Microbench: Automated metadata management for systems biology benchmarking and reproducibility in Python

Mapping Intimacies ◽

10.1101/2021.09.14.460317 ◽

2021 ◽

Author(s):

Alexander Lyulph Robert Lubbock ◽

Carlos F Lopez

Keyword(s):

Systems Biology ◽

Virtual Environments ◽

Software Package ◽

Source Code ◽

Metadata Management ◽

Computational Systems Biology ◽

Environment Variables ◽

Computational Systems ◽

Python Package ◽

Over Time

Motivation: Computational systems biology analyses typically make use of multiple software and their dependencies, which 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. Results: 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: Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench.

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Faculty Opinions recommendation of Computational systems biology and in silico modeling of the human microbiome.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717697961.793153071 ◽

2012 ◽

Author(s):

Nicola Mulder

Keyword(s):

Systems Biology ◽

In Silico ◽

Human Microbiome ◽

Computational Systems Biology ◽

In Silico Modeling ◽

Computational Systems

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Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Cancers ◽

10.3390/cancers13010035 ◽

2020 ◽

Vol 13 (1) ◽

pp. 35

Author(s):

Sahar Aghakhani ◽

Naouel Zerrouk ◽

Anna Niarakis

Keyword(s):

Rheumatoid Arthritis ◽

Extracellular Matrix ◽

Systems Biology ◽

Healing Process ◽

Metabolic Reprogramming ◽

Critical Event ◽

Wound Healing Process ◽

Computational Systems Biology ◽

Structural Framework ◽

Computational Systems

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

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