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Computational Techniques for a Comprehensive Understanding of Different Genotype-Phenotype Factors in Biological Systems and Their Applications

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Synthetic Biology

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Abstract

Identification and analyses of the discrete genotype and phenotype components of a biological system are complex, multi-scale problems. Computational techniques are widely used to extract some meaningful information from the underlying heterogeneous, raw biological data. Here, we review the use of different approaches to mathematically model biological data at different levels of complexities. At the raw sequence level, we discuss about the various techniques that model biological sequences based on frequency, geometry and spectral representation of nucleotides/amino acids. We also discuss about techniques that can be used to capture quantitative patterns of codons and briefly present an application in identification of evolutionary mechanisms that preserve codon usage patterns in trypanosomatids. Lastly, we discuss about the integration of the genotype and phenotype components into a systems-level mathematical representation of functional interactions in the form of a reconstructed genome-scale metabolic network and discern its role in governing variations in metabolic behaviours under different environmental conditions.

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Authors and Affiliations

  1. Chemical Engineering and Process Development, CSIR-National Chemical Laboratory, Pune, Maharashtra, India

    Abhishek Subramanian & Ram Rup Sarkar

  2. Academy of Scientific & Innovative Research (AcSIR), CSIR-NCL Campus, Pune, India

    Abhishek Subramanian & Ram Rup Sarkar

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  1. Abhishek Subramanian
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  2. Ram Rup Sarkar
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Correspondence to Ram Rup Sarkar .

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  1. Department of Pathogenesis and Cellular Response, National Centre for Cell Science, Computational and Systems Biology Lab, Pune, Maharashtra, India

    Shailza Singh

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Subramanian, A., Sarkar, R.R. (2018). Computational Techniques for a Comprehensive Understanding of Different Genotype-Phenotype Factors in Biological Systems and Their Applications. In: Singh, S. (eds) Synthetic Biology. Springer, Singapore. https://doi.org/10.1007/978-981-10-8693-9_8

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