An Analysis of Tinker Air Force Base Thermal Spray Hazardous Waste Stream from 2003-2019 and its Potential Reclamation

The disposal of hazardous waste threatens the environment and human health. However, certain hazardous wastes can be recycled or reclaimed to alleviate environmental and financial impacts associated with its disposal and resource demand reduction. A hazardous powder produced by the United States Air Force stems from aircraft maintenance and is composed of metals and ceramics, called thermal spray. This waste can be purchased by an industrial recycler if the waste is composed of valuable metals. Historic data from the depot-level maintenance base Tinker Air Force Base initiated an analysis of annual thermal spray hazardous waste disposal fees, which combined with a sample analysis resulted in an expected value of the waste to a recycler, and potential profits if a contract to recycle the waste was created. Tinker Air Force Base’s thermal spray waste stream was valued at $0.16/lb, resulting in an estimated annual profit of$10,856.64 and the saving of an additional $26,463.06 of disposal fees. It is recommended that this research initiates the recycling of thermal spray waste stream throughout the Department of Defense in order to save money and lessen the burden to the environment through disposal and resource extraction reduction

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 ($\textit{FLT3}$) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in $\textit{FLT3}$, tyrosine kinase 2 ($\textit{TYK2}$), platelet-derived growth factor receptor alpha ($\textit{PDGFRA}$), and fibroblast growth factor receptor 1 ($\textit{FGFR1}$) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition. Cancer Res; 77(4); 827–38.We would also like to thank Bloodwise for supporting BG, and the Israeli ministry of science, technology and space and Edmond J. Safra Center for Bioinformatics at Tel-Aviv University for supporting DS

Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution

Cancer develops via the progressive accumulation of somatic mutations, which subvert the normal operation of the gene regulatory network of the cell. However, little is known about the order in which mutations are acquired in successful clones. A particular sequence of mutations may confer an early selective advantage to a clone by increasing survival or proliferation, or lead to negative selection by triggering cell death. The space of allowed sequences of mutations is therefore constrained by the gene regulatory network. Here, we introduce a methodology for the systematic exploration of the effect of every possible sequence of oncogenic mutations in a cancer cell modelled as a qualitative network. Our method uses attractor identification using binary decision diagrams and can be applied to both synchronous and asynchronous systems. We demonstrate our method using a recently developed model of ER-negative breast cancer. We show that there are differing levels of constraint in the order of mutations for different combinations of oncogenes, and that the effects of ErbB2/HER2 over-expression depend on the preceding mutations

Network orientation via shortest paths

The graph orientation problem calls for orienting the edges of a graph so as to maximize the number of pre-specified source–target vertex pairs that admit a directed path from the source to the target. Most algorithmic approaches to this problem share a common preprocessing step, in which the input graph is reduced to a tree by repeatedly contracting its cycles. Although this reduction is valid from an algorithmic perspective, the assignment of directions to the edges of the contracted cycles becomes arbitrary, and the connecting source–target paths may be arbitrarily long. In the context of biological networks, the connection of vertex pairs via shortest paths is highly motivated, leading to the following problem variant: given a graph and a collection of source–target vertex pairs, assign directions to the edges so as to maximize the number of pairs that are connected by a shortest (in the original graph) directed path. This problem is NP-complete and hard to approximate to within sub-polynomial factors. Here we provide a first polynomial-size integer linear program formulation for this problem, which allows its exact solution in seconds on current networks. We apply our algorithm to orient protein–protein interaction networks in yeast and compare it with two state-of-the-art algorithms. We find that our algorithm outperforms previous approaches and can orient considerable parts of the network, thus revealing its structure and function. Availability and implementation: The source code is available at www.cs.tau.ac.il/*roded/shortest.zip

ANAT 2.0: reconstructing functional protein subnetworks

Abstract Background ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. Results Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets. Conclusions ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species

Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses

MOTIVATION: In recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging single-cell sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made. RESULTS: We present Simulated Annealing Single-Cell inference (SASC): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS datasets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real datasets and in comparison with some other available methods. AVAILABILITY AND IMPLEMENTATION: The SASC tool is open source and available at https://github.com/sciccolella/sasc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online