Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints

Desai, Ruchir P.; Nielsen, Lars K.; Papoutsakis, Eleftherios T.

doi:10.1016/s0168-1656(99)00022-x

Cited by 77 publications

(50 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Metabolic flux analysis uses measurable substrate and product data to solve a system of linear equations based on metabolic reaction stoichiometry to determine in vivo fluxes (22). We used a program specifically designed for C. acetobutylicum (9) to examine pathway fluxes (expressed in millimolar concentrations per hour per gram of cell weight) obtained in this study.…”

Section: Methodsmentioning

confidence: 99%

Transcriptional Analysis of spo0A Overexpression in Clostridium acetobutylicum and Its Effect on the Cell's Response to Butanol Stress

2004

Self Cite

View full text Add to dashboard Cite

Spo0A is the regulator of stationary-phase events and is required for transcription of solvent formation genes in Clostridium acetobutylicum. In order to elucidate the role of spo0A in differentiation, we performed transcriptional analysis of 824(pMSPOA) (a spo0A-overexpressing C. acetobutylicum strain with enhanced sporulation) against a plasmid control strain. DNA microarray data were contrasted to data from a spo0A knockout strain (SKO1) that neither sporulates nor produces solvents. Transcripts of fatty acid metabolism genes, motility and chemotaxis genes, heat shock protein genes, and genes encoding the Fts family of cell division proteins were differentially expressed in the two strains, suggesting that these genes play roles in sporulation and the solvent stress response. 824(pMSPOA) alone showed significant downregulation of many glycolytic genes in stationary phase, which is consistent with metabolic flux analysis data. Surprisingly, spo0A overexpression resulted in only nominal transcriptional changes of regulatory genes (abrB and sigF) whose expression was significantly altered in SKO1. Overexpression of spo0A imparted increased tolerance and prolonged metabolism in response to butanol stress. While most of the differentially expressed genes appear to be part of a general stress response (similar to patterns in two plasmid control strains and a groESLoverexpressing strain), several genes were expressed at higher levels at early time points after butanol challenge only in 824(pMSPOA). Most of these genes were related to butyryl coenzyme A and butyrate formation and/or assimilation, but they also included the cell division gene ftsX, the gyrase subunit-encoding genes gyrB and gyrA, DNA synthesis and repair genes, and fatty acid synthesis genes, all of which might play a role in the immediate butanol stress response, and thus in enhanced butanol tolerance. Stationary-phase events in gram-positive bacteria, includingBacillus subtilis and the solventogenic clostridia Clostridium acetobutylicum (13) and C. beijerinckii (24), are regulated by Spo0A, a response regulator that acts as both an activator and a repressor of gene expression. The effects of Spo0A have been examined in several sporulation studies of B. subtilis (11,27), but these events occur under nutrient-limiting conditions and involve several genes not found in C. acetobutylicum (21). Expression of spo0A in C. acetobutylicum is of particular interest because it promotes expression of the solvent formation genes (aad, ctfA, ctfB, and adc) during stationary phase and therefore promotes the conversion of acetate and butyrate into acetone, butanol, and ethanol (13). In order to metabolically engineer strains of C. acetobutylicum that can withstand a toxic solvent environment, it would be necessary to understand the expression and regulation of the events that occur in the transitional and stationary phases of culture. While inactivation of spo0A has been examined extensively in B. subtilis (11) and to some extent in C. acetobutylicum (13), overexpre...

show abstract

Section: Methodsmentioning

confidence: 99%

Transcriptional Analysis of spo0A Overexpression in Clostridium acetobutylicum and Its Effect on the Cell's Response to Butanol Stress

2004

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first challenge addressed in this paper is to develop a systematic computational framework to identify which functionalities to add to an organism-specific metabolic network (e.g., E. coli [Edwards and Palsson 2000;Reed et al 2003], S. cerevisiae [Forster et al 2003], Clostridium acetobutylicum [Papoutsakis 1984;Desai et al 1999], etc.) to enable a desired biotransformation.…”

Section: The Optstrain Proceduresmentioning

confidence: 99%

“…We next explored, as in the E. coli case, whether hydrogen production could be enhanced by judiciously removing competing functionalities using the OptKnock framework. To this end, we used the stoichiometric model for C. acetobutylicum developed by Papoutsakis and coworkers (Papoutsakis 1984;Desai et al 1999). OptKnock suggested the deletion of the acetate-forming and butyrate-transport reactions.…”

mentioning

confidence: 99%

OptStrain: A computational framework for redesign of microbial production systems

Pharkya¹,

Burgard²,

Maranas³

2004

Genome Res.

446

311

View full text Add to dashboard Cite

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.[Supplemental material is available online at www.genome.org. The Universal database can be found at http://fenske.che.psu.edu/Faculty/CMaranas/pubs.html.]A fundamental goal in systems biology is to elucidate the complete "palette" of biotransformations accessible to nature in living systems. This goal parallels the continuing quest in biotechnology to construct microbial strains capable of accomplishing an ever-expanding array of desired biotransformations. These biotransformations are aimed at products that range from simple precursor chemicals (Nakamura and Whited 2003;Causey et al. 2004) or complex molecules such as carotenoids (Misawa et al. 1991), to electrons in biofuel cells (Liu et al. 2004) or batteries (Bond et al. 2002;Bond and Lovley 2003), to even microbes capable of precipitating heavy metal complexes in bioremediation applications (Finneran et al. 2002;Lovley 2003;Methe et al. 2003). Recent developments in molecular biology and recombinant DNA technology have ushered in a new era in the ability to shape the gene content and expression levels for microbial production strains in a direct and targeted fashion (Stephanopoulos 2002). The astounding range and diversity of these newly acquired capabili...

show abstract

“…Мы не обсуждаем здесь различные соображения о возможных вариантах построения и трактовки целевой функции в оптимизационной задаче, высказанные в литературе [1,3,4], потому что это выходит за рамки настоящей статьи.…”

Section: Discussionunclassified

“…Построенная модель, включающая 14 реакций, позволила количественно описать процесс биосинтеза растворителей культурой продуцента Clostridium acetobutylicum и некоторых его штаммов при росте на глюкозе в хорошем соответствии с экспериментом. Предложенный подход был развит в1999 г [3] путем введения и учета некоторого количества нелинейных ограничений, полученных с использованием измеренных в эксперименте кинетических констант.…”

Section: Introductionunclassified

Rate Calculation for Metabolic Reactions in a Living and Growing Cell by the Method of Steady-State Stoichiometric Flux Balance

Nazipova¹

2007

Math.Biol.Bioinf.

View full text Add to dashboard Cite

Аннотация. Разработано программное обеспечение для построения математической модели стационарного метаболизма растущей клетки, позволяющее производить расчёт оптимальных экономических коэффициентов роста биомассы и выделения продуктов биосинтеза, достижимых при идеальной регуляции процессов, и соответствующее этим оптимальным режимам метаболизма распределение скоростей реакций внутри клетки и в её компартментах, включая скорости обмена метаболитами между компартментами. Созданная программа FLUX II работает в среде Windows. Программа имеет удобный интерфейс, обеспечена средствами, исключающими зацикливание алгоритма и позволяющими производить содержательный анализ построенной модели. Программа обеспечивает также представление результатов расчета и анализа модели в цифровой и графической форме. С помощью созданной программы построена математическая модель стационарного метаболизма клетки E.coli, и рассчитан наилучший возможный экономический коэффициент для роста биомассы этой клетки в аэробных условиях на глюкозе и соответствующее этому режиму метаболизма распределение скоростей реакций. Показано удовлетворительное согласие результатов расчетов и анализа с данными, полученными в эксперименте. Обсуждены возможные пути использования программы при решении практических задач современной биотехнологии. ВведениеСуществование живой клетки полностью определяется её метаболизмом -совокупностью большого числа взаимозависимых химических реакций, протекающих согласованно во времени, скорость протекания которых определяется белками-ферментами. В результате метаболических реакций осуществляется воспроизведение всех структурных и функциональных молекулярных компонентов клетки, определяющих её существование в качестве живой системы, т.е. реализуется основное свойство живой клетки -её способность к самовоспроизведению. Метаболизм клетки включает в себя сотни реакций и является сложной саморегулирующейся системой.В настоящее время метаболизм клеток многих прокариотических организмов (таких как Escherichia coli, Bacillus subtilis, Clostridium acetobutylicum и ряда других) * drozdov@img.ras.ru

show abstract

Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints

Cited by 77 publications

References 34 publications

Transcriptional Analysis of spo0A Overexpression in Clostridium acetobutylicum and Its Effect on the Cell's Response to Butanol Stress

Transcriptional Analysis of spo0A Overexpression in Clostridium acetobutylicum and Its Effect on the Cell's Response to Butanol Stress

OptStrain: A computational framework for redesign of microbial production systems

Rate Calculation for Metabolic Reactions in a Living and Growing Cell by the Method of Steady-State Stoichiometric Flux Balance

Contact Info

Product

Resources

About