Model certainty in cellular network-driven processes with missing data.

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data.

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq.

Motivation: LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in LIBRA-seq antigen count data is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies. Results: In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of technical noise. We apply this method to a dataset of nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method used in LIBRA-seq, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow.

Techniques and assays for the study of angiogenesis.

The importance of studying angiogenesis, the formation of new blood vessels from pre-existing vessels, is underscored by its involvement in both normal physiology, such as embryonic growth and wound healing, and pathologies, such as diabetes and cancer. Treatments targeting the molecular drive of angiogenesis have been developed, but many of the molecular mechanisms that mediate vascularization, as well as how these mechanisms can be targeted in therapy, remain poorly understood. The limited capacity to quantify angiogenesis properly curtails our molecular understanding and development of new drugs and therapies. Although there are a number of assays for angiogenesis, many of them strip away its important components and/or limit control of the variables that direct this highly cooperative and complex process. Here we review assays commonly used in endothelial cell biology and describe the progress toward development of a physiologically realistic platform that will enable a better understanding of the molecular and physical mechanisms that govern angiogenesis.