Hybrid stochastic simulations of intracellular reaction-diffusion systems.

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

THE EQUILIBRIUM CONSTANT FOR THE GLYCINE SYNTHASE REACTION (ONE-CARBON, METABOLISM, FOLATE)

The equilibrium constant (K(,c)) under physiological conditions (38(DEGREES)C, 0.25 M ionic strength (I), pH 7.0) for the glycine synthase (GS) reaction (E C 2.1.2.1.0) (Equation 1) has been determined. (UNFORMATTED TABLE FOLLOWS)^ 5,10-CH(,2)-H(,4)Folate NADH NH (,4)+ CO(,2) ^ K(,c) = Eq. 1^ H(,4)Folate NAD('+) GLY ^(TABLE ENDS)^ The enzymatic instability of the GS enzyme complex itself has made it necessary to determine the overall K(,c) from the product of constants for the partial reactions of GS determined separately under the same conditions. The partial reactions are the H(,4)Folate-formaldehyde (CH(,2)(OH)(,2)) condensation reaction (Reaction 1) the K(,c) for which has been reported by this laboratory (3.0 x 10('4)), the lipoate (LipS(,2)) dehydrogenase reaction (LipDH) (Reaction 2) and the Gly-Lip^ decarboxylase reaction (Reaction 3) forming reduced lipoate (Lip(SH)(,2)), NH(,4)('+), CO(,2) and CH(,2)(OH)(,2.) (UNFORMATTED TABLE FOLLOWS)(,)^ H(,4)Fote + CH(,2)(OH)(,2) 5,10-CH(,2)-H(,4)Folate (1)^ Lip(SH)(,2) + NAD('+) LipS(,2) + NADH + H('+) (2)^ H('+) + Gly + LipS(,2) Lip(SH)(,2) + NH(,4)('+) CO(,2) + CH(,2)(OH)(,2) (3)^(TABLE ENDS)^ In this work the K(,c) for Reactions 2 and 3 are reported.^ The K(,c)' for the LipDH reaction described by other authors was reported with unexplainable conclusions regarding the pH depend- ence for the reaction. These conclusions would imply otherwise unexpected acid dissociation constants for reduced and oxidized lipoate. The pK(,a)',s for these compounds have been determined to resolve discrepancy. The conclusions are as follows: (1) The K(,c) for the LipDH reaction is 2.08 x 10('-8); (2) The pK(,a)',s for Lip(SH)(,2) are 4.77(-COOH), 9.91(-SH), 11.59(-SH); for LipS(,2) the carboxyl pK(,a)' is 4.77; (3) Contrary to previous literature, the log K(,c)' for the LipDH reaction is a linear function of the pH, a conclusion supported by the values for the dissociation constants.^ The K(,c) for Reaction 3 is the product of constants for Reactions 4-7. (UNFORMATTED TABLE FOLLOWS)^ LipSHSCH(,2)OH + H(,2)O Lip(SH)(,2) + CH(,2)(OH)(,2) (4)^ H(,2)O + LipSHSCH(,2)NH(,3)('+) LipSHSCH(,2)OH + NH(,4)('+) (5)^ LipSHSCH(,2)NH(,2) + H('+) LipSHSCH(,2)NH(,3)('+) (6)^ Gly + LipS(,2) LipSHSCH(,2)NH(,2) + CO(,2) (7)^(TABLE ENDS)^ Reactions 4-6 are non-enzymatic reactions whose constants were determined spectrophotometrically. Reaction 7 was catalyzed by the partially purified P-protein of GS with equilibrium approached from both directions. The value for K(,c) for this reaction is 8.15 x 10('-3). The combined K(,c) for Reactions 4-7 or Reaction 3 is 2.4 M.^ The overall K(,c) for the GS reaction determined by combination of values for Reactions 1-3 is 1.56 x 10('-3). ^