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Special Purpose Parallel Computing
 Lectures on Parallel Computation
, 1993
"... A vast amount of work has been done in recent years on the design, analysis, implementation and verification of special purpose parallel computing systems. This paper presents a survey of various aspects of this work. A long, but by no means complete, bibliography is given. 1. Introduction Turing ..."
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Cited by 81 (6 self)
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A vast amount of work has been done in recent years on the design, analysis, implementation and verification of special purpose parallel computing systems. This paper presents a survey of various aspects of this work. A long, but by no means complete, bibliography is given. 1. Introduction Turing [365] demonstrated that, in principle, a single general purpose sequential machine could be designed which would be capable of efficiently performing any computation which could be performed by a special purpose sequential machine. The importance of this universality result for subsequent practical developments in computing cannot be overstated. It showed that, for a given computational problem, the additional efficiency advantages which could be gained by designing a special purpose sequential machine for that problem would not be great. Around 1944, von Neumann produced a proposal [66, 389] for a general purpose storedprogram sequential computer which captured the fundamental principles of...
Molecular Modeling Of Proteins And Mathematical Prediction Of Protein Structure
 SIAM Review
, 1997
"... . This paper discusses the mathematical formulation of and solution attempts for the socalled protein folding problem. The static aspect is concerned with how to predict the folded (native, tertiary) structure of a protein, given its sequence of amino acids. The dynamic aspect asks about the possib ..."
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Cited by 59 (5 self)
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. This paper discusses the mathematical formulation of and solution attempts for the socalled protein folding problem. The static aspect is concerned with how to predict the folded (native, tertiary) structure of a protein, given its sequence of amino acids. The dynamic aspect asks about the possible pathways to folding and unfolding, including the stability of the folded protein. From a mathematical point of view, there are several main sides to the static problem:  the selection of an appropriate potential energy function;  the parameter identification by fitting to experimental data; and  the global optimization of the potential. The dynamic problem entails, in addition, the solution of (because of multiple time scales very stiff) ordinary or stochastic differential equations (molecular dynamics simulation), or (in case of constrained molecular dynamics) of differentialalgebraic equations. A theme connecting the static and dynamic aspect is the determination and formation of...
Kinematic Manipulation of Molecular Chains Subject to Rigid Constraints
 In Proceedings of 2nd International Symposium on Molecular Biology
, 1994
"... We present algorithms for kinematic manipulation of molecular chains subject to fixed bond lengths and bond angles. They are useful for calculating conformations of a molecule subject to geometric constraints, such as those derived from twodimensional NMR experiments. Other applications include sea ..."
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We present algorithms for kinematic manipulation of molecular chains subject to fixed bond lengths and bond angles. They are useful for calculating conformations of a molecule subject to geometric constraints, such as those derived from twodimensional NMR experiments. Other applications include searching out the full range of conformations available to a molecule such as cyclic configurations. We make use of results from robot kinematics and recently developed algorithms for solving polynomial systems. In particular, we model the molecule as a serial chain using the DenavitHartenberg formulation and reduce these problems to inverse kinematics of a serial chain. We also highlight the relationship between molecular embedding problems and inverse kinematics. As compared to earlier methods, the main advantages of the kinematic formulation are its generality to all molecular chains without any restrictions on the geometry and efficiency in terms of performance. The algorithms give us real...
MOLecular Structure GENeration with MOLGEN, new features and future developments
 Fresenius J. Anal. Chem
, 1997
"... MOLGEN is a computer program system which is designed for generating molecular graphs fast, redundancy free and exhaustively. In the present paper we describe its basic features, new features of the current release MOLGEN 3.5, and future developments which provide considerable improvements and ex ..."
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Cited by 6 (4 self)
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MOLGEN is a computer program system which is designed for generating molecular graphs fast, redundancy free and exhaustively. In the present paper we describe its basic features, new features of the current release MOLGEN 3.5, and future developments which provide considerable improvements and extensions. 1 Introduction MOLGEN [17] is a generator for molecular graphs (=connectivity isomers or constitutional formulae) allowing to generate all isomers that correspond to a given molecular formula and (optional) further conditions like prescribed and forbidden substructures, ring sizes etc. The input consists of ffl the empirical formula, together with ffl an optional list of macroatoms, which means prescribed substructures that must not overlap, ffl an optional goodlist, that consists of prescribed substructures which may overlap, ffl an optional badlist, containing forbidden substructures, ffl an optional interval for the minimal and maximal size of rings, ffl an optional num...
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"... Constraining volume by matching the moments of a distance distribution C.C.Chen, R.O.Chen and R.B.AItman 1 The problem of computing a molecular structure from a set of distances arises in the interpretation ofNMR data as well as other experimental methods that yield distance information. Techniques ..."
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Constraining volume by matching the moments of a distance distribution C.C.Chen, R.O.Chen and R.B.AItman 1 The problem of computing a molecular structure from a set of distances arises in the interpretation ofNMR data as well as other experimental methods that yield distance information. Techniques for computing structures must find conformations consistent with the distance data. There are often other constraints on the structure that must be satisfied as well. One of the most problematic constraints is the constraint on the total volume occupied by the atoms. In this paper, we use the first two moments (mean and variance) of an estimated distance distribution to constrain the volume of a computed structure. We show that a probabilistic algorithm for matching the first two moments of the estimated distance distribution significantly improves the quality of the solution, especially when the distance information alone is not sufficient to define the structure precisely. We also show that our method is not sensitive to small errors in the estimates of mean and variance of the distance distribution. Finally, we demonstrate the use of this constraint in computing a lowresolution structure of the 30S prokaryotic ribosomal subunit. Quantitative analysis of our results allows us to assess the information content contained in constraints on volume, and to show that in some cases addition of a volume constraint adds information roughly equivalent to doubling the number of input distances. Our results also demonstrate the flexibility of probabilistic representations of structural constraints, and the importance of including volume information to constrain structural computations—especially in the case of sparse data.
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"... Constraining volume by matching the moments of a distance distribution C.C.Chen, R.O.Chen and R.B.AItman1 The problem of computing a molecular structure from a set of distances arises in the interpretation ofNMR data as well as other experimental methods that yield distance information. Techniques ..."
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Constraining volume by matching the moments of a distance distribution C.C.Chen, R.O.Chen and R.B.AItman1 The problem of computing a molecular structure from a set of distances arises in the interpretation ofNMR data as well as other experimental methods that yield distance information. Techniques for computing structures must find conformations consistent with the distance data. There are often other constraints on the structure that must be satisfied as well. One of the most problematic constraints is the constraint on the total volume occupied by the atoms. In this paper, we use the first two moments (mean and variance) of an estimated distance distribution to constrain the volume of a computed structure. We show that a probabilistic algorithm for matching the first two moments of the estimated distance distribution significantly improves the quality of the solution, especially when the distance information alone is not sufficient to define the structure precisely. We also show that our method is not sensitive to small errors in the estimates of mean and variance of the distance distribution. Finally, we demonstrate the use of this constraint in computing a lowresolution structure of the 30S prokaryotic ribosomal subunit. Quantitative analysis of our results allows us to assess the information content contained in constraints on volume, and to show that in some cases addition of a volume constraint adds information roughly equivalent to doubling the number of input distances. Our results also demonstrate the flexibility of probabilistic representations of structural constraints, and the importance of including volume information to constrain structural computations—especially in the case of sparse data.
Parallel Hierarchical Molecular Structure Estimation
, 1996
"... Determining the threedimensional structure of biological molecules such as proteins and nucleic acids is an important element of molecular biology because of the intimate relation between form and function of these molecules. Individual sources of data about molecular structure are subject to varyi ..."
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Determining the threedimensional structure of biological molecules such as proteins and nucleic acids is an important element of molecular biology because of the intimate relation between form and function of these molecules. Individual sources of data about molecular structure are subject to varying degrees of uncertainty. We have previously examined the parallelization of a probabilistic algorithm for combining multiple sources of uncertain data to estimate the structure of molecules and predict a measure of the uncertainty in the estimated structure. In this paper we extend our work on two fronts. First we present a hierarchical decomposition of the original algorithm which reduces the sequential computational complexity tremendously. The hierarchical decomposition in turn reveals a new axis of parallelism not present in the "flat" organization of the problems, as well as new parallelization issues. We demonstrate good speedups on two cachecoherent sharedmemory multiprocessors, the Stanford DASH and the SGI Challenge, with distributed and centralized memory organization, respectively. Our results point to several areas of further study to make both the hierarchical and the parallel aspects more flexible for general problems: automatic structure decomposition, processor load balancing across the hierarchy, and data locality management in conjunction with load balancing. We outline the directions we are investigating to incorporate these extensions.
Mathematical Simulations in Combinatorial Chemistry
, 1996
"... A novel technique for chemical synthesis in drug research is combinatorial chemistry, where usually a set of buildingblock molecules is attached to a core structure in all the combinatorially possible ways. The resulting set of compounds (called a library) can then be systematically screened for a ..."
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A novel technique for chemical synthesis in drug research is combinatorial chemistry, where usually a set of buildingblock molecules is attached to a core structure in all the combinatorially possible ways. The resulting set of compounds (called a library) can then be systematically screened for a desired biological activity. In this paper we discuss ways and limits of a mathematical simulation of this procedure. At first, two methods for selecting the buildingblocks from a given structure pool are presented with the objective to obtain only dissilimar library entries. Next an algorithm is described for the exhaustive and redundancyfree generation of a combinatorial library, illustrated by a singlestep and a multicomponent reaction. Finally equations for the enumeration of the library sizes are derived and the limits of the virtual combinatorial chemistry, i.e. purely in computer and without experiment, are discussed. 1
Cluster Distance Geometry of Polypeptide Chains
, 2004
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.