A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems.

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections.

Multiagent Consensus Equilibrium in Molecular Structure Determination.

Multiagent consensus equilibrium (MACE) is demonstrated for the integration of experimental observables as constraints in molecular structure determination and for the systematic merging of multiple computational architectures. MACE is founded on simultaneously determining the equilibrium point between multiple experimental and/or computational agents; the returned state description (e.g.

Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation.

We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics. We employ the kernelized Lipschitz estimation to learn multiplier matrices that are used in semidefinite programming frameworks for computing admissible initial control policies with provably high probability. Such admissible controllers enable safe initialization and constraint enforcement while providing exponential stability of the equilibrium of the closed-loop system.

Maximally informative next experiments for nonlinear models.

Mathematical modeling is a powerful tool in systems biology; we focus here on improving the reliability of model predictions by reducing the uncertainty in model dynamics through experimental design. Model-based experimental design is a process by which experiments can be systematically chosen to reduce dynamic uncertainty in a given model. We discuss the Maximally Informative Next Experiment (MINE) method for group-wise selection of points in an experimental design and present a convergence result for MINE with nonlinear models.

Dynamic Sparse Sampling for Confocal Raman Microscopy.

The total number of data points required for image generation in Raman microscopy was greatly reduced using sparse sampling strategies, in which the preceding set of measurements informed the next most information-rich sampling location. Using this approach, chemical images of pharmaceutical materials were obtained with >99% accuracy from 15.8% sampling, representing an ∼6-fold reduction in measurement time relative to full field of view rastering with comparable image quality.

Binary Complementary Filters for Compressive Raman Spectroscopy.

The previously described optimized binary compressive detection (OB-CD) strategy enables fast hyperspectral Raman (and fluorescence) spectroscopic analysis of systems containing two or more chemical components. However, each OB-CD filter collects only a fraction of the scattered photons and the remainder of the photons are lost. Here, we present a refinement of OB-CD, the OB-CD2 strategy, in which all of the collected Raman photons are detected using a pair of complementary binary optical filters that direct photons of different colors to two photon counting detectors.

Synchrotron X-Ray Diffraction Dynamic Sampling for Protein Crystal Centering.

A supervised learning approach for dynamic sampling (SLADS) was developed to reduce X-ray exposure prior to data collection in protein structure determination. Implementation of this algorithm allowed reduction of the X-ray dose to the central core of the crystal by up to 20-fold compared to current raster scanning approaches. This dose reduction corresponds directly to a reduction on X-ray damage to the protein crystals prior to data collection for structure determination.

Dynamic X-ray diffraction sampling for protein crystal positioning.

A sparse supervised learning approach for dynamic sampling (SLADS) is described for dose reduction in diffraction-based protein crystal positioning. Crystal centering is typically a prerequisite for macromolecular diffraction at synchrotron facilities, with X-ray diffraction mapping growing in popularity as a mechanism for localization. In X-ray raster scanning, diffraction is used to identify the crystal positions based on the detection of Bragg-like peaks in the scattering patterns; however, this additional X-ray exposure may result in detectable damage to the crystal prior to data collection.

A Computational Study of the Effects of Syk Activity on B Cell Receptor Signaling Dynamics.

The kinase Syk is intricately involved in early signaling events in B cells and is required for proper response when antigens bind to B cell receptors (BCRs). Experiments using an analog-sensitive version of Syk (Syk-AQL) have better elucidated its role, but have not completely characterized its behavior. We present a computational model for BCR signaling, using dynamical systems, which incorporates both wild-type Syk and Syk-AQL.

Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty.

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters.

Fluorescence modeling for optimized-binary compressive detection Raman spectroscopy.

The recently-developed optimized binary compressive detection (OB-CD) strategy has been shown to be capable of using Raman spectral signatures to rapidly classify and quantify liquid samples and to image solid samples. Here we demonstrate that OB-CD can also be used to quantitatively separate Raman and fluorescence features, and thus facilitate Raman-based chemical analyses in the presence of fluorescence background. More specifically, we describe a general strategy for fitting and suppressing fluorescence background using OB-CD filters trained on third-degree Bernstein polynomials.

Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive control.

The hypothalamic-pituitary-adrenal (HPA) axis is critical in maintaining homeostasis under physical and psychological stress by modulating cortisol levels in the body. Dysregulation of cortisol levels is linked to numerous stress-related disorders. In this paper, an automated treatment methodology is proposed, employing a variant of nonlinear model predictive control (NMPC), called explicit MPC (EMPC).

Specifying informative experiment stimulation conditions for resolving dynamical uncertainty in biological systems.

A computationally efficient model-based design of experiments (MBDOE) strategy is developed to plan an optimal experiment by specifying the experimental stimulation magnitudes and measurement points. The strategy is extended from previous work which optimized the experimental design over a space of measurable species and time points. We include system inputs (stimulation conditions) in the experiment design search to investigate if the addition of perturbations enhances the ability of the MBDOE method to resolve uncertainties in system dynamics.

Multiple model-informed open-loop control of uncertain intracellular signaling dynamics.

Computational approaches to tune the activation of intracellular signal transduction pathways both predictably and selectively will enable researchers to explore and interrogate cell biology with unprecedented precision. Techniques to control complex nonlinear systems typically involve the application of control theory to a descriptive mathematical model. For cellular processes, however, measurement assays tend to be too time consuming for real-time feedback control and models offer rough approximations of the biological reality, thus limiting their utility when considered in isolation.

Model-based analysis for qualitative data: an application in Drosophila germline stem cell regulation.

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques.

Experimental design for dynamics identification of cellular processes.

We address the problem of using nonlinear models to design experiments to characterize the dynamics of cellular processes by using the approach of the Maximally Informative Next Experiment (MINE), which was introduced in W. Dong et al. (PLoS ONE 3(8):e3105, 2008) and independently in M.

Digital compressive chemical quantitation and hyperspectral imaging.

Digital compressive detection, implemented using optimized binary (OB) filters, is shown to greatly increase the speed at which Raman spectroscopy can be used to quantify the composition of liquid mixtures and to chemically image mixed solid powders. We further demonstrate that OB filters can be produced using multivariate curve resolution (MCR) to pre-process mixture training spectra, thus facilitating the quantitation of mixtures even when no pure chemical component samples are available for training.

Model-based design of experiments for cellular processes.

Model-based design of experiments (MBDOE) assists in the planning of highly effective and efficient experiments. Although the foundations of this field are well-established, the application of these techniques to understand cellular processes is a fertile and rapidly advancing area as the community seeks to understand ever more complex cellular processes and systems. This review discusses the MBDOE paradigm along with applications and challenges within the context of cellular processes and systems.

Efficient calculation of steady state probability distribution for stochastic biochemical reaction network.

The Steady State (SS) probability distribution is an important quantity needed to characterize the steady state behavior of many stochastic biochemical networks. In this paper, we propose an efficient and accurate approach to calculating an approximate SS probability distribution from solution of the Chemical Master Equation (CME) under the assumption of the existence of a unique deterministic SS of the system. To find the approximate solution to the CME, a truncated state-space representation is used to reduce the state-space of the system and translate it to a finite dimension.

Photon level chemical classification using digital compressive detection.

A key bottleneck to high-speed chemical analysis, including hyperspectral imaging and monitoring of dynamic chemical processes, is the time required to collect and analyze hyperspectral data. Here we describe, both theoretically and experimentally, a means of greatly speeding up the collection of such data using a new digital compressive detection strategy. Our results demonstrate that detecting as few as ~10 Raman scattered photons (in as little time as ~30 μs) can be sufficient to positively distinguish chemical species.

Sparse-grid-based adaptive model predictive control of HL60 cellular differentiation.

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 MPC scheme in which multiple data-consistent regions of the model parameter space are identified and used to calculate a control compromise.

Secreted, receptor-associated bone morphogenetic protein regulators reduce stochastic noise intrinsic to many extracellular morphogen distributions.

Morphogens are secreted molecules that specify cell-fate organization in developing tissues. Patterns of gene expression or signalling immediately downstream of many morphogens such as the bone morphogenetic protein (BMP) decapentaplegic (Dpp) are highly reproducible and robust to perturbations. This contrasts starkly with our expectation of a noisy interpretation that would arise out of the experimentally determined low concentration (approximately picomolar) range of Dpp activity, tight receptor binding and very slow kinetic rates.

A bioenergetic model of the mitochondrial population undergoing permeability transition.

Mitochondrial permeability transition (MPT) is a highly regulated complex phenomenon that is a type of ischemia/reperfusion injury that can lead to cell death and ultimately organ dysfunction. A novel population transition and detailed permeability transition pore regulation model were integrated with an existing bioenergetics model to describe MPT induction under a variety of conditions. The framework of the MPT induction model includes the potential states of the mitochondria (aggregated, orthodox and post-transition), their transitions from one state to another as well as their interaction with the extra-mitochondrial environment.