Software, Algorithms and Computational Methods

MCMICRO is the end-to-end processing pipeline for multiplexed whole tissue imaging and tissue microarrays. It comprises stitching and registration, segmentation, and single-cell feature extraction. Each step of the pipeline is containerized to enable portable deployment across an array of compute environments, including local machines, job-scheduling clusters and cloud environments like AWS. The pipeline execution is implemented in Nextflow, a workflow language that facilitates caching of partial results, dynamic restarts, extensive logging and resource usage reports.

INDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system, originally developed for molecular systems biology and currently being generalized to other domains. INDRA draws on natural language processing systems and structured databases to collect mechanistic and causal assertions, represents them in a standardized form (INDRA Statements), and assembles them into various modeling formalisms including causal graphs and dynamical models.

At the core of INDRA are its knowledge-level assembly procedures, allowing sources to be assembled into coherent models, a process that involves correcting systematic input errors, finding and resolving redundancies, inferring missing information, filtering to a relevant scope and assessing the reliability of causal information.

ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration) is an open source Python package that stiches together successive microscopy image tiles to generate a single, seamless image. ASHLAR also registers images from different fluorescent channels at a high level of accuracy.

shinyDepMap is a web-tool to explore the Cancer Dependency Map (DepMap) project data of the Broad Institute (version 19q3), to help users identify and characterize essential genes among all protein-encoding genes. DepMap is a powerful drug discovery tool. Its portal website provides individual genes’ dependency from both CRISPR and RNAi data. We combined the CRISPR and RNAi dependency data into a unified score, and built this non-programmer biologist-friendly tool, shinyDepMap, which allows 1) to compare efficacy and selectivity of a gene loss across 15,847 protein-encoding genes, and 2) genes that work together in pathways or complexes.

This web-tool can be used to predict the efficacy and selectivity of future drugs with a known target gene, identify targets of highly selective drugs, identify maximally sensitive cell lines for testing a drug, “Target hop”, i.e., navigate from an undruggable protein with the desired selectivity profile to more druggable targets with similar profiles, and identify novel pathways needed to cancer cell growth and survival.

The Small Molecule Suite (SMS) is a free, open-access tool developed by the Harvard Program in Therapeutic Sciences (HiTS) and funded by the NIH. The goal of the SMS is to help scientists understand and work with the targets of molecular probes, approved drugs and other drug-like molecules, while acknowledging the complexity of polypharmacology — the phenomenon that virtually all drug-like molecules bind multiple target proteins. The SMS combines data from the ChEMBL database with pre-published data from the Laboratory of Systems Pharmacology. The methodology of calculating selectivities and similarities are explained in Moret et al. Cell Chem Biol 2019 (which can also be used to cite the Small Molecule Suite).

SYLARAS (SYstemic Lymphoid Architecture Response ASsessment) is a preclinical research platform for the interrogation of systemic immune response to disease and therapy. The approach combines multiplex immunophenotyping with biological computation to transform complex single-cell datasets into a visual compendium of the time and tissue-dependent changes occurring in immune cell frequency and/or function in response to an arbitrary immune stimulus (e.g. tumor model, infectious or autoimmune disease, vaccine, immunotherapy, etc.).

SYLARAS is deployed in three stages. In the first stage, longitudinal immunophenotyping data are collected from mouse lymphoid organs of test and control subjects in a high-throughput manner by multiplex flow cytometry. In the second stage, raw FCS files are spectrally compensated and filtered for viable cells before undergoing a systematic immune cell subset identification procedure. In the final stage, the pre-processed data are computationally analyzed using an open-source data analysis tool scripted in the Python programming language and run at the command-line of a personal computer. This leads to the generation of a set of data-rich graphical dashboards (1 per immune cell type) that together portray systemic immune response to a given experimental perturbation.

The Ras Executable Model (REM) is a model of the signaling pathway of the Ras family proteins (K-RAS, N-RAS, H-RAS), including their regulators and effectors, at a biochemical level of detail. It is a rule-based model written in PySB. REM consists of three interlinked levels. Model components consist of PySB modules that implement pathway mechanisms. A model scenario corresponds to a specific use case and imports one or more model components in a fit-to-purpose manner to instantiate an executable model. Each model scenario can have a set of corresponding model analysis scripts. REM is built via a combination of automated assembly using INDRA (Integrated Network and Dynamical Reasoning Assembler), and manual development by a team of modelers.