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solveME: fast and reliable solution of nonlinear ME models

BMC BIOINFORMATICS 2016年第1期17卷 391-391页

作者： Yang, Laurence Ma, Ding Ebrahim, Ali Lloyd, Colton J. Saunders, Michael A. Palsson, Bernhard O. Stanford Univ Dept Management Sci & Engn Stanford CA 94305 USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA

Background: Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (> 30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Results: Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60x speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints. Conclusions: Just as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.

关键词： Nonlinear optimization constraint-based modeling Metabolism Proteome Quasiconvex

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Characterizing metabolic pathway diversification in the context of perturbation size

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METABOLIC ENGINEERING 2015年 28卷 114-122页

作者： Yang, Laurence Srinivasan, Shyamsundhar Mahadevan, Radhakrishnan Cluett, William R. Univ Toronto Dept Chem Engn & Appl Chem Toronto ON M5S 3E5 Canada Univ Toronto Inst Biomat & Biomed Engn Toronto ON M5S 3G9 Canada

Cell metabolism is an important platform for sustainable biofuel, chemical and pharmaceutical production but its complexity presents a major challenge for scientists and engineers. Although in silica strains have been designed in the past with predicted performances near the theoretical maximum, real-world performance is often sub-optimal. Here, we simulate how strain performance is impacted when subjected to many randomly varying perturbations, including discrepancies between gene expression and in vivo flux, osmotic stress, and substrate uptake perturbations due to concentration gradients in bioreactors. This computational study asks whether robust performance can be achieved by adopting robustness-enhancing mechanisms from naturally evolved organisms-in particular, redundancy. Our study shows that redundancy, typically perceived as a ubiquitous robustness-enhancing strategy in nature, can either improve or undermine robustness depending on the magnitude of the perturbations. We also show that the optimal number of redundant pathways used can be predicted for a given perturbation size. (C) 2015 International Metabolic Engineering Society. Published by Elsevier Inc.

关键词： Strain design Optimization Robustness Succinate Amino acid constraint-based modeling

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Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment

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PLANT JOURNAL 2015年第5期81卷 822-835页

作者： Kleessen, Sabrina Irgang, Susann Klie, Sebastian Giavalisco, Patrick Nikoloski, Zoran Targenomix GmbH D-14476 Potsdam Germany Max Planck Inst Mol Plant Physiol Genes & Small Mol Grp D-14476 Potsdam Germany Max Planck Inst Mol Plant Physiol Expt Syst Biol Grp D-14476 Potsdam Germany Max Planck Inst Mol Plant Physiol Syst Biol & Math Modeling Grp D-14476 Potsdam Germany

Flux phenotypes predicted by constraint-based methods can be refined by the inclusion of heterogeneous data. While recent advances facilitate the integration of transcriptomics and proteomics data, purely stoichiometry-based approaches for the prediction of flux phenotypes by considering metabolomics data are lacking. Here we propose a constraint-based method, termed TREM-Flux, for integrating time-resolved metabolomics and transcriptomics data. We demonstrate the applicability of TREM-Flux in the dissection of the metabolic response of Chlamydomonas reinhardtii to rapamycin treatment by integrating the expression levels of 982 genes and the content of 45 metabolites obtained from two growth conditions. The findings pinpoint cysteine and methionine metabolism to be most affected by the rapamycin treatment. Our study shows that the integration of time-resolved unlabeled metabolomics data in addition to transcriptomics data can specify the metabolic pathways involved in the system's response to a studied treatment. Significance StatementIntegration of transcriptomics and metabolomics time-series data in large-scale models of biochemical reactions is used to derive representative flux phenotypes. Comparative analysis of the flux phenotypes from Chlamydomonas pinpoints the most affected metabolic functions in response to the rapamycin treatment under different growth conditions.

关键词： constraint-based modeling data integration Chlamydomonas reinhardtii metabolomics rapamycin technical advance

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Quantitative prediction of genome-wide resource allocation in bacteria

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METABOLIC ENGINEERING 2015年 32卷 232-243页

作者： Goelzer, Anne Muntel, Jan Chubukov, Victor Jules, Matthieu Prestel, Eric Noelker, Rolf Mariadassou, Mahendra Aymerich, Stephane Hecker, Michael Noirot, Philippe Becher, Doerte Fromion, Vincent INRA UR1404 MaIAGE F-78350 Jouy En Josas France Ernst Moritz Arndt Univ Greifswald Inst Microbiol D-17489 Greifswald Germany Swiss Fed Inst Technol Inst Mol Syst Biol CH-8093 Zurich Switzerland INRA UMR Micalis F-78350 Jouy En Josas France AgroParisTech UMR Micalis F-78350 Jouy En Josas France

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications. (C) 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

关键词： Resource allocation constraint-based modeling Strain design Systems biology

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Development and application of efficient pathway enumeration algorithms for metabolic engineering applications

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015年第2期118卷 134-146页

作者： Liu, F. Vilaca, P. Rocha, I. Rocha, M. Univ Minho Ctr Biol Engn P-4710057 Braga Portugal SilicoLife Lda P-4715387 Braga Portugal

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modeling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cereoisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

关键词： Synthetic biology Optimal pathway design Pathway enumeration Hypergraphs constraint-based modeling Metabolic engineering

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modeling and Simulation Tools: From Systems Biology to Systems Medicine

Modeling and Simulation Tools: From Systems Biology to Syste...

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作者： Brett G. Olivier Maciej J. Swat Martijn J. Moné

modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of “Software as a Medical Device” and what this may imply for the migration of research-oriented, simulation software into the domain of human health. In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools. less

关键词： modeling And Simulation Systems biology Pharmacodynamics constraint-based modeling Kinetic modeling Software design Standards development SBML Quantitative and systems pharmacology Physiology-based pharmacokinetics Pharmacometrics

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Interactive design exploration for constrained meshes

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COMPUTER-AIDED DESIGN 2015年 61卷 13-23页

作者： Deng, Bailin Bouaziz, Sofien Deuss, Mario Kaspar, Alexandre Schwartzburg, Yuliy Pauly, Mark Ecole Polytech Fed Lausanne Comp Graph & Geometry Lab CH-1015 Lausanne Switzerland

In architectural design, surface shapes are commonly subject to geometric constraints imposed by material, fabrication or assembly. Rationalization algorithms can convert a freeform design into a form feasible for production, but often require design modifications that might not comply with the design intent. In addition, they only offer limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm. We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimization subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a unified way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved efficiently and accurately. based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system offers full control over the exploration process, by providing direct access to the specification of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Architectural geometry Design exploration Fabrication-aware design constraint-based modeling

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Computational modeling of Human Metabolism and Its Application to Systems Biomedicine

Computational Modeling of Human Metabolism and Its Applicati...

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作者： Maike K. Aurich Ines Thiele

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model’s topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data. An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine. less

关键词： Metabolomics Homo sapiens Systems biology Cancer metabolism Flux balance analysis constraint-based modeling OMICS COBRA Personalized health Personalized models

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modeling the effects of commonly used drugs on human metabolism

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FEBS JOURNAL 2015年第2期282卷 297-317页

作者： Sahoo, Swagatika Haraldsdottir, Hulda S. Fleming, Ronan M. T. Thiele, Ines Univ Luxembourg Luxembourg Ctr Syst Biomed Belval Luxembourg

Metabolism contributes significantly to the pharmacokinetics and pharmacodynamics of a drug. In addition, diet and genetics have a profound effect on cellular metabolism with respect to both health and disease. In the present study, we assembled a comprehensive, literature-based drug metabolic reconstruction of the 18 most highly prescribed drug groups, including statins, anti-hypertensives, immunosuppressants and analgesics. This reconstruction captures in detail our current understanding of their absorption, intracellular distribution, metabolism and elimination. We combined this drug module with the most comprehensive reconstruction of human metabolism, Recon 2, yielding Recon2_DM1796, which accounts for 2803 metabolites and 8161 reactions. By defining 50 specific drug objectives that captured the overall drug metabolism of these compounds, we investigated the effects of dietary composition and inherited metabolic disorders on drug metabolism and drug-drug interactions. Our main findings include: (a) a shift in dietary patterns significantly affects statins and acetaminophen metabolism;(b) disturbed statin metabolism contributes to the clinical phenotype of mitochondrial energy disorders;and (c) the interaction between statins and cyclosporine can be explained by several common metabolic and transport pathways other than the previously established CYP3A4 connection. This work holds the potential for studying adverse drug reactions and designing patient-specific therapies.

关键词： constraint-based modeling drug metabolism genome-scale metabolic network reconstruction inborn errors of metabolism reconstruction module

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Prediction of intracellular metabolic states from extracellular metabolomic data

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METABOLOMICS 2015年第3期11卷 603-619页

作者： Aurich, Maike K. Paglia, Giuseppe Rolfsson, Ottar Hrafnsdottir, Sigrun Magnusdottir, Manuela Stefaniak, Magdalena M. Palsson, Bernhard O. Fleming, Ronan M. T. Thiele, Ines Univ Iceland Ctr Syst Biol Reykjavik Iceland Univ Luxembourg Luxembourg Ctr Syst Biomed Esch Sur Alzette Luxembourg Univ Iceland Sch Hlth Sci Fac Food Sci & Nutr Reykjavik Iceland Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

关键词： constraint-based modeling Metabolomics Multi-omics Metabolic network Transcriptomics

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