Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Stuart R. Gallant is active.

Publication


Featured researches published by Stuart R. Gallant.


Journal of Chromatography A | 1996

Optimization of preparative ion-exchange chromatography of proteins: linear gradient separations

Stuart R. Gallant; Suresh Vunnum; Steven M. Cramer

In this study, the Steric Mass Action (SMA) model of ion exchange is employed in concert with appropriate mass balance equations to predict the separation performance of preparative ion-exchange chromatography. The model is able to accurately predict linear gradient separations of the proteins α-chymotrypsinogen A, cytochrome c, and lysozyme under overloaded conditions. The optimization behavior of preparative ion-exchange chromatography is examined under conditions of baseline resolution and induced sample displacement. This work demonstrates that a simple iterative procedure can be employed to establish optimal gradient conditions for preparative ion-exchange chromatography of proteins. The results also indicate that under appropriate conditions, sample displacement can be employed to dramatically improve the production rate with minor losses in product yield or purity. Linear gradient separations with sample displacement are also less sensitive to the adsorption properties of the feed stream than baseline resolved separations, resulting in simplified methods development.


Journal of Chromatography A | 1995

Modeling non-linear elution of proteins in ion-exchange chromatography

Stuart R. Gallant; Amitava Kundu; Steven M. Cramer

Abstract The problem of non-linear elution of a band of protein in isocratic ion-exchange chromatography leads to a pair of coupled non-linear partial differential equations. The equilibrium may be modeled using the Steric Mass Action (SMA) model of ion exchange, which treats both the salt dependence of protein adsorption and the steric shielding present under non-linear conditions. Neglecting axial dispersion, a model of ideal chromatography is formulated that may be solved by the method of characteristics. The predictions of this relatively simple model are shown to agree with experimental results concerning the non-linear elution of cytochrome c in a strong cation-exchange column. Of particular interest is the existence of two plateaus in the solution of this problem for large injection volumes. While this result cannot be understood or predicted on the basis of the traditional Langmuir isotherm or other currently available descriptions of adsorption, the chromatographic model presented in this work makes this otherwise anomalous result clear. Further, the use of such a model during parameter estimation is discussed.


Journal of Chromatography A | 1997

Productivity and operating regimes in protein chromatography using low-molecular-mass displacers

Stuart R. Gallant; Steven M. Cramer

Abstract A theoretical and experimental study of ion-exchange displacement chromatography using the low-molecular-mass displacer neomycin sulfate, is carried out. A chromatography model utilizing the steric mass action (SMA) ion-exchange formalism is employed to predict the displacement behavior of proteins displaced by neomycin sulfate. An operating regime plot is developed from the SMA model to predict selective displacement chromatography using low-molecular-mass displacers. Numerical simulations are employed to examine the behavior of selective displacement systems and to investigate the productivity of displacement chromatography. In this study, it is seen that use of low-molecular-mass displacers allows “selective displacement” separations in which some impurities are removed by elution or by desorption within the displacer front. Further, low-molecular-mass displacers, like previously studied high-molecular-mass displacers, can provide high productivity chromatographic separations even for feed streams characterized by low separation factors.


Chemical Engineering Science | 1995

Immobilized metal affinity chromatography: Modeling of nonlinear multicomponent equilibrium

Suresh Vunnum; Stuart R. Gallant; Young J. Kim; Steven M. Cramer

A rigorous multicomponent isotherm for preparative immobilized metal affinity chromatography (IMAC) must consider the multipointed nature of adsorption, the possibility of steric hindrance of the stationary phase upon binding of macromolecules, and the role of the mobile-phase modifier. In this paper, the metal affinity interaction chromatography (MAIC) model, a formalism which addresses all three of these issues, is presented. The linear and nonlinear adsorption behavior of proteins as a function of mobile-phase imidazole content is considered. Numerical simulations of nonlinear chromatography, employing MAIC equilibrium, are seen to accurately predict the experimental results in various modes of nonlinear IMAC chromatography. In frontal chromatography, the model is seen to accurately describe the adsorption phenomena, including induced imidazole gradients. In step gradient and displacement chromatography, the results presented in this manuscript demonstrate the ability of the MAIC model to predict multicomponent equilibrium in IMAC systems and establish this model as a powerful tool for studying the operation of IMAC separations.


Biotechnology Progress | 1996

Immobilized Metal Affinity Chromatography: Displacer Characteristics of Traditional Mobile Phase Modifiers

Suresh Vunnum; Stuart R. Gallant; Steven M. Cramer

Imidazole was examined as a potential displacer for protein purification in metal affinity chromatographic systems. A dynamic affinity plot was developed for predicting the elution order and efficacy of displacers in metal affinity displacement chromatography. Theoretical predictions and experimental results indicate that small molecular weight compounds, such as imidazole, with a single coordination site can indeed displace proteins with multiple coordination sites to the surface. In addition, the theory predicts the concentration dependence ofimidazole displacement behavior observed in the experiments. In the presence of additional mobile phase modifiers, imidazole was shown to induce relatively high modifier gradients due to its high affinity and negligible steric factor. N‐protected histidines and tryptophan were also shown to act as protein displacers in IMAC systems. While the N‐protected histidines resulted in relatively sharp displacement boundaries, tryptophan was a less effective displacer, producing significant protein tailing during the separation. The results in this manuscript are expected to have major implications for downstream processing of proteins using IMAC in the displacement mode of operation.


Chemometrics and Intelligent Laboratory Systems | 1993

Deconvolution of overlapping chromatographic peaks using a cerebellar model arithmetic computer neural network

Stuart R. Gallant; Steven P. Fraleigh; Steven M. Cramer

Abstract In this paper, we report on the use of a cerebellar model arithmetic computer (CMAC) neural network for the deconvolution of overlapping chromatographic peaks. Features derived from the chromatogram and its second derivative were employed to map an unresolved signal onto its component peaks. The accuracy of the CMAC network was examined as a function of the training-set size, training method, and network parameters. The accuracy of the network was further verified using ultraviolet absorption traces produced by two peptides, N -benzoyl- l -arginine ethyl ester and N -benzoyl- l -alanine, chromatographed on a C 18 reversed-phase column. CMAC was found to provide rapid, accurate deconvolutions for a wide range of peak-height ratios, peak widths, and resolutions. In addition, CMAC has significant advantages in ease of training and in detection of inadequate training that do not apply for a back-propagation neural network.


Separation Science and Technology | 1998

Nonlinear Multicomponent Gradient Chromatography in Metal Affinity Systems

Suresh Vunnum; Venkatesh Natarajan; Stuart R. Gallant; Steven M. Cramer

ABSTRACT In this paper the metal affinity interaction chromatography (MAIC) model is employed in concert with appropriate mass transport equations to study preparative linear gradient chromatography in immobilized metal affinity chromatography (IMAC) systems. The MAIC model accounts for the nonlinear adsorption of proteins and mobile phase modulators (e.g., imidazole), and is shown to accurately predict gradient separations of proteins under overloaded conditions. Experimental and simulation results indicate that the concentration-dependent sorption of imidazole and protein-imidazole interference effects can severely deform linear gradients in IMAC systems. The steric accessibility and displacer characteristics of imidazole together with multicomponent interference effects can lead to unusual protein elution profiles and the spiking of imidazole between the feed components. Due to their ability to act as displacers, these imidazole spikes can sharpen protein tails, decreasing the interface shock layer thi...


Biotechnology and Bioengineering | 1995

Optimization of step gradient separations: consideration of nonlinear adsorption

Stuart R. Gallant; Amitava Kundu; Steven M. Cramer


Journal of Chromatography A | 2004

Modeling ion-exchange adsorption of proteins in a spherical particle

Stuart R. Gallant


Aiche Journal | 1995

Transient profiles in ion‐exchange displacement chromatography

Shishir D. Gadam; Stuart R. Gallant; Steven M. Cramer

Collaboration


Dive into the Stuart R. Gallant's collaboration.

Top Co-Authors

Avatar

Steven M. Cramer

Rensselaer Polytechnic Institute

View shared research outputs
Top Co-Authors

Avatar

Suresh Vunnum

Rensselaer Polytechnic Institute

View shared research outputs
Top Co-Authors

Avatar

Amitava Kundu

Rensselaer Polytechnic Institute

View shared research outputs
Top Co-Authors

Avatar

Shishir D. Gadam

Rensselaer Polytechnic Institute

View shared research outputs
Top Co-Authors

Avatar

Venkatesh Natarajan

Rensselaer Polytechnic Institute

View shared research outputs
Top Co-Authors

Avatar
Researchain Logo
Decentralizing Knowledge