anti-cancer drug cocktails have several important advantages over single drug therapies that can include a) lowered concentrations of each drug than if used singularly; b) reduced development of drug resistance; and c) increased drug efficacy through synergistic drug interactions (Sawyers 2007, Catley et al. 2005). The mouse B cell leukemia line WEHI-231 is being tested in combination with mouse embryonic fibroblast (MEF) cells. Six drugs were chosen for study including etoposide, 5-fluorouracil, doxorubicin, docetaxel, vincristine, and rapamycin. Three of the six drugs specifically targets DNA (5-fluorouracil, etoposide, doxorubicin), 2 drugs target microtubules (vincristine, docetaxel), and 1 drug targets the mTOR signaling pathway (rapamycin). Preliminary studies have determined the effective concentration ranges of each of the six drugs in both cell types for testing. The Differential Evolution (DE) algorithm was used in this study with a total of 22 test cases for each generation (Storn and Price, 1997). Cells were treated with drug cocktails and analyzed using flow cytometry after 48 hours. Non-optimized drug cocktails in the initial DE generations resulted in more than 90% cell death of both WEHI and MEF cells. After 15 search generations, optimized drug cocktails were attained that has up to 90% cell death in WEHI Cells and only 5~10% cell death in MEF cells. Future plans include testing an optimized anti-cancer drug cocktail, developed on the feedback-control platform, in vivo using adoptive transfer of B cell leukemia cells into immunodeficient mice, and integration of Phosphoflow and TNPR technologies to elucidate the signalosome network that operates to control leukemia.
Feedback System Control (FSC) for HSV (Xianting Ding)
In human disease, many molecular assemblies and pathways within the cell’s signaling and regulatory network function aberrantly, resulting in a complex disease state. Therefore, the most effective way to treat such complex diseases is to use multiple approaches. For example, treating HIV infection and AIDS with a combination of multiple drugs is more effective than single drug therapy. However, determining the optimal combination of several drugs at specific dosages to reach an objective endpoint result (e.g., high efficacy and low toxicity) is a major challenge. The combination of N drugs at M dosage levels results in MN possible combinations, a testing pool size that makes complete screening cost prohibitive and infeasible. Furthermore, many high efficacy drug combinations will also exhibit high toxicity, making these inappropriate for clinical use. We addressed the challenge of identifying high efficacy/low toxicity drug combinations by introducing a feedback system control (FSC) method. With this method, we identified the optimal drug combination that inhibits all HSV-1 infection in vitro and attain limited dosage of the highly toxic drug Ribavirin. This study demonstrates this FSC platform is capable of rapidly screening a large search space using multiple parameters to identify optimal drug combinations and doses for the treatment of a viral infection.
The FSC platform technology consists of four modules. The first module is the input stimulations, namely, the drug combinations. The second module is the bio-complex system of interest, which in this case is the virus and host cell. The third module is the objective function readout, which is the goal for optimization, such as efficacy, toxicity, drug resistance, etc. The fourth module is the search algorithm, which provides the next set of stimulant dosages for directing the bio-complex system toward the desired phenotype (See figure above). For the FSC approach, we started with a set of drugs at arbitrarily chosen concentrations to stimulate the cells infected with HSV-1. The percentage of the host cells that become infected is used as the endpoint readout of the objective function in the third FSC module, and will most likely not be satisfactory in the first permutation. The fourth module of the FSC will use a search algorithm to determine a selection of drug concentrations with potentially better performance that will be used in the next iteration of the experiment and be fed back into the bio-complex system. Iterations of this feedback will continue until the optimal drug combination is reached (i.e., when the system objective function becomes satisfactory).