Development of a passive and remote magnetic microsensor with thin-film giant magnetoimpedance element and surface acoustic wave transponder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98710/1/JApplPhys_109_07E524.pd

Exploring the gap between dynamic and constraint-based models of metabolism

Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of E. coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identied by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.This research was supported by PhD Grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the Development of Improved Microbial Cell Factories" (MIT-Pt/BS-BB/0082/2008)

Experimental evaluation of an adaptive Joule--Thomson cooling system including silicon-microfabricated heat exchanger and microvalve components

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98726/1/JVA021005.pd

Accelerated search for biomolecular network models to interpret high-throughput experimental data

<p>Abstract</p> <p>Background</p> <p>The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.</p> <p>Results</p> <p>Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.</p> <p>Conclusion</p> <p>Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.</p

Alternative Post-Processing on a CMOS Chip to Fabricate a Planar Microelectrode Array

We present an alternative post-processing on a CMOS chip to release a planar microelectrode array (pMEA) integrated with its signal readout circuit, which can be used for monitoring the neuronal activity of vestibular ganglion neurons in newborn Wistar strain rats. This chip is fabricated through a 0.6 μm CMOS standard process and it has 12 pMEA through a 4 × 3 electrodes matrix. The alternative CMOS post-process includes the development of masks to protect the readout circuit and the power supply pads. A wet etching process eliminates the aluminum located on the surface of the p+-type silicon. This silicon is used as transducer for recording the neuronal activity and as interface between the readout circuit and neurons. The readout circuit is composed of an amplifier and tunable bandpass filter, which is placed on a 0.015 mm2 silicon area. The tunable bandpass filter has a bandwidth of 98 kHz and a common mode rejection ratio (CMRR) of 87 dB. These characteristics of the readout circuit are appropriate for neuronal recording applications

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods

Energy scavenging from insect flight

This paper reports the design, fabrication and testing of an energy scavenger that generates power from the wing motion of a Green June Beetle (C otinis nitida ) during its tethered flight. The generator utilizes non-resonant piezoelectric bimorphs operated in the d 31 bending mode to convert mechanical vibrations of a beetle into electrical output. The available deflection, force, and power output from oscillatory movements at different locations on a beetle are measured with a meso-scale piezoelectric beam. This way, the optimum location to scavenge energy is determined, and up to ~115 µW total power is generated from body movements. Two initial generator prototypes were fabricated, mounted on a beetle, and harvested 11.5 and 7.5 µW in device volumes of 11.0 and 5.6 mm 3 , respectively, from 85 to 100 Hz wing strokes during the beetle's tethered flight. A spiral generator was designed to maximize the power output by employing a compliant structure in a limited area. The necessary technology needed to fabricate this prototype was developed, including a process to machine high-aspect ratio devices from bulk piezoelectric substrates with minimum damage to the material using a femto-second laser. The fabricated lightweight spiral generators produced 18.5–22.5 µW on a bench-top test setup mimicking beetles' wing strokes. Placing two generators (one on each wing) can result in more than 45 µW of power per insect. A direct connection between the generator and the flight muscles of the insect is expected to increase the final power output by one order of magnitude.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90804/1/0960-1317_21_9_095016.pd

Gradient Descent Optimization in Gene Regulatory Pathways

BACKGROUND: Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. High-throughput technologies allow studying various aspects of gene regulatory networks on a genome-wide scale and we will discuss recent advances as well as limitations and future challenges for gene network modeling. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms. METHODOLOGY: In the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings. CONCLUSIONS: We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example

Spontaneous Reaction Silencing in Metabolic Optimization

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical non-optimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.Comment: 34 pages, 6 figure