A Search for the Physical Basis of the Genetic Code and Modeling Cancer Cell Response to Chemotherapy Using the Ising Model

  • Author / Creator
    Arbabimoghadam, Sahar
  • Genetic code and its origin are one the most challenging problems in biochemistry and cell biology. Studying the genetic code evolution and the logic behind it is an interesting but a very complicated problem. The logic of the genetic code from an energetic and probabilistic perspective, the occurrence frequency of protein mutations, and statistics of cytotoxicity effects on surviving cancer cell have been the main investigated topics in this thesis. The aim of this research is to implement the methods rooted in statistics, thermodynamics, and the physics of phase transitions in order to better face the challenges that experimental observations from genetics, molecular, and cell biology bring to the field of computational biophysics.
    In this thesis, the first aim has been to find an underlying correlation between the Gibbs free energy and the naturally occurring frequency of codons and amino acids across extant life forms analyzed statistically. Using GAMESS software, the amino acid thermochemistry was estimated. For these calculations, we used the Hartee-Fock method with the PM3 basis sets. These energies were compared to the codon energies obtaining involving three energetic terms; nearest neighbor, stacking and nucleotide Gibbs free energy. The correlation between codon and amino acid energies could shed light on the rules behind the codon assignments in the genetic code. Unfortunately, only weak correlations were found in our study. Moreover, our investigation showed that, in human, amino acids that have a higher redundancy occur more commonly in nature, with examples including arginine and leucine. However, the higher abundance amino acids were not energetically cheaper to make in nature. In addition, among the dataset we studied such as; animal and fungal mitochondrial proteins, human body tissues and various species according to the phylogenetic tree of life (from bacteria to homo sapiens), the amino acid occurrence frequency was highly conserved. Also, we attempted to address the entropy reduction paradox in the transcription and translation process by accounting for the involvement of macromolecules ATP and GTP in these process and affecting the overall thermodynamic energy balance. We also investigated the hypothesis whether the amino acids have a higher affinity for their codons or anticodons according to the binding energy values obtained using computational docking simulations. However, the obtained docking scores showed no correlation between the codons or anticodons and the corresponding amino acids, and we have found some paradoxical examples that disprove the proposed hypothesis.
    The next goal was to study p53 proteins mutations across a large set of various cancer types. The p53 protein has been selected due to its significant role in the cell cycle, cancer initiation, and progression. We showed that the highly represented mutants are R-H(79%), R-W(71%), R-Q(73%), G-S(55%), and R-S(48%) and at least one of these amino acid mutations occurs in 84% of the cases. Moreover, the Shannon entropy of p53 mutations has been computed in an effort to shed light on the epidemiological findings in terms of five-year-survival rate for cancer patients. However, the entropic approach to the analysis of the role of these important somatic mutations in cancer did not emerge as a prognostic factor in the analysis of cancer epidemiology data.
    Finally, using the physical concepts of bistability and phase transitions, we were able to model the cancer cell response to a number of cytotoxic agents used in cancer chemotherapy. We applied the well-known model in the physics of critical phenomena, namely the Ising model and represented the two spin states (spin ‘up’ and ‘down’) in the context of cancer cell biology as a ‘dead’ and ‘alive’ state of cancerous cells, respectively. We explored both an interacting and non-interacting case of cancer cells in a culture with the latter corresponding to the well-studied “bystander effect”. The proposed model has been tested on 13 different cytotoxic compounds applied to various cancer cell lines in culture. The results were in strong agreement with our model showing high consistency among the tested chemotherapy agents. Also the results confirmed the prediction that the EC50 value corresponds to the peak of the susceptibility function, which is an important characteristic of systems at a critical point. The model has been tested successfully on experimental data from both a two-dimensional well-plate cell culture and a three-dimensional spheroid model.

  • Subjects / Keywords
  • Graduation date
    Fall 2020
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
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