Sarah L. Noble

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Previous efforts to control cellular differentiation have been largely experimental. Although some mathematical models for this process exist, rarely has a quantitative approach been employed to design experiments that predictably direct the cell fate. As an initial step towards this aim, a control strategy for sustaining a desired constant level of(More)
Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model(More)
Mathematical models are commonly used to interrogate and control biological systems. However, such models are often uncertain and sloppy, with multiple parameter sets equally capable of reproducing the experimental data. These features make systems biology models unreliable when used to support a model-based control strategy. Multi-scenario control can help(More)
Quantitative methods such as model-based predictive control are known to facilitate the design of strategies to manipulate biological systems. This study develops a sparse-grid-based adaptive model predictive control (MPC) strategy to direct HL60 cellular differentiation. Sparse-grid sampling and interpolation support a computationally efficient adaptive(More)
Cellular differentiation is a complex process for which systematic design of control strategies has not been widely investigated. As a first step towards this aim, a control strategy for achieving a desired percentage of differentiating cells is proposed. A population balance model structure parallels the known granulocyte/monocyte differentiation pathway.(More)
In this paper we present an enhancement of model-based trajectory selection algorithms – a popular class of collision avoidance techniques for autonomous ground vehicles. Rather than dilate a set of individual candidate trajectories in an ad hoc way to account for uncertainty, we generate a set of trajectory clouds – sets of states that represent possible(More)
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