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33
The Open Motion Planning Library
, 2012
"... We describe the Open Motion Planning Library (OMPL), a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that allows the user to easily solve a variety of complex motion planning problems with ..."
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Cited by 90 (17 self)
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We describe the Open Motion Planning Library (OMPL), a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos and programming assignments have been designed to teach students about sampling-based motion planning. Finally, the library is also available for use through the
Metrics for analyzing the evolution of c-space models
, 2006
"... Abstract — There are many sampling-based motion planning methods that model the connectivity of a robot’s configuration space (C-space) with a graph whose nodes are valid configurations and whose edges represent valid transitions between nodes. One of the biggest challenges faced by users of these m ..."
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Cited by 17 (10 self)
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Abstract — There are many sampling-based motion planning methods that model the connectivity of a robot’s configuration space (C-space) with a graph whose nodes are valid configurations and whose edges represent valid transitions between nodes. One of the biggest challenges faced by users of these methods is selecting the right planner for their problem. While researchers have tried to compare different planners, most accepted metrics for comparing planners are based on efficiency, e.g., number of collision detection calls or samples needed to solve a particular set of queries, and there is still a lack of useful and efficient quantitive metrics that can be used to measure the suitability of a planner for solving a problem. That is, although there is great interest in determining which planners should be used in which situations, there are still many questions we cannot answer about the relative performance of different planning methods. In this paper we make some progress towards this goal. We propose a metric that can be applied to each new sample considered by a samplingbased planner to characterize how that sample improves, or not, the planner’s current C-space model. This characterization requires only local information and can be computed quite efficiently, so that it can be applied to every sample. We show how this characterization can be used to analyze and compare how different planning strategies explore the configuration space. In particular, we show that it can be used to identify three phases that planners go through when building C-space models: quick learning (rapidly building a coarse model), model enhancement (refining the model), and learning decay (oversampling – most samples do not provide additional information). Hence, our work can also provide the basis for determining when a particular planning strategy has ‘converged ’ on the best C-space model that it is capable of building. I.
A Sampling-Based Tree Planner for Systems with Complex Dynamics
, 2012
"... This paper presents a kinodynamic motion planner, ..."
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Cited by 14 (10 self)
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This paper presents a kinodynamic motion planner,
Tools for simulating and analyzing rna folding kinetics
- in Proc. Int. Conf. Comput. Molecular Biology (RECOMB
, 2007
"... It has recently been found that some RNA functions are determined by the actual folding process itself and not just the RNA’s nucleotide sequence or its native structure. In this paper, we present new computational tools that can be used to study kinetics-based functions for RNA such as population k ..."
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Cited by 12 (9 self)
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It has recently been found that some RNA functions are determined by the actual folding process itself and not just the RNA’s nucleotide sequence or its native structure. In this paper, we present new computational tools that can be used to study kinetics-based functions for RNA such as population kinetics, folding rates, and the folding of particular subsequences. Previously, these properties could only be studied for small RNA whose conformation space could be enumerated (e.g., RNA with 30-40 nucleotides) or for RNA whose kinetics were restricted in some way. Our method follows our previous work by first building an approximate map (or model) of the RNA’s folding energy landscape. Next, we use our new analysis technique, called Map-based Monte Carlo (MMC) simulation, to stochastically extract folding pathways from the map. MMC, in conjunction with an improved sampling strategy for building the map, enables us to study kinetics-based functions for larger RNA than we could before, e.g., RNA with 200+ nucleotides. We validate our method against known experimental data and analyze two case studies in detail. First, we compare simulated folding rates for ColE1 RNAII and its mutants against experimental rates and show that our method identifies the same relative folding order as seen in experiment. Second, we predict the gene expression rates of wild-type MS2 phage RNA and three of its mutants and and show that our approach
Kinetics analysis methods for approximate folding landscapes
- Special issue of Int. Conf. on Intelligent Systems for Molecular Biology (ISMB) & European Conf. on Computational Biology (ECCB
, 2007
"... Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements o ..."
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Cited by 11 (8 self)
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Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-type protein and its mutants. We present two new techniques, Map-based Master Equation solution and Map-based Monte Carlo simulation, to study protein kinetics through folding rates and population kinetics from approximate folding landscapes, models called maps. From these two new techniques, interesting metrics that describe the folding process, such as reaction coordinates, can also be studied. In this paper we focus on two metrics, formation of helices and structure formation around tryptophan residues. These two metrics are often studied in the lab through CD spectra analysis and tryptophan fluorescence experiments, respectively. The approximated landscape models we use here are the maps of protein conformations and their associated transitions that we have presented and validated previously. In contrast to other methods such as the traditional master equation and Monte Carlo simulation, our techniques are both fast and can easily be computed for full length detailed protein models. We validate our map-based kinetics techniques by comparing folding rates to known experimental results. We also look in depth at the population kinetics, helix formation, and structure near tryptophan residues for a variety of proteins.
A MOTION PLANNING APPROACH TO STUDYING MOLECULAR MOTIONS
, 2010
"... While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer’s disease are associated with protein misfolding and aggregation. S ..."
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Cited by 7 (1 self)
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While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer’s disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we present an overview of our work with a computational method we have adapted from robotic motion planning to study molecular motions. We have validated against experimental data and have demonstrated that our method can capture biological results such as stochastic folding pathways, population kinetics of various conformations, and relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We have validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis has also been able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants.
Analysis of the evolution of c-space models built through incremental exploration
- in Robotics and Automation, 2007 IEEE International Conference on
, 2007
"... Many sampling methods for motion planning explore the robot’s configuration space (C-space) starting from a set of configuration(s) and incrementally explore surrounding areas to produce a growing model of the space. Although there is a common understanding of the strengths and weaknesses of these t ..."
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Cited by 6 (2 self)
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Many sampling methods for motion planning explore the robot’s configuration space (C-space) starting from a set of configuration(s) and incrementally explore surrounding areas to produce a growing model of the space. Although there is a common understanding of the strengths and weaknesses of these techniques, metrics for analyzing the incremental exploration process and for evaluating the performance of incremental samplers have been lacking. We propose the use of local metrics that provide insight into the complexity of the different regions in the model and global metrics that describe the process as a whole. These metrics only require local information and can be efficiently computed. We illustrate the use of our proposed metrics to analyze representative incremental strategies including the Rapidly-exploring Random Trees, Expansive Space Trees, and the original Randomized Path Planner. We show how these metrics model the efficiency of C-space exploration and help to identify different modeling stages. In addition, these metrics are ideal for adapting space exploration to improve performance. “Analysis... Incremental Exploration”, Morales et. al. TR06-013, Parasol Lab, Texas A&M, Sep. 2006 1 1
Tracing Conformational Changes in Proteins
, 2009
"... Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to lar ..."
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Cited by 5 (1 self)
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Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions. In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on three well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions.
Structural Improvement Filtering Strategy for PRM
"... Sampling based motion planning methods have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. Recent work in metrics for sampling ..."
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Cited by 4 (2 self)
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Sampling based motion planning methods have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. Recent work in metrics for sampling based planners provide tools to analyze the model building process at three levels of detail: sample level, region level, and global level. These tools are useful for comparing the evolution of sampling methods, and have shown promise to improve the process altogether [15], [17], [24]. Here, we introduce a filtering strategy for the Probabilistic Roadmap Methods (PRM) with the aim to improve roadmap construction performance by selecting only the samples that are likely to produce roadmap structure improvement. By measuring a new sample’s maximum potential structural improvement with respect to the current roadmap, we can choose to only accept samples that have an adequate potential for improvement. We show how this approach can improve the standard PRM framework in a variety of motion planning situations using popular sampling techniques. I.