Three-dimensional biochemical sensors are developed that can be used for chemical and biological detection in aqueous solutions and suspensions. The sensors are fabricated using a standard polycrystalline silicon process, PolyMUMPs, and can detect chemicals and biomarkers in low concentrations in near real time. The sensors made of a stack of electrodes allowing the solution to occupy the space between the layers of electrodes and have a larger interface with the electrodes. The sensors use electrochemistry impedance spectroscopy (EIS) for detection and therefore increasing the solution-electrode interface improves the sensitivity of the sensor. To demonstrate the applicability of the proposed sensor design, experimental measurements are used to characterize and compare the 3D sensors with conventional 2D interdigitated sensors. Diethylhexyl phthalate (DEHP) solution is used as the target chemical, and the 2D and 3D biochemical sensors are exposed to different concentrations of DEHP solution. An LCR meter is used to sweep the frequency and determine the impedance of the sensor-solution combination. The test results show that the three-dimensional sensors have higher sensitivity than 2D interdigitated ones verifying the advantage of the new sensor design over existing conventional sensors. The proposed sensors can also be used for detection of biological markers such as cells, proteins and enzymes in aqueous solutions.

This paper presented a MEMS based differential scanning calorimeter (DSC) for biomolecular characterization. In this MEMS based DSC, PDMS (Polydimethylsiloxane) and Flexdyne thin film were used to construct the microfluidic chamber. Polyimide were used to fabricate the flexible substrate and temperature sensitive vanadium oxide was used as the thermistor material. A heating stage was used to heat the sample and reference up at a certain rate. The resolution study and step response characterization indicated the high sensitivity (6.1V/W) of the device. The test with Bovine Serum Albumin (BSA) samples showed clear phase transitions and the data was confirmed to be reasonable by comparing it with the results of commercial DSC’s test. This device used 0.63uL sample amount and could complete the scanning process in 3 minutes, significantly increasing the throughput of the biomolecular thermodynamics study like protein denaturation process compared to the traditional DSC (1 to 2 hours).

The effects of mechanical impact forces on neurological health is a critical concern, likely due to issues of traumatic brain injury (TBI) in sports and brain damage stemming from the potential of “sonic terrorism.” The quantitative analysis and evaluation of such forces on brain tissue function is very difficult. To address this issue, this research proposes a novel approach of using a cellular model subjected to mechanical vibration for analysis. Here, neuron-like differentiated neuroblastoma cells were subjected to vibration at frequencies of 20, 200, 2000, and 20000 Hz for a period of 24 hours at constant amplitude. Cell proliferation and inflammatory cytokine production, including IL-6, IL-1β, and TGF-β1, was measured as response of the cells and indicators of cellular health after vibrational treatment. Cell proliferation was found to increase after 20, 200, and 20000 Hz treatments; p<0.05) and decrease after 2000 Hz treatment (p<0.05). IL-6 production was found to decrease after 200 and 20000 Hz treatments (p<0.01) and increase after 20 and 2000 Hz treatments (p<0.01). IL-1β protein production was found to decrease after 20 Hz and increase after 200 Hz treatments (p<0.001), while TGF-β1 was found to decrease after 200 Hz treatment (p<0.001). The results suggest that cell proliferation and cytokine production serve as a sensitive measure to external impact forces applied to the cells. In addition, it is suggested that inflammatory mechanisms exhibit inhibitory “cross-talk” between IL-6 and IL-1β signaling pathways at 20 and 200 Hz. Inflammatory cytokine data suggest frequency-specific responses, which can be used not only to better understand the mechanism of vibration induced cellular damage, but also to unveil the cellular signaling processes.

Bacteriophage T4 is one of the most common and complex of the tailed viruses that infect host bacteria using an intriguing contractile tail assembly. Despite extensive progress in resolving the structure of T4, the dynamics of the injection machinery remains largely unknown. This paper contributes a first model of the injection machinery that is driven by elastic energy stored in a structure known as the sheath. The sheath is composed of helical strands of protein that suddenly collapse from an energetic, extended conformation prior to infection to a relaxed, contracted conformation during infection. We employ Kirchhoff rod theory to simulate the nonlinear dynamics of a single protein strand coupled to a model for the remainder of the virus, including the coupled translation and rotation of the head (capsid), neck and tail tube. Doing so provides an important building block towards the future goal of modeling the entire sheath structure which is composed of six interacting helical protein strands. The resulting numerical model exposes fundamental features of the injection machinery including the time scale and energetics of the infection process, the nonlinear conformational change experienced by the sheath, and the contribution of hydrodynamic drag on the head (capsid).

Recent researches have demonstrated that cells ingest mitochondrial by endocytosis to repair cell damage. This new mitochondrial therapy approach can be used for curing particular disease of neuropathy related diseases. Hence the obtainment of high quality and healthy mitochondria play an important role in mitochondrial based disease therapy. In this study, we propose a cascade microfluidic device for green extraction of healthy mitochondria. The geometry of the device was designed using the commercially available COMSOL package. Soft lithography process was than conducted to realize the device of PDMS. We used C2-GFP cells to demonstrate the efficiency of the proposed cascade microfluidic device. The total protein assay kit (complex I-V) was conducted to examine the extractive protein and the SDS page (Tom 20) was used for measuring the activity of the extracted mitochondria. Experimental results illustrate that the complex I-V expression of the extracted mitochondria by the proposed device is much higher than that of the extracted mitochondria by conventional kit. Furthermore, the results of the Tom 20 expression also demonstrate that our device is able to extract more amounts of mitochondria than the conventional kit.

An organism grows from very small to the whole body, while an engineering product is assembled from elements. An organism is formed autonomously and adaptable to his/her/its environment, while an engineering product can only execute very limited actions. The formation of a product determines its functionality. Nature is the best teacher for learning how structures are formed for specific functionality. This paper compares the design process with the developmental process of embryo and proposes a qualitative development framework that simulates the gene transcription and translation in biology. The key step in design is transforming behaviors to structures. This is a process from information to the form and it bears some similarity with the process from DNA to the protein in embryogenesis. Three basic steps are required from DNA to the protein: gene transcription, transport and protein synthesis, which is named as gene expression. Key mechanisms contributing to this transformation process are investigated and a qualitative development framework are constructed for a growth design process. Simple examples are presented for illustration of proposed methods.

Flagella are active, beam-like, sub-cellular organelles that use wavelike oscillations to propel the cell. The mechanisms underlying the coordinated beating of flagella remain incompletely understood despite the fundamental importance of these organelles. The axoneme (the cytoskeletal structure of flagella) consists of microtubule doublets connected by passive and active elements. The motor protein dynein is known to drive active bending, but dynein activity must be regulated to generate oscillatory, propulsive waveforms. Mathematical models of flagella motion generate quantitative predictions that can be analyzed to test hypotheses concerning dynein regulation. Here we investigate the emergence of unstable modes in a mathematical model of flagella motion with feedback from inter-doublet separation (the “geometric clutch” or GC model). The unstable modes predicted by the model may be used to critically evaluate the underlying hypothesis. The least stable mode of the GC model exhibits switching at the base and robust base-to-tip propagation.

On the basis of robot kinematics, we have thus far developed a method for predicting the motion of proteins from their 3D structural data given in the Protein Data Bank (PDB data). In this method, proteins are modeled as serial manipulators constrained by springs and the structural compliance properties of the models are evaluated. We focus on localized instead of whole structures of proteins. Employing the same model used in our method of motion prediction, the motion properties of the localized structures and the relation between the motion properties of localized and whole structures are analyzed. First, we present a method for graphically expressing the deformation of objects with a complex shape, such as proteins, by approximating the shape as a rectangular prism with a mesh on its surface. We then formulate a method for comparing the motion properties of localized structures cleaved from the whole structure and those remaining in it by expressing the motion of the latter using the decomposed motion modes of the former according to the structural compliance. Finally, we show a method for evaluating the effect of a localized structure on the motion properties of proteins by applying forces to localized structures. In the formulations, we demonstrate applications as illustrative examples using the PDB data of a real protein.

In this paper, it is shown how rigid-body kinematics can be used to assist in determining the atomic structure of proteins and nucleic acids when using x-ray crystallography, which is a powerful method for structure determination. The importance of determining molecular structures for understanding biological processes and for the design of new drugs is well known. Phasing is a necessary step in determining the three-dimensional structure of molecules from x-ray diffraction patterns. A computational approach called molecular replacement (MR) is a well-established method for phasing of x-ray diffraction patterns for crystals composed of biological macromolecules. In MR, a search is performed over positions and orientations of a known biomolecular structure within a model of the crystallographic asymmetric unit, or, equivalently, multiple symmetry-related molecules in the crystallographic unit cell. Unlike the discrete space groups known to crystallographers and the continuous rigid-body motions known to kinematicians, the set of motions over which molecular replacement searches are performed does not form a group. Rather, it is a coset space of the group of continuous rigid-body motions, SE(3), with respect to the crystallographic space group of the crystal, which is a discrete sub-group of SE(3). Properties of these ‘motion spaces’ (which are compact manifolds) are investigated here.

In mechanisms and robotics it is common to describe motions relative to a ground link, or robot base, and the position and orientation of the distal link (or robot hand) is viewed as a rigid-body motion relative to this fixed world frame. Assessing preferred relative rigid-body position and orientation in interacting biomolecules (such as proteins) often uses this approach as well by artificially calling one molecule the ground, and considering the motions of another molecule relative to it. But since both molecules are floating, it is not as natural to take this perspective as it is in the field of mechanisms and robotics. Therefore, this paper introduces a ‘symmetrical’ parameterization of relative biomolecular motions in which the structure of the equations is the same when each molecule views the other. In this way, there is no bias in terms of labeling one molecule as being fixed and the other as moving. The properties of this new parameterization are evaluated and compared with traditional ones known to the kinematics community.

Understanding the 3D structure and consequently the motion of protein molecules contributes to simulate their function. Modeling protein molecules as kinematic chains has been used to predict protein molecules flexible and rigid regions as well as their degrees of freedom to predict their mobility. However, high computational cost for relatively large molecules is one of the major challenges in this field. In this paper we have combined our previously developed rigidity analysis (ProtoFold) with pebble game thus improving computational cost of our simulation. Here, we have determined the required time for all steps of ProtoFold and subsequently the most time consuming step. Results have shown that finding rigid loops inside the protein structure using graph theory and Grübler-Kutzbach criterion is the slowest part of the procedure, taking an average of 75% of the time required for the rigidity analysis. Therefore we have replaced this step with pebble game. The modified method has been applied to a random group of protein molecules and its efficiency in significantly improving the simulation speed has been verified.

In this paper the dynamics and stability of a linear system with stochastic delay are investigated. We assume that the delay may take finitely many different values and its dynamics are modeled by a continuous-time Markov chain. Semi-discretization is used to derive the dynamics of the second moment which leads to necessary and sufficient stability conditions for the trivial solution. We apply these results to investigate the stability of the steady state of an auto-regulatory gene-protein network. We demonstrate that stochastic delay may stabilize the system when the corresponding deterministic system with average delay is unstable.

On the basis of an analogy between the kinematic structures of proteins and robotic mechanisms, we have so far developed methods for predicting the internal motion of proteins from three-dimensional structural data in the protein data bank (PDB). With these methods, we model proteins as serial manipulators constrained by springs, and calculate the structural compliance of the protein model. In this study, toward more practical purposes, we reformulate and extend the existing methods by broadening the definition of structural compliance and reducing the number of variables for expressing the conformation of the model. The broadening is performed by separating the parts whose deformations are evaluated from those where forces are applied. This separation allows the calculation of the effective forces causing deformation in other specified parts. We also reduce the number of conformation variables from the consideration based on the algebraic structure of the basic equations. The size of the matrix whose inverse must be calculated is thus minimized, and the computational cost is reduced. We verify the effectiveness of these extensions by analyzing the PDB data of some proteins.

There is an analogy between the kinematic structures of proteins and robotic mechanisms. On the basis of this analogy, we have so far developed some methods for predicting the internal motions of proteins from their three-dimensional structural data in protein data bank (PDB). However, these methods are basically applicable to a single protein molecule. In this study, we extended these methods to apply them to systems that consist of multiple molecules including proteins (protein systems), and developed a computational framework for predicting the motions of the molecules. The model used in this method is a type of elastic network model. In particular, proteins are modeled as a robot manipulator constrained by the springs (the dihedral angles on the main chains correspond to the joint angles). The interactions between molecules are also modeled as springs. The basic concept for predicting the motions is based on the analysis of structural compliance. By applying statically balanced forces to the model in various directions, we extracted those motions with larger structural compliance. To reduce the computational time, we formulated the method with the prospect of efficient computation including parallel computation. In addition, we developed a preparatory computer program implementing the proposed algorithms, and analyzed some protein systems. The results showed that the proposed computational framework can efficiently analyze large protein systems.

Protein structure prediction remains one of the significant challenges in computational biology. We have previously shown that our kinetostatic compliance method can overcome some of the key difficulties faced by other de novo structural prediction methods, such as the very small time steps required by the molecular dynamics approaches, or the very large number of samples required by the sampling based techniques. In this paper we extend the previous free energy formulation by adding the solvent effects, which contribute predominantly to the folding phenomena. We show that the addition of the solvation effects, which complement the existing Coulombic and van der Waals interactions, lead to a physically effective energy function. Furthermore, we achieve significant computational speed-up by employing efficient algorithms and data structures that effectively reduce the time complexity from O(n 2 ) to O(n), n being the number of atoms. Our simulations are consistent with the general behavior observed in protein folding, and show that the hydrophobic atoms tend to pack inside the core of the molecule in an aqueous solvent, while a vacuum environment produces no such effect.

This paper presents a multiscale dynamic model for the simulation and analysis of flexibility in myosin V. A three dimensional (3D) flexible multibody model is developed to mechanically model the biological structure of myosin V. Experimental studies have shown that myosin’s neck domain can be considered as three pairs of tandem elements which can bend at junctures between them. Therefore, each neck is modeled by three rigid bodies connected by flexible spherical joints. One of the most important issues in dynamic modeling of micro-nanoscale sized biological structures, likes DNA and motor proteins, is the long simulation run time due to the disproportionality between physical parameters involved in their dynamics such as mass, drag coefficient, and stiffness. In order to address this issue, the mostly used models, based on the famous overdamped Langevin dynamics, omit the inertial terms in the equations of motion; that leads to a first order model which is inconsistent with the Newton’s second law. However, the proposed model uses the concept of the method of multiple scales (MMS) that brings all terms of the equations of motion into proportion with each other that helps to retain the inertia terms. This keeps consistency of the model with the physical laws and increases time step size of numerical integration from commonly used sub-femto seconds to sub-milli seconds. Therefore, simulation run time will be many orders of magnitude less than ones based on the other approaches. The simulation results obtained by the proposed multiscale model show more realistic dynamic behavior of myosin V in compared with other models.

This paper describes “digital origami” from geometrically frustrated tiles: arrays of structures that cannot attain an energetically favorable flat state because of internal constraints. Each tile can typically snap between two symmetric energy-minimizing states, and neighboring tiles are coupled so that a collection of binary tile states determines the local curvature of the entire sheet. Modular structures like these tiles give great advantages in manufacturing and in predictive simulation, and their discrete nature is a good match for digital readout and control of self-folding systems. The digital origami concept applies to materials from the nanoscale to the macroscale. An example from previous researchers is a metal sheet with an array of dimples that can flex up or down. In this paper we investigate more general techniques that can develop planar sheets into bistable structures. Such methods include installing compressed pieces into a flat sheet of material, or tying together parts of a sheet (smocking). These methods work with a large variety of technologically important materials including circuit boards and semiconductor substrates. While there are clear benefits to such structures, significant obstacles to design exist in manufacturing, in evaluating their mechanical properties, and in choosing the best arrangement of tile states to match a desired shape. Determining the optimal flipping order of tile states to change the sheet from one shape to another is a sequencing problem analogous to protein folding, and origami from non-planar surfaces is a little-explored area in the fine arts. The paper discusses algorithms for curve-matching with one-dimensional arrays by error diffusion, and shape prediction for two-dimensional sheets with pre-programmed tile states. Low computational cost is required for creating structures that can predict, detect, and even change their own three-dimensional shapes using low-power onboard microprocessors. Motivators for this challenge include shape measurement over a large size range — for example, detecting the changing shapes of biological microstructures or endowing robots with a spatial sense similar to human proprioception — and self-modeling of structural properties for lightweight morphing structures that can strengthen for impact in a given direction using a limited amount of material.

Bio-filaments at sub-micron scales such as DNA perform their biological functions via well-regulated structural deformations that involve large twisting and bending. The strain energies associated with these deformations are of the order of the thermal kinetic energies of surrounding solvent molecules. Therefore, the bio-filaments at such small length scales also exhibit large fluctuations in their shape due to the random collisions of the solvent molecules with them. These thermal fluctuations may, on one hand, help the bio-filaments explore functionally desirable configuration space, while, on the other hand, hinder the regulation of their deformations by motor proteins. Nevertheless, it seems indispensable to model the thermal fluctuations to accurately study the dynamics of deformation of bio-filaments. This paper presents the first elastic rod formulation that incorporates the thermal fluctuations by modeling the impacts of solvent molecules as distributed stochastic force. For quasi-static fluctuations, this formulation leverages the simplicity of a rod formulation noted by Anker et al. [1] that allows solving it as an initial value problem (IVP) in single iteration, and yet capturing arbitrarily large (nonlinear) deformations with rigorous description of constitutive laws.

A realistic computer simulation of protein folding requires a comprehensive account of interaction energetics, placing a substantial demand on processing power. This paper presents an improved computational framework for protein folding software package PROTOFOLD, that enables efficient computation of solvation free energy effects in addition to Coulombic and van der Waals interactions. Efficient data structures have been utilized to speed-up the sequential running times from O(n 2 ) to O(n), n being the number of atoms. It turns out, however, that an accurate evaluation of molecular surface areas characterizing the solvation effects imposes a computational bottleneck to the entire simulation. Massive computational power offered by Graphics Processing Units (GPU) was exploited to develop a simple and efficient Single-Instruction Multiple-Thread (SIMT) algorithm for the latter step. The running times were monitored for different steps of the folding simulation for different molecular sizes. Significant performance improvements were observed, yielding promising results where numerous iterative runs are needed.

Recent trends in technology are challenging engineers to configure products at ever smaller scales. At the nano-scale, biological protein machines are commonly chosen as a power-source for a broad-range of nano-devices. This paper explores the challenges in designing these and similar systems, such as improving the emergent system performance that arises from the interactions of many stochastic components. We develop a domain-independent methodology, using multi-agent simulations as a means of modeling and predicting emergent system behavior across scales and structure-behavior-function representations for understanding and navigating the resulting design space. This methodology is validated with an application of synthetic myosin motor design at the nanoscale, with simulation results aligning well with the macroscopic performance of myosin-powered muscular contractions. The multi-agent simulation is implemented with myosins modeled as agents, allowing for the virtual design and experimentation of synthetic myosins with altered structures and mechanochemical behaviors. Four myosin populations are designed and simulated, with their emergent system performance determined by aggregating the contributions of each myosin agent over time. Although the multi-agent simulation successfully recreates the emergent behaviors of the myosins, it is difficult to draw conclusions about how each structural variation influences aggregate performance. SBF representations of the system are then developed to describe how the aggregate performance of the system is explainable in terms of myosin behaviors, which map directly to altered myosin structures. It is then demonstrated how an engineer may utilize these representations and experimental results to reason about, and configure a myosin system with optimal performance. The methodology is domain-independent, ensuring its extendibility to similar complex systems while aiding a designer in simplifying a complex physical phenomenon to a design space consisting of only a few critical parameters. The methodology is particularly suited for complex systems with many parts operating stochastically across scales, and should prove invaluable for engineers facing the challenges of biological nanoscale design, where designs with unique properties require novel approaches or useful configurations in nature await discovery.