Abstract
Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a ...significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered
Escherichia coli
strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-
d
-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.
Despite considerable advancements in biological measurement technologies, capturing the simultaneous temporal changes in various biomolecular concentrations remains a challenge. Overcoming this ...technical difficulty via data preprocessing could not only clarify the principles of biological functions but also reduce the costs associated with advancing measurement technologies. This review introduces a novel approach to harmonizing heterogeneous time-series data related to molecular signaling and cellular movement. In response to this challenge, we developed and employed a motion-trigger average (MTA) algorithm. The MTA comprehensively screens and averages intracellular molecular activities that coincide with targeted velocity patterns of the moving cell edge. Given that the MTA filters out cell individuality-dependent noise from the data, a straightforward regression equation can correlate edge moving velocities with the molecular activities of various species within the cell. This methodology not only integrates fragmented datasets but also enables the reuse of past data for new analyses. The crux of our discovery is the elucidation of the role that Rho GTPases play in regulating cellular edge dynamics, a finding made possible by adopting the MTA algorithm. Our study suggests that the MTA could become an indispensable tool in data-driven biology, potentially paving the way for considerable insights into dynamic cellular behaviors and the underlying biological principles.
The mitogen-activated protein (MAP) kinase pathway is comprised of a three-tiered kinase cascade. The distributive kinetic mechanism of two-site MAP kinase phosphorylation inherently generates a ...nonlinear switch-like response. However, a linear graded response of MAP kinase has also been observed in mammalian cells, and its molecular mechanism remains unclear. To dissect these input-output behaviors, we quantitatively measured the kinetic parameters involved in the MEK (MAPK/ERK kinase)-ERK MAP kinase signaling module in HeLa cells. Using a numerical analysis based on experimentally determined parameters, we predicted in silico and validated in vivo that ERK is processively phosphorylated in HeLa cells. Finally, we identified molecular crowding as a critical factor that converts distributive phosphorylation into processive phosphorylation. We proposed the term quasi-processive phosphorylation to describe this mode of ERK phosphorylation that is operated under the physiological condition of molecular crowding. The generality of this phenomenon may provide a new paradigm for a diverse set of biochemical reactions including multiple posttranslational modifications.
Fed-batch culture is widely used in biopharmaceutical production owing to its superior productivity; however, optimizing feeding trajectories remains a challenge. In this study, we investigated the ...feasibility and benefits of using a nonlinear model predictive controller (NLMPC) for on-line reoptimization in mammalian fed-batch culture to compensate for process-model mismatch (PMM). We simulated a monoclonal antibody production process using a standard kinetic model and deliberately introduced PMM via parameter errors. The NLMPC optimized feeding trajectories for a single-feed case, in which a mixture of glucose and glutamine is fed, and for a multiple-feed case, in which glucose and glutamine are fed independently. Our results demonstrate that on-line reoptimization successfully compensates for PMM, improving the final product mass compared to off-line optimization. This study highlights the potential of on-line reoptimization using NLMPCs in mammalian fed-batch culture, which can enhance product yield even in the presence of insufficient parameter estimation.
The dynamic activity of the serine/threonine kinase Akt is crucial for the regulation of diverse cellular functions, but the precise spatiotemporal control of its activity remains a critical issue. ...Herein, we present a photo-activatable Akt (PA-Akt) system based on a light-inducible protein interaction module of Arabidopsis thaliana cryptochrome2 (CRY2) and CIB1. Akt fused to CRY2phr, which is a minimal light sensitive domain of CRY2 (CRY2-Akt), is reversibly activated by light illumination in several minutes within a physiological dynamic range and specifically regulates downstream molecules and inducible biological functions. We have generated a computational model of CRY2-Akt activation that allows us to use PA-Akt to control the activity quantitatively. The system provides evidence that the temporal patterns of Akt activity are crucial for generating one of the downstream functions of the Akt-FoxO pathway; the expression of a key gene involved in muscle atrophy (Atrogin-1). The use of an optical module with computational modeling represents a general framework for interrogating the temporal dynamics of biomolecules by predictive manipulation of optogenetic modules.
Cell migration plays an important role in many physiological processes. Rho GTPases (Rac1, Cdc42, RhoA) and phosphatidylinositols have been extensively studied in directional cell migration. However, ...it remains unclear how Rho GTPases and phosphatidylinositols regulate random cell migration in space and time. We have attempted to address this issue using fluorescence resonance energy transfer (FRET) imaging and statistical signal processing. First, we acquired time-lapse images of random migration of HT-1080 fibrosarcoma cells expressing FRET biosensors of Rho GTPases and phosphatidyl inositols. We developed an image-processing algorithm to extract FRET values and velocities at the leading edge of migrating cells. Auto- and cross-correlation analysis suggested the involvement of feedback regulations among Rac1, phosphatidyl inositols and membrane protrusions. To verify the feedback regulations, we employed an acute inhibition of the signaling pathway with pharmaceutical inhibitors. The inhibition of actin polymerization decreased Rac1 activity, indicating the presence of positive feedback from actin polymerization to Rac1. Furthermore, treatment with PI3-kinase inhibitor induced an adaptation of Rac1 activity, i.e. a transient reduction of Rac1 activity followed by recovery to the basal level. In silico modeling that reproduced the adaptation predicted the existence of a negative feedback loop from Rac1 to actin polymerization. Finally, we identified MLCK as the probable controlling factor in the negative feedback. These findings quantitatively demonstrate positive and negative feedback loops that involve actin, Rac1 and MLCK, and account for the ordered patterns of membrane dynamics observed in randomly migrating cells.
Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape ...cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube
Limitations in simultaneously observing the activity of multiple molecules in live cells prevent researchers from elucidating how these molecules coordinate the dynamic regulation of cellular ...functions. Here, we propose the motion-triggered average (MTA) algorithm to characterize pseudo-simultaneous dynamic changes in arbitrary cellular deformation and molecular activities. Using MTA, we successfully extract a pseudo-simultaneous time series from individually observed activities of three Rho GTPases: Cdc42, Rac1, and RhoA. To verify that this time series encoded information on cell-edge movement, we use a mathematical regression model to predict the edge velocity from the activities of the three molecules. The model accurately predicts the unknown edge velocity, providing numerical evidence that these Rho GTPases regulate edge movement. Data preprocessing using MTA combined with mathematical regression provides an effective strategy for reusing numerous individual observations of molecular activities.
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•Screening of Rho GTPase activity patterns based on velocity patterns of cell edges•Mean of extracted patterns approximates simultaneously observed activities•A function exists that quantitatively converts these activities to edge velocities
Kunida et al. report that a linear sum of the activities of three Rho GTPases and their time derivatives can decode cell-edge velocity with reasonable accuracy. Their results provide numerical evidence that the signals of the three Rho GTPases distributively represent information about cell-edge velocity.
Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and ...regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.
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•We reconstructed signal flow of insulin from multiple “omic” data•Insulin induced phosphorylation in 71 and allosteric regulation in 226•Insulin signal flow revealed global control of metabolic homeostasis.•Mutant experiments and modeling identified insulin-dependent glycolysis regulation
Cellular homeostasis is regulated by signals that are transmitted across multiple “omic” layers. However, molecular networks bridging these layers have not been identified comprehensively. Here, Yugi et al. describe comprehensive reconstruction of the molecular network between phosphoproteomic and metabolomic layers of rat hepatoma cells that is responsible for acute insulin action. Analysis of the network reveals the global landscape of insulin-signaling-dependent metabolic control and a novel mode of regulation of glycolysis by phosphorylation of liver-type phosphofructokinase-1.
Secretion of insulin transiently increases after eating, resulting in a high circulating concentration. Fasting limits insulin secretion, resulting in a low concentration of insulin in the ...circulation. We analyzed transcriptional responses to different temporal patterns and doses of insulin in the hepatoma FAO cells and identified 13 up-regulated and 16 down-regulated insulin-responsive genes (IRGs). The up-regulated IRGs responded more rapidly than did the down-regulated IRGs to transient stepwise or pulsatile increases in insulin concentration, whereas the down-regulated IRGs were repressed at lower concentrations of insulin than those required to stimulate the up-regulated IRGs. Mathematical modeling of the insulin response as two stages-(i) insulin signaling to transcription and (ii)transcription and mRNA stability-indicated that the first stage was the more rapid stage for the down-regulated IRGs, whereas the second stage of transcription was the more rapid stage for the up-regulated IRGs. A subset of the IRGs that were up-regulated or down-regulated in the FAO cells was similarly regulated in the livers of rats injected with a single dose of insulin. Thus, not only can cells respond to insulin but they can also interpret the intensity and pattern of signal to produce distinct transcriptional responses. These results provide insight that may be useful in treating obesity and type 2 diabetes associated with aberrant insulin production or tissue responsiveness.
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