Methods for Calculating Coronary Perfusion Pressure During CPR

Coronary perfusion pressure (CPP) is a major indicator of the effectiveness of cardiopulmonary resuscitation in human and animal research studies; however methods for calculating CPP differ among research groups. Here we compare the 6 published methods for calculating CPP using the same data set of aortic (Ao) and right atrial (RA) blood pressures. CPP was computed using each of the 6 calculation methods in an anesthetized pig model, instrumented with catheters with Cobe pressure transducers. Aortic and right atrial pressures were recorded continuously during electrically induced ventricular fibrillation and standard CPR. CPP calculated from the same raw data set by the 6 calculation methods ranged from -1 (signifying retrograde blood flow) to 26 mmHg (mean ± SD of 15 ± 11 mmHg). The CPP achieved by standard closed chest CPR is typically reported as 10–20 mmHg. Within a single study the CPP values may be comparable; however, the CPP values for different studies may not be reliable indicators of the relative efficacies of different CPR methods. Electronically derived, true mean coronary perfusion pressure is arguably the gold standard metric for representing coronary perfusion pressure

Model-Based Analysis for Qualitative Data: An Application in Drosophila Germline Stem Cell Regulation.

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques. This is the case for stem cell regulation mechanisms explored in the Drosophila germarium through fluorescent immunohistochemistry. To enable better integration of biological data with modeling in this and similar situations, we have developed a general parameter estimation process to quantitatively optimize models with qualitative data. The process employs a modified version of the Optimal Scaling method from social and behavioral sciences, and multi-objective optimization to evaluate the trade-off between fitting different datasets (e.g. wild type vs. mutant). Using only published imaging data in the germarium, we first evaluated support for a published intracellular regulatory network by considering alternative connections of the same regulatory players. Simply screening networks against wild type data identified hundreds of feasible alternatives. Of these, five parsimonious variants were found and compared by multi-objective analysis including mutant data and dynamic constraints. With these data, the current model is supported over the alternatives, but support for a biochemically observed feedback element is weak (i.e. these data do not measure the feedback effect well). When also comparing new hypothetical models, the available data do not discriminate. To begin addressing the limitations in data, we performed a model-based experiment design and provide recommendations for experiments to refine model parameters and discriminate increasingly complex hypotheses

Relaxation oscillations and hierarchy of feedbacks in MAPK signaling

We formulated a computational model for a MAPK signaling cascade downstream of the EGF receptor to investigate how interlinked positive and negative feedback loops process EGF signals into ERK pulses of constant amplitude but dose-dependent duration and frequency. A positive feedback loop involving RAS and SOS, which leads to bistability and allows for switch-like responses to inputs, is nested within a negative feedback loop that encompasses RAS and RAF, MEK, and ERK that inhibits SOS via phosphorylation. This negative feedback, operating on a longer time scale, changes switch-like behavior into oscillations having a period of 1 hour or longer. Two auxiliary negative feedback loops, from ERK to MEK and RAF, placed downstream of the positive feedback, shape the temporal ERK activity profile but are dispensable for oscillations. Thus, the positive feedback introduces a hierarchy among negative feedback loops, such that the effect of a negative feedback depends on its position with respect to the positive feedback loop. Furthermore, a combination of the fast positive feedback involving slow-diffusing membrane components with slower negative feedbacks involving faster diffusing cytoplasmic components leads to local excitation/global inhibition dynamics, which allows the MAPK cascade to transmit paracrine EGF signals into spatially non-uniform ERK activity pulses.Peer reviewe

Akt regulation of glycolysis mediates bioenergetic stability in epithelial cells

Cells use multiple feedback controls to regulate metabolism in response to nutrient and signaling inputs. However, feedback creates the potential for unstable network responses. We examined how concentrations of key metabolites and signaling pathways interact to maintain homeostasis in proliferating human cells, using fluorescent reporters for AMPK activity, Akt activity, and cytosolic NADH/NAD+ redox. Across various conditions, including glycolytic or mitochondrial inhibition or cell proliferation, we observed distinct patterns of AMPK activity, including both stable adaptation and highly dynamic behaviors such as periodic oscillations and irregular fluctuations that indicate a failure to reach a steady state. Fluctuations in AMPK activity, Akt activity, and cytosolic NADH/NAD+ redox state were temporally linked in individual cells adapting to metabolic perturbations. By monitoring single-cell dynamics in each of these contexts, we identified PI3K/Akt regulation of glycolysis as a multifaceted modulator of single-cell metabolic dynamics that is required to maintain metabolic stability in proliferating cells

Quantitative analysis for complex biological models using qualitative data: Applications in developmental biology

Better understanding the many complex processes governing living organisms relies on the combination of efficient experimentation and careful consideration in a theoretical framework. Informed by experimental data, mathematical modeling offers many tools to aid comprehension of complex systems, providing critical support throughout biological sciences. However, the technical challenges to performing precise experiments and making many molecular measurements, all in fragile living systems, limit the ability to quantify data. Much biological data is instead qualitative, especially in fields such as developmental biology, which emphasizes imaging molecular distributions across many cells or whole tissues. In contrast with quantitative measurements, there is an absence of tools to incorporate information from these qualitative data into the mathematical models used to understand complex interactions, compare and distinguish hypotheses, predict behavior, and plan experiments. The work presented in this dissertation develops strategies to address the technical limitations to quantitative modeling with qualitative data, applied in the context of developmental biology. Two parallel objectives are discussed. The theoretical objective is the development of a parameter estimation procedure for complex models that accommodates qualitative information, based on existing qualitative and quantitative techniques. The biological objective is the elucidation of stem cell regulatory mechanisms through study of the Drosophila germarium, a stem cell niche in the ovary. Mathematical representations of the germarium system are formulated based on experimental evidence, and employed to evaluate the viability and potential effects of several proposed mechanisms. Through the newly developed parameter estimation procedure, multiple hypothetical mechanisms are compared based on a compilation of published qualitative data from wild type flies and genetic mutants. The extent to which these experiments can distinguish hypotheses is shown, and the quantitatively tuned models are used to estimate the utility of feasible future experiments to refine models and better discriminate among them. The framework and procedure developed herein offer benefits to many applications of mathematical modeling in biology, biotechnology and other fields where qualitative data are prevalent

Live‐Cell Imaging and Analysis with Multiple Genetically Encoded Reporters

Genetically encoded live-cell reporters measure signaling pathway activity at the cellular level with high temporal resolution, often revealing a high degree of cell-to-cell heterogeneity. By using multiple spectrally distinct reporters within the same cell, signal transmission from one node to another within a signaling pathway can be analyzed to quantify factors such as signaling efficiency and delay. With other reporter configurations, correlation between different signaling pathways can be quantified. Such analyses can be used to establish the mechanisms and consequences of cell-to-cell heterogeneity and can inform new models of the functional properties of signaling pathways. In this unit, we describe an approach for designing and executing live-cell multiplexed reporter experiments. We also describe approaches for analyzing the resulting time-course data to quantify correlations and trends between the measured parameters at the single-cell level. © 2018 by John Wiley & Sons, Inc

Experimental and engineering approaches to intracellular communication.

Communication between and within cells is essential for multicellular life. While intracellular signal transduction pathways are often specified in molecular terms, the information content they transmit remains poorly defined. Here, we review research efforts to merge biological experimentation with concepts of communication that emerge from the engineering disciplines of signal processing and control theory. We discuss the challenges of performing experiments that quantitate information transfer at the molecular level, and we highlight recent studies that have advanced toward a clearer definition of the information content carried by signaling molecules. Across these studies, we emphasize a theme of increasingly well-matched experimental and theoretical approaches to decode the data streams directing cellular behavior

Transient phases of OXPHOS inhibitor resistance reveal underlying metabolic heterogeneity in single cells

Cell-to-cell heterogeneity in metabolism plays an unknown role in physiology and pharmacology. To functionally characterize cellular variability in metabolism, we treated cells with inhibitors of oxidative phosphorylation (OXPHOS) and monitored their responses with live-cell reporters for ATP, ADP/ATP, or activity of the energy-sensing kinase AMPK. Across multiple OXPHOS inhibitors and cell types, we identified a subpopulation of cells resistant to activation of AMPK and reduction of ADP/ATP ratio. This resistant state persists transiently for at least several hours and can be inherited during cell divisions. OXPHOS inhibition suppresses the mTORC1 and ERK growth signaling pathways in sensitive cells, but not in resistant cells. Resistance is linked to a multi-factorial combination of increased glucose uptake, reduced protein biosynthesis, and G0/G1 cell-cycle status. Our results reveal dynamic fluctuations in cellular energetic balance and provide a basis for measuring and predicting the distribution of cellular responses to OXPHOS inhibition

Brat Promotes Stem Cell Differentiation via Control of a Bistable Switch that Restricts BMP Signaling

Drosophila ovarian germline stem cells (GSCs) are maintained by Dpp signaling and the Pumilio (Pum) and Nanos (Nos) translational repressors. Upon division, Dpp signaling is extinguished, and Nos is downregulated in one daughter cell, causing it to switch to a differentiating cystoblast (CB). However, downstream effectors of Pum-Nos remain unknown, and how CBs lose their responsiveness to Dpp is unclear. Here, we identify Brain Tumor (Brat) as a potent differentiation factor and target of Pum-Nos regulation. Brat is excluded from GSCs by Pum-Nos but functions with Pum in CBs to translationally repress distinct targets, including the Mad and dMyc mRNAs. Regulation of both targets simultaneously lowers cellular responsiveness to Dpp signaling, forcing the cell to become refractory to the self-renewal signal. Mathematical modeling elucidates bistability of cell fate in the Brat-mediated system, revealing how autoregulation of GSC number can arise from Brat coupling extracellular Dpp regulation to intracellular interpretation