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Creators/Authors contains: "Lopez, Carlos F."

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  1. Abstract Motivation

    Computational systems biology analyses typically make use of multiple software and their dependencies, which are often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility.


    Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes.

    Availability and implementation

    Install from the Python Package Index using pip install microbench. Source code is available from

    Supplementary information

    Supplementary data are available at Bioinformatics online.

  2. Abstract

    Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

  3. Saucerman, Jeffrey J. (Ed.)
    Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.
  4. Abstract High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose–response curves, and derived dose–response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from
  5. Scaffold proteins tether and orient components of a signaling cascade to facilitate signaling. Although much is known about how scaffolds colocalize signaling proteins, it is unclear whether scaffolds promote signal amplification. Here, we used arrestin-3, a scaffold of the ASK1-MKK4/7-JNK3 cascade, as a model to understand signal amplification by a scaffold protein. We found that arrestin-3 exhibited >15-fold higher affinity for inactive JNK3 than for active JNK3, and this change involved a shift in the binding site following JNK3 activation. We used systems biochemistry modeling and Bayesian inference to evaluate how the activation of upstream kinases contributed to JNK3 phosphorylation. Our combined experimental and computational approach suggested that the catalytic phosphorylation rate of JNK3 at Thr-221 by MKK7 is two orders of magnitude faster than the corresponding phosphorylation of Tyr-223 by MKK4 with or without arrestin-3. Finally, we showed that the release of activated JNK3 was critical for signal amplification. Collectively, our data suggest a “conveyor belt” mechanism for signal amplification by scaffold proteins. This mechanism informs on a long-standing mystery for how few upstream kinase molecules activate numerous downstream kinases to amplify signaling.