Enterprise Change: Improving Complex Enterprises with System Models

Student research poster description shee

Enterprise Change

Student research poste

Improving Complex Enterprises with System Models

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Improving complex enterprises with system models

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.Includes bibliographical references (leaves 96-98).Air Force sustainment operations are the focus of an intensive internal effort to improve performance and reduce costs. Past improvement initiatives have often failed to produce the intended results, and have caused performance to decline in some cases. Exploratory research was conducted at an Air Logistics Center to study how improvements are executed. Two conclusions are drawn from this research. The first is that changing sustainment operations is a problem of high dynamic and behavioral complexity. The second conclusion is that system models are well suited to coordinating change at the ALC because they provide insight into how a complicated system can be managed and improved. Three key findings support these conclusions. First, there is significant correlation between categories of unavailable F-16 aircraft such that reductions in one category are associated with increases in another. Second, an analysis of change efforts in two parts of the ALC shows that systemic influences, such as the inability to reinvest in improvements, are hindering change initiatives in one part of the ALC.(cont.) The third finding is that a model of sustainment operations suggests that independent improvement initiatives are outperformed by coordinated efforts driven with an understanding of systemic interactions. Leaders throughout the sustainment community have expressed their desire to understand how sustainment operations function as a system. A hybrid approach to change is offered as a method for understanding and improving sustainment operations. System models are used to quantify and model system interactions; then policies and recommendations are drawn from the models. Recommendations may include process-level improvements utilizing change methods already in use at the ALC.by Justin M. Hemann.S.M

Understanding resistance to combination chemotherapy

available in PMC 2014 April 04The current clinical application of combination chemotherapy is guided by a historically successful set of practices that were developed by basic and clinical researchers 50–60 years ago. Thus, in order to understand how emerging approaches to drug development might aid the creation of new therapeutic combinations, it is critical to understand the defining principles underlying classic combination therapy and the original experimental rationales behind them. One such principle is that the use of combination therapies with independent mechanisms of action can minimize the evolution of drug resistance. Another is that in order to kill sufficient cancer cells to cure a patient, multiple drugs must be delivered at their maximum tolerated dose – a condition that allows for enhanced cancer cell killing with manageable toxicity. In light of these models, we aim to explore recent genomic evidence underlying the mechanisms of resistance to the combination regimens constructed on these principles. Interestingly, we find that emerging genomic evidence contradicts some of the rationales of early practitioners in developing commonly used drug regimens. However, we also find that the addition of recent targeted therapies has yet to change the current principles underlying the construction of anti-cancer combinatorial regimens, nor have they made substantial inroads into the treatment of most cancers. We suggest that emerging systems/network biology approaches have an immense opportunity to impact the rational development of successful drug regimens. Specifically, by examining drug combinations in multivariate ways, next generation combination therapies can be constructed with a clear understanding of how mechanisms of resistance to multi-drug regimens differ from single agent resistance.Massachusetts Institute of Technology (Eisen and Chang Career Development Associate Professor of Biology)National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), #U54-CA112967-06)National Institutes of Health (U.S.) (NIH RO1-CA128803-04

Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy

Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases, the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine preclinical lymphoma model. Altogether, our approach provides new insights about design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program (ICBP), Grant U54-CA112967-06)National Institutes of Health (U.S.) (NIH/National Institute of General Medical Sciences (NIGMS) Interdepartmental Biotechnology Training Program, 5T32GM008334)National Cancer Institute (U.S.) (Koch Institute Support (core) Grant P30-CA14051

Predicting cancer drug mechanisms of action using molecular network signatures

Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify “New” mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these “New” mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena.National Cancer Institute (U.S.) (NCI Integrative Cancer Biology Program grant U54-CA112967

Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death

Multitarget compounds that act on a diverse set of regulatory pathways are emerging as a therapeutic approach for a variety of cancers. Toward a more specified use of this approach, we hypothesize that the desired efficacy can be recreated in terms of a particular combination of relatively more specific (i.e., ostensibly single target) compounds. We test this hypothesis for the geldanamycin analogue 17-Allylamino-17-demethoxygeldanamycin (17AAG) in hepatocellular carcinoma cells, measuring critical phosphorylation levels that indicate the kinase pathway effects correlating with apoptotic responsiveness of the Hep3B cell line in contrast to the apoptotic resistance of the Huh7 cell line. A principal components analysis (PCA) constructed from time course measurements of seven phosphoprotein signaling levels identified modulation of the AKT, IκB kinase, and signal transducer and activator of transcription 3 pathways by 17AAG treatment as most important for distinguishing these cell-specific death responses. The analysis correctly suggested from 17AAG-induced effects on these phosphoprotein levels that the FOCUS cell line would show apoptotic responsiveness similarly to Hep3B. The PCA also guided the inhibition of three critical pathways and rendered Huh7 cells responsive to 17AAG. Strikingly, in all three hepatocellular carcinoma lines, the three-inhibitor combination alone exhibited similar or greater efficacy to 17AAG. We conclude that (a) the PCA captures and clusters the multipathway phosphoprotein time courses with respect to their 17AAG-induced apoptotic responsiveness and (b) we can recreate, in a more specified manner, the cellular responses of a prospective multitarget cancer therapeutic.National Institute of General Medical Sciences (U.S.). Cell Decision Processes CenterNational Cancer Institute (U.S.). Integrative Cancer Biology ProgramMassachusetts Institute of Technology. Presidential FellowshipNational Institutes of Health (U.S.

BCL-2 family genetic profiling reveals microenvironment-specific determinants of chemotherapeutic response

The Bcl-2 family encompasses a diverse set of apoptotic regulators that are dynamically activated in response to various cell-intrinsic and -extrinsic stimuli. An extensive variety of cell culture experiments have identified effects of growth factors, cytokines, and drugs on Bcl-2 family functions, but in vivo studies have tended to focus on the role of one or two particular members in development and organ homeostasis. Thus, the ability of physiologically relevant contexts to modulate canonical dependencies that are likely to be more complex has yet to be investigated systematically. In this study, we report findings derived from a pool-based shRNA assay that systematically and comprehensively interrogated the functional dependence of leukemia and lymphoma cells upon various Bcl-2 family members across many diverse in vitro and in vivo settings. This approach permitted us to report the first in vivo loss of function screen for modifiers of the response to a front-line chemotherapeutic agent. Notably, our results reveal an unexpected role for the extrinsic death pathway as a tissue-specific modifier of therapeutic response. In particular, our findings show that particular tissue sites of tumor dissemination play critical roles in demarcating the nature and extent of cancer cell vulnerabilities and mechanisms of chemoresistance. Cancer Res; 71(17); 5850–8. ©2011 AACR.National Institutes of Health (U.S.) (NIH RO1 CA128803)National Cancer Institute (U.S.) (Integrated Cancer Biology Program grant NCI 1-U54-CA112967)David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship)Massachusetts Institute of Technology. Dept. of Biology (training grant

A genome-scale in vivo loss-of-function screen identifies Phf6 as a lineage-specific regulator of leukemia cell growth

We performed a genome-scale shRNA screen for modulators of B-cell leukemia progression in vivo. Results from this work revealed dramatic distinctions between the relative effects of shRNAs on the growth of tumor cells in culture versus in their native microenvironment. Specifically, we identified many “context-specific” regulators of leukemia development. These included the gene encoding the zinc finger protein Phf6. While inactivating mutations in PHF6 are commonly observed in human myeloid and T-cell malignancies, we found that Phf6 suppression in B-cell malignancies impairs tumor progression. Thus, Phf6 is a “lineage-specific” cancer gene that plays opposing roles in developmentally distinct hematopoietic malignancies.Massachusetts Institute of Technology. Department of Biology (Training Grant)National Cancer Institute (U.S.). Integrative Cancer Biology Program (U54-CA112967-06)National Institutes of Health (U.S.) (RO1-CA128803-05