James B. Rawlings Research Group


Eric Haseltine
B.S. Ch.E., Clemson University, 1999
Ph.D., University of Wisconsin-Madison, 2005
haseltin[AT]bevo[DOT]che[DOT]wisc[DOT]edu

Current Work

One of the simplest, yet most intriguing biological organisms is the virus. Although technically not a `living' organism because it cannot reproduce by itself, the virus contains enough genetic information to replicate itself given the machinery of a living host. This replication requires a surprisingly large amount of complex chemistry at the cellular level. Accordingly, chemical reaction models present one method of quantifying and interpreting these infections.

To date, most chemical reaction models of viral infections have focused exclusively on either the intracellular level or the extracellular level. To more realistically model these infections, we propose incorporating both levels of information into the description. Cell population balances present one way of performing this task in a deterministic setting. We have already applied such a balance to obtain a two-level model of a viral infection [2]. Our results demonstrate that, in contrast to commonly used models, the cell population balance provides a more intuitive and flexible modeling framework for incorporating all events occurring during viral infections. This improved capability to represent the trends in the biological measurements of interest offers a more systematic and quantitative understanding of how viral infections propagate and how to best control this propagation.

We are extending this modeling technique to spatially resolving the propagation of virus in a growing plaque [1, 3]. The goal of this research is to use experimental data (obtained from the on-campus laboratory of Professor John Yin) in conjunction with population balance models to elucidate the quantitative and qualitative features of the host-specific intracellular signaling pathways limiting viral propagation. Figure 1 presents a rough schematic of this experimental system along with a digital image acquired during such an infection, and Figure 2 presents a reduced schematic of the population balance model. These types of models have received little attention from the biological community. We therefore seek to explore their utility in explaining biological phenomena. We believe that refined versions of these models will eventually lead to insights on how to best control viral propagation via excitation of these innate signaling pathways.

Figure 1. Overview of the experiment and measurement.
Virus initially infects a focal region, then the infection propagates outward.
Viral propagation is detected by immunofluorescent labeling of a viral surface glycoprotein.

Figure 2. General form of the population balance for species i (i.e. infected cells,
uninfected cells, viral species, and signaling species). Examples of external
and internal characteristics are spatial coordinates and cell age, respectively.
These types of models consist of coupled integro-partial differential equations.
In contrast, both current intracellular and extracellular models consist of coupled
ordinary differential equations. Population balance models require an additional
level of complexity to describe both intracellular and extracellular events.

References

1
K. A. Duca, V. Lam, I. Keren, E. E. Endler, G. J. Letchworth, I. S. Novella, and J. Yin.
Quantifying viral propagation in vitro: Toward a method for characterization of complex phenotypes.
Biotech. Prog., 17(6):1156-1165, November-December 2001.

2
E. L. Haseltine, J. B. Rawlings, and J. Yin.
Dynamics of viral infections: Incorporating both the intracellular and extracellular levels.
Submitted for publication in Computers and Chemical Engineering, 2003.

3
V. Lam, K. A. Duca, and J. Yin.
Cytokine-induced resistance to viral spread in vitro.
Submitted for publication in Biotech. Prog., 2003.

Publications

1
Eric L. Haseltine and James B. Rawlings.
On the origins of approximations for stochastic chemical kinetics.
J. Chem. Phys., 123(16):Art. No. 164115, October 2005.

2
Eric L. Haseltine and James B. Rawlings.
Critical evaluation of extended Kalman filtering and moving horizon estimation.
Ind. Eng. Chem. Res., 44(8):2451-2460, April 2005.

3
Eric L. Haseltine.
Systems Analysis of Stochastic and Population Balance Models for Chemically Reacting Systems.
PhD thesis, University of Wisconsin-Madison, February 2005.

4
Eric L. Haseltine, Daniel B. Patience, and James B. Rawlings.
On the stochastic simulation of particulate systems.
Chem. Eng. Sci., 60(10):2627-2641, 2005.

5
Eric L. Haseltine, John Yin, and James B. Rawlings.
Multi-level dynamics of viral infections.
Annual AIChE Meeting, Austin, TX, November 2004.

6
Ethan A. Mastny, Eric L. Haseltine, James B. Rawlings, and Ioannis G. Kevrekidis.
Control of a kinetic Monte Carlo lattice gas model.
Annual AIChE Meeting, San Francisco, CA, November 2003.

7
Eric L. Haseltine and James B. Rawlings.
Using moving horizon estimation to overcome extended Kalman filtering failure.
Annual AIChE Meeting, San Francisco, CA, November 2003.

8
James B. Rawlings and Eric L. Haseltine.
Estimating the state of a system: The moving horizon approach.
Gordon Research Conference on Statistics in Chemistry and Chemical Engineering, South Hadley, MA, July 2003.

9
Eric L. Haseltine and James B. Rawlings.
A critical evaluation of extended Kalman filtering and moving horizon estimation.
TWMCC Technical Report 2002-03, Department of Chemical Engineering, University of Wisconsin-Madison, February 2003.

10
Eric L. Haseltine and James B. Rawlings.
Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics.
J. Chem. Phys., 117(15):6959-6969, October 2002.

11
James B. Rawlings, Daniel B. Patience, Eric L. Haseltine, and Philip Dell'Orco.
Stochastic population modeling and application to particle size control in pharmaceutical crystallization.
Annual AIChE Meeting, Indianapolis, IN, November 2002.

12
Daniel B. Patience, Eric L. Haseltine, Philip Dell'Orco, and James B. Rawlings.
Stochastic modeling and control of particle size in crystallization of a pharmaceutical.
World Congress On Particle Technology 4, Sydney, Australia, July 2002.

13
Daniel B. Patience, Eric L. Haseltine, Phillip Dell'Orco, and James B. Rawlings.
Crystallization of a pharmaceutical. experimental data and stochastic modeling of particle size and shape.
Annual AIChE Meeting, Reno, Nevada, November 2001.

Personal Web Page: www.its.caltech.edu/~haseltin

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University of Wisconsin
Department of Chemical Engineering
Madison WI 53706