Comparison of the approximate hazard function given by (6) with the exact numerical solution of hazard function given by (9).

All parameters of the model are set as follows: N = 107, , λi = 0.1 + (i − 1) * 0.05.</p

Systems Metabolic Engineering of Escherichia coli Coculture for <i>De Novo</i> Production of Genistein

Genistein is a plant-derived isoflavone possessing various bioactivities to prevent aging, carcinogenesis, and neurodegenerative and inflammation diseases. As a typical complex flavonoid, its microbial production from sugar remains to be completed. Here, we use systems metabolic engineering stategies to design and develop a three-strain commensalistic Escherichia coli coculture that for the first time realized the de novo production of genistein. First, we reconstituted the naringenin module by screening and incorporating chalcone isomerase-like protein, an auxiliary component to rectify the chalcone synthase promiscuity. Furthermore, we devised and constructed the genistein module by N-terminal modifications of plant P450 enzyme 2-hydroxyisoflavanone synthase and cytochrome P450 enzyme reductase. When naringenin-producing strain was cocultivated with p-coumaric acid-overproducing strain (a phenylalanine-auxotroph), two-strain coculture worked as commensalism through a unidirectional nutrient flow, which favored the efficient production of naringenin with a titer of 206.5 mg/L from glucose. A three-strain commensalistic coculture was subsequently engineered, which produced the highest titer to date of 60.8 mg/L genistein from a glucose and glycerol mixture. The commensalistic coculture is a flexible and versatile platform for the production of flavonoids, indicating a promising future for production of complex natural products in engineered E. coli

Estimate values of parameters in the pathway with <i>APC</i>^+/− → <i>TP</i>53^+/− → <i>APC</i>^−/− → <i>KRAS</i>⁺ → <i>TP</i>53^−/− or <i>TP</i>53^+/− → <i>APC</i>^+/− → <i>APC</i>^−/− → <i>KRAS</i>⁺ → <i>TP</i>53^−/−.

Estimate values of parameters in the pathway with APC+/− → TP53+/− → APC−/− → KRAS+ → TP53−/− or TP53+/− → APC+/− → APC−/− → KRAS+ → TP53−/−.</p

Estimate values of parameters in the pathway with <i>APC</i>^+/− → <i>APC</i>^−/− → <i>TP</i>53^+/− → <i>TP</i>53^−/− → <i>KRAS</i>⁺.

Estimate values of parameters in the pathway with APC+/− → APC−/− → TP53+/− → TP53−/− → KRAS+.</p

Estimate values of parameters in the pathway with <i>APC</i>^+/− → <i>APC</i>^−/− → <i>TP</i>53^+/− → <i>KRAS</i>⁺ → <i>TP</i>53^−/−.

Estimate values of parameters in the pathway with APC+/− → APC−/− → TP53+/− → KRAS+ → TP53−/−.</p

The fitting result and Probabilistic distributions of the estimated parameters using approximate Bayesian computation schemes involving the simulated likelihood density for the pathway with <i>APC</i>^+/− → <i>KRAS</i>⁺ → <i>APC</i>^−/− → <i>TP</i>53^+/− → <i>TP</i>53^−/−.

(a) (NμN of model), (b) ( of model), (c) ( of model), (d) ( of model), (e) ( of model), (f) The colorectal cancer incidence rate per 100,000 patients from SEER registry and rates predicted by the model.</p

The fitting result and Probabilistic distributions of the estimated parameters using approximate Bayesian computation schemes involving the simulated likelihood density for the pathway with <i>KRAS</i>⁺ → <i>APC</i>^+/− → <i>APC</i>^−/− → <i>TP</i>53^+/− → <i>TP</i>53^−/−.

(a) (NμN of model), (b) ( of model), (c) ( of model), (d) ( of model), (e) ( of model), (f) The colorectal cancer incidence rate per 100,000 patients from SEER registry and rates predicted by the model.</p

Mathematical derivation and supplementary figures.

Fig A: All pathways of gene mutations for the case (A) in colorectal cancer. Fig B: All pathways of gene mutations for the case (B) in colorectal cancer. Fig C: All pathways of gene mutations for the case (C) in colorectal cancer. Fig D: All pathways of gene mutations for the case (D) in colorectal cancer. Fig E: All pathways of gene mutations for the case (E) in colorectal cancer. Fig F: All pathways of gene mutations for the case (F) in colorectal cancer. (PDF)</p

The fitting result and Probabilistic distributions of the estimated parameters using approximate Bayesian computation schemes involving the simulated likelihood density for the pathway with <i>APC</i>^+/− → <i>APC</i>^−/− → <i>TP</i>53^+/− → <i>TP</i>53^−/− → <i>KRAS</i>⁺.

(a) (NμN of model), (b) ( of model), (c) ( of model), (d) ( of model), (e) ( of model), (f) The colorectal cancer incidence rate per 100,000 patients from SEER registry and rates predicted by the model.</p

The fitting result and Probabilistic distributions of the estimated parameters using approximate Bayesian computation schemes involving the simulated likelihood density for the pathway with <i>APC</i>^+/− → <i>TP</i>53^+/− → <i>APC</i>^−/− → <i>KRAS</i>⁺ → <i>TP</i>53^−/− or <i>TP</i>53^+/− → <i>APC</i>^+/− → <i>APC</i>^−/− → <i>KRAS</i>⁺ → <i>TP</i>53^−/−.

(a) or (NμN of model), (b) or ( of model), (c) ( of model), (d) ( of model), (e) ( of model), (f) The colorectal cancer incidence rate per 100,000 patients from SEER registry and rates predicted by the model.</p