scholarly journals Top‐down versus bottom‐up—rediscovering physiology via systems biology?

2007 ◽  
Vol 3 (1) ◽  
pp. 113 ◽  
Ian Wilson
Top Down ◽  
Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. SCI-38-SCI-38
Scott L. Diamond

Abstract Abstract SCI-38 Systems Biology seeks to provide patient-specific prediction of dynamic cellular response to multiple stimuli, critical information toward predicting risk, disease progression, or response to therapy. We deployed two distinct approaches, bottom-up and top-down analyses, to gain insight into platelet signaling. The bottom-up approach required a definition of reaction network and kinetic equations (topology), kinetic parameters, and initial concentrations in order to simulate platelet signaling. We developed a computational platelet model – assembled from 24 peer-reviewed platelet studies to yield 132 measured kinetic rate constants – that accurately predicts resting levels of cytosolic calcium, IP3, diacylglycerol, phosphatidic acid, phosphoinositol, PIP, and PIP2. The model accurately predicts the full transient calcium dynamics in response to increasing levels of ADP. In the first full stochastic simulation of single platelet response to ADP, the model provides an extremely accurate prediction of the statistics of the asynchronous [Ca]i spikes observed in single platelets. Specifically, this is the first work to provide a quantitative molecular explanation of the asynchronous calcium spiking observed in ADP-activated human platelets. We show the asynchronous spiking is a result of the fundamentally stochastic nature of signal transduction in cells as small as human platelets. Specific testable predictions have emerged about the requirement of high SERCA/IP3R ratios in functional platelets, limits on the concentration of calcium in the DTS, and relative potencies of PAR peptides and ADP. For functional phenotyping platelets, a top-down approach linking multiple inputs to functional outputs was used to understand how human platelets integrate diverse signals encountered during thrombosis. We developed a high-throughput platform that measures the human platelet calcium mobilization in response to all pairwise combinations of six major agonists. Agonists tested in this study were: convulxin (CVX; GPVI activator), ADP, the thromboxane analog U46619, PAR1 agonist peptide (SFLLRN), PAR4 agonist peptide (AYPGKF), and PGE2 (activator of IP and EP receptor). The calcium responses to single agonists at 0.1, 1, 10′ EC50 and 135 pairwise combinations trained a neural network (NN) model to predict the entire 6-dimensional platelet response space. The NN model successfully predicted responses to sequential additions and 27 ternary combinations of [ADP], [convulxin], and [SFLLRN] (R=0.881). With 4077 NN simulations spanning the 6-dimensional agonist space, 45 combinations of 4–6 agonists (ranging from synergism to antagonism) were selected and confirmed experimentally (R=0.883), revealing a highly synergistic condition of high U46619/PGE2 ratio, consistent with the risk of COX-2 therapy. Furthermore, pairwise agonist scanning (PAS) provided a direct measurement of 135 synergy values, thus allowing a unique phenotypic scoring of 10 human donors. Patient-specific training of NNs represent a compact and robust approach for prediction of cellular integration of multiple signals in a complex disease milieu. Either bottom-up models or top-down NN models are ideal for incorporation into systems biology simulations of thrombotic pathways under flow conditions. Disclosures: No relevant conflicts of interest to declare.

2009 ◽  
Vol 33 (1) ◽  
pp. 1-2 ◽  
Víctor De Lorenzo ◽  
Michael Galperin

2007 ◽  
Vol 292 (1) ◽  
pp. C164-C177 ◽  
Thuy D. Vo ◽  
Bernhard O. Palsson

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

Cytokine ◽  
2011 ◽  
Vol 56 (1) ◽  
pp. 50
Ronald N. Germain ◽  
Martin Meier-Schellersheim ◽  
Bastian Angermann ◽  
Frederick Klauschen ◽  
Fenghai Zhang ◽  
Top Down ◽  

2005 ◽  
Vol 50 (19) ◽  
Michael Cole
Top Down ◽  

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