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Estimation, Modeling, and Simulation of Patterned Growth in Extreme Environments

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Software Tools and Algorithms for Biological Systems

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 696))

Abstract

In the search for life on Mars and other extraterrestrial bodies or in our attempts to identify biological traces in the most ancient rock record of Earth, one of the biggest problems facing us is how to recognize life or the remains of ancient life in a context very different from our planet’s modern biological examples. Specific chemistries or biological properties may well be inapplicable to extraterrestrial conditions or ancient Earth environments. Thus, we need to develop an arsenal of techniques that are of broader applicability. The notion of patterning created in some fashion by biological processes and properties may provide such a generalized property of biological systems no matter what the incidentals of chemistry or environmental conditions. One approach to recognizing these kinds of patterns is to look at apparently organized arrangements created and left by life in extreme environments here on Earth, especially at various spatial scales, different geologies, and biogeochemical circumstances.

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Authors and Affiliations

  1. California State University, San Bernardino, CA, 92407, USA

    K. E. Schubert

Authors
  1. B. Strader
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  2. K. E. Schubert
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  3. M. Quintana
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  4. E. Gomez
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  5. J. Curnutt
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  6. P. Boston
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Corresponding author

Correspondence to K. E. Schubert .

Editor information

Editors and Affiliations

  1. Dept. Computer Science, University of Georgia, Athens, 30602-7404, Georgia, USA

    Hamid R. Arabnia

  2. , Department of Computer Science, Lamar University, Beaumont, 77710, Texas, USA

    Quoc-Nam Tran

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Strader, B., Schubert, K.E., Quintana, M., Gomez, E., Curnutt, J., Boston, P. (2011). Estimation, Modeling, and Simulation of Patterned Growth in Extreme Environments. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_16

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