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One of the fundamental problems in nucleic acids biophysics is to predict the different forces that stabilize nucleic acid tertiary folds. Here we provide a quantitative estimation and analysis for the forces between DNA helices in an ionic solution. Using the generalized Born model and the improved atomistic tightly binding ions model, we evaluate ion correlation and solvent polarization effects in interhelix interactions. The results suggest that hydration, Coulomb correlation and ion entropy act together to cause the repulsion and attraction between nucleic acid helices in Mg(2+) and Mn(2+) solutions, respectively. The theoretical predictions are consistent with experimental findings. Detailed analysis further suggests that solvent polarization and ion correlation both are crucial for the interhelix interactions. The theory presented here may provide a useful framework for systematic and quantitative predictions of the forces in nucleic acids folding.

The nanopore sensor can detect cancer-derived nucleic acid biomarkers such as microRNAs (miRNAs), providing a noninvasive tool potentially useful in medical diagnostics. However, the nanopore-based detection of these biomarkers remains confounded by the presence of numerous other nucleic acid species found in biofluid extracts. Their nonspecific interactions with the nanopore inevitably contaminate the target signals, reducing the detection accuracy. Here we report a novel method that utilizes a polycationic peptide-PNA probe as the carrier for selective miRNA detection in the nucleic acid mixture. The cationic probe hybridized with microRNA forms a dipole complex, which can be captured by the pore using a voltage polarity that is opposite the polarity used to capture negatively charged nucleic acids. As a result, nontarget species are driven away from the pore opening, and the target miRNA can be detected accurately without interference. In addition, we demonstrate that the PNA probe enables accurate discrimination of miRNAs with single-nucleotide difference. This highly sensitive and selective nanodielectrophoresis approach can be applied to the detection of clinically relevant nucleic acid fragments in complex samples.

Experiments have suggested that ion correlation and fluctuation effects can be potentially important for multivalent ions in RNA folding. However, most existing computational methods for the ion electrostatics in RNA folding tend to ignore these effects. The previously reported tightly bound ion (TBI) model can treat ion correlation and fluctuation but its applicability to biologically important RNAs is severely limited by the low computational efficiency. Here, on the basis of Monte Carlo sampling for the many-body ion distribution, we develop a new computational model, the Monte Carlo tightly bound ion (MCTBI) model, for ion-binding properties around an RNA. Because of an enhanced sampling algorithm for ion distribution, the model leads to a significant improvement in computational efficiency. For example, for a 160-nt RNA, the model causes a more than 10-fold increase in the computational efficiency, and the improvement in computational efficiency is more pronounced for larger systems. Furthermore, unlike the earlier model that describes ion distribution using the number of bound ions around each nucleotide, the current MCTBI model is based on the three-dimensional coordinates of the ions. The higher efficiency of the model allows us to treat the ion effects for medium to large RNA molecules, RNA-ligand complexes, and RNA-protein complexes. This new model together with proper RNA conformational sampling and the energetics model may serve as a starting point for further development for the ion effects in RNA folding and conformational changes and for large nucleic acid systems.

RNA-RNA interactions are essential for genomic RNA dimerization, mRNA splicing, and many RNA-related gene expression and regulation processes. The prediction of the structure and folding stability of RNA-RNA complexes is a problem of significant biological importance and receives substantial interest in the biological community. The VfoldCPX server provides a new web interface to predict the two-dimensional (2D) structures of RNA-RNA complexes from the nucleotide sequences. The VfoldCPX server has several novel advantages including the ability to treat RNAs with tertiary contacts (crossing base pairs) such as loop-loop kissing interactions and the use of physical loop entropy parameters. Based on a partition function-based algorithm, the server enables prediction for structure with and without tertiary contacts. Furthermore, the server outputs a set of energetically stable structures, ranked by their stabilities. The results allow users to gain extensive physical insights into RNA-RNA interactions and their roles in RNA function. The web server is freely accessible at "http://rna.physics.missouri.edu/vfoldCPX".

RNA/RNA interactions are essential for genomic RNA dimerization and regulation of gene expression. Predicting the structure and folding stabilities of RNA/RNA complexes is a biologically significant problem. We recently developed a new web server, VfoldCPX, to predict two-dimensional (2D) structures of RNA/RNA complexes from the sequence. The server is based on a new Vfold model that employs coarse-grained entropy parameters for the different structural motifs. By taking into account both the intra- and inter-molecular interactions, the model can predict RNA/RNA structures with cross-linked tertiary contacts. Furthermore, in addition to the minimum free energy structure, the server can predict multiple energetically stable structures ranked by their stabilities. The predicted structures can offer users with extensive insights into the folding and conformational switches in RNA biological functions. The web server is freely accessible at “http://rna.physics.missouri.edu”.

Aptamers are single-stranded DNA or RNA oligonucleotides that can bind with exquisitely high affinity and specificity to target molecules and are thus often referred to as 'nucleic acid' antibodies. Oligonucleotide aptamers are derived through a process of directed chemical evolution called SELEX (Systematic Evolution of Ligands by Exponential enrichment). This chemical equivalent of Darwinian evolution was first described in 1990 by Tuerk & Gold and Ellington & Szostak and has since yielded aptamers for a wide-range of applications, including biosensor technologies, in vitro diagnostics, biomarker discovery, and therapeutics. Since the inception of the original SELEX method, numerous modifications to the protocol have been described to fit the choice of target, specific conditions or applications. Technologies such as high-throughput sequencing methods and microfluidics have also been adapted for SELEX. In this chapter, we outline key steps in the SELEX process for enabling the rapid identification of RNA aptamers for in vivo applications. Specifically, we provide a detailed protocol for the selection of chemically-optimized RNA aptamers using the original in vitro SELEX methodology. In addition, methods for performing next-generation sequencing of the RNAs from each round of selection, based on Illumina sequencing technology, are discussed.

RNA/RNA interactions are essential for genomic RNA dimerization and regulation of gene expression. Intermolecular loop-loop base pairing is a widespread and functionally important tertiary structure motif in RNA machinery. However, computational prediction of intermolecular loop-loop base pairing is challenged by the entropy and free energy calculation due to the conformational constraint and the intermolecular interactions. In this chapter, we describe a recently developed statistical mechanics-based method for the prediction of RNA/RNA complex structures and stabilities. The method is based on the virtual bond RNA folding model (Vfold). The main emphasis in the method is placed on the evaluation of the entropy and free energy for the loops, especially tertiary kissing loops. The method also uses recursive partition function calculations and two-step screening algorithm for large, complicated structures of RNA/RNA complexes. As case studies, we use the HIV-1 Mal dimer and the siRNA/HIV-1 mutant (T4) to illustrate the method.

RNAs are negatively charged molecules residing in macromolecular crowding cellular environments. Macromolecular confinement can influence the ion effects in RNA folding. In this work, using the recently developed tightly bound ion model for ion fluctuation and correlation, we investigate the confinement effect on the ion-mediated RNA structural collapse for a simple model system. We found that, for both Na$^+$ and Mg$^{2+}$, ion efficiencies in mediating structural collapse/folding are significantly enhanced by the structural confinement. Such an enhancement in the ion efficiency is attributed to the decreased electrostatic free energy difference between the compact conformation ensemble and the (restricted) extended conformation ensemble due to the spatial restriction., 22 pages, 5 figures

The recently developed Tightly Bound Ion (TBI) model offers improved predictions for ion effect in nucleic acid systems by accounting for ion correlation and fluctuation effects. However, further application of the model to larger systems is limited by the low computational efficiency of the model. Here, we develop a new computational efficient TBI model using free energy landscape-guided sampling method. The method leads to drastic reduction in the computer time by a factor of 50 for RNAs of 50-100 nucleotides long. The improvement in the computational efficiency would be more significant for larger structures. To test the new method, we apply the model to predict the free energies and the number of bound ions for a series of RNA folding systems. The validity of this new model is supported by the nearly exact agreement with the results from the original TBI model and the agreement with the experimental data. The method may pave the way for further applications of the TBI model to treat a broad range of biologically significant systems such as tetraloop-receptor and riboswitches.

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

MicroRNAs (miRNAs) are a class of short RNA molecules that play an important role in post-transcriptional gene regulation. Computational prediction of the miRNA target sites in mRNA is crucial for understanding the mechanism of miRNA-mRNA interactions. We here develop a new computational model that allows us to treat a variety of miRNA-mRNA kissing interactions, which have been ignored in the currently existing miRNA target prediction algorithms. By including all the different inter- and intra-molecular base pairs, this new model can predict both the structural accessibility of the target sites and the binding affinity (free energy). Applications of the model to a test set of 105 miRNA-gene systems show a notably improved success rate of 83/105. We found that although the binding affinity alone predicts the miRNA repression efficiency with a high success rate of 73/105, the structure in the seed region can significantly influence the miRNA activity. The method also allows us to efficiently search for the potent miRNA from a pool of miRNA candidates for any given gene target. Furthermore, extension of the method may enable predictions of the three-dimensional (3D) structures of miRNA/mRNA complexes.

RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model.

We develop a statistical mechanical model to predict the structure and folding stability of the RNA/RNA kissing-loop complex. One of the key ingredients of the theory is the conformational entropy for the RNA/RNA kissing complex. We employ the recently developed virtual bond-based RNA folding model (Vfold model) to evaluate the entropy parameters for the different types of kissing loops. A benchmark test against experiments suggests that the entropy calculation is reliable. As an application of the model, we apply the model to investigate the structure and folding thermodynamics for the kissing complex of the HIV-1 dimerization initiation signal. With the physics-based energetic parameters, we compute the free energy landscape for the HIV-1 dimer. From the energy landscape, we identify two minimal free energy structures, which correspond to the kissing-loop dimer and the extended-duplex dimer, respectively. The results support the two-step dimerization process for the HIV-1 replication cycle. Furthermore, based on the Vfold model and energy minimization, the theory can predict the native structure as well as the local minima in the free energy landscape. The root-mean-square deviations (RMSDs) for the predicted kissing-loop dimer and extended-duplex dimer are ∼3.0 Å. The method developed here provides a new method to study the RNA/RNA kissing complex.

The 3′-untranslated region (UTR) of the group 2 coronavirus mouse hepatitis virus (MHV) genome contains a predicted bulged stem–loop (designated P0ab), a conserved cis-acting pseudoknot (PK), and a more distal stem–loop (designated P2). Base-pairing to create the pseudoknot-forming stem (P1pk) is mutually exclusive with formation of stem P0a at the base of the bulged stem–loop; as a result, the two structures cannot be present simultaneously. Herein, we use thermodynamic methods to evaluate the ability of individual subdomains of the 3′ UTR to adopt a pseudoknotted conformation. We find that an RNA capable of forming only the predicted PK (58 nt; 3′ nucleotides 241–185) adopts the P2 stem–loop with little evidence for P1pk pairing in 0.1 M KCl and the absence of Mg2+; as Mg2+ or 1 M KCl is added, a new thermal unfolding transition is induced and assignable to P1pk pairing. The P1pk helix is only marginally stable, ΔG25 ≈ 1.2 ± 0.3 kcal/mol (5.0 mM Mg2+, 100 mM K+), and unfolded at 37°C. Similar findings characterize an RNA 5′ extended through the P0b helix only (89 nt; 294–185). In contrast, an RNA capable of forming either the P0a helix or the pseudoknot (97 nt; 301–185) forms no P1pk helix. Thermal unfolding simulations are fully consistent with these experimental findings. These data reveal that the PK forms weakly and only when the competing double-hairpin structure cannot form; in the UTR RNA, the double hairpin is the predominant conformer under all solution conditions.

Multicentric gliomas are considered to be well recognized but uncommon; often scatter widely in different lobes or hemispheres; and cannot be attributed to a definite pathway[1]. A patient diagnosed as multicentric gliomas is presented in this paper. He was fi rstly misdiagnosed as cerebral metastatic tumors, but later the histopathological examination revealed them to be glioblastoma (WHO grade IV). Additionally, the aim of the paper was to describe the case history of the patient and the problems encountered in the pathogenesis, pathophysiology, diagnosis and treatment.

Recent experiments pointed to the potential importance of ion correlation for multivalent ions such as Mg(2+) ions in RNA folding. In this study, we develop an all-atom model to predict the ion electrostatics in RNA folding. The model can treat ion correlation effects explicitly by considering an ensemble of discrete ion distributions. In contrast to the previous coarse-grained models that can treat ion correlation, this new model is based on all-atom nucleic acid structures. Thus, unlike the previous coarse-grained models, this new model allows us to treat complex tertiary structures such as HIV-1 DIS type RNA kissing complexes. Theory-experiment comparisons for a variety of tertiary structures indicate that the model gives improved predictions over the Poisson-Boltzmann theory, which underestimates the Mg(2+) binding in the competition with Na(+). Further systematic theory-experiment comparisons for a series of tertiary structures lead to a set of analytical formulas for Mg(2+)/Na(+) ion-binding to various RNA and DNA structures over a wide range of Mg(2+) and Na(+) concentrations.

Tertiary interactions between loops and helical stems play critical roles in the biological function of many RNA pseudoknots. However, quantitative predictions for RNA tertiary interactions remain elusive. Here we report a statistical mechanical model for the prediction of noncanonical loop–stem base-pairing interactions in RNA pseudoknots. Central to the model is the evaluation of the conformational entropy for the pseudoknotted folds with defined loop–stem tertiary structural contacts. We develop an RNA virtual bond-based conformational model (Vfold model), which permits a rigorous computation of the conformational entropy for a given fold that contains loop–stem tertiary contacts. With the entropy parameters predicted from the Vfold model and the energy parameters for the tertiary contacts as inserted parameters, we can then predict the RNA folding thermodynamics, from which we can extract the tertiary contact thermodynamic parameters from theory–experimental comparisons. These comparisons reveal a contact enthalpy (ΔH) of −14 kcal/mol and a contact entropy (ΔS) of −38 cal/mol/K for a protonated C+•(G–C) base triple at pH 7.0, and (ΔH = −7 kcal/mol, ΔS = −19 cal/mol/K) for an unprotonated base triple. Tests of the model for a series of pseudoknots show good theory–experiment agreement. Based on the extracted energy parameters for the tertiary structural contacts, the model enables predictions for the structure, stability, and folding pathways for RNA pseudoknots with known or postulated loop–stem tertiary contacts from the nucleotide sequence alone.

RNAs are highly negatively charged chain molecules. Metal ions play a crucial role in RNA folding stability and conformational changes. In this work, we employ the recently developed tightly bound ion (TBI) model, which accounts for the correlation between ions and the fluctuation of ion distributions, to investigate the ion-dependent free energy landscape for the three-way RNA junction in a 16S rRNA domain. The predicted electrostatic free energy landscape suggests that 1), ion-mediated electrostatic interactions cause an ensemble of unfolded conformations narrowly populated around the maximally extended structure; and 2), Mg(2+) ion-induced correlation effects help bring the helices to the folded state. Nonelectrostatic interactions, such as noncanonical interactions within the junctions and between junctions and helix stems, might further limit the conformational diversity of the unfolded state, resulting in a more ordered unfolded state than the one predicted from the electrostatic effect. Moreover, the folded state is predominantly stabilized by the coaxial stacking force. The TBI-predicted folding stability agrees well with the experimental measurements for the different Na(+) and Mg(2+) ion concentrations. For Mg(2+) solutions, the TBI model, which accounts for the Mg(2+) ion correlation effect, gives more improved predictions than the Poisson-Boltzmann theory, which tends to underestimate the role of Mg(2+) in stabilizing the folded structure. Detailed control tests indicate that the dominant ion correlation effect comes from the charge-charge Coulombic correlation rather than the size (excluded volume) correlation between the ions. Furthermore, the model gives quantitative predictions for the ion size effect in the folding energy landscape and folding cooperativity.

Single-stranded junctions/loops are frequently occurring structural motifs in nucleic acid structures. Due to the polyanionic nature of the nucleic acid backbone, metal ions play a crucial role in the loop stability. Here we use the tightly bound ion theory, which can account for the possible ion correlation and ensemble (fluctuation) effects, to predict the ion-dependence of loop and stem-loop (hairpin) free energies. The predicted loop free energy is a function of the loop length, the loop end-to-end distance, and the ion (Na(+) and Mg(2+) in this study) concentrations. Based on the statistical mechanical calculations, we derive a set of empirical formulas for the loop thermodynamic parameters as functions of Na(+) and Mg(2+) concentrations. For three specific types of loops, namely, hairpin, bulge, and internal loops, the predicted free energies agree with the experimental data. Further applications of these empirical formulas to RNA and DNA hairpin stability lead to good agreements with the available experimental data. Our results indicate that the ion-dependent loop stability makes significant contribution to the overall ion-dependence of the hairpin stability.

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5–3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson–Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

The ever increasing discoveries of noncoding RNA functions draw a strong demand for RNA structure determination from the sequence. In recently years, computational studies for RNA structures, at both the two-dimensional and the three-dimensional levels, led to several highly promising new developments. In this chapter, we describe a recently developed RNA structure prediction method based on the virtual bond-based coarse-grained folding model (Vfold). The main emphasis in the Vfold method is placed on the loop entropy calculations, the treatment of noncanonical (mismatch) interactions and the 3D structure assembly from motif-based template library. As case studies, we use the glycine riboswitch and the G310-U376 domain of MLV RNA to illustrate the Vfold-based prediction of RNA 3D structures from the sequences.

In concurrent engineering projects, tasks are usually interdependent among each other that require much iteration before completion where the critical path method/program evaluation and review technique (CPM/PERT) may not be applicable to help estimate the project duration. In addition, carrying out a large-scaled project in a dynamic environment has to deal with various factors at the same time. When estimating the project completion time, previous research often focused on one subject of interest and assumed the other factors causing little effect on the overall project duration. The objective of this article is to develop a research framework to help estimate the project completion time and analyze the major factors that affect the estimation for complex concurrent engineering projects. The framework consists of three major components: (1) data collection, where the needed data for simulation are prepared including project task structure, task relations, and quantified team member characteristics; (2) simulation, where tasks are dynamically assigned to the appropriate members/engineers according to each member's knowledge level of the task, teamwork capability, work schedule availability, and learning curve improvement; and (3) data analysis, where significant factors to the project completion time are studied by the ANOVA analysis based on the simulation results. The effectiveness of our framework and the simulation model is demonstrated by an illustrative example.

Metal ions are crucial for nucleic acid folding. From the free energy landscapes, we investigate the detailed mechanism for ion-induced collapse for a paradigm system: loop-tethered short DNA helices. We find that Na + and Mg 2+ play distinctive roles in helix–helix assembly. High [Na + ]( >0.3 M) causes a reduced helix–helix electrostatic repulsion and a subsequent disordered packing of helices. In contrast, Mg 2+ of concentration >1 mM is predicted to induce helix–helix attraction and results in a more compact and ordered helix–helix packing. Mg 2+ is much more efficient in causing nucleic acid compaction. In addition, the free energy landscape shows that the tethering loops between the helices also play a significant role. A flexible loop, such as a neutral loop or a polynucleotide loop in high salt concentration, enhances the close approach of the helices in order to gain the loop entropy. On the other hand, a rigid loop, such as a polynucleotide loop in low salt concentration, tends to de-compact the helices. Therefore, a polynucleotide loop significantly enhances the sharpness of the ion-induced compaction transition. Moreover, we find that a larger number of helices in the system or a smaller radius of the divalent ions can cause a more abrupt compaction transition and a more compact state at high ion concentration, and the ion size effect becomes more pronounced as the number of helices is increased.

Based on the experimentally determined atomic coordinates for RNA helices and the self-avoiding walks of the P (phosphate) and C4 (carbon) atoms in the diamond lattice for the polynucleotide loop conformations, we derive a set of conformational entropy parameters for RNA pseudoknots. Based on the entropy parameters, we develop a folding thermodynamics model that enables us to compute the sequence-specific RNA pseudoknot folding free energy landscape and thermodynamics. The model is validated through extensive experimental tests both for the native structures and for the folding thermodynamics. The model predicts strong sequence-dependent helix-loop competitions in the pseudoknot stability and the resultant conformational switches between different hairpin and pseudoknot structures. For instance, for the pseudoknot domain of human telomerase RNA, a native-like and a misfolded hairpin intermediates are found to coexist on the (equilibrium) folding pathways, and the interplay between the stabilities of these intermediates causes the conformational switch that may underlie a human telomerase disease.

We develop a statistical mechanical model for RNA/RNA complexes with both intramolecular and intermolecular interactions. As an application of the model, we compute the free energy landscapes, which give the full distribution for all the possible conformations, for U4/U6 and U2/U6 in major spliceosome and U4atac/U6atac and U12/U6atac in minor spliceosome. Different snRNA experiments found contrasting structures, our free energy landscape theory shows why these structures emerge and how they compete with each other. For yeast U2/U6, the model predicts that the two distinct experimental structures, the four-helix junction structure and the helix Ib-containing structure, can actually coexist and specifically compete with each other. In addition, the energy landscapes suggest possible mechanisms for the conformational switches in splicing. For instance, our calculation shows that coaxial stacking is essential for stabilizing the four-helix junction in yeast U2/U6. Therefore, inhibition of the coaxial stacking possibly by protein-binding may activate the conformational switch from the four-helix junction to the helix Ib-containing structure. Moreover, the change of the energy landscape shape gives information about the conformational changes. We find multiple (native-like and misfolded) intermediates formed through base-pairing rearrangements in snRNA complexes. For example, the unfolding of the U2/U6 undergoes a transition to a misfolded state which is functional, while in the unfolding of U12/U6atac, the functional helix Ib is found to be the last one to unfold and is thus the most stable structural component. Furthermore, the energy landscape gives the stabilities of all the possible (functional) intermediates and such information is directly related to splicing efficiency.

The complexity of RNA hairpin folding arises from the interplay between the loop formation, the disruption of the slow-breaking misfolded states, and the formation of the slow-forming native base stacks. We investigate the general physical mechanism for the dependence of the RNA hairpin folding kinetics on the sequence and the length of the hairpin loop and the helix stem. For example, 1), the folding would slow down when a stable GC basepair moves to the middle of the stem; 2), hairpin with GC basepair near the loop would fold/unfold faster than the one with GC near the tail of the stem; 3), within a certain range of the stem length, a longer stem can cause faster folding; and 4), certain misfolded states can assist folding through the formation of scaffold structures to lower the entropic barrier for the folding. All our findings are directly applicable and quantitatively testable in experiments. In addition, our results can be useful for molecular design to achieve desirable fast/slow-folding hairpins, hairpins with/without specific misfolded intermediates, and hairpins that fold along designed pathways.

Background Metal ions play a critical role in the stabilization of RNA structures. Therefore, accurate prediction of the ion effects in RNA folding can have a far-reaching impact on our understanding of RNA structure and function. Multivalent ions, especially Mg2+, are essential for RNA tertiary structure formation. These ions can possibly become strongly correlated in the close vicinity of RNA surface. Most of the currently available software packages, which have widespread success in predicting ion effects in biomolecular systems, however, do not explicitly account for the ion correlation effect. Therefore, it is important to develop a software package/web server for the prediction of ion electrostatics in RNA folding by including ion correlation effects. Results The TBI web server http://rna.physics.missouri.edu/tbi_index.html provides predictions for the total electrostatic free energy, the different free energy components, and the mean number and the most probable distributions of the bound ions. A novel feature of the TBI server is its ability to account for ion correlation and ion distribution fluctuation effects. Conclusions By accounting for the ion correlation and fluctuation effects, the TBI server is a unique online tool for computing ion-mediated electrostatic properties for given RNA structures. The results can provide important data for in-depth analysis for ion effects in RNA folding including the ion-dependence of folding stability, ion uptake in the folding process, and the interplay between the different energetic components.

Complex products or projects with long development times always involve multiple disciplines and a great deal of effort. Especially in concurrent engineering, the design needs to simultaneously consider various downstream activities throughout the entire product life cycle. To accomplish this, tasks in the concurrent engineering environment generally involve multifunctional teams in which team members from different functional departments interact in every phase of development tasks. However, as products become more complex, so do the design process and the project teams. This will inevitably degrade team performance when the team size becomes too large to handle. Moreover, to ensure a successful multifunctional team, it is also important to understand the characteristics of team members that affect team performance. Thus, an efficient multifunctional team can be established with all the right team members being assigned. The objective of this research is to develop an integrated methodological framework for project task coordination and team organization from the concurrent engineering perspective in order to assign the right team members to the right tasks. The framework includes three models: (1) a project task model to understand the complex task structure of the project; (2) a team member model to provide a quantitative representation of three important team member characteristics; and (3) a task-member assignment model to accomplish the research goal - assign the right team members to the right tasks. The effectiveness of this framework is demonstrated by an illustrative example. The result shows that the proposed integrated framework is capable of coordinating the project tasks and helpful in organizing project teams.

In concurrent engineering, project tasks generally involve the establishment of multifunctional design teams in order to simultaneously consider various activities throughout the entire product life cycle. Team members from different functional departments of the company interact in every phase of development tasks to design the products and processes concurrently. To ensure a successful multifunctional team, it is important to understand the characteristics of team members. Three fundamental descriptors of team members are developed in this research. The first is to represent the multifunctional knowledge of team members due to the need of concurrent engineering. Second, to build a successful project team, teamwork capability of team members is needed by taking their experience, communication skills, and flexibility in job assignment into account. Multifunctional knowledge and teamwork capability ratings are captured from each member using analytic hierarchy process. Third, since the team members work closely, their collegiality directly affects team performance, regardless of their knowledge. Thus, the working relationship model is developed to provide such a metric. Personality profiling using Myers-Briggs type indicator serves as the basis of assessing each team member's abilities to work with others. Finally, we complete this paper by providing a step-by-step procedure with an example for selecting the best multifunctional team using the three ratings. This research helps establish an efficient multifunctional team because every team member will be capable of communicating to each other within the team due to their multifunctional knowledge, teamwork skills, as well as an established good working relationship.

A kinetic cluster method enables us to analyze biopolymer folding kinetics with discrete rate-limiting steps by classifying biopolymer conformations into pre-equilibrated clusters. The overall folding kinetics is determined by the intercluster transitions. Due to the complex energy landscapes of biopolymers, the intercluster transitions have multiple pathways and can have kinetic intermediates (local free-energy minima) distributed on the intercluster pathways. We focus on the RNA secondary structure folding kinetics. The dominant folding pathways and the kinetic partitioning mechanism can be identified and quantified from the rate constants for different intercluster pathways. Moreover, the temperature dependence of the folding rate can be analyzed from the interplay between the stabilities of the on-pathway (nativelike) and off-pathway (misfolded) conformations and from the kinetic partitioning between different intercluster pathways. The predicted folding kinetics can be directly tested against experiments.

Engineering designs involving multiple factors that interact with one another have drawn researchers' attention for decades. In today's competitive global market, which demands rapid response to customer needs, speeding up the product development process has become more crucial than ever. Axiomatic Design (AD) is one design theory that provides a systematic approach to engineering design. Based on the independence axiom in AD, unacceptable solutions can be identified in a domain mapping process during the early design stage. As AD accepts only uncoupled and decoupled designs, design solutions with interacting parameters are not considered appropriate. However, many engineering designs do involve couplings, especially for the design of large and complex systems, even though these couplings may have various degrees of strength. To expedite the design process, design engineers may ignore some weak couplings that have little influence on the design outcome so that the design tasks can proceed with fewer interactions. The intention of this research is to develop methods for measuring functional dependency and sequencing of coupled tasks in engineering design so as to improve the design process. First, we rearrange the design matrix using partitioning algorithm to reveal the structure and sequence of design processes. Next, the Analytic Hierarchy Process (AHP) is applied to measure the strength of the coupled design tasks. To reduce the comparison effort in AHP, a comparison scale assignment algorithm is developed. Finally, we provide an algorithm for finding the best processing sequence of the coupled tasks in terms of the measured coupling strengths. The effectiveness of our proposed method is demonstrated by an illustrative example showing the design of a one-time-use camera.

In concurrent engineering, project tasks generally involve the establishment of multifunctional teams in which team members from different functional departments interact in every phase of development tasks to design the products and processes concurrently. However, the increasing complexity of product development and design process often come up with large interdependent task groups due to the nature of the concurrent strategy. The large size of interdependent task groups makes it difficult for team organization and thus delays the project completion. This calls for the intention of this research to develop an effective model to: (1) transform the binary task relationships into the quantifiable task coupling strengths; and (2) to decompose the large interdependent task group into smaller and manageable sub-groups. Design structure matrix (DSM), analytic hierarchy process and cluster analysis are used to represent task relationships, quantify task couplings and decompose large size of task groups. Clustering performance between numerical DSM versus binary DSM is evaluated using a simulation experiment. The experimental results show that the clustering performance of using numerical DSM is better than the use of binary DSM. The effectiveness of this model is then demonstrated by an illustrative example. The result shows that our proposed model is capable of decomposing the large coupling task group that helps team organization for concurrent engineering project.

A master equation approach is developed to find the rate-limiting steps in biopolymer folding, where the folding kinetics is described as a linear combination of basic kinetic modes determined from the eigenvalues and eigenvectors of the rate matrix. Because the passage of a rate-limiting step is intrinsically related to the folding speed, it is possible to probe and to identify the rate-limiting steps through the folding from different unfolded initial conformations. In a master equation approach, slow and fast folding speeds are directly correlated to the large and small contributions of the (rate-limiting) slow kinetic modes. Because the contributions from the slow modes can be computed from the corresponding eigenvectors, the rate-limiting steps can be identified from the eigenvectors of the slow modes. Our rate-limiting searching method has been tested for a simplified hairpin folding kinetics model, and it may provide a general transition state searching method for biopolymer folding.

In a concurrent engineering environment, product design needs to simultaneously consider various downstream activities throughout the entire product life cycle, in addition to meeting the functional requirements of the products. To accomplish this, multifunctional design teams are organized with team members from different functional departments interacting in every phase of development tasks. However, without proper task coordination and team organization, communication and cooperation among a large number of dependent or interdependent tasks and team members could seriously delay project completion. This calls for the intention of this research to study the underlying structure of the project tasks. The objectives of this research are: (1) to develop a project task coordination model that identifies the sequence of all project tasks and reveals the underlying structure of the design process; (2) to decompose large interdependent task groups into smaller and manageable task groups by transforming the binary form of task relationships into the quantifiable task coupling strength and (3) to provide a structural foundation for the efficient team organization in concurrent engineering. Design Structure Matrix (DSM) is used to represent relationship among project tasks. Analytic Hierarchy Process (AHP), as a multi-criteria decision analysis, is employed to quantify the binary DSM. The effectiveness of this model is demonstrated by an illustrative example. The result shows that our proposed model is not only capable of coordinating the entire project tasks but also helpful in organizing project teams.

RNA loop–loop interactions are essential for genomic RNA dimerization and regulation of gene expression. In this article, a statistical mechanics-based computational method that predicts the structures and thermodynamic stabilities of RNA complexes with loop–loop kissing interactions is described. The method accounts for the entropy changes for the formation of loop–loop interactions, which is a notable advancement that other computational models have neglected. Benchmark tests with several experimentally validated systems show that the inclusion of the entropy parameters can indeed improve predictions for RNA complexes. Furthermore, the method can predict not only the native structures of RNA/RNA complexes but also alternative metastable structures. For instance, the model predicts that the SL1 domain of HIV-1 RNA can form two different dimer structures with similar stabilities. The prediction is consistent with experimental observation. In addition, the model predicts two different binding sites for hTR dimerization: One binding site has been experimentally proposed, and the other structure, which has a higher stability, is structurally feasible and needs further experimental validation.

Based on a graphical representation of intrachain contacts, we have developed a new three-dimensional model for the statistical mechanics of double-stranded chain molecules. The theory has been tested and validated for the cubic lattice chain conformations. The statistical mechanical model can be applied to the equilibrium folding thermodynamics of a large class of chain molecules, including protein β-hairpin conformations and RNA secondary structures. The application of a previously developed two-dimensional model to RNA secondary structure folding thermodynamics generally overestimates the breadth of the melting curves [S-J. Chen and K. A. Dill, Proc. Natl. Acad. Sci. U.S.A. 97, 646 (2000)], suggesting an underestimation for the sharpness of the conformational transitions. In this work, we show that the new three-dimensional model gives much sharper melting curves than the two-dimensional model. We believe that the new three-dimensional model may give much improved predictions for the thermodynamic prop...

We develop a statistical mechanical theory for the free energy landscapes for complexes of double-stranded chain molecules. The theory is based on the generalized polymer graph, a graphical representation for the conformations of the complexes. We compute the partition functions by “dividing and conquering” on the generalized polymer graph: we decompose a graph into simple subunits, calculate the partition function of each subunit exactly, and treat the interactions between subunits approximately, by calculating the localized interactions (of the nearest neighbor and the next-nearest neighbor monomers) at the interface of subunits. Our tests against the exact computer enumeration on the two-dimensional (2D) square lattice show that the theory is accurate. We apply the theory to the computation of the free energy landscapes of three representative systems: homopolymer–homopolymer, homopolymer–heteropolymer, and heteropolymer–heteropolymer complexes, using contact-based energy functions for the homopolymer–homopolymer and homopolymer–heteropolymer complexes, and stacking energies for the heteropolymer–heteropolymer complexes (to mimic RNA secondary structures). We find that the systems involving homopolymers show smooth free energy landscapes, and undergo noncooperative structural transitions during the melting process, and that the system of heteropolymers show rugged free energy landscapes, and the thermal denaturation involves intermediate states and cooperative structural transitions. We believe this approach maybe useful for computing the free energy landscapes and the thermodynamics of DNA or RNA interactions and RNA binding to a DNA or RNA target.

Using a new theoretical model, we explore the conformational energy landscapes and the statistical thermodynamics of RNA secondary structure folding. The model is physical, treats the conformational entropies and excluded volume explicitly, and has previously been shown to give reasonable agreement with experimental melting curves. Here we use it to design energy landscapes that can be tested by experiments. The model predicts that even simple RNA’s, less than 60 nucleotides long and having only secondary structures, can have remarkably complex and bumpy landscapes that can be altered substantially by small mutations. Moving a GC block around in a sequence of fixed composition can lead from one melting peak to two. A mutation can switch it back to one. Two melting peaks do not imply two simple stem melting events. In one system, we find 5 transitions, each of which can be either 2-state or 1-state. In some sequences we find triple points, where 3 macroscopically identifiable species have equal populations at the same temperature. We show designs of RNA’s with multiple native states, and conformational switching between them, as functions of mutations or temperature.

Simulation is a widely used approach for assisting design and improvement of manufacturing systems. It is a complex activity and needs a great deal of human expertise. Since the knowledge of analyzing simulation output for decision making is not inherently captured in the simulation modeling methodology, a framework that integrates simulation and knowledge-based decision analysis is needed. In this paper, we develop a knowledge-based system that cooperates with simulation for improving the performance of manufacturing cells. Using Axiomatic Design as a guideline, a hierarchical knowledge base structure that corresponds to the decision process is built. Our proposed knowledge-based system consists of a set of facts and three levels of rules in a hierarchy that is consistent with the manufacturing cell system configuration. The system demonstrates the effectiveness of utilizing Axiomatic Design concept when developing a knowledge-based system. The results of an industrial study show that our method contributes to improving the performance of manufacturing cells.

To investigate the clinical value of surgical treatment for cerebral schistosomiasis.The clinical data of 42 patients with cerebral schistosomiasis caused by Schistosoma japonicum undergoing surgical therapy were analyzed retrospectively.There were 25 cases undergoing total resection of schistosomal granulomas and 17 cases undergoing partial resection of the lesions involving the functional areas or more than 2 lobes, and none died of the surgery. The post-surgical follow-up showed that 31 cases recovered completely and returned to the normal work and life, 2 developed mild weaknesses of all extremities, 3 had post-surgical epileptic seizures, 2 died of schistosomal hepatic cirrhosis, and 1 died naturally.Surgical treatment is an effective approach for cerebral schistosomiasis.

Current literature about scheduling problems contains numerous studies on the use of dispatching rules with performance measures that are based only on time criteria. Various methods have been proposed to consider job processing time, job completion time, job due date, and combinations thereof. Considerations of tardiness cost, which offers a realistic reflection on the manufacturers' business concern, are usually ignored. One exception is the Weighted COVERT rule with the weighted tardiness cost concern. It has been tested and performs well in costbased performance. However, the performance of Weighted COVERT strongly depends upon the selected parameter values for estimating the expected waiting time of jobs. In this research, we present a multi-factor priority rule to improve the Weighted COVERT rule. Our new rule combines job processing time, job routing, job due date, and job-dependent tardiness cost for the scheduling in a manufacturing cell. The objective is to reduce the total tardiness cost. The c...

We develop statistical mechanical theory to predict the thermodynamic properties of chain molecules having noncovalent double-stranded conformations, as in RNA molecules and β-sheets in proteins. Sequence dependence and excluded volume interactions are explicitly taken into account. We classify conformations by their polymer graphs and enumerate all the conformations corresponding to each graph by a recently developed matrix method [S-J. Chen and K. A. Dill, J. Chem. Phys. 103, 5802 (1995)]. All such graphs are summed by a recursive method. Tests against exact computer enumeration for short chains on a 2D lattice show that the density of states and partition function are given quite accurately. So far, we have explored two classes of conformations; hairpins, which model small β-sheets, and RNA secondary structures. The main folding transition is predicted to be quite different for these two conformational classes: the hairpin transition is two-state while the RNA secondary structure transition is one-stat...