Biology subjects

Laubenbacher, R.

Publications and source records attributed to Laubenbacher, R..

3 recordsLinked to original sources

bioRxiv2018

An important role for periplasmic storage in Pseudomonas aeruginosa copper homeostasis revealed by a combined experimental and computational modeling study

Biological systems require precise copper homeostasis enabling metallation of cuproproteins while preventing metal toxicity. In bacteria, sensing, transport and storage molecules act in coordination to fulfill these roles. However, there is not yet a kinetic schema explaining the system integration. Here, we report a model emerging from experimental and computational approaches that describes the dynamics of copper distribution in Pseudomonas aeruginosa. Based on copper uptake experiments, a minimal kinetic model describes well the copper distribution in the wild type bacteria but is unable to explain the behavior of the mutant strain lacking CopA1, a key Cu+ efflux ATPase. The model was expanded through an iterative hypothesis-driven approach, arriving to a mechanism that considers the induction of compartmental pools and the parallel function of CopA and Cus efflux systems. Model simulations support the presence of a periplasmic copper storage with a crucial role under dyshomeostasis conditions in P. aeruginosa. Importantly, the model predicts not only the interplay of periplasmic and cytoplasmic pools but also the existence of a threshold in the concentration of external copper beyond which cells lose their ability to control copper levels.

microbiology↗

bioRxiv2018

Comparative Analysis of Linear and Nonlinear Dimension Reduction Techniques on Mass Cytometry Data

Mass cytometry, also known as CyTOF, is a newly developed technology for quantification and classification of immune cells that can allow for analysis of over three dozen protein markers per cell. The high dimensional data that is generated requires innovative methods for analysis and visualization. We conducted a comparative analysis of four dimension reduction techniques - principal component analysis (PCA), isometric feature mapping (Isomap), t-distributed stochastic neighbor embedding (t-SNE), and Diffusion Maps by implementing them on benchmark mass cytometry data sets. We compare the results of these reductions using computation time, residual variance, a newly developed comparison metric we term neighborhood proportion error (NPE), and two-dimensional visualizations. We find that t-SNE and Diffusion Maps are the two most effective methods for preserving relationships of interest among cells and providing informative visualizations. In low dimensional embeddings, t-SNE exhibits well-defined phenotypic clustering. Additionally, Diffusion Maps can represent cell differentiation pathways with long projections along each diffusion component. We thus recommend a complementary approach using t-SNE and Diffusion Maps in order to extract diverse and informative cell relationship information in a two-dimensional setting from CyTOF data.

bioinformatics↗

bioRxiv2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patients immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.

systems biology↗