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Paper_v1.bib
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Created for Jeffrey Varner at 2016-03-19 07:13:05 -0400
%% Saved with string encoding Unicode (UTF-8)
@article{Song:2011aa,
Abstract = {In a recent article, Song and Ramkrishna (Song and Ramkrishna [2010]. Biotechnol Bioeng 106(2):271-284) proposed a lumped hybrid cybernetic model (L-HCM) towards extracting maximum information about metabolic function from a minimum of data. This approach views the total uptake flux as distributed among lumped elementary modes (L-EMs) so as to maximize a prescribed metabolic objective such as growth or uptake rate. L-EM is computed as a weighted average of EMs where the weights are related to the yields of vital products (i.e., biomass and ATP). In this article, we further enhance the predictive power of L-HCMs through modifications in lumping weights with additional parameters that can be tuned with data viewed to be critical. The resulting model is able to make predictions of diverse metabolic behaviors varying greatly with strain types as evidenced from case studies of anaerobic growth of various Escherichia coli strains. Incorporation of the new lumping formula into L-HCM remarkably improves model predictions with a few critical data, thus presenting L-HCM as a dynamic tool as being not only qualitatively correct but also quantitatively accurate.},
Author = {Song, Hyun-Seob and Ramkrishna, Doraiswami},
Date-Added = {2016-03-19 11:12:51 +0000},
Date-Modified = {2016-03-19 11:12:51 +0000},
Doi = {10.1002/bit.22922},
Journal = {Biotechnol Bioeng},
Journal-Full = {Biotechnology and bioengineering},
Mesh = {Anaerobiosis; Computer Simulation; Escherichia coli; Models, Biological; Systems Biology},
Month = {Jan},
Number = {1},
Pages = {127-40},
Pmid = {20830732},
Pst = {ppublish},
Title = {Cybernetic models based on lumped elementary modes accurately predict strain-specific metabolic function},
Volume = {108},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1002/bit.22922}}
@article{Gadkar:2003aa,
Abstract = {The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.},
Author = {Gadkar, Kapil G and Doyle, 3rd, Francis J and Crowley, Timothy J and Varner, Jeffrey D},
Date = {2003 Sep-Oct},
Date-Added = {2016-03-19 11:09:54 +0000},
Date-Modified = {2016-03-19 11:09:54 +0000},
Doi = {10.1021/bp025776d},
Journal = {Biotechnol Prog},
Journal-Full = {Biotechnology progress},
Mesh = {Algorithms; Bioreactors; Cell Culture Techniques; Computer Simulation; Cupriavidus necator; Cybernetics; Energy Metabolism; Feedback; Homeostasis; Hydroxybutyrates; Models, Biological; Multienzyme Complexes; Polyesters},
Number = {5},
Pages = {1487-97},
Pmid = {14524710},
Pst = {ppublish},
Title = {Cybernetic model predictive control of a continuous bioreactor with cell recycle},
Volume = {19},
Year = {2003},
Bdsk-Url-1 = {http://dx.doi.org/10.1021/bp025776d}}
@article{Song:2012aa,
Abstract = {Metabolic engineering is the field of introducing genetic changes in organisms so as to modify their function towards synthesizing new products of high impact to society. However, engineered cells frequently have impaired growth rates thus seriously limiting the rate at which such products are made. The problem is attributable to inadequate understanding of how a metabolic network functions in a dynamic sense. Predictions of mutant strain behavior in the past have been based on steady state theories such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and regulatory on/off minimization (ROOM). Such predictions are restricted to product yields and cannot address productivity, which is of focal interest to applications. We demonstrate that our framework ( [Song and Ramkrishna, 2010] and [Song and Ramkrishna, 2011]), based on a ``cybernetic'' view of metabolic systems, makes predictions of the dynamic behavior of mutant strains of Escherichia coli from a limited amount of data obtained from the wild-type. Dynamic frameworks must necessarily address the issue of metabolic regulation, which the cybernetic approach does by postulating that metabolism is an optimal dynamic response of the organism to the environment in driving reactions towards ensuring survival. The predictions made in this paper are without parallel in the literature and lay the foundation for rational metabolic engineering.},
Author = {Song, Hyun-Seob and Ramkrishna, Doraiswami},
Date-Added = {2016-03-19 11:09:34 +0000},
Date-Modified = {2016-03-19 11:09:34 +0000},
Journal = {Metab Eng},
Journal-Full = {Metabolic engineering},
Mesh = {Computer Simulation; Escherichia coli; Metabolic Engineering; Mutation},
Month = {Mar},
Number = {2},
Pages = {69-80},
Pmid = {22500302},
Pst = {ppublish},
Title = {Prediction of dynamic behavior of mutant strains from limited wild-type data},
Volume = {14},
Year = {2012}}
@article{Wiback:2003aa,
Abstract = {The move towards genome-scale analysis of cellular functions has necessitated the development of analytical (in silico) methods to understand such large and complex biochemical reaction networks. One such method is extreme pathway analysis that uses stoichiometry and thermodynamic irreversibly to define mathematically unique, systemic metabolic pathways. These extreme pathways form the edges of a high-dimensional convex cone in the flux space that contains all the attainable steady state solutions, or flux distributions, for the metabolic network. By definition, any steady state flux distribution can be described as a nonnegative linear combination of the extreme pathways. To date, much effort has been focused on calculating, defining, and understanding these extreme pathways. However, little work has been performed to determine how these extreme pathways contribute to a given steady state flux distribution. This study represents an initial effort aimed at defining how physiological steady state solutions can be reconstructed from a network's extreme pathways. In general, there is not a unique set of nonnegative weightings on the extreme pathways that produce a given steady state flux distribution but rather a range of possible values. This range can be determined using linear optimization to maximize and minimize the weightings of a particular extreme pathway in the reconstruction, resulting in what we have termed the alpha-spectrum. The alpha-spectrum defines which extreme pathways can and cannot be included in the reconstruction of a given steady state flux distribution and to what extent they individually contribute to the reconstruction. It is shown that accounting for transcriptional regulatory constraints can considerably shrink the alpha-spectrum. The alpha-spectrum is computed and interpreted for two cases; first, optimal states of a skeleton representation of core metabolism that include transcriptional regulation, and second for human red blood cell metabolism under various physiological, non-optimal conditions.},
Author = {Wiback, Sharon J and Mahadevan, Radhakrishnan and Palsson, Bernhard {\O}},
Date-Added = {2016-03-19 10:15:48 +0000},
Date-Modified = {2016-03-19 10:15:48 +0000},
Journal = {J Theor Biol},
Journal-Full = {Journal of theoretical biology},
Mesh = {Computer Simulation; Energy Metabolism; Erythrocytes; Humans; Models, Chemical; Models, Statistical},
Month = {Oct},
Number = {3},
Pages = {313-24},
Pmid = {12941590},
Pst = {ppublish},
Title = {Reconstructing metabolic flux vectors from extreme pathways: defining the alpha-spectrum},
Volume = {224},
Year = {2003}}
@article{Covert:2004aa,
Abstract = {The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.},
Author = {Covert, Markus W and Knight, Eric M and Reed, Jennifer L and Herrgard, Markus J and Palsson, Bernhard O},
Date-Added = {2016-03-19 09:55:26 +0000},
Date-Modified = {2016-03-19 09:55:26 +0000},
Doi = {10.1038/nature02456},
Journal = {Nature},
Journal-Full = {Nature},
Mesh = {Aerobiosis; Anaerobiosis; Computational Biology; Computer Simulation; Escherichia coli; Gene Expression Profiling; Genes, Bacterial; Models, Biological; Phenotype},
Month = {May},
Number = {6987},
Pages = {92-6},
Pmid = {15129285},
Pst = {ppublish},
Title = {Integrating high-throughput and computational data elucidates bacterial networks},
Volume = {429},
Year = {2004},
Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature02456}}
@article{Schuster:2000aa,
Abstract = {A set of linear pathways often does not capture the full range of behaviors of a metabolic network. The concept of 'elementary flux modes' provides a mathematical tool to define and comprehensively describe all metabolic routes that are both stoichiometrically and thermodynamically feasible for a group of enzymes. We have used this concept to analyze the interplay between the pentose phosphate pathway (PPP) and glycolysis. The set of elementary modes for this system involves conventional glycolysis, a futile cycle, all the modes of PPP function described in biochemistry textbooks, and additional modes that are a priori equally entitled to pathway status. Applications include maximizing product yield in amino acid and antibiotic synthesis, reconstruction and consistency checks of metabolism from genome data, analysis of enzyme deficiencies, and drug target identification in metabolic networks.},
Author = {Schuster, S and Fell, D A and Dandekar, T},
Date-Added = {2016-03-19 09:48:13 +0000},
Date-Modified = {2016-03-19 09:48:13 +0000},
Doi = {10.1038/73786},
Journal = {Nat Biotechnol},
Journal-Full = {Nature biotechnology},
Mesh = {Algorithms; Computational Biology; Glycolysis; Metabolism; Models, Biological; Monosaccharides; Pentose Phosphate Pathway},
Month = {Mar},
Number = {3},
Pages = {326-32},
Pmid = {10700151},
Pst = {ppublish},
Title = {A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks},
Volume = {18},
Year = {2000},
Bdsk-Url-1 = {http://dx.doi.org/10.1038/73786}}
@article{Schilling:2000aa,
Abstract = {Cellular metabolism is most often described and interpreted in terms of the biochemical reactions that make up the metabolic network. Genomics is providing near complete information regarding the genes/gene products participating in cellular metabolism for a growing number of organisms. As the true functional units of metabolic systems are its pathways, the time has arrived to define metabolic pathways in the context of whole-cell metabolism for the analysis of the structural design and capabilities of the metabolic network. In this study, we present the theoretical foundations for the identification of the unique set of systemically independent biochemical pathways, termed extreme pathways, based on system stochiometry and limited thermodynamics. These pathways represent the edges of the steady-state flux cone derived from convex analysis, and they can be used to represent any flux distribution achievable by the metabolic network. An algorithm is presented to determine the set of extreme pathways for a system of any complexity and a classification scheme is introduced for the characterization of these pathways. The property of systemic independence is discussed along with its implications for issues related to metabolic regulation and the evolution of cellular metabolic networks. The underlying pathway structure that is determined from the set of extreme pathways now provides us with the ability to analyse, interpret, and perhaps predict metabolic function from a pathway-based perspective in addition to the traditional reaction-based perspective. The algorithm and classification scheme developed can be used to describe the pathway structure in annotated genomes to explore the capabilities of an organism.},
Author = {Schilling, C H and Letscher, D and Palsson, B O},
Date-Added = {2016-03-18 19:49:59 +0000},
Date-Modified = {2016-03-18 19:49:59 +0000},
Doi = {10.1006/jtbi.2000.1073},
Journal = {J Theor Biol},
Journal-Full = {Journal of theoretical biology},
Mesh = {Algorithms; Animals; Cells; Models, Biological; Signal Transduction},
Month = {Apr},
Number = {3},
Pages = {229-48},
Pmid = {10716907},
Pst = {ppublish},
Title = {Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective},
Volume = {203},
Year = {2000},
Bdsk-Url-1 = {http://dx.doi.org/10.1006/jtbi.2000.1073}}
@article{Bell:2005aa,
Abstract = {UNLABELLED: The set of extreme pathways, a generating set for all possible steady-state flux maps in a biochemical reaction network, can be computed from the stoichiometric matrix, an incidence-like matrix reflecting the network topology. Here, we describe the implementation of a well-known algorithm to compute these pathways and give a summary of the features of the available software.
AVAILABILITY: The C-code, along with a Windows executable and sample network reaction files, are available at http://systemsbiology.ucsd.edu
CONTACT: [email protected].},
Author = {Bell, Steven L and Palsson, Bernhard {\O}},
Date-Added = {2016-03-18 19:48:54 +0000},
Date-Modified = {2016-03-18 19:48:54 +0000},
Doi = {10.1093/bioinformatics/bti228},
Journal = {Bioinformatics},
Journal-Full = {Bioinformatics (Oxford, England)},
Mesh = {Animals; Biochemistry; Cell Physiological Phenomena; Energy Metabolism; Gene Expression Regulation; Humans; Proteome; Signal Transduction; Software},
Month = {Apr},
Number = {8},
Pages = {1739-40},
Pmid = {15613397},
Pst = {ppublish},
Title = {Expa: a program for calculating extreme pathways in biochemical reaction networks},
Volume = {21},
Year = {2005},
Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bti228}}
@article{2012_kim_ramkrishna_BiotechProg,
Abstract = {We demonstrate strong experimental support for the cybernetic model based on maximizing carbon uptake rate in describing the microorganism's regulatory behavior by verifying exacting predictions of steady state multiplicity in a chemostat. Experiments with a feed mixture of glucose and pyruvate show multiple steady state behavior as predicted by the cybernetic model. When multiplicity occurs at a dilution (growth) rate, it results in hysteretic behavior following switches in dilution rate from above and below. This phenomenon is caused by transient paths leading to different steady states through dynamic maximization of the carbon uptake rate. Thus steady state multiplicity is a manifestation of the nonlinearity arising from cybernetic mechanisms rather than of the nonlinear kinetics. The predicted metabolic multiplicity would extend to intracellular states such as enzyme levels and fluxes to be verified in future experiments.},
Author = {Kim, Jin Il and Song, Hyun-Seob and Sunkara, Sunil R and Lali, Arvind and Ramkrishna, Doraiswami},
Date = {2012 Sep-Oct},
Date-Added = {2016-03-18 19:47:13 +0000},
Date-Modified = {2016-03-18 19:47:13 +0000},
Doi = {10.1002/btpr.1583},
Journal = {Biotechnol Prog},
Journal-Full = {Biotechnology progress},
Mesh = {Bioreactors; Culture Media; Cybernetics; Escherichia coli; Glucose; Kinetics; Models, Biological; Models, Theoretical; Pyruvic Acid},
Number = {5},
Pages = {1160-6},
Pmid = {22736577},
Pst = {ppublish},
Title = {Exacting predictions by cybernetic model confirmed experimentally: steady state multiplicity in the chemostat},
Volume = {28},
Year = {2012},
Bdsk-Url-1 = {http://dx.doi.org/10.1002/btpr.1583}}
@article{1999_varner_ramkrishna_MetaEng,
Abstract = {Using the modular cybernetic framework developed by Varner and Ramkrishna (Varner and Ramkrishna; 1998a, b) a cybernetic model is formulated that describes the time evolution of the aspartate family of amino acids in Corynebacterium lactofermentum ATCC 21799. The network model formulation is employed in the role of a diagnostic tool for the overproduction of threonine. More precisely, having determined a parameter set that describes the time evolution of a base strain (lysine producer), the model predicted response to genetic perturbations, designed to enhance the level of threonine, are simulated using an appropriately modified cybernetic model and compared with the experimental results of Stephanopoulos and Sinskey (Col{\'o}n et al., 1995a, Appl. Environ. Microbiol. 61, 74-78) for identical genetic perturbations. It is found that the model predicted response to enzymatic over-expression in the aspartate pathway agrees, for the most part, with experimental observations within the experimental error bounds. This result lends credence to the hypothesis that cybernetic models can be employed to predict the local response of a metabolic network to genetic perturbation, thereby, affording cognizance of the potential pitfalls of a particular genetic alteration strategy a priori.},
Author = {Varner, J and Ramkrishna, D},
Date-Added = {2016-03-18 19:45:48 +0000},
Date-Modified = {2016-03-18 19:45:48 +0000},
Doi = {10.1006/mben.1998.0104},
Journal = {Metab Eng},
Journal-Full = {Metabolic engineering},
Mesh = {Aspartic Acid; Biomedical Engineering; Corynebacterium; Cybernetics; Models, Biological; Models, Theoretical},
Month = {Jan},
Number = {1},
Pages = {88-116},
Pmid = {10935757},
Pst = {ppublish},
Title = {Metabolic engineering from a cybernetic perspective: aspartate family of amino acids},
Volume = {1},
Year = {1999},
Bdsk-Url-1 = {http://dx.doi.org/10.1006/mben.1998.0104}}
@article{1986_kompala_ramkrishna_tsao_BiotechBioeng,
Abstract = {Cybernetic models, developed earlier by the authors, have been evaluated experimentally for the growth of Klebsiella oxytoca in batch cultures using mixed substrates from glucose, xylose, arabinose, lactose, and fructose. Based entirely on information procured from batch growth on single substrates, the models accurately predict without further parameter fitting, diauxic growth on mixed substrates, automatically predicting the order in which the substrates are consumed. Even triauxic growth on a mixture of glucose, xylose, and lactose is predicted by the model based on single substrate data. Growth on glucose-fructose mixtures appears to need a slightly modified strategy for cybernetic variables.},
Author = {Kompala, D S and Ramkrishna, D and Jansen, N B and Tsao, G T},
Date-Added = {2016-03-18 19:45:40 +0000},
Date-Modified = {2016-03-18 19:45:40 +0000},
Doi = {10.1002/bit.260280715},
Journal = {Biotechnol Bioeng},
Journal-Full = {Biotechnology and bioengineering},
Month = {Jul},
Number = {7},
Pages = {1044-55},
Pmid = {18555426},
Pst = {ppublish},
Title = {Investigation of bacterial growth on mixed substrates: experimental evaluation of cybernetic models},
Volume = {28},
Year = {1986},
Bdsk-Url-1 = {http://dx.doi.org/10.1002/bit.260280715}}
@article{Julia,
Abstract = {The Julia programming language is gaining enormous popularity. Julia was designed to be easy and fast. Most importantly, Julia shatters deeply established notions widely held in the applied community:
1. High-level, dynamic code has to be slow by some sort of law of nature.
2. It is sensible to prototype in one language and then recode in another language.
3. There are parts of a system for the programmer, and other parts best left untouched as they are built by the experts.
Julia began with a deep understanding of the needs of the scientific programmer and the needs of the computer in mind. Bridging cultures that have often been distant, Julia combines expertise from computer science and computational science creating a new approach to scientific computing. This note introduces the programmer to the language and the underlying design theory. It invites the reader to rethink the fundamental foundations of numerical computing systems.
In particular, there is the fascinating dance between specialization and abstraction. Specialization allows for custom treatment. We can pick just the right algorithm for the right circumstance and this can happen at runtime based on argument types (code selection via multiple dispatch). Abstraction recognizes what remains the same after differences are stripped away and ignored as irrelevant. The recognition of abstraction allows for code reuse (generic programming). A simple idea that yields incredible power. The Julia design facilitates this interplay in many explicit and subtle ways for machine performance and, most importantly, human convenience.},
Author = {Jeff Bezanson and Alan Edelman and Stefan Karpinski and Viral B. Shah},
Date-Added = {2016-03-14 14:19:02 +0000},
Date-Modified = {2016-03-14 14:19:13 +0000},
Eprint = {1411.1607},
Eprintclass = {cs.MS},
Eprinttype = {arXiv},
Month = {November},
Title = {{J}ulia: A Fresh Approach to Numerical Computing},
Year = {2014}}
@article{2008_kim_varner_ramkrishna_BiotechProg,
Author = {Kim, JI and Varner, JD and Ramkrishna, D},
Doi = {10.1002/btpr.73},
Issn = {1520-6033},
Journal = {Biotechnol. Prog.},
Number = {5},
Pages = {993--1006},
Publisher = {Wiley Subscription Services, Inc., A Wiley Company},
Title = {A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables},
Url = {http://dx.doi.org/10.1002/btpr.73},
Volume = {24},
Year = {2008},
Bdsk-Url-1 = {http://dx.doi.org/10.1002/btpr.73}}
@article{1994_varma_palsson_ApplEnvMicro,
Abstract = {Flux balance models of metabolism use stoichiometry of metabolic pathways, metabolic demands of growth, and optimality principles to predict metabolic flux distribution and cellular growth under specified environmental conditions. These models have provided a mechanistic interpretation of systemic metabolic physiology, and they are also useful as a quantitative tool for metabolic pathway design. Quantitative predictions of cell growth and metabolic by-product secretion that are experimentally testable can be obtained from these models. In the present report, we used independent measurements to determine the model parameters for the wild-type Escherichia coli strain W3110. We experimentally determined the maximum oxygen utilization rate (15 mmol of O2 per g [dry weight] per h), the maximum aerobic glucose utilization rate (10.5 mmol of Glc per g [dry weight] per h), the maximum anaerobic glucose utilization rate (18.5 mmol of Glc per g [dry weight] per h), the non-growth-associated maintenance requirements (7.6 mmol of ATP per g [dry weight] per h), and the growth-associated maintenance requirements (13 mmol of ATP per g of biomass). The flux balance model specified by these parameters was found to quantitatively predict glucose and oxygen uptake rates as well as acetate secretion rates observed in chemostat experiments. We have formulated a predictive algorithm in order to apply the flux balance model to describe unsteady-state growth and by-product secretion in aerobic batch, fed-batch, and anaerobic batch cultures. In aerobic experiments we observed acetate secretion, accumulation in the culture medium, and reutilization from the culture medium. In fed-batch cultures acetate is cometabolized with glucose during the later part of the culture period.(ABSTRACT TRUNCATED AT 250 WORDS)},
Author = {Varma, A and Palsson, B{\O}},
Journal = {Appl. Environ. Microbiol.},
Number = {10},
Pages = {3724-3731},
Title = {Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110.},
Url = {http://aem.asm.org/content/60/10/3724.abstract},
Volume = {60},
Year = {1994},
Bdsk-Url-1 = {http://aem.asm.org/content/60/10/3724.abstract}}
@article{2007_schuetz_etal_MolSysBio,
Abstract = {To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C-determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.Mol Syst Biol. 3: 119},
Author = {Schuetz, R and Kuepfer, L and Sauer, U},
Doi = {10.1038/msb4100162},
Issn = {1744-4292},
Journal = {Mol. Syst. Biol.},
Number = {1},
Publisher = {EMBO Press},
Title = {Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli},
Url = {http://msb.embopress.org/content/3/1/119},
Volume = {3},
Year = {2007},
Bdsk-Url-1 = {http://msb.embopress.org/content/3/1/119},
Bdsk-Url-2 = {http://dx.doi.org/10.1038/msb4100162}}
@book{2006_Palsson_model,
Address = {New York, NY, USA},
Author = {Palsson, B{\O}},
Isbn = {0521859034},
Publisher = {Cambridge University Press},
Title = {Systems Biology: Properties of Reconstructed Networks},
Year = {2006}}
@article{2004_lee_varner_ko_ieee,
Address = {Los Alamitos, CA, USA},
Author = {Lee, L and Varner, JD and Ko, K},
Doi = {http://doi.ieeecomputersociety.org/10.1109/CSB.2004.1332526},
Isbn = {0-7695-2194-0},
Journal = {Comput Syst Bioinformatics Conf, Int IEEE CS},
Pages = {636-639},
Publisher = {IEEE Computer Society},
Title = {Parallel Extreme Pathway Computation for Metabolic Networks},
Volume = {0},
Year = {2004},
Bdsk-Url-1 = {http://doi.ieeecomputersociety.org/10.1109/CSB.2004.1332526}}
@article{2010_orth_NatBiotech,
Author = {Orth, JD and Thiele, I and Palsson, B{\O}},
Journal = {Nat. Biotechnol.},
Number = {3},
Pages = {245--248},
Publisher = {Nature Publishing Group},
Title = {What is flux balance analysis?},
Volume = {28},
Year = {2010}}
@article{2002_Mahadevan_BiophysJ,
Abstract = {Flux Balance Analysis (FBA) has been used in the past to analyze microbial metabolic networks. Typically, \{FBA\} is used to study the metabolic flux at a particular steady state of the system. However, there are many situations where the reprogramming of the metabolic network is important. Therefore, the dynamics of these metabolic networks have to be studied. In this paper, we have extended \{FBA\} to account for dynamics and present two different formulations for dynamic FBA. These two approaches were used in the analysis of diauxic growth in Escherichia coli. Dynamic \{FBA\} was used to simulate the batch growth of E. coli on glucose, and the predictions were found to qualitatively match experimental data. The dynamic \{FBA\} formalism was also used to study the sensitivity to the objective function. It was found that an instantaneous objective function resulted in better predictions than a terminal-type objective function. The constraints that govern the growth at different phases in the batch culture were also identified. Therefore, dynamic \{FBA\} provides a framework for analyzing the transience of metabolism due to metabolic reprogramming and for obtaining insights for the design of metabolic networks.},
Author = {Mahadevan, R and Edwards, JS and Doyle III, FJ},
Doi = {http://dx.doi.org/10.1016/S0006-3495(02)73903-9},
Issn = {0006-3495},
Journal = {Biophys. J.},
Number = {3},
Pages = {1331 - 1340},
Title = {Dynamic Flux Balance Analysis of Diauxic Growth in Escherichia coli},
Url = {http://www.sciencedirect.com/science/article/pii/S0006349502739039},
Volume = {83},
Year = {2002},
Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0006349502739039},
Bdsk-Url-2 = {http://dx.doi.org/10.1016/S0006-3495(02)73903-9}}
@article{2007_young_ramkrishna_BiotechProg,
Author = {Young, JD and Ramkrishna, D},
Doi = {10.1021/bp060176q},
Issn = {1520-6033},
Journal = {Biotechnol. Progr.},
Number = {1},
Pages = {83--99},
Publisher = {American Chemical Society},
Title = {On the Matching and Proportional Laws of Cybernetic Models},
Url = {http://dx.doi.org/10.1021/bp060176q},
Volume = {23},
Year = {2007},
Bdsk-Url-1 = {http://dx.doi.org/10.1021/bp060176q}}
@webpage{GLPK,
Date-Modified = {2016-03-17 12:52:17 +0000},
Month = {March},
Title = {{GNU Linear Programming Kit, Version 4.52}},
Url = {http://www.gnu.org/software/glpk/glpk.html},
Year = {2016}}
@article{SALIB,
Author = {Herman, JD},
Title = {SALib. Available online: https://github.com/jdherman/SALib}}
@article{Sundials,
Acmid = {1089020},
Author = {Hindmarsh, Alan C. and Brown, Peter N. and Grant, Keith E. and Lee, Steven L. and Serban, Radu and Shumaker, Dan E. and Woodward, Carol S.},
Doi = {10.1145/1089014.1089020},
Issn = {0098-3500},
Issue_Date = {September 2005},
Journal = {ACM Trans. Math. Softw.},
Month = sep,
Number = {3},
Numpages = {34},
Pages = {363--396},
Publisher = {ACM},
Title = {SUNDIALS: Suite of Nonlinear and Differential/Algebraic Equation Solvers},
Url = {http://doi.acm.org/10.1145/1089014.1089020},
Volume = {31},
Year = {2005},
Bdsk-Url-1 = {http://doi.acm.org/10.1145/1089014.1089020},
Bdsk-Url-2 = {http://dx.doi.org/10.1145/1089014.1089020}}
@article{2001_covert_schilling_palsson,
Author = {Covert, MW and Schilling, CH and Palsson, B{\O}},
Doi = {http://dx.doi.org/10.1006/jtbi.2001.2405},
Issn = {0022-5193},
Journal = {J. Theor. Biol.},
Number = {1},
Pages = {73 - 88},
Title = {Regulation of Gene Expression in Flux Balance Models of Metabolism},
Url = {http://www.sciencedirect.com/science/article/pii/S0022519301924051},
Volume = {213},
Year = {2001},
Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0022519301924051},
Bdsk-Url-2 = {http://dx.doi.org/10.1006/jtbi.2001.2405}}
@article{2006_vonKamp_Metatool,
Abstract = {Summary: Elementary modes analysis is a powerful tool in the constraint-based modeling of metabolic networks. In recent years, new approaches to calculating elementary modes in biochemical reaction networks have been developed. As a consequence, the program Metatool, which is one of the first programs dedicated to this purpose, has been reimplemented in order to make use of these new approaches. The performance of Metatool has been significantly increased and the new version 5.0 can now be run inside the GNU octave or Matlab environments to allow more flexible usage and integration with other tools.Availability: The script files and compiled shared libraries can be downloaded from the Metatool website at http://pinguin.biologie.uni-jena.de/bioinformatik/networks/index.html. Metatool consists of script files (m-files) for GNU octave as well as Matlab and shared libraries. The scripts are licensed under the GNU Public License and the use of the shared libraries is free for academic users and testing purposes. Commercial use of Metatool requires a special contract.Contact:[email protected]},
Author = {Kamp, A and Schuster, S},
Doi = {10.1093/bioinformatics/btl267},
Journal = {Bioinformatics},
Number = {15},
Pages = {1930-1931},
Title = {Metatool 5.0: fast and flexible elementary modes analysis},
Url = {http://bioinformatics.oxfordjournals.org/content/22/15/1930.abstract},
Volume = {22},
Year = {2006},
Bdsk-Url-1 = {http://bioinformatics.oxfordjournals.org/content/22/15/1930.abstract},
Bdsk-Url-2 = {http://dx.doi.org/10.1093/bioinformatics/btl267}}
@article{2010_song,
author = {Song, Hyun-Seob and Ramkrishna, Doraiswami},
title = {Prediction of metabolic function from limited data: Lumped hybrid cybernetic modeling (L-HCM)},
journal = {Biotechnology and Bioengineering},
volume = {106},
number = {2},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
issn = {1097-0290},
url = {http://dx.doi.org/10.1002/bit.22692},
doi = {10.1002/bit.22692},
pages = {271--284},
keywords = {lumped hybrid cybernetic model, elementary mode, Saccharomyces cerevisiae, Crabtree effect},
year = {2010},
}