Multiplexed model predictive control of interconnected systems

A Multiplexed Model Predictive Control (MMPC) scheme with Quadratic Dissipativity Constraint (QDC) for interconnected systems is presented in this paper. A centralized MMPC is designed for the global system, wherein the controls of subsystems are updated sequentially to reduce the computational time. In MMPC, the global state vector of the interconnected system is required by the optimization. The QDC is converted into an enforced stability constraint for the MMPC as an alternative to the terminal constraint and terminal cost in this approach. The nominal recursive feasibility for the global system and the iterative feasibility for the local subsystems are obtained via set operations on the invariant sets. The admissible sets for the control inputs are obtained and employed in this approach for the QDC-based stability constraint. The set operations are speed up by multiple magnitudes thanks to the implementation of multiplexed inputs in MMPC. Numerical simulations with Automatic Generation Control (AGC) in power systems having tie-lines demonstrate the theoretical development.The authors acknowledge the support by the Singapore National Research Foundation (NRF) under its Campus for Research Excellence And Technological Enterprise (CREATE) programme and the Cambridge Centre for Advanced Research in Energy Efficiency in Singapore (Cambridge CARES), C4T project.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/CDC.2015.740256

Pre-clinical trials with precision-medicine based therapeutics in basal-like patient-derived xenografts

Breast cancer treatments have improved over time, but the diseases seeing the most benefit from these improvements have the estrogen receptor, progesterone receptor, or are positive for HER2. Basal-like breast cancer tends to not have these biomarkers, which necessitates their treatment to be traditional, untargeted therapeutics which are less effective and tend to have harsh adverse effect profiles – this is an important unmet need. These studies utilize a variety of techniques, including tissue culture, viability assays, high-throughput screening, in vivo drug treatments and imaging, pathway analyses, molecular techniques such as Western blot, antibody arrays, RNA sequencing, sc RNA sequencing, and many others. This project is divided into 4 studies, each with important findings. First, a characterization of the Glowing Head mouse model showed that the model is suitable for most metastatic settings, though the endogenous signal of the luciferase produced by the mouse will confound studies which study bone or brain metastasis. Then, a study in EGFR inhibitor resistance identified LCN2 as an upregulated marker of resistance which could reduce sensitivity to erlotinib by aiding in the recycling of EGFR. Another study discovered compounds that doxorubicin treated cells will become resistant to and which show a trend of reducing senescent cells’ viability. Finally, the core project identified three compounds when combined with BYL-719, a PIK3CA inhibitor, have synergistic activity in the reduction of tumor sizes of basal-like PI3K aberrant PDXs. It is my hope that these studies may be used as preliminary data for further study, both preclinical and clinical

RAS/MAPK activation is associated with reduced Tumor-infiltrating lymphocytes in Triple-Negative Breast Cancer: Therapeutic Cooperation Between MEK and PD-1/PD-L1 Immune Checkpoint Inhibitors

PURPOSE: Tumor-infiltrating lymphocytes (TIL) in the residual disease (RD) of triple-negative breast cancers (TNBC) after neoadjuvant chemotherapy (NAC) are associated with improved survival, but insight into tumor cell-autonomous molecular pathways affecting these features are lacking. EXPERIMENTAL DESIGN: We analyzed TILs in the RD of clinically and molecularly characterized TNBCs after NAC and explored therapeutic strategies targeting combinations of MEK inhibitors with PD-1/PD-L1-targeted immunotherapy in mouse models of breast cancer. RESULTS: Presence of TILs in the RD was significantly associated with improved prognosis. Genetic or transcriptomic alterations in Ras-MAPK signaling were significantly correlated with lower TILs. MEK inhibition upregulated cell surface MHC expression and PD-L1 in TNBC cells both in vivo and in vitro. Moreover, combined MEK and PD-L1/PD-1 inhibition enhanced antitumor immune responses in mouse models of breast cancer. CONCLUSIONS: These data suggest the possibility that Ras-MAPK pathway activation promotes immune-evasion in TNBC, and support clinical trials combining MEK- and PD-L1-targeted therapies. Furthermore, Ras/MAPK activation and MHC expression may be predictive biomarkers of response to immune checkpoint inhibitors

Statistical Learning and Stochastic Process for Robust Predictive Control of Vehicle Suspension Systems

Predictive controllers play an important role in today's industry because of their capability of verifying optimum control signals for nonlinear systems in a real-time fashion. Due to their mathematical properties, such controllers are best suited for control problems with constraints. Also, these interesting controllers can be equipped with different types of optimization and learning modules. The main goal of this thesis is to explore the potential of predictive controllers for a challenging automotive problem, known as active vehicle suspension control. In this context, it is intended to explore both modeling and optimization modules using different statistical methodologies ranging from statistical learning to random process control. Among the variants of predictive controllers, learning-based model predictive controller (LBMPC) is becoming more and more interesting to the researchers of control society due to its structural flexibility and optimal performance. The current investigation will contribute to the improvement of LBMPC by adopting different statistical learning strategies and forecasting methods to improve the efficiency and robustness of learning performed in LBMPC. Also, advanced probabilistic tools such as reinforcement learning, absorbing state stochastic process, graphical modelling, and bootstrapping are used to quantify different sources of uncertainty which can affect the performance of the LBMPC when it is used for vehicle suspension control. Moreover, a comparative study is conducted using gradient-based as well as deterministic and stochastic direct search optimization algorithms for calculating the optimal control commands. By combining the well-established control and statistical theories, a novel variant of LBMPC is developed which not only affords stability and robustness, but also surpasses a wide range of conventional controllers for the vehicle suspension control problem. The findings of the current investigation can be interesting to the researchers of automotive industry (in particular those interested in automotive control), as several open issues regarding the potential of statistical tools for improving the performance of controllers for vehicle suspension problem are addressed

Drug discovery for psychiatric disorders using high-content single-cell screening of signaling network responses ex vivo

There is a paucity of efficacious new compounds to treat neuropsychiatric disorders. We present a novel approach to neuropsychiatric drug discovery based on high-content characterization of druggable signaling network responses at the single-cell level in patient-derived lymphocytes ex vivo. Primary T lymphocytes showed functional responses encompassing neuropsychiatric medications and central nervous system ligands at established (e.g., GSK-3?) and emerging (e.g., CrkL) drug targets. Clinical application of the platform to schizophrenia patients over the course of antipsychotic treatment revealed therapeutic targets within the phospholipase C?1-calcium signaling pathway. Compound library screening against the target phenotype identified subsets of L-type calcium channel blockers and corticosteroids as novel therapeutically relevant drug classes with corresponding activity in neuronal cells. The screening results were validated by predicting in vivo efficacy in an independent schizophrenia cohort. The approach has the potential to discern new drug targets and accelerate drug discovery and personalized medicine for neuropsychiatric conditions

Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases

Current antibiotics tend to be broad spectrum, leading to indiscriminate killing of commensal bacteria and accelerated evolution of drug resistance. Here, we use CRISPR-Cas technology to create antimicrobials whose spectrum of activity is chosen by design. RNA-guided nucleases (RGNs) targeting specific DNA sequences are delivered efficiently to microbial populations using bacteriophage or bacteria carrying plasmids transmissible by conjugation. The DNA targets of RGNs can be undesirable genes or polymorphisms, including antibiotic resistance and virulence determinants in carbapenem-resistant Enterobacteriaceae and enterohemorrhagic Escherichia coli. Delivery of RGNs significantly improves survival in a Galleria mellonella infection model. We also show that RGNs enable modulation of complex bacterial populations by selective knockdown of targeted strains based on genetic signatures. RGNs constitute a class of highly discriminatory, customizable antimicrobials that enact selective pressure at the DNA level to reduce the prevalence of undesired genes, minimize off-target effects and enable programmable remodeling of microbiota.National Institutes of Health (U.S.) (New Innovator Award 1DP2OD008435)National Centers for Systems Biology (U.S.) (Grant 1P50GM098792)United States. Defense Threat Reduction Agency (HDTRA1-14-1-0007)Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (W911NF13D0001)National Institute of General Medical Sciences (U.S.) (Interdepartmental Biotechnology Training Program 5T32 GM008334)Fonds de la recherche en sante du Quebec (Master's Training Award

Delivery of PEGylated liposomal doxorubicin by bispecific antibodies improves treatment in models of high-risk childhood leukemia

High-risk childhood leukemia has a poor prognosis because of treatment failure and toxic side effects of therapy. Drug encapsulation into liposomal nanocarriers has shown clinical success at improving biodistribution and tolerability of chemotherapy. However, enhancements in drug efficacy have been limited because of a lack of selectivity of the liposomal formulations for the cancer cells. Here, we report on the generation of bispecific antibodies (BsAbs) with dual binding to a leukemic cell receptor, such as CD19, CD20, CD22, or CD38, and methoxy polyethylene glycol (PEG) for the targeted delivery of PEGylated liposomal drugs to leukemia cells. This liposome targeting system follows a "mix-and-match" principle where BsAbs were selected on the specific receptors expressed on leukemia cells. BsAbs improved the targeting and cytotoxic activity of a clinically approved and low-toxic PEGylated liposomal formulation of doxorubicin (Caelyx) toward leukemia cell lines and patient-derived samples that are immunophenotypically heterogeneous and representative of high-risk subtypes of childhood leukemia. BsAb-assisted improvements in leukemia cell targeting and cytotoxic potency of Caelyx correlated with receptor expression and were minimally detrimental in vitro and in vivo toward expansion and functionality of normal peripheral blood mononuclear cells and hematopoietic progenitors. Targeted delivery of Caelyx using BsAbs further enhanced leukemia suppression while reducing drug accumulation in the heart and kidneys and extended overall survival in patient-derived xenograft models of high-risk childhood leukemia. Our methodology using BsAbs therefore represents an attractive targeting platform to potentiate the therapeutic efficacy and safety of liposomal drugs for improved treatment of high-risk leukemia

Designing Rational Combination Strategies for Overcoming Drug Resistance in Breast Cancer

Drug resistance is a ubiquitous problem in the therapeutic management of breast cancer, even in the context of next-generation targeted therapies where only modest clinical improvements have been observed despite a tumors mutational load for a given target pathway or intrinsic subtype. To devise effective anti-cancer treatment strategies, new systems-based methods are needed to fully interpret factors underlying drug responses encompassing both genetic and non-genetic mechanisms. Here we developed two approaches towards designing novel combination strategies for overcoming drug resistance. First, using an unbiased chemoproteomics approach, we profiled kinome dynamics across breast cancer cells in response to various targeted therapies and identified signaling changes that correlate with drug sensitivity. This signaling map identified survival factors whose presence limits the efficacy of targeted therapies and revealed AURKA as a new co-targeting opportunity to enhance the therapeutic efficacy of PI3K-pathway inhibitors in breast cancer. Second, we used single-cell transcriptomics data and pharmacogenomic modeling as a way to inform upfront drug combinations based on systematic analysis of tumor subpopulation architectures. Using in silico and experimental approaches, our study provides an effective new framework to discover drug combinations capable of counteracting intrinsic cell variability by predicting drug responses of single cells within tumor cell subpopulations and systematically links transcriptional heterogeneity with drug actionability to optimize therapy combinations