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Additional file 1. Supplementary Figures S1–S5.

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.

Evidence that some influential biomedical results cannot be recapitulated has increased calls for data that is findable, accessible, interoperable, and reproducible (FAIR). Here, we study factors influencing the reproducibility of a prototypical cell-based assay: responsiveness of cultured cell lines to anti-cancer drugs. Such assays are important for drug development, mechanism of action studies, and patient stratification. This study involved seven research centers comprising the NIH LINCS Program Consortium, which aims to systematically characterize the responses of human cells to perturbation by gene disruption, small molecule drugs, and components of the microenvironment. We found that factors influencing the measurement of drug response vary substantially with the compound being analyzed and thus, the underlying biology. For example, substitution of a surrogate assay such as CellTiter-Glo(r) for direct microscopy-based cell counting is acceptable in the case of neratinib or alpelisib, but not palbociclib or etoposide. Uncovering and controlling for such context sensitivity requires systematic measurement of assay robustness in the face of biological variation, which is distinct from assay precision and sensitivity. Conversely, validating assays only over a narrow range of conditions has the potential to introduce serious systematic error in a large dataset spanning many compounds and cell lines.

Step-by-Step GR Calculator Example. Supplementary document describing step by step example of using GRcalculator (PDF 1256 kb)

Large-scale dose-response data and genomic datasets can be combined to discover novel drug-response biomarkers. There exist numerous online datasets of drug-response assays, but they are currently poorly accessible and their potential as a big data resource is limited due to lack of access and connection. Furthermore, it has recently been found that drug-response data often vary from one study to the next. A major reason for this variance is that traditional metrics of drug sensitivity such as IC50, Emax, and AUC values are confounded by the number of cell divisions taking place over the course of an assay. To solve this problem, we have developed GRcalculator, a suite of online tools found at www.grcalculator.org. The tools use GR metrics (proposed recently by Hafner et al. in Nature), a set of alterative drug-response metrics based on growth rate inhibition that are robust to differences in nominal division rate and assay duration. GRcalculator is a powerful, user-friendly and free tool for mining drug-response data using GR metrics. These metrics harmonize drug-response data, improving the discovery of novel drug-response biomarkers using big data as well as allowing for comparisons with patient-derived tumor cells that are generally slow growing. Direct access to LINCS drug-response datasets and, in the future, other public domain datasets is a unique functionality that will facilitate re-use of the valuable resources that these data represent. As well as mining datasets, the tool also offers calculation and visualization of GR metrics (and traditional metrics), generates publication-ready figures, and provides a unified platform for researchers analyzing drug sensitivity. For offline calculation and analysis, we have developed the GRmetrics R package (available via Bioconductor), which allows for use of larger datasets and inclusion of GR metrics calculations within existing R analysis pipelines.

Feedback loops are major components of biochemical systems. Many systems show multiple such (positive or negative) feedback loops. Nevertheless, very few quantitative analyses address the question how such multiple feedback loops evolved. Based on published models from the mitotic cycle in embryogenesis, we build a few case studies. Using a simple core architecture (transcription, phosphorylation and degradation), we define oscillatory models having either one positive feedback or one negative feedback, or both loops. With these models, we address the following questions about evolvability: could a system evolve from a simple model to a more complex one with a continuous transition in the parameter space? How do new feedback loops emerge without disrupting the proper function of the system? Our results show that progressive formation of a second feedback loop is possible without disturbing existing oscillatory behavior. For this process, the parameters of the system have to change during evolution to maintain predefined properties of oscillations like period and amplitude., Comment: Proceedings of the 2009 FOSBE conference in Denver, CO, USA. 4 pages

When cells are exposed to death ligands such as TRAIL a fraction undergoes apoptosis and a fraction survives; if surviving cells are re exposed to TRAIL fractional killing is once again observed. Fractional killing is a consequence of cell to cell variability arising from fluctuations in protein levels (extrinsic noise) and is observed even when death receptors are saturated. How extrinsic noise results in a clean bifurcation between life and death remains unclear however. In this paper we characterize a threshold in the rate and timing of initiator caspase activation that distinguishes cells that live from those that die; by mapping this threshold we can predict fractional killing in HeLa cells and a subset of other cells types exposed to natural and synthetic receptor agonists. A phenomenological model of the threshold also quantifies the effects of two resistance genes (c FLIP and Bcl 2) providing new insight into the control of cell fate by opposing pro death and pro survival proteins and suggesting new criteria for evaluating synthetic agonists of TRAIL receptors; this may help to rescue a once promising class of cancer therapeutics.

Most cellular oscillators rely on interlocked positive and negative regulatory feedback loops. While a negative circuit is necessary and sufficient to have limit-cycle oscillations, the role of positive feedbacks is not clear. Here we investigate the possible role of positive feedbacks in the robustness of the oscillations in presence of molecular noise. We performed stochastic simulations of a minimal 3-variable model of the cell cycle. We compare the robustness of the oscillations in the 3-variable model and in a modified model which incorporates a positive feedback loop through an auto-catalytic activation. We find that the model with a positive feedback loop is more robust to molecular noise than the model without the positive feedback loop. This increase of robustness is parameter-independent and can be explained by the attractivity properties of the limit-cycle. © 2010 Springer-Verlag Berlin Heidelberg.

With the advances in measurement technology for molecular biology, predictive mathematical models of cellular processes come in reach. A large fraction of such models addresses the kinetics of interaction between biomolecules such as proteins, transcription factors, genes and messenger RNA. In contrast to classical chemical kinetics - utilizing the reaction-rate equation, the small volume of cellular compartments requires to account for the stochasticity of chemical kinetics. In this chapter we discuss methods to generate sample paths of this underlying stochastic process for situations where the well-stirredness or fast-diffusion assumption holds true. We introduce various approximations to exact simulation algorithms that are more efficient in terms of computational complexity. Moreover, we discuss algorithms that account for the multi-scale nature of cellular reaction events.

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated., National Institutes of Health (U.S.) (NIGMS) (Grant number R01GM086881), National Science Foundation (U.S.). (Award number 1001092), National Science Foundation (U.S.). Graduate Research Fellowship Program, SystemsX.ch initiative (IPhD project)

The existence of a length threshold, of about 35 residues, above which polyglutamine repeats can give rise to aggregation and to pathologies, is one of the hallmarks of polyglutamine neurodegenerative diseases such as Huntington's disease. The reason why such a minimal length exists at all has remained one of the main open issues in research on the molecular origins of such classes of diseases. Following the seminal proposals of Perutz, most research has focused on the hunt for a special structure, attainable only above the minimal length, able to trigger aggregation. Such a structure has remained elusive and there is growing evidence that it might not exist at all. Here we review some basic polymer and statistical physics facts and show that the existence of a threshold is compatible with the modulation that the repeat length imposes on the association and dissociation rates of polyglutamine polypeptides to and from oligomers. In particular, their dramatically different functional dependence on the length rationalizes the very presence of a threshold and hints at the cellular processes that might be at play, in vivo, to prevent aggregation and the consequent onset of the disease.