Reverse-engineering of biochemical reaction networks from spatio-temporal correlations of fluorescence fluctuations

Gibbs free energy change of a discrete chemical reaction event

In modeling the interior of cells by simulating a reaction-diffusion master equation over a grid of compartments, one employs the assumption that the copy numbers of various chemical species are small, discrete quantities. We show that, in this case, textbook expressions for the change in Gibbs free energy accompanying a chemical reaction or diffusion between adjacent compartments are inaccurate. We derive exact expressions for these free energy changes for the case of discrete copy numbers and show how these expressions reduce to traditional expressions under a series of successive approximations leveraging the relative sizes of the stoichiometric coefficients and the copy numbers of the solutes and solvent. Numerical results are presented to corroborate the claim that if the copy numbers are treated as discrete quantities, then only these more accurate expressions lead to correct behavior. Thus, the newly derived expressions are critical for correctly computing entropy production in mesoscopic simulations based on the reaction-diffusion master equation formalism.

Methods of biological network inference for reverse engineering cancer chemoresistance mechanisms

We review recent Bayesian network inference methodologies we developed to infer genetic and metabolic pathways associated to oncological drug chemoresistance. Bayesian inference is supported by a rigorous and widely accepted mathematical formalization of predictive analytics. It is an inherently integrative approach allowing the incorporation of prior knowledge and constraints. Moreover, it is recommended to treat noisy data, and large amount of data whose dynamics laws are mostly unknown. We focus on variational Bayesian methods for the inference of stochastic reaction processes and we present a compendium of the recent results of inference of gene and metabolic networks presiding at the development of pancreas cancer resistance to gemcitabine.

Reverse engineering to suggest biologically relevant redox activities of phenolic materials

Phenolics are among the most abundant redox-active organics in nature, but the intractability of phenolic materials (e.g., melanin) has precluded study of their biological activities and functions. Previous studies demonstrated that a model abiotic catecholic matrix can rapidly exchange electrons with biological oxidants and reductants without the need for enzymes. Here, a novel electrochemically based reverse engineering approach was employed to probe redox interactions between this model matrix and a population of bacteria. Specifically, this method employs redox-active natural products (e.g., pyocyanin) to shuttle electrons between the bacteria and the abiotic matrix, and imposed oscillating potential inputs to engage redox-cycling mechanisms that switch the matrix's redox state. The oscillating output currents were observed to be amplified, gated, and partially rectified, while the overall magnitude and direction of electron flow across the matrix depended on the biological and environmental context. These response characteristics support hypotheses that natural phenolic materials may be integral to extracellular electron transport for processes that include anaerobic respiration, redox signaling, and redox-effector action.