15. ゼミノームの放射線治療成績(第5回佐藤外科例会,第488回千葉医学会例会)

Performance of the MDSINE inference algorithms on simulated data with different sequencing depths. Simulations assumed an underlying dynamical systems model with ten species observed over 30 days with 27 time points sampled and an invading species at day 10. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: (a) root mean-square error (RMSE) for microbial growth rates; (b) RMSE for microbial interaction parameters; (c) RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject; and (d) area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network. Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance. (PDF 182 kb

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users

Nonlinear Constraint-Based Modeling Of The Function And Evolution Of C4 Photosynthesis

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. The C4 photosynthetic system is a key target of efforts to improve crop yield through biotechnology, and its independent development in dozens of plant species widely separated geographically and phylogenetically is an intriguing example of convergent evolution. The availability of extensive high-throughput experimental data from C4 and non-C4 plants, as well as the origin of the biochemical pathways of C4 photosynthesis in the recruitment of enzymatic reactions already present in the ancestral state, makes it natural to study the development, function and evolution of the C4 system in the context of a plant's complete metabolic network, but the essentially nonlinear relationship between rates of photosynthesis, rates of photorespiration, and carbon dioxide and oxygen levels prevents the application of conventional, linear methods for genome-scale metabolic modeling to these questions. I present an approach which incorporates nonlinear constraints on reaction rates arising from enzyme kinetics and diffusion laws into flux balance analysis problems, and software to enable it. Applying the technique to a new genomescale model, suitable for describing metabolism in the leaves of either Zea mays or generic plants, I show it can reproduce known nonlinear physiological re- sponses of C3 and C4 plants. In combination with a novel method for inferring metabolic activity from enzyme expression data, I use the nonlinear model to interpret multiple channels of transcriptomic and biochemical data in the developing maize leaf, showing that the predicted metabolic state reproduces the transition between carbonimporting tissue at the leaf base and carbon-exporting tissue at the leaf tip while making additional testable predictions about metabolic shifts along the developmental axis. Adapting a method for simulating transition paths in physical and chemical systems, I find the highest-fitness paths connecting C3 and C4 states in the model's high-dimensional parameter space, show that such paths reproduce known aspects of the evolutionary history of the C4 position, and study their response to variation in environmental conditions and C4 biochemistry

Additional file 6: Figure S4. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Forecasts of microbial concentration trajectories for the gnotobiotic mice probiotic stability experiments. The forecasts were obtained using a hold-one-subject-out procedure. Briefly, MDSINE was run on all data from all but one of the mice (the held-out subject) and model parameters were inferred. Using the inferred model parameters (including for the perturbation) and the measured concentrations of the microbiota at an initial time point for the held-out mouse, the trajectories of the microbiota for the held-out mouse were then forecast for all the remaining time points; the procedure was repeated for each mouse in turn. Solid lines denote predicted trajectories and symbols denote actual data. (PDF 7552 kb

Additional file 1: Tables S1–4. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Supplemental tables. (DOCX 21 kb

Additional file 5: Figure S3. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Experimental design for probiotic stability studies in gnotobiotic mice. Seven adult germ-free mice were gavaged with 13 Clostridia strains from the VE202 probiotic cocktail [24]. Five mice were maintained on a standard high-fiber diet for 5 weeks, after which mice were switched to a low-fiber diet for 2 weeks and then switched back to the high-fiber diet for another 2 weeks; an additional two mice were inoculated with the same strains but were not subjected to the low-fiber dietary perturbation. Fecal pellets were collected at days 1–21 (daily), 23, 25, 27, 29, 31, 33, 35–60 (daily), 62, 63, and 65 for the five mice receiving the low-fiber dietary perturbation and at days 1–21 (daily), 23, 25, 27, and 29 for the two mice not receiving the perturbation. (PDF 42 kb

Additional file 7: Figure S5. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Predicted stability and steady state concentrations (log10 ng strain DNA/μg total fecal DNA) for all combinations of the 13 Clostridia strains in mice fed either high-fiber (standard) or low-fiber diets in the probiotic stability experiment. Columns and rows were ordered using hierarchical clustering using Euclidean distance with Ward linkage. No significant differences were found in the predicted stable biodiversity profiles between the high-fiber and low-fiber dietary regimes (number of strains across all predicted stable states was not significantly different; Wilcoxon rank sum test p value = 0.096). (PDF 4845 kb

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses (Software Source Code)

<p>Predicting dynamics of host-microbial ecosystems is crucial for rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then demonstrate MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with <em>Clostridium difficile</em> and an immune-modulatory probiotic. On these datasets, we demonstrate new capabilities including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity (keystoneness) in response to perturbations.</p