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Jia G.,Chemical and Pharmaceutical Engineering | Stephanopoulos G.N.,Massachusetts Institute of Technology | Gunawan R.,ETH Zurich
Bioinformatics | Year: 2011

Motivation: Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints. Results: In this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this twophase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing. © The Author 2011. Published by Oxford University Press. All rights reserved. Source


News Article
Site: http://news.mit.edu/topic/mitchemistry-rss.xml

The Department of Chemical Engineering is pleased to announce the establishment of its Raymond F. Baddour (1949) Chemical Engineering Professorship, which will support a distinguished faculty member in the department. Baddour, the Lammot du Pont Professor Emeritus of Chemical Engineering, received his MS in chemical engineering practice in 1949 and, upon earning his ScD from MIT in 1951, became an assistant professor in the department. He became a full professor in 1963 and founded MIT's Environmental Laboratory in 1970, becoming its first director. Baddour was head of the chemical engineering department from 1969 to 1976; during that time, he put together and executed a visionary plan to expand the department's programs in applied chemistry, bioengineering, and energy/environmental engineering. He launched a bold and aggressive hiring initiative that broke with longstanding departmental traditions; now, 30 years later, Course X continues to see the positive impact of his vision. Another impact of Baddour's hard work is more tangible: In order to revitalize and address space concerns for new faculty and research programs, Baddour conceived of the plan to create the current home of the chemical engineering department, the Ralph Landau Building (Building 66). He also raised funds for the building completely through private sources, an act that has not been duplicated at the Institute since. Baddour is also a role model for entrepreneurship: He started his first company in 1962, and has founded a total of 16 companies, including the 1980 co-founding of California-based biopharmaceutical giant Amgen. He has been on the board of directors of 13 companies, and has influenced some notably successful entrepreneurs, including Noubar Afeyan PhD '87, David Lam ScD ’74, and William Koch ’62, ScD ’71. In 1998, Baddour presented the department's venerable Warren K. Lewis Lecture, entitled, "Start-ups and Letdowns — Reflections of a Professor in Venture Land." Baddour earned his BS in chemical engineering from the University of Notre Dame in 1945. He has more 65 publications, and holds 16 patents. The first Raymond F. Baddour Professor of Chemical Engineering will be Bernhardt L. Trout. Trout is currently director of the Novartis-MIT Center for Continuous Manufacturing and co-chair of the Singapore-MIT Alliance Program on Chemical and Pharmaceutical Engineering. He received his SB and SM degrees from MIT and his PhD from the University of California at Berkeley. In addition, he performed postdoctoral research at the Max-Planck Institute. Trout's research focuses on molecular engineering, specifically the development and application of both computational and experimental molecular-based methods to engineering pharmaceutical formulations and processes with unprecedented specificity. Since 1999, he has focused on molecular engineering for biopharmaceutical formulation, pharmaceutical crystallization, and pharmaceutical manufacturing. In 2007, with several colleagues from MIT, he set up the Novartis-MIT Center for Continuous Manufacturing, a $85 million partnership that aims to transform pharmaceutical manufacturing. In addition to Novartis, Trout has worked with many other pharmaceutical companies in research or consulting. He has published more than 150 papers and currently has 17 patent applications. With his MIT and Novartis colleagues in the MIT Novartis-Center and at the U.S. Foods and Drug Administration, he is the recipient of the Council for Chemical Collaboration Award in 2014, and will receive the 2014 AIChE Excellence in Process Development Research Award in November.


Perumal T.M.,University of Luxembourg | Madgula Krishna S.,Chemical and Pharmaceutical Engineering | Tallam S.S.,Chemical and Pharmaceutical Engineering | Gunawan R.,ETH Zurich
Computers and Chemical Engineering | Year: 2013

The development of detailed chemical kinetic models is necessary for the design and optimization of complex chemical systems. However, it is also often desired to reduce the model size by excluding inconsequential chemical species and/or reactions for end-point applications, usually due to computational reasons. In this work, new model reduction methods based on dynamic sensitivities from the impulse parametric sensitivity analysis (iPSA) and the Green's function matrix (GFM) analysis have been developed. The iPSA and GFM were originally formulated to provide dynamical parameter-by-parameter and species-by-species information on how a system output behavior is achieved, respectively. The efficacies of the proposed reduction methods were compared with existing methods through applications to reduce detailed kinetic models of alkane pyrolysis and natural gas combustion (GRI Mech 3.0) and an ab initio kinetic model of industrial steam cracking of ethane. © 2013 Elsevier Ltd. Source


Zhou Z.,Chemical and Pharmaceutical Engineering | Cheng J.-H.,National University of Singapore | Chung T.-S.,Chemical and Pharmaceutical Engineering | Chung T.-S.,National University of Singapore | And 5 more authors.
Chemical Engineering Journal | Year: 2011

The optical resolution efficiency of a membrane system that integrates stereoselective affinity dialysis and ion-exchange membrane partitioned free flow isoelectric focusing (FFIEF) is shown to be superior to normal affinity dialysis (AD) and affinity ultrafiltration (AUF) membrane processes under similar experimental conditions, i.e. by using the same sulfonated polyetherketone (SPEK) membranes and identical human serum albumin (HSA) to tryptophan ratio of 0.75. The chiral separation is achieved by isolating the unbound tryptophan, which contains D-tryptophan in excess, and the protein-tryptophan complex into the permeation and feed chambers, respectively, by controlling their migration under an external electric field. The separation factor is increased with increasing protein concentration while the permeation flux can be enhanced by increasing the operating current. The rationale for using HSA instead of BSA as the chiral selector, and the use of four-chamber system rather than two are also discussed. © 2011 Elsevier B.V. Source


Zhou K.,Chemical and Pharmaceutical Engineering | Zhou L.,Chemical and Pharmaceutical Engineering | Zhou L.,National University of Singapore | Lim Q.,National University of Singapore | And 5 more authors.
BMC Molecular Biology | Year: 2011

Background: Accurate interpretation of quantitative PCR (qPCR) data requires normalization using constitutively expressed reference genes. Ribosomal RNA is often used as a reference gene for transcriptional studies in E. coli. However, the choice of reliable reference genes has not been systematically validated. The objective of this study is to identify a set of reliable reference genes for transcription analysis in recombinant protein over-expression studies in E. coli.Results: In this study, the meta-analysis of 240 sets of single-channel Affymetrix microarray data representing over-expressions of 63 distinct recombinant proteins in various E. coli strains identified twenty candidate reference genes that were stably expressed across all conditions. The expression of these twenty genes and two commonly used reference genes, rrsA encoding ribosomal RNA 16S and ihfB, was quantified by qPCR in E. coli cells over-expressing four genes of the 1-Deoxy-D-Xylulose 5-Phosphate pathway. From these results, two independent statistical algorithms identified three novel reference genes cysG, hcaT, and idnT but not rrsA and ihfB as highly invariant in two E. coli strains, across different growth temperatures and induction conditions. Transcriptomic data normalized by the geometric average of these three genes demonstrated that genes of the lycopene synthetic pathway maintained steady expression upon enzyme overexpression. In contrast, the use of rrsA or ihfB as reference genes led to the mis-interpretation that lycopene pathway genes were regulated during enzyme over-expression.Conclusion: This study identified cysG/hcaT/idnT to be reliable novel reference genes for transcription analysis in recombinant protein producing E. coli. © 2011 Zhou et al; licensee BioMed Central Ltd. Source

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