Publications by authors named "Michael A Saunders"

14 Publications

  • Page 1 of 1

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Nat Protoc 2019 03;14(3):639-702

Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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http://dx.doi.org/10.1038/s41596-018-0098-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635304PMC
March 2019

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression.

BMC Syst Biol 2019 01 9;13(1). Epub 2019 Jan 9.

Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA.

Background: Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype.

Results: We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation.

Conclusions: Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.
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http://dx.doi.org/10.1186/s12918-018-0675-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327497PMC
January 2019

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression.

Sci Rep 2017 01 18;7:40863. Epub 2017 Jan 18.

Stanford University, Dept of Management Science and Engineering, Stanford, CA 94305, USA.

Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We have developed a quadruple-precision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.
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http://dx.doi.org/10.1038/srep40863DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5241643PMC
January 2017

Principles of proteome allocation are revealed using proteomic data and genome-scale models.

Sci Rep 2016 11 18;6:36734. Epub 2016 Nov 18.

Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the "generalist" (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and "hedging" against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σ. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.
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http://dx.doi.org/10.1038/srep36734DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114563PMC
November 2016

solveME: fast and reliable solution of nonlinear ME models.

BMC Bioinformatics 2016 Sep 22;17(1):391. Epub 2016 Sep 22.

Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, 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 60× 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.
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http://dx.doi.org/10.1186/s12859-016-1240-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5034503PMC
September 2016

Conditions for duality between fluxes and concentrations in biochemical networks.

J Theor Biol 2016 11 23;409:1-10. Epub 2016 Jun 23.

Dept of Management Science and Engineering, Stanford University, Stanford, CA, USA. Electronic address:

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We also provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality.The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048525PMC
http://dx.doi.org/10.1016/j.jtbi.2016.06.033DOI Listing
November 2016

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.

Proc Natl Acad Sci U S A 2015 Aug 10;112(34):10810-5. Epub 2015 Aug 10.

Department of Bioengineering, University of California at San Diego, La Jolla, CA 92093; Novo Nordisk Foundation Center for Biosustainability, 2970 Hørsholm, Denmark;

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.
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http://dx.doi.org/10.1073/pnas.1501384112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4553782PMC
August 2015

LSRN: A PARALLEL ITERATIVE SOLVER FOR STRONGLY OVER- OR UNDERDETERMINED SYSTEMS.

SIAM J Sci Comput 2014 ;36(2):C95-C118

Department of Mathematics, Stanford University, Stanford, CA 94305 ( ).

We describe a parallel iterative least squares solver named LSRN that is based on random normal projection. LSRN computes the min-length solution to min ‖ - ‖, where ∈ ℝ with ≫ or ≪ , and where may be rank-deficient. Tikhonov regularization may also be included. Since is involved only in matrix-matrix and matrix-vector multiplications, it can be a dense or sparse matrix or a linear operator, and LSRN automatically speeds up when is sparse or a fast linear operator. The preconditioning phase consists of a random normal projection, which is embarrassingly parallel, and a singular value decomposition of size ⌈γ min()⌉ × min(), where γ is moderately larger than 1, e.g., γ = 2. We prove that the preconditioned system is well-conditioned, with a strong concentration result on the extreme singular values, and hence that the number of iterations is fully predictable when we apply LSQR or the Chebyshev semi-iterative method. As we demonstrate, the Chebyshev method is particularly efficient for solving large problems on clusters with high communication cost. Numerical results show that on a shared-memory machine, LSRN is very competitive with LAPACK's DGELSD and a fast randomized least squares solver called Blendenpik on large dense problems, and it outperforms the least squares solver from SuiteSparseQR on sparse problems without sparsity patterns that can be exploited to reduce fill-in. Further experiments show that LSRN scales well on an Amazon Elastic Compute Cloud cluster.
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http://dx.doi.org/10.1137/120866580DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4238893PMC
January 2014

Algorithm 937: MINRES-QLP for Symmetric and Hermitian Linear Equations and Least-Squares Problems.

ACM Trans Math Softw 2014 Feb;40(2)

Stanford University.

We describe algorithm MINRES-QLP and its FORTRAN 90 implementation for solving symmetric or Hermitian linear systems or least-squares problems. If the system is singular, MINRES-QLP computes the unique minimum-length solution (also known as the pseudoinverse solution), which generally eludes MINRES. In all cases, it overcomes a potential instability in the original MINRES algorithm. A positive-definite pre-conditioner may be supplied. Our FORTRAN 90 implementation illustrates a design pattern that allows users to make problem data known to the solver but hidden and secure from other program units. In particular, we circumvent the need for reverse communication. Example test programs input and solve real or complex problems specified in Matrix Market format. While we focus here on a FORTRAN 90 implementation, we also provide and maintain MATLAB versions of MINRES and MINRES-QLP.
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http://dx.doi.org/10.1145/2527267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199394PMC
February 2014

Robust flux balance analysis of multiscale biochemical reaction networks.

BMC Bioinformatics 2013 Jul 30;14:240. Epub 2013 Jul 30.

Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA.

Background: Biological processes such as metabolism, signaling, and macromolecular synthesis can be modeled as large networks of biochemical reactions. Large and comprehensive networks, like integrated networks that represent metabolism and macromolecular synthesis, are inherently multiscale because reaction rates can vary over many orders of magnitude. They require special methods for accurate analysis because naive use of standard optimization systems can produce inaccurate or erroneously infeasible results.

Results: We describe techniques enabling off-the-shelf optimization software to compute accurate solutions to the poorly scaled optimization problems arising from flux balance analysis of multiscale biochemical reaction networks. We implement lifting techniques for flux balance analysis within the openCOBRA toolbox and demonstrate our techniques using the first integrated reconstruction of metabolism and macromolecular synthesis for E. coli.

Conclusion: Our techniques enable accurate flux balance analysis of multiscale networks using off-the-shelf optimization software. Although we describe lifting techniques in the context of flux balance analysis, our methods can be used to handle a variety of optimization problems arising from analysis of multiscale network reconstructions.
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http://dx.doi.org/10.1186/1471-2105-14-240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750362PMC
July 2013

A higher-order generalized singular value decomposition for comparison of global mRNA expression from multiple organisms.

PLoS One 2011 22;6(12):e28072. Epub 2011 Dec 22.

Department of Electrical and Computer Engineering, University of Texas at Austin, Texas, USA.

The number of high-dimensional datasets recording multiple aspects of a single phenomenon is increasing in many areas of science, accompanied by a need for mathematical frameworks that can compare multiple large-scale matrices with different row dimensions. The only such framework to date, the generalized singular value decomposition (GSVD), is limited to two matrices. We mathematically define a higher-order GSVD (HO GSVD) for N≥2 matrices D(i)∈R(m(i) × n), each with full column rank. Each matrix is exactly factored as D(i)=U(i)Σ(i)V(T), where V, identical in all factorizations, is obtained from the eigensystem SV=VΛ of the arithmetic mean S of all pairwise quotients A(i)A(j)(-1) of the matrices A(i)=D(i)(T)D(i), i≠j. We prove that this decomposition extends to higher orders almost all of the mathematical properties of the GSVD. The matrix S is nondefective with V and Λ real. Its eigenvalues satisfy λ(k)≥1. Equality holds if and only if the corresponding eigenvector v(k) is a right basis vector of equal significance in all matrices D(i) and D(j), that is σ(i,k)/σ(j,k)=1 for all i and j, and the corresponding left basis vector u(i,k) is orthogonal to all other vectors in U(i) for all i. The eigenvalues λ(k)=1, therefore, define the "common HO GSVD subspace." We illustrate the HO GSVD with a comparison of genome-scale cell-cycle mRNA expression from S. pombe, S. cerevisiae and human. Unlike existing algorithms, a mapping among the genes of these disparate organisms is not required. We find that the approximately common HO GSVD subspace represents the cell-cycle mRNA expression oscillations, which are similar among the datasets. Simultaneous reconstruction in the common subspace, therefore, removes the experimental artifacts, which are dissimilar, from the datasets. In the simultaneous sequence-independent classification of the genes of the three organisms in this common subspace, genes of highly conserved sequences but significantly different cell-cycle peak times are correctly classified.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0028072PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245232PMC
May 2012

Purification and characterization of a cyclooxygenase-2 and angiogenesis suppressing factor produced by human fibroblasts.

FASEB J 2002 Aug 7;16(10):1286-8. Epub 2002 Jun 7.

Vascular Biology Research Center, Institute of Molecular Medicine, and Division of Hematology, University of Texas-Houston Medical School, Houston, Texas, USA.

Cyclooxygenase-2 (COX-2) is an inducible enzyme that plays an important role in several pathophysiological processes, including inflammation, angiogenesis, and tumorigenesis. We have recently observed that COX-2 induction is restrained in proliferating fibroblasts. The mechanism by which this occurs is unclear. Here, we report the detection and isolation from the conditioned medium of proliferating fibroblasts a factor that suppressed COX-2 expression. This factor, which was named cytoguardin, suppressed COX-2 protein levels induced by phorbol 12-myristate 13-acetate, interleukin-1beta, tumor necrosis factor alpha, and lipopolysaccharide (LPS) in fibroblasts and LPS-induced COX-2 protein levels and promoter activities in human endothelial cells and murine RAW 264.7 cells in a comparable concentration-dependent manner. It inhibited COX-2 expression induced by angiogenic factors and endothelial tube formation induced by angiogenic factors and colon cancer cell medium. These findings provide evidence for the control of COX-2 transcription by an endogenous cellular factor.
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http://dx.doi.org/10.1096/fj.01-0844fjeDOI Listing
August 2002

Obligatory role of cyclic adenosine monophosphate response element in cyclooxygenase-2 promoter induction and feedback regulation by inflammatory mediators.

Circulation 2002 Jun;105(23):2760-5

Institut für Pharmakologie und Klinische Pharmakologie, Heinrich-Heine-Universtät Düsseldorf, Düsseldorf, Germany.

Background: Cyclooxygenase-2 (COX-2) plays a key role in human inflammatory disorders such as vascular inflammation. COX-2 promoter activity is induced by proinflammatory mediators, but the role of cyclic adenosine monophosphate response element (CRE) in promoter stimulation remains unclear.

Methods And Results: Transient transfection of a 0.9-kb COX-2 promoter fragment bearing CRE mutation abrogated COX-2 promoter activity induced by proinflammatory mediators in human endothelial cells and fibroblasts. Dual mutations of CRE and an upstream CCAAT/enhancer binding protein (C/EBP) site did not have an additional effect. Binding of CREB-2, ATF-2, USF-2, and c-Jun transactivators to a wild-type and CRE-mutated oligonucleotide was analyzed by a novel DNA-binding assay. CREB-2 and ATF-2 in nuclear extracts of unstimulated endothelial cells bound to CRE, whereas USF-2 and c-Jun or c-Fos bound to non-CRE sites. CREB-2 and c-Fos binding was increased by phorbol 12-myristate 13-acetate but not tumor necrosis factor-alpha. The binding assay and chromatin immunoprecipitation revealed binding of P300 coactivator to the COX-2 promoter region.

Conclusions: CRE plays an obligatory role in COX-2 promoter activation by diverse stimuli. CREB-2 and ATF-2 bound to CRE serve as an anchor for P300 interaction with upstream transactivators and downstream transcription machinery.
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http://dx.doi.org/10.1161/01.cir.0000018127.10968.34DOI Listing
June 2002

Dynamic regulation of cyclooxygenase-2 promoter activity by isoforms of CCAAT/enhancer-binding proteins.

J Biol Chem 2002 Mar 10;277(9):6923-8. Epub 2001 Dec 10.

Vascular Biology Research Center, Institute of Molecular Medicine, University of Texas-Houston Medical School, 6431 Fannin, Houston, TX 77030, USA.

To elucidate the mechanism by which isoforms of CCAAT/enhancer-binding proteins regulate cyclooxygenase-2 expression, we determined by a novel technique binding of six isoforms of this transactivator to two sequence-specific CCAAT/enhancer-binding protein (-132/-125) and cyclic AMP (-59/-53) regulatory elements in human foreskin fibroblasts treated with phorbol 12-myristate 13-acetate for 4 h. The delta isoform bound to these two elements at basal state, which was displaced by full-length as well as two truncated beta isoforms, a 41-kDa liver-enriched activating protein and a 16-kDa liver-enriched inhibitory protein, after phorbol ester stimulation. Kinetic analysis shows time-dependent changes in beta and delta binding that were concordant with time-dependent increase in cyclooxygenase-2 induction. Overexpression of the 16-kDa beta isoform blocked the promoter activity and protein level induced by phorbol ester. Paradoxically, it increased binding of beta isoforms to the sequence-specific promoter DNA but suppressed cyclooxygenase-2 promoter activation by p300 cotransfection. These findings provide new insight into the regulation of cyclooxygenase-2 promoter by an interplay between two opposite beta isoforms and p300 co-activator.
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http://dx.doi.org/10.1074/jbc.M108075200DOI Listing
March 2002
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