Here we propose an objective scheme to quantify the precise amount of negative entropy, present in an extremely important biochemical pathway; namely, the TCA cycle. Our approach is based on the computational implementation of two-person non-cooperative finite zero-sum game between positive entropy and negative entropy . Biochemical analogue of Nash equilibrium condition between these positive and negative entropy, could unambiguously provide a quantitative marker that describes the 'edge of life' for TCA cycle . Difference between concentration-profiles prevalent at the 'edge of life' and biologically observed TCA cycle, could quantitatively express the precise amount of negative entropy present in a typical biochemical network. We show here that it is not the existence of mere order, but the synchronization profile between ordered fluctuations, which accounts for biological robustness. An exhaustive sensitivity analysis could identify the concentrations, for which slightest perturbation can account for enormous increase in positive entropy. Since our algorithm is general, the same analysis can as well be performed on larger networks and (ideally) for an entire cell, if numerical data for concentration is available .
Biological systems possess negative entropy. In them, one form of order produces another, URLanized form of order. We propose a formal scheme to calculate robustness of an entire biological system by quantifying the negative entropy present in it. Our Methodology is based upon a computational implementation of two-person non-cooperative finite zero-sum game between positive (physico-chemical) and negative (biological) entropy, present in the system(TCA cycle, for this work) . Biochemical analogue of Nash equilibrium , proposed here, could measure the robustness in TCA cycle in exact numeric terms, whereas the mixed strategy game between these entropies could quantitate the progression of stages of biological adaptation. Synchronization profile amongst macromolecular concentrations (even under environmental perturbations) is found to account for negative entropy and biological robustness. Emergence of synchronization profile was investigated with dynamically varying metabolite concentrations. Obtained results were verified with that from the deterministic simulation methods. Categorical plans to apply this algorithm in Cancer studies and anti-viral therapies are proposed alongside. From theoretical perspective, this work proposes a general, rigorous and alternative view of immunology .
R
Here we propose an objective scheme to quantify the precise amount of negative entropy, present in an extremely important biochemical pathway; namely, the TCA cycle. Our approach is based on the
Biological systems possess negative entropy. In them, one form of order produces another, URLanized form of order. We propose a formal scheme to calculate robustness of an entire biological system by quantifying the negative entropy present in it. Our Methodology is based upon a
R
entropy and negative entropy
(physico-chemical) and negative (biological) entropy, present in the system(TCA cycle, for this work)
R
condition between these positive and negative entropy, could unambiguously provide a quantitative marker that describes the 'edge of life' for TCA cycle . Difference between concentration-profiles prevalent at the 'edge of life' and biologically observed TCA cycle, could quantitatively express the precise amount of negative entropy present in a typical biochemical network. We show here that it is not the existence of mere order, but the synchronization profile between ordered fluctuations, which accounts for biological robustness. An exhaustive sensitivity analysis could identify the concentrations, for which slightest perturbation can account for enormous increase in positive entropy. Since our algorithm is general, the same analysis can as well be performed on larger networks and (ideally) for an entire cell, if numerical data for concentration is available
, proposed here, could measure the robustness in TCA cycle in exact numeric terms, whereas the mixed strategy game between these entropies could quantitate the progression of stages of biological adaptation. Synchronization profile amongst macromolecular concentrations (even under environmental perturbations) is found to account for negative entropy and biological robustness. Emergence of synchronization profile was investigated with dynamically varying metabolite concentrations. Obtained results were verified with that from the deterministic simulation methods. Categorical plans to apply this algorithm in Cancer studies and anti-viral therapies are proposed alongside. From theoretical perspective, this work proposes a general, rigorous and alternative view of immunology