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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.
Molecular computation is fashionable because of the small device sizes that should be possible. For instance, information processing in nanospaces is widespread in biology. However, experimental cases with computing molecules occupy rather large volumes until now (ca. 1012 nm3). Now we demonstrate small-scale computation using a designed logic gate in spheres of 3 nm radius (volume = ca. 102 nm3) which are produced by detergent micelles. The logic gate accommodated in the small nanospace fluoresces only when both H+ and Na+ ions are available.
Since the introduction of molecular computation, experimental molecular computational elements have grown to encompass small-scale integration, arithmetic and games, among others. However, the need for a practical application has been pressing. Here we present molecular computational identification (MCID), a demonstration that molecular logic and computation can be applied to a widely relevant issue. Examples of populations that need encoding in the microscopic world are cells in diagnostics or beads in combinatorial chemistry (tags). Taking advantage of the small size (about 1 nm) and large 'on/off' output ratios of molecular logic gates and using the great variety of logic types, input chemical combinations, switching thresholds and even gate arrays in addition to colours, we produce unique identifiers for members of populations of small polymer beads (about 100 microm) used for synthesis of combinatorial libraries. Many millions of distinguishable tags become available. This method should be extensible to far smaller objects, with the only requirement being a 'wash and watch' protocol. Our focus on converting molecular science into technology concerning analog sensors, turns to digital logic devices in the present work.
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.
Parallelism is one of the major advantages of molecular computation. A large number of data encoded in DNA molecules can be processed simultaneously by molecular biology techniques, although only a single set of instructions has been implemented in a solution. We have developed a computing machine, called the "whiplash" machine, which is made of DNA polymerase and a hairpin DNA. This machine simulates a finite state machine, executing its own instructions encoded in the DNA moiety, and would thus be applicable to multiple-instruction operation in a solution. In the present study, we explored the feasibility of this novel type of parallelism by applying the whiplash machine in a computation of the directed Hamiltonian path problem. The possible paths in a given graph were represented with different instruction sets, which were then implemented separately by whiplash machines in a test tube. After an autonomous operation of the machines, only the machine that implemented the instruction set corresponding to the Hamiltonian path was recovered from the tube. On the basis of the efficiency of machine operation, which was experimentally determined, 10(10) different instruction sets could be implemented simultaneously in a 1-ml solution.
Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.
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.
Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems. Recently, simple molecular-scale autonomous programmable computers were demonstrated allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for 'logical' control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.