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The demonstration that DNA molecules can act as parallel processors to solve hard problems has excited interest in the possibility of developing molecular computers based on recombinant DNA techniques.
Because of their wide use in molecular modeling, methods to compute molecular surfaces have received a lot of interest in recent years. However, most of the proposed algorithms compute the analytical representation of only the solvent-accessible surface. There are a few programs that compute the analytical representation of the solvent-excluded surface, but they often have problems handling singular cases of self-intersecting surfaces and tend to fail on large molecules (more than 10,000 atoms). We describe here a program called MSMS, which is shown to be fast and reliable in computing molecular surfaces. It relies on the use of the reduced surface that is briefly defined here and from which the solvent-accessible and solvent-excluded surfaces are computed. The four algorithms composing MSMS are described and their complexity is analyzed. Special attention is given to the handling of self-intersecting parts of the solvent-excluded surface called singularities. The program has been compared with Connolly's program PQMS [M.L. Connolly (1993) Journal of Molecular Graphics, Vol. 11, pp. 139-141] on a set of 709 molecules taken from the Brookhaven Data Base. MSMS was able to compute topologically correct surfaces for each molecule in the set. Moreover, the actual time spent to compute surfaces is in agreement with the theoretical complexity of the program, which is shown to be O[n log(n)] for n atoms. On a Hewlett-Packard 9000/735 workstation, MSMS takes 0.73 s to produce a triangulated solvent-excluded surface for crambin (1 crn, 46 residues, 327 atoms, 4772 triangles), 4.6 s for thermolysin (3tln, 316 residues, 2437 atoms, 26462 triangles), and 104.53 s for glutamine synthetase (2gls, 5676 residues, 43632 atoms, 476665 triangles).
A course called "Molecular Biology Computer Techniques" was implemented in 1987 and has been evolving ever since. Currently the semester-long three credit course consists of thirty hours of lecture (three hours/week for the first ten weeks of the semester) and a minimum of 45 hours of laboratory instruction (three hours/week). The lectures survey both bioinformatics and structure based methods. The laboratory has two tracks, one that can be described loosely as "sequence analysis" and the other as "molecular modelling." Most students choose one of the two laboratory tracks, although a small number have done both, either simultaneously or in successive years. For each student, the goal of the course is the completion of a student-initiated research project. The culmination of the course is the presentation of the completed projects at a "Poster Session Final." During this final, which is conducted like a poster session at a typical biological science meeting, students are examined, not only by the instructors in the course, but also by a diverse cross-section of the university community at large, including non-scientists (who are specially invited to attend). Questioning by non-scientists provides opportunity for the students to improve their communication skills with the lay public. In this manuscript we discuss our views regarding the rationale for the development of formal courses in computational molecular biology, relate our experiences in the development of our course, and describe the course as it stood the last time it was taught, which was in the Fall of 1994.
In the whiplash polymerase chain reaction (WPCR), autonomous molecular computation is implemented in vitro by the recursive, self-directed polymerase extension of a mixture of DNA hairpins. Although computational efficiency is known to be reduced by a tendency for DNAs to self-inhibit by backhybridization, both the magnitude of this effect and its dependence on the reaction conditions have remained open questions. In this paper, the impact of backhybridization on WPCR efficiency is addressed by modeling the recursive extension of each strand as a Markov chain. The extension efficiency per effective polymerase-DNA encounter is then estimated within the framework of a statistical thermodynamic model. Model predictions are shown to provide close agreement with the premature halting of computation reported in a recent in vitro WPCR implementation, a particularly significant result, given that backhybridization had been discounted as the dominant error process. The scaling behavior further indicates completion times to be sufficiently long to render WPCR-based massive parallelism infeasible. A modified architecture, PNA-mediated WPCR (PWPCR) is then proposed in which the occupancy of backhybridized hairpins is reduced by targeted PNA(2)/DNA triplex formation. The efficiency of PWPCR is discussed using a modified form of the model developed for WPCR. Predictions indicate the PWPCR efficiency is sufficient to allow the implementation of autonomous molecular computation on a massive scale.
Moore's Law states that the processing power of microchips doubles every one to two years. This observation might apply to the nascent field of molecular computing, in which biomolecules carry out logical operations. Incorporation of new technologies that improve sensitivity and throughput has increased the complexity of problems that can be addressed. It is an ultimate goal for molecular computers to use the full potential of massive parallelism.
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The review concentrates on practical applications of computer molecular modeling in peptide drug design. The examples of the predictions (successful or not) made by computational modeling before synthesis of peptide analogs, not the explanations provided after synthesis and biological testing of peptides, are discussed. The review spans over 20 years of predictions made by computer molecular modeling for bradykinin, angiotensin, thyrotropin-releasing factor, tuftsin, substance P, CCK-related peptides, luliberin, alpha-melanotropin and opioid peptides. The described examples are discussed in terms of finding the optimal way to use computer modeling for peptide design. The step-by-step 'technology' of peptide design is outlined in detail.
The pioneering work of Adleman (1994) demonstrated that DNA molecules in test tubes can be manipulated to perform a certain type of mathematical computation. This has stimulated a theoretical interest in the possibility of constructing DNA-based molecular computers. To gauge the practicality of realizing such microscopic computers, it was thought necessary to learn as much as possible from the biology of the living cell--presently the only known DNA-based molecular computer in existence. Here the recently developed theoretical model of the living cell (the Bhopalator) and its associated theories (e.g. cell language), principles, laws and concepts (e.g. conformons, IDS's) are briefly reviewed and summarized in the form of a set of five laws of 'molecular semiotics' (synonyms include 'microsemiotics', 'cellular semiotics', or 'cytosemiotics') the study of signs mediating measurement, computation, and communication on the cellular and molecular levels. Hopefully, these laws will find practical applications in designing DNA-based computing systems.
Recently, Boneh, Dunworth, and Lipton (1996) described the potential use of molecular computation in attacking the United States Data Encryption Standard (DES). Here, we provide a description of such an attack using the sticker model of molecular computation. Our analysis suggests that such an attack might be mounted on a tabletop machine using approximately a gram of DNA and might succeed even in the presence of a large number of errors.
The principle of minimum expenditure of "price of action" [5] provides in the liquid cell membrane such operative memory which uses for recording information from any receptor (multidigit number) the transmission of an electron with the loss smaller than 250 mev. According to the hypothesis such memory is constructed from equal protein molecules and different lipid addresses for different receptors. It works on Brown collisions, weak interactions between the molecules and capture bonds controlled by MCC with a minimum expenditure of free energ- for the formation and breakage of bonds.
Adaptive behaviors and dynamic activities within living cells are organized by the cytoskeleton: intracellular networks of interconnected protein polymers which include microtubules (MTs), actin, intermediate filaments, microtubule associated proteins (MAPs) and other protein structures. Cooperative interactions among cytoskeletal protein subunit conformational states have been used to model signal transmission and information processing. In the present work we present a theoretical model for molecular computing in which Boolean logic is implemented in parallel networks of individual MTs interconnected by MAPs. Conformational signals propagate on MTs as in data buses and in the model MAPs are considered as Boolean operators, either as bit-lines (like MTs) where a signal can be transported unchanged between MTs ('BUS-MAP'), or as bit-lines where a Boolean operation is performed in one of the two MAP-MT attachments ('LOGIC-MAP'). Three logic MAPs have been defined ('NOT-MAP, 'AND-MAP', 'XOR-MAP') and used to demonstrate addition, subtraction and other arithmetic operations. Although our choice of Boolean logic is arbitrary, the simulations demonstrate symbolic manipulation in a connectionist system and suggest that MT-MAP networks can perform computation in living cells and are candidates for future molecular computing devices.
A novel approach to designing a DNA library for molecular computation is presented. The method is employed for encoding binary information in DNA molecules. It aims to achieve a practical discrimination between perfectly matched DNA oligomers and those with mismatches in a large pool of different molecules. The approach takes into account the ability of DNA strands to hybridize in complex structures like hairpins, internal loops, or bulge loops and computes the stability of the hybrids formed based on thermodynamic data. A dynamic programming algorithm is applied to calculate the partition function for the ensemble of structures, which play a role in the hybridization reaction. The applicability of the method is demonstrated by the design of a twelve-bit DNA library. The library is constructed and experimentally tested using molecular biology tools. The results show a high level of specific hybridization achieved for all library words under identical conditions. The method is also applicable for the design of primers for PCR, DNA sequences for isothermal amplification reactions, and capture probes in DNA-chip arrays. The library could be applied for integrated DNA computing of twelve-bit instances of NP-complete combinatorial problems by multi-step DNA selection in microflow reactors.
Hairpin formation by single-stranded DNA molecules was exploited in a DNA-based computation in order to explore the feasibility of autonomous molecular computing. An instance of the satisfiability problem, a famous hard combinatorial problem, was solved by using molecular biology techniques. The satisfiability of a given Boolean formula was examined autonomously, on the basis of hairpin formation by the molecules that represent the formula. This computation algorithm can test several clauses in the given formula simultaneously, which could reduce the number of laboratory steps required for computation.
In this paper we provide a model for micro-flow based bio-molecular computation (MF-BMC). It provides an abstraction for the design of algorithms which account for the constraints of the model. Our MF-BMC model uses abstractions of both the recombinant DNA (RDNA) technology as well as of the micro-flow technology and takes into account both of their limitations. For example, when considering the efficiency of the recombinant DNA operation of annealing, we take into account the limitation imposed by the concentration of the reactants. The fabrication technology used to construct MEMS is limited to constructing relatively thin 3D structures. We abstract this by limiting the model to a small constant number of layers (as is done with VLSI models). Besides our contribution of the MF-BMC model, the paper contains two other classes of results. The main result is the volume and time efficient algorithm for message routing in the MF-BMC model, specifically useful for PA-Match. We will show that routing of strands between chambers will occur in time O(N x D/ m x n), where N is the number of strands in the MF-BMC, n is the number of chambers where RDNA operations are occurring, D is the diameter of the topology of the layout of the chambers, and m is proportional to the channel width. Operations that need annealing, such as PA-Match, are shown feasible in O(N2logN/n/n) volume instead of the previous use of omega(N2) volume, with reasonable time constraints. Applications of the volume efficient algorithm include the use of the Join operation for databases, logarithmic depth solutions to SAT (Boolean formula satisfiability) problems and parallel algorithms that execute on a PRAM. Existent algorithms can be mapped to ones that work efficiently in the MF-BMC model, whereas previous methods for applications such as PRAM simulation in BMC were not both time and volume efficient. Our other class of results are theoretical lower bounds on the quantities of DNA and the time needed to solve a problem in the MF-BMC model, analogous to lower bounds in VLSI. We bound the product BT from below, and further show that BT2 has a stronger lower bound of I2. Here B is the maximum amount of information encoded in the MF-BMC system at a time. T is the time for an algorithm to complete, and I is the information content of a problem.
A concept for molecular electronics exploiting carbon nanotubes as both molecular device elements and molecular wires for reading and writing information was developed. Each device element is based on a suspended, crossed nanotube geometry that leads to bistable, electrostatically switchable ON/OFF states. The device elements are naturally addressable in large arrays by the carbon nanotube molecular wires making up the devices. These reversible, bistable device elements could be used to construct nonvolatile random access memory and logic function tables at an integration level approaching 10(12) elements per square centimeter and an element operation frequency in excess of 100 gigahertz. The viability of this concept is demonstrated by detailed calculations and by the experimental realization of a reversible, bistable nanotube-based bit.
In the past few years two fascinating and new scientific fields, the science of DNA-structure and topology and the theory of molecular computers have been growing independently. The main goal of this paper is to establish an interesting connection between them and to propose a novel paradigm for the future construction of DNA-computing devices based on supercoil energetics. The basic principle of the proposed model can also be applied to describe the communication between topologically closed segments in real genomes, which is believed to take part in the complex process of gene regulation. An implementation of the recent model is proposed by which polynomials of one real variable can be evaluated in a simple in vitro recombination assay.
Methods of coding the number and search for molecular program in a molecular computer are considered. The limited length of the nucleotide code is (see formula) where Pi -- probability of request of the given program, N -- total number of programs. Energetic expenditures for the synthesis of the code with the length l (in an ideal case without noise) E approximately 10 kT X l. Protein-nucleic recognition allows the work of the cell with almost the same expenditures on the account of Brown search in the presence of noise.
MOTIVATION: Rapid software prototyping can significantly reduce development times in the field of computational molecular biology and molecular modeling. Biochemical Algorithms Library (BALL) is an application framework in C++ that has been specifically designed for this purpose. RESULTS: BALL provides an extensive set of data structures as well as classes for molecular mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility which results from an object-oriented and generic programming approach distinguishes it from other software packages. BALL is well suited to serve as a public repository for reliable data structures and algorithms. We show in an example that the implementation of complex methods is greatly simplified when using the data structures and functionality provided by BALL.