Catalyzing Inquiry at the Interface of Computing and Biology (2005) / Chapter Skim
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9 Illustrative Problem Domains at the Interface of Computing and Biology
9 Illustrative Problem Domains at the Interface of Computing and Biology
Pages 299-330
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1 D.B. Searls, "Grand Challenges in Computational Biology," Computational Methods in Molecular Biology, S.L.
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Currently, however, we have very incomplete knowledge about all of the components that make up cells and how these components interact to perform those functions. Understanding how cells work is one of biology's grand challenges.
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In this context, the model is intended to integrate -- as a real cell would -- different aspects of its functionality. Although the grand challenge may well be unachievable, almost by definition, the goal of increasing degrees of integration of what is known and understood about various aspects of cellular function remains something for which researchers strive.
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It will be important to develop ways of measuring many more aspects of internal cellular state, and in particular, new techniques for measuring rates of processes and biochemical reactions in situ in living cells will be necessary. Besides additional reporter molecules, selective fluorescent dyes, and so on, a particular need is to develop good ways of tracking cellular state at different points in time, so that cellular dynamics can be better understood.
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Cellular modification has a long history ranging from the development of plasmids carrying biosynthetic genes, or serving as "engineering blanks" for production of new materials, to the creation of small genetic circuits for the control of gene expression. However, the synthetic cells being imagined today would differ from the original cell much more substantially than those that have resulted from modifications to date.
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In turn, the mechanisms underlying synthetic cells are likely to be more easily understood than comparable ones in natural cells. Using this combined information, the behavior of biological processes in living cells can slowly be unraveled.
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Inputs to the synthetic cell would take the form of environmental sensitivities of various kinds that direct cellular function. (Another perspective on "artificial cells" similar to this report's notion of synthetic cells is offered by Pohorille.7 In general, synthetic cells share much with artificial cells, and the dividing line between them is both blurry and somewhat arbitrary.
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. 9.4 NEURAL INFORMATION PROCESSING AND NEURAL PROSTHETICS Brain research is a grand challenge area for the coming decades.
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Given the desirability of computers that can learn and adapt, an ability to answer this question might provide some guidance in the engineering of such systems. Some things are known about neural information processing: · Animal brains find good solutions to real-time problems in image and speech processing, motor control, and learning.
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It is thus the higher-level organization underlying neural computation that is of interest and relevance. Note also that for the purposes of understanding neural signaling or computation, a neuron-by-neuron simulation of nervous tissue per se cannot be expected to reveal very much about the principles of organization, though it may be necessary for the development of useful artifacts (e.g., neural prostheses)
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15It is also known that not all neural signaling is carried by spikes. A phenomenon known as graded synaptic transmission also carries neural information and is based on a release of neurotransmitter at synaptic junctions whose volume is voltage dependent and continuous.
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The microfluidic chip has a two-dimensional array of small controllable pores, corresponding to pixels in an image. An image is created by the selective drip of neurotransmitters onto specific bipolar cells, which are the cells that carry retinal information to the brain.21 A third example of work in this area is that of Musallam et al., who have demonstrated the feasibility of a neural interface that enables a monkey to control the movement of a cursor on a computer screen by thinking about a goal the monkey would like to achieve and assigning a value to that goal.22 The interesting twist to this work is the reliance of signals from parts of the brain related to higher-order ("cognitive")
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Still, developing such a function may be the first step toward such understanding. As suggested above, building a successful neural prosthetic implies some understanding of the semantics of neural information processing: how the relevant nerve tissue stores and replicates and processes information.
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, more molecular variability was found in the 1960s than had been expected, and this finding stimulated Kimura's neutral theory of molecular evolution.26 Phylogenetics (the study of the evolutionary history of life) makes use of a variety of different kinds of data, of which DNA sequences are the most important, as well as whole-genome, metabolic, morphological, geographical, and geological data.27 Evolutionary biology is founded on the concept that organisms share a common origin and have diverged through time.
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The comparative method has provided much of the evidence for natural selection and is probably the most widely used statistical method in evolutionary biology. But comparative analyses must account for phylogenetic history, since the similarity in features common to multiple species that originate in a common evolutionary history can inappropriately and seriously bias the analyses.
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Similarly, Sheldon et al.33 observed that the size spectrum, from the smallest particles to large fish, follows a power law with a characteristic exponent, valid across a range of trophic levels. Ecosystems and the biosphere are complex adaptive systems,34 in which macroscopic patterns emerge from interactions at lower levels of organization and feed back to influence dynamics on those scales.
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. Today, computational ecology makes use of continuum and individual descriptions.
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The development of algorithms implementing parallelization for individualbased ecological models has enabled a number of simulations, including simulations for fish populations in the Everglades42 and for more general models aimed ultimately at resource management.43 Data issues in computational ecology are also critical. Information technology has been a key enabler for a great deal of ecological data.
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that will be critical to address the grand challenges of the environmental sciences (biogeochemical cycles, biological diversity and ecosystem functioning, climate variability, hydrologic forecasting, infectious disease and the environment, institutions and resource use, land-use dynamics, reinventing the use of materials) proposed by the National Research Council.45 Similarly, an explosion of data and of information will arise from sensors carried by individual animals.
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. It is useful to distinguish between genetic signatures that are highly penetrant and those that are highly prevalent.
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Nevertheless, for a variety of reasons, identifying the relevant genetic signatures over multiple genes that account for disease susceptibility will pose significant intellectual challenges. Probably the most important point is that the contribution of any given gene involved is likely to be weak; hence detecting its clinical significance may be problematic.
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Detailed "omic" knowledge about individuals may help to identify the set of people who might benefit from certain drugs without incurring undesirable side effects, although some degree of empirical testing will be needed if such individuals can be identified.56 In addition, some individuals may be more sensitive than others to specific drugs, requiring differential dosages for optimal effect. As in the case of disease susceptibility, the best-understood genetic polymorphisms that affect drug responses in individuals are those that involve single genes.
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Here, researchers hope to establish and utilize antihypertensive drugs that are matched to the genetic variations among individuals, and thus to optimize blood pressure control and reduce side effects.57 A number of monogenic polymorphisms have been found, encoding drug-metabolizing enzymes, drug transporters, and drug targets, as well as disease-modifying genes, that have been linked to drug effects in humans. However, these are the "low-hanging fruit" of pharmacogenetics, and for most drug effects and treatment outcomes, monogenic polymorphisms with clearly recognizable drug-response phenotypes do not characterize the situation.
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Ordovas and Corella write:60 Nutritional genomics has tremendous potential to change the future of dietary guidelines and personal recommendations. Nutrigenetics will provide the basis for personalized dietary recommendations based on the individual's genetic makeup.
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In the context of nutritional genomics, metabolomic studies attempt to characterize the levels, activities, regulation, and interactions of all metabolites in an individual and determine how this characterization changes in response to various foods that are consumed. Genomics is important because genetic makeup is an important influence on the specific nature of the metabolomic changes that result as a function of food consumption.
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Box 9.4 A Virtual Reality Simulation of Regional Anesthesia A collaborative effort between researchers at the Ohio State University Hospitals, Immersion Corporation, and the Ohio Supercomputer Center has led to the creation of a virtual reality simulator that enables anesthesiol ogists-in-training to practice in a realistic environment the injection of a local anesthetic into the epidural space of the spinal column. The system includes a workstation capable of stereo display, a real-time spatial volume renderer, a voice-activated interface, and most importantly, a one-dimensional haptic probe capable of simulating the resistive forces of penetrated tissues.
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That is, a distance parameter should represent in a formalized manner the extent to which two structures differ -- and a distance of zero should indicate that they are identical. In the approach to computational anatomy pioneered by Grenander and Miller,1 the distance parameter is the square root of the energy re quired to transform the first structure onto the metric of the second with the assumption that normal transfor mations follow the least-energy path.
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Most important among aperiodic patterns are Wang tiles, a set of tiles for which the act of tiling a plane was shown to be equivalent to the operation of a universal Turing machine.66 (Because of the grounding in the theory of Wang tiles in particular, the components of self-assembled systems are often referred to as "tiles" and collections of tiles and rules for attaching them as "tiling systems.") With a fundamental link between nonperiodic tilings and computation being established, it becomes possible to consider the possibility of programming matter to form desired shapes, just as Turing machines can be programmed to perform certain computations.
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are reflected in reorganized neural patterns. Thus, a deep understanding of how biology organizes self-modification in using DNA or in a neural brain may lead to insights about how one might approach human problems that call for self-modifying computer programs.
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Other attempts at measuring biological complexity include enumerating various macroscopic properties of an organism, such as the number of distinct parts, number of distinct cell types, number of biological functions performed, and so forth. In practice this can be difficult (what is considered a "distinct" part?
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Generally, the types of complexity measures applied to DNA sequences are defined by their relationship to the process of computation. For example, a string might be considered to be a program, an input to a program, or the output of a program, and the resulting complexity measure might include the size of the Turing machine that produced it, its running time, or the number of states.
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Regardless, this is an area of active research, and further integration with actual biological investigation is likely to produce further progress in identifying accurate and useful measures of complexity.
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