PubMed Health⌕ Search

Biomedical subjects

Yandi Dharmadi

Publications and source records attributed to Yandi Dharmadi.

3 recordsLinked to original sources

Anaerobic fermentation of glycerol by Escherichia coli: a new platform for metabolic engineering.

The worldwide surplus of glycerol generated as inevitable byproduct of biodiesel fuel and oleochemical production is resulting in the shutdown of traditional glycerol-producing/refining plants and new applications are needed for this now abundant carbon source. In this article we report our finding that Escherichia coli can ferment glycerol in a pH-dependent manner. We hypothesize that glycerol fermentation is linked to the availability of CO(2), which under acidic conditions is produced by the oxidation of formate by the enzyme formate hydrogen lyase (FHL). In agreement with this hypothesis, glycerol fermentation was severely impaired by blocking the activity of FHL. We demonstrated that, unlike CO(2), hydrogen (the other product of FHL-mediated formate oxidation) had a negative impact on cell growth and glycerol fermentation. In addition, supplementation of the medium with CO(2) partially restored the ability of an FHL-deficient strain to ferment glycerol. High pH resulted in low CO(2) generation (low activity of FHL) and availability (most CO(2) is converted to bicarbonate), and consequently very inefficient fermentation of glycerol. Most of the fermented glycerol was recovered in the reduced compounds ethanol and succinate (93% of the product mixture), which reflects the highly reduced state of glycerol and confirms the fermentative nature of this process. Since glycerol is a cheap, abundant, and highly reduced carbon source, our findings should enable the development of an E. coli-based platform for the anaerobic production of reduced chemicals from glycerol at yields higher than those obtained from common sugars, such as glucose.

Anaerobiosis↗

A better global resolution function and a novel iterative stochastic search method for optimization of high-performance liquid chromatographic separation.

HPLC optimization strategy consists of four elements; experimental design, retention modeling, quality criteria function, and optimum search method. In this paper we present a simple, superior alternative to general classes of classical resolution functions (S function) and a novel optimum search algorithm (iterative stochastic search, ISS) for HPLC optimization. Comparison of S with general classes of resolution-based quality criteria functions (Rs, Rp, and Rmin) shows superior features such as correct assessment of favorable separation conditions, preservation of peak pair contributions, elimination of arbitrary cut-off values, and a unique capability to interpret absolute significance of function values through a simple inequality. The proposed ISS algorithm is more robust than standard methods and it is easily applicable to hyperdimensional optimization. ISS also shows clear advantages in its ability to correctly identify the global optimum (instead of local optimum), with higher precision, with more efficient use of computation cycles, and with easier implementation. Successful application of S and ISS to HPLC optimization was demonstrated in the separation of representative functionalities (sugars, alcohols, and organic acids) present in microbial fermentations. Both the optimal and pathological (worst) conditions were successfully predicted and experimentally verified.

Algorithms↗

DNA microarrays: experimental issues, data analysis, and application to bacterial systems.

DNA microarrays are currently used to study the transcriptional response of many organisms to genetic and environmental perturbations. Although there is much room for improvement of this technology, its potential has been clearly demonstrated in the past 5 years. The general consensus is that the bottleneck is now located in the processing and analysis of transcriptome data and its use for purposes other than the quantification of changes in gene expression levels. In this article we discuss technological aspects of DNA microarrays, statistical and biological issues pertinent to the design of microarray experiments, and statistical tools for microarray data analysis. A review on applications of DNA microarrays in the study of bacterial systems is presented. Special attention is given to studies in the following areas: (1) bacterial response to environmental changes; (2) gene identification, genome organization, and transcriptional regulation; and (3) genetic and metabolic engineering. Soon, the use of DNA microarray technologies in conjunction with other genome/system-wide analyses (e.g., proteomics, metabolomics, fluxomics, phenomics, etc.) will provide a better assessment of genotype-phenotype relationships in bacteria, which serve as a basis for understanding similar processes in more complex organisms.

Algorithms↗