Catalyzing Inquiry at the Interface of Computing and Biology (2005) / Chapter Skim
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6 A Computational and Engineering View of Biology
6 A Computational and Engineering View of Biology
Pages 205-226
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From page 205... ...
To facilitate the understanding and construction of such artifacts, computer science has developed information abstractions that seek to capture and encapsulate certain kinds of functional behavior in manipulating and managing information; such abstractions are a primary focus of study of the computer scientist (Box 6.1)
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From page 206... ...
So viewed, the information circle becomes the unit of life."3 The current state of intellectual affairs with respect to biological information and complexity may have some historical analogy with the concept of energy at the beginning of the 19th century. Although the concept was intuitively obvious, it was not formally defined or measured at that time.
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From page 207... ...
Cells coordinate their internal activity because they have harnessed intracellular Shannon information channels. Multicellular organisms coordinate their internal activity because they have harnessed intercellular Shannon information channels.
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From page 208... ...
and a "high-level" view that focuses on functionality (what the system does -- analogous to a logic gate or a computational device) .10 For example, one might distinguish between the pathways involved in regulating the circadian rhythm of an organism and its functional behavior as an oscillator.
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From page 209... ...
Fernandez and R.V. Sole, "The Role of Computation in Complex Regulatory Networks," Santa Fe Institute Working Paper, 2003, available at http://www.santafe.edu/sfi/publications/Working-Papers/03-10-055.pdf; to appear in a chapter in Power Laws, Scale-Free Networks and Genome Biology, Landes Bioscience.
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argue, "Similar messy and probabilistic intermediates appear in engineering systems based on artificial neural networks -- mathematical characterizations of information processing that are directly inspired by biology. A neural network can usefully describe complicated deterministic input-output relationships, even though the intermediate calculations through which it proceeds lack any obvious meaning and their choice depends on random noise in a training process."14 6.2 AN ENGINEERING PERSPECTIVE ON BIOLOGICAL ORGANISMS 6.2.1 Biological Organisms as Engineered Entities Engineering insights can be useful in understanding biological organisms as engineered entities, and the rationale for seeking insights from engineering is based on three notions.
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A second view of biological organisms as engineered entities -- as novel entities to be constructed by human beings rather than as existing organisms to be understood by human beings -- is discussed in Section 8.4.2 on synthetic biology. 6.2.2 Biology as Reverse Engineering Biological organisms are generally presented to scientists as completed entities, so the challenge of achieving an engineering understanding of them is in fact a challenge of reverse engineering.
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From page 212... ...
Systems engineered by humans, even very poorly engineered ones and even though they too often show their historical origins, are seldom if ever as arcane and kludgy as evolved biological organisms. Finally, it is helpful to distinguish between two different approaches to reverse engineering.
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in metabolic or gene regulatory networks. Another approach mentioned earlier is clustering expres sion profiles to produce groups of genes that appear to be co-regulated that should ideally reveal the functional modules.
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From page 214... ...
Negative feedback loops can maintain an output parameter within a narrow range, despite widely fluctuating input. For example, negative feedback in bacterial chemotaxis2 allows the sensory system to detect subtle variations in an input signal whose absolute size can vary by several orders of magnitude.3 (This topic -- robustness against noise -- is described in more detail in Section 6.2.5.)
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Tang, "Transcriptional Regulatory Networks in Saccharomyces cerevisiae," Science 298(5594)
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Calestani, and E.H. Davidson, "New Early Zygotic Regulators of Endomesoderm Specification in Sea Urchin Embryos Discovered by Differential Array Hybridization," Developmental Biology 246(1)
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Yuh, T Minokawa, et al., "A Provisional Regulatory Gene Network for Specification of Endomesoderm in the Sea Urchin Embryo," Developmental Biology 246(1)
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Simon, and J Doyle, "Robust Perfect Adaptation in Bacterial Chemotaxis Through Integral Feedback Control," Proceedings of the National Academy of Sciences 97(9)
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This strongly implies that such a network, taken as a whole, is a robust developmental module, able to produce a particular effect despite wide variation in reaction parameters. In a refinement to that work, Ingolia investigated the architecture of that network to attempt to determine the structural sources of such robust behavior.41 He determined that the source of the robustness at the network level was a pair of positive feedback loops of gene expression, which led to cells being forced to one of two stable states (bistability)
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Biological organisms exhibit high degrees of robustness in the face of changing environments. Engineered artifacts designed by human beings have used mechanisms such as negative feedback to provide stability, redundancy to provide backup, and modularity for the isolation of failures to enhance robustness.
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Simon, and J Doyle, "Robust Perfect Adaptation in Bacterial Chemotaxis Through Integral Feedback Control," Proceedings of the National Academy of Sciences 97(9)
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the model of A Wagner, who treats development as the interaction of a network of transcriptional regulatory genes, phenotype as the equilibrium state of this network, and fitness as a function of the distance between an individual's equilibrium state and the optimum state.
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The network that controls circadian rhythms consists of multiple, complex, interlocking feedback loops. Both deterministic and stochastic mechanisms for noise resistance in circadian rhythms have been explored,67 and it turns out that stochastic models are able to produce regular oscillations when the deterministic models do not,68 suggesting that the regulatory networks may utilize molecular fluctuations to their advantage.
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From page 224... ...
These strategies are rare in singlecell organisms but nearly universal in multicellular organisms, and evolved before or coincident with the emergence of multicellular life. As described in Table 6.1, each of these strategies may be analogous to trends seen in computing today.
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In the late 1980s and early 1990s, David B Searls and collaborators made the metaphor much more concrete, applying formal language theory to the analysis of nucleic acid sequences.76 Linguistics theory considers four levels of interpretation of text: lexical (the 76D.B.
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From page 226... ...
Some sequences of nucleic acids result in ambiguous linguistic interpretations; while this is a difficulty for computer languages, it represents a strength of biological linguistic analysis, because these ambiguities correctly represent alternative secondary structures.78 This approach has been fruitful for analyzing genetic sequences and characterizing the complexity and structure of genes. GenLang, a software system that employs linguistic approaches, has successfully identified tRNA genes, group I introns, protein-encoding genes, and the specification of gene regulatory elements.79 Other important findings include placing RNA in the Chomsky hierarchy as at least beyond context-free languages.
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