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Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation, and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.

Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 10(9) or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.

Motivation Stitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods. Results We describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open-source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines. Availability and implementation ASHLAR is written in Python and is available under the MIT license at https://github.com/labsyspharm/ashlar. The newly published data underlying this article are available in Sage Synapse at https://dx.doi.org/10.7303/syn25826362; the availability of other previously published data re-analyzed in this article is described in Supplementary Table S4. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationStitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly-multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods.ResultsWe describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines.Availability and implementationASHLAR is written in Python and available under an MIT license at https://github.com/labsyspharm/ashlar. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/.

High-throughput, high-content imaging technologies and multiplex slide scanning have become widely used. Advantages of these approaches include the ability to archive digital copies of slides, review slides as teams using virtual microscopy software, and standardize analytical approaches. The cost and hardware and software inflexibility of dedicated slide scanning devices can, however, complicate implementation. Here, we describe a simple method that allows any microscope to be used for slide scanning. The only requirements are that the microscope be equipped with a motorized filter turret or wheels (for multi-channel fluorescence) and a motorized xyz stage. This example uses MetaMorph software, but the same principles can be used with any microscope control software that includes a few standard functions and allows programming of simple command routines, or journals. The series of journals that implement the method perform key functions, including assistance in defining an unlimited number of regions of interest (ROI) and imaging parameters. Fully automated acquisition is rapid, taking less than 3 hr to image fifty 2.5-mm ROIs in four channels. Following acquisition, images can be easily stitched and displayed using open-source or commercial image-processing and virtual microscope applications. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Hardware and software configuration Basic Protocol 2: Create a preliminary scan Basic Protocol 3: Select, save, and position ROIs Basic Protocol 4: Determine and set autofocus parameters Basic Protocol 5: Acquire tiled images Basic Protocol 6: Review the scans Basic Protocol 7: Reimage ROIs as needed Basic Protocol 8: Stitch, stack, and assemble images Basic Protocol 9: Repeat scanning for multiplex immunostaining or background subtraction.

Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software. MCMICRO is a modular and open-source computational pipeline for transforming highly multiplexed whole-slide images of tissues into single-cell data. MCMICRO is versatile and can be used with CODEX, mxIF, CyCIF, mIHC and H&E staining data.

The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify biomarkers that not only predict nephrotoxic compounds but also provide hints toward their mechanism of toxicity. Gene expression and high-content imaging-derived phenotypical data from 46 diverse kidney toxicants were analyzed using Random Forest machine learning. Imaging features capturing changes in cell morphology and nucleus texture along with mRNA levels of HMOX1 and SQSTM1 were identified as the most powerful predictors of toxicity. These biomarkers were validated by their ability to accurately predict kidney toxicity of four out of six candidate therapeutics that exhibited toxicity only in late stage preclinical/clinical studies. Network analysis of similarities in toxic phenotypes was performed based on live-cell high-content image analysis at seven time points. Using compounds with known mechanism as reference, we could infer potential mechanisms of toxicity of candidate therapeutics. In summary, we report an approach to generate a multidimensional biomarker panel for mechanistic de-risking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities.

The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or “digital docents” that allow experts to help make complex images intelligible. We illustrate this idea usingMinervasoftware and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

SummaryIndividual cancers rely on distinct essential genes for their survival. The Cancer Dependency Map (DepMap) is an ongoing project to uncover gene dependency in hundreds of cancer cell lines. DepMap is a powerful drug discovery tool, but can be challenging to use without professional bioinformatics assistance. We combined CRISPR and shRNA screening data from DepMap and built a non-programmer-friendly browser (https://labsyspharm.shinyapps.io/depmap) that reports, for each gene, the growth reduction that can be expected on loss of a gene or inhibition of its action (efficacy) and the selectivity of this effect across cell lines. Cluster analysis revealed proteins that work together in pathways or complexes. This tool can be used to 1) predict the efficacy and selectivity of candidate drugs; 2) identify targets for highly selective drugs; 3) identify maximally sensitive cell lines for testing a drug; 4) target hop, i.e., navigate from an undruggable protein with the desired selectively profile, such as an activated oncogene, to more druggable targets with a similar profile; and 5) identify novel pathways needed for cancer cell growth and survival.

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools., Measurement(s)immunofluorescence • biomarker • cellular featureTechnology Type(s)immunofluorescence microscopy assay • computational modeling techniqueFactor Type(s)Lung carcinoma • Reactive tonsilSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11184539

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Nephrotoxicity due to drugs and environmental chemicals accounts for significant patient mortality and morbidity, but there is no high throughput in vitro method for predictive nephrotoxicity assessment. We show that primary human proximal tubular epithelial cells (HPTECs) possess characteristics of differentiated epithelial cells rendering them desirable to use in such in vitro systems. To identify a reliable biomarker of nephrotoxicity, we conducted multiplexed gene expression profiling of HPTECs after exposure to six different concentrations of nine human nephrotoxicants. Only overexpression of the gene encoding heme oxygenase-1 (HO-1) significantly correlated with increasing dose for six of the compounds, and significant HO-1 protein deregulation was confirmed with each of the nine nephrotoxicants. Translatability of HO-1 increase across species and platforms was demonstrated by computationally mining two large rat toxicogenomic databases for kidney tubular toxicity and by observing a significant increase in HO-1 after toxicity using an ex vivo three-dimensional microphysiologic system (kidney-on-a-chip). The predictive potential of HO-1 was tested using an additional panel of 39 mechanistically distinct nephrotoxic compounds. Although HO-1 performed better (area under the curve receiver-operator characteristic curve [AUC-ROC]=0.89) than traditional endpoints of cell viability (AUC-ROC for ATP=0.78; AUC-ROC for cell count=0.88), the combination of HO-1 and cell count further improved the predictive ability (AUC-ROC=0.92). We also developed and optimized a homogenous time-resolved fluorescence assay to allow high throughput quantitative screening of nephrotoxic compounds using HO-1 as a sensitive biomarker. This cell-based approach may facilitate rapid assessment of potential nephrotoxic therapeutics and environmental chemicals.

Targeted inhibition of oncogenic pathways can be highly effective in halting the rapid growth of tumors but often leads to the emergence of slowly dividing persister cells, which constitute a reservoir for the selection of drug-resistant clones. In BRAF(V600E) melanomas, RAF and MEK inhibitors efficiently block oncogenic signaling, but persister cells emerge. Here, we show that persister cells escape drug-induced cell-cycle arrest via brief, sporadic ERK pulses generated by transmembrane receptors and growth factors operating in an autocrine/paracrine manner. Quantitative proteomics and computational modeling show that ERK pulsing is enabled by rewiring of mitogen-activated protein kinase (MAPK) signaling: from an oncogenic BRAF(V600E) monomer-driven configuration that is drug sensitive to a receptor-driven configuration that involves Ras-GTP and RAF dimers and is highly resistant to RAF and MEK inhibitors. Altogether, this work shows that pulsatile MAPK activation by factors in the microenvironment generates a persistent population of melanoma cells that rewires MAPK signaling to sustain non-genetic drug resistance.

SUMMARY Accurately profiling systemic immune responses to cancer initiation and progression is necessary for understanding tumor surveillance and, ultimately, improving therapy. Here, we describe the SYLARAS software tool (systemic lymphoid architecture response assessment) and a dataset collected with SYLARAS that describes the frequencies of immune cells in primary and secondary lymphoid organs and in the tumor microenvironment of mice engrafted with a standard syngeneic glioblastoma (GBM) model. The data resource involves profiles of 5 lymphoid tissues in 48 mice and shows that GBM causes wide-spread changes in the local and systemic immune architecture. We use SYLARAS to identify a subset of CD45R/B220+ CD8+ T cells that is depleted from circulation but accumulates in the tumor mass and confirm this finding using multiplexed immunofluorescence microscopy. SYLARAS is freely available for download at (https://github.com/gjbaker/sylaras). A record of this paper’s transparent peer review process is included in the Supplemental Information., Graphical Abstract, In Brief Localized tumors such as glioblastoma alter the composition of the immune system in peripheral organs including the spleen, lymph nodes, bone marrow, and thymus. SYLARAS enables efficient, systematic analysis of immune system architecture across many organs and samples to reveal subtle, recurrent changes on a background of between-sample biological variability.

Accurately profiling systemic immune responses to cancer initiation and progression is necessary for understanding tumor surveillance and, ultimately, improving therapy. Here, we describe the SYLARAS software tool (SYstemic Lymphoid Architecture Response ASsessment) and a data set collected with SYLARAS that describes the frequencies of immune cells in primary and secondary lymphoid organs and in the tumor microenvironment of mice engrafted with a standard syngeneic glioblastoma (GBM) model. The data resource involves profiles of 5 lymphoid tissues in 48 mice and shows that GBM causes wide-spread changes in the local and systemic immune architecture. We perform in-depth analysis of one significant tumor-induced change: depletion of a specialized subset of CD45R/B220+ CD8+ T cells from the circulation and their accumulation in the tumor mass. Immunoprofiling of tissue microarrays demonstrates the presence of similar cells in human GBM.

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage; (ii) adaptive drug resistance in BRAF-V600E mutant melanomas; and (iii) the RAS signaling pathway. The use of natural language for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community.Standfirst textINDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.HighlightsINDRA decouples the curation of knowledge as word models from model implementationINDRA is connected to multiple natural language processing systems and can draw on information from curated databasesINDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formatsWe used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language

Anti-cancer drugs commonly target signal transduction proteins activated by mutation. In patients with BRAFV600E melanoma, small molecule RAF and MEK kinase inhibitors cause dramatic but often transient tumor regression. Emerging evidence suggests that cancer cells adapting by non-genetic mechanisms constitute a reservoir for the development of drug-resistant tumors. Here, we show that few hours after exposure to RAF/MEK inhibitors, BRAFV600E melanomas undergo adaptive changes involving disruption of negative feedback and sporadic pulsatile reactivation of the MAPK pathway, so that MAPK activity is transiently high enough in some cells to drive proliferation. ERK pulses are locally triggered by autocrine/paracrine factors in the local microenvironment so that ERK activity levels are low on average, but transiently high enough in patches of cells to explain the infrequent division characteristic of persister cells. Quantitative proteomics and computational modeling show that pulsatile MAPK reactivation is possible due to the co-existence in cells of two MAPK cascades: one driven by BRAFV600E that is drug-sensitive and a second driven by receptors that is drug-resistant. Paradoxically, this may account both for the frequent emergence of drug resistance and for the tolerability of RAF/MEK therapy in patients. Citation Format: Luca Gerosa, Christopher Chidley, Fabian Froehlich, Gabriela Sanchez, Sang Kyun Lim, Jeremy Muhlich, Jia-Yun Chen, Gregory J. Baker, Denis Schapiro, Tujin Shi, Lian Yi, Carrie D. Nicora, Wei-Jun Qian, H. Steven Wiley, Peter Sorger. ERK pulses drive non-genetic resistance in drug-adapted BRAFV600E melanoma cells [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr LB-B09. doi:10.1158/1535-7163.TARG-19-LB-B09

For the Library of Integrated Network-based Cellu- lar Signatures (LINCS) project many gene expres- sion signatures using the L1000 technology have been produced. The L1000 technology is a cost- effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interac- tive HTML5 web-based software application that fa- cilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB im- plements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an ex- perimental condition highlights gene-sets enriched for the differentially expressed genes from the se- lected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprece- dented potential for new discoveries in systems biol- ogy and systems pharmacology. The LCB application is available athttp://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of thehttp:// lincscloud.organdhttp://lincs.hms.harvard.eduweb- sites.

Motivation: Linking experimental data to mathematical models in biology is impeded by the lack of suitable software to manage and transform data. Model calibration would be facilitated and models would increase in value were it possible to preserve links to training data along with a record of all normalization, scaling, and fusion routines used to assemble the training data from primary results. Results: We describe the implementation of DataRail, an open source MATLAB-based toolbox that stores experimental data in flexible multi-dimensional arrays, transforms arrays so as to maximize information content, and then constructs models using internal or external tools. Data integrity is maintained via a containment hierarchy for arrays, imposition of a metadata standard based on a newly proposed MIDAS format, assignment of semantically typed universal identifiers, and implementation of a procedure for storing the history of all transformations with the array. We illustrate the utility of DataRail by processing a newly collected set of ∼22 000 measurements of protein activities obtained from cytokine-stimulated primary and transformed human liver cells. Availability: DataRail is distributed under the GNU General Public License and available at http://code.google.com/p/sbpipeline/ Contact: sbpipeline@hms.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available.

A Bayesian framework is used to calibrate a mass-action model of receptor-mediated apoptosis. Despite parameter non-identifiability and model ‘sloppiness', Bayes factor analysis discriminates between two alternative models of mitochondrial outer membrane permeabilization., Bayesian estimation returns statistically complete joint parameter distribution for mass-action models of receptor-mediated apoptosis calibrated to dynamic, live-cell data. Analysis of joint distributions reveals strong, non-linear correlations between parameters that are poorly captured by a conventional table of mean values and covariances; a high-dimensional distribution must therefore be reported as the true estimate of parameter values. Despite non-identifiablility and model ‘sloppiness,' a Bayesian framework returns probabilistic predictions for cell death dynamics that have tight confidence intervals and match experimental data. Use of a Bayesian framework to discriminate between two competing models of mitochondrial outer membrane permeabilization shows that a ‘direct' mechanism has ∼20-fold greater plausibility than an ‘indirect' mechanism, even though both models exhibit equally good fits to data for some parameters., Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (∼20-fold) for competing ‘direct' and ‘indirect' apoptosis models having different numbers of parameters. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty.

ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.

Experiments often result in observations that can lead to conflicting interpretations of biochemical mechanisms in signaling networks. We aim to use mathematical modeling of extrinsic apoptosis signaling networks to address such experimental observations and provide a theoretical explanation to seemingly discordant interpretations. Probing multiple mechanistic hypotheses in biological modeling often involves the instantiation of complex systems of equations, which despite their usefulness can make model revision, extension, and sharing extremely challenging. To address these modeling barriers, we have developed a modeling framework that allows biological models to be written as native Python programs that encode biological functions. Our modeling framework, PySB, offers access to a large set of existing numerical and programming methods to biological systems modeling. We discuss the implementation of our approach and show how it can be used to explore multiple hypotheses to describe the regulation of mitochondrial outer membrane permeabilization among the Bcl-2 family of proteins. We use our modeling framework to systematically explore proposed mechanisms, both from the literature and from our own experimental work, resulting in the instantiation, comparison, and calibration of multiple model topologies for numerical exploration. Our preliminary results, based on simulations calibrated to experimental data, suggest that the so-called indirect mechanism does not accurately reproduce experimental observations. Preliminary simulations of the so-called direct, and embedded mechanisms, with different topological variants, suggest that the latter model has better relation with experimental data and combinations of these hypothetical mechanisms could more likely account for the experimental observations. We will also aim to provide a programmatic foundation to probe biochemical model topology, physicochemical parameter-space exploration, and model likelihood with the goal of developing novel testable hypotheses about the interactions among these proteins and their role in apoptosis cancer biology.

Background: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. Results: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2 amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR + ) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. Conclusions: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.

PySB is a framework for creating biological models as Python programs using a high-level, action-oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development., PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development. Reusable functions can encode common low-level biochemical processes as well as high-level modules, making models transparent and concise. Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software. We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together' model based on recent biochemical findings., Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Experiments often result in observations that suggest conflicting biochemical mechanisms in signaling networks. Mathematical modeling of biological systems is a method used to probe such experimental observations and provide a consensus among seemingly discordant observations. However, probing multiple mechanistic hypotheses in biological modeling often involves the instantiation of complex systems of equations, which despite their usefulness, makes model revision, extension, and sharing extremely challenging. To address these modeling barriers, we have developed a modeling framework that brings a process-based approach to biological modeling. In our approach, biological models are written as programs that encode biological functions as needed. Our modeling framework, developed in the Python language, offers access to a large set of existing numerical and programming methods to biological systems modeling. Given our choice of programming language, the resulting models are easier to share, distribute, extend, and revise, adding transparency to the model-creation and execution process. We demonstrate our approach by exploring three competing mechanisms that describe the interactions among the Bcl-2 family of proteins and their contributions to mitochondrial outer membrane permeabilization in extrinsic apoptosis. We used our modeling framework to systematically explore these three proposed mechanisms resulting in the instantiation of sixteen biochemical model topologies for numerical exploration. Our preliminary results, based on simulations calibrated to experimental data, suggest that the so-called indirect mechanism does not accurately reproduce experimental observations. Results from simulations suggest that the so-called embedded model more seems to better align with existing experimental data. Despite these observations, further topology- and numerical-based characterization of the model topology and parameter-space is being carried out to more accurately understand the role of these Bcl-2 proteins in apoptosis. We aim to formulate hypotheses, testable through experiments, about the interaction among these proteins and their role in Bcl-2 cancer biology. This proffered talk is also presented as Poster A9. Citation Format: Carlos F. Lopez, Jeremy L. Muhlich, John J. Bachman, Peter K. Sorger. Considering alternate signaling mechanisms models in extrinsic apoptosis using programmatic modeling approaches [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR3.

We describe a systems approach to combine mathematical modeling and experimental measurement in the study of signal transduction in mammalian cells. Our focus is on the BCL-2 family of proteins and their interplay in extrinsic apoptosis. Construction of mathematical signal transduction models that recapitulate key features of signaling pathways as they exist in cells is currently very difficult. To circumvent this, we employ a novel rules-based modeling approach to manage and track high-level biological knowledge and translate this knowledge to mathematical models of extrinsic apoptosis. We present results that use experimental data as a foundation to explain seemingly contradictory interactions among Bcl-2 proteins and their contributions to mitochondrial outer membrane permeabilization as inhibitors, promoters, or sensitizers of apoptosis.

Background Shared-usage high throughput screening (HTS) facilities are becoming more common in academe as large-scale small molecule and genome-scale RNAi screening strategies are adopted for basic research purposes. These shared facilities require a unique informatics infrastructure that must not only provide access to and analysis of screening data, but must also manage the administrative and technical challenges associated with conducting numerous, interleaved screening efforts run by multiple independent research groups. Results We have developed Screensaver, a free, open source, web-based lab information management system (LIMS), to address the informatics needs of our small molecule and RNAi screening facility. Screensaver supports the storage and comparison of screening data sets, as well as the management of information about screens, screeners, libraries, and laboratory work requests. To our knowledge, Screensaver is one of the first applications to support the storage and analysis of data from both genome-scale RNAi screening projects and small molecule screening projects. Conclusions The informatics and administrative needs of an HTS facility may be best managed by a single, integrated, web-accessible application such as Screensaver. Screensaver has proven useful in meeting the requirements of the ICCB-Longwood/NSRB Screening Facility at Harvard Medical School, and has provided similar benefits to other HTS facilities.