Estimation of non-equilibrium transition rate from gene expression data.

The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.

Landscape quantifies the intermediate state and transition dynamics in ecological networks.

Understanding the ecological mechanisms associated with the collapse and restoration is especially critical in promoting harmonious coexistence between humans and nature. So far, it remains challenging to elucidate the mechanisms of stochastic dynamical transitions for ecological systems. Using an example of plant-pollinator network, we quantified the energy landscape of ecological system. The landscape displays multiple attractors characterizing the high, low and intermediate abundance stable states. Interestingly, we detected the intermediate states under pollinator decline, and demonstrated the indispensable role of the intermediate state in state transitions. From the landscape, we define the barrier height (BH) as a global quantity to evaluate the transition feasibility. We propose that the BH can serve as a new early-warning signal (EWS) for upcoming catastrophic breakdown, which provides an earlier and more accurate warning signal than traditional metrics based on time series. Our results promote developing better management strategies to achieve environmental sustainability.

Early-initiated childhood reading for pleasure: associations with better cognitive performance, mental well-being and brain structure in young adolescence.

BACKGROUND: Childhood is a crucial neurodevelopmental period. We investigated whether childhood reading for pleasure (RfP) was related to young adolescent assessments of cognition, mental health, and brain structure. METHODS: We conducted a cross-sectional and longitudinal study in a large-scale US national cohort (10 000 + young adolescents), using the well-established linear mixed model and structural equation methods for twin study, longitudinal and mediation analyses. A 2-sample Mendelian randomization (MR) analysis for potential causal inference was also performed. Important factors including socio-economic status were controlled. RESULTS: Early-initiated long-standing childhood RfP (early RfP) was highly positively correlated with performance on cognitive tests and significantly negatively correlated with mental health problem scores of young adolescents. These participants with higher early RfP scores exhibited moderately larger total brain cortical areas and volumes, with increased regions including the temporal, frontal, insula, supramarginal; left angular, para-hippocampal; right middle-occipital, anterior-cingulate, orbital areas; and subcortical ventral-diencephalon and thalamus. These brain structures were significantly related to their cognitive and mental health scores, and displayed significant mediation effects. Early RfP was longitudinally associated with higher crystallized cognition and lower attention symptoms at follow-up. Approximately 12 h/week of youth regular RfP was cognitively optimal. We further observed a moderately significant heritability of early RfP, with considerable contribution from environments. MR analysis revealed beneficial causal associations of early RfP with adult cognitive performance and left superior temporal structure. CONCLUSIONS: These findings, for the first time, revealed the important relationships of early RfP with subsequent brain and cognitive development and mental well-being.

Controlling brain dynamics: Landscape and transition path for working memory.

Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.

Probiotic-derived amphiphilic exopolysaccharide self-assembling adjuvant delivery platform for enhancing immune responses.

Enhancing immune response activation through the synergy of effective antigen delivery and immune enhancement using natural, biodegradable materials with immune-adjuvant capabilities is challenging. Here, we present NAPSL.p that can activate the Toll-like receptor 4 (TLR4) pathway, an amphiphilic exopolysaccharide, as a potential self-assembly adjuvant delivery platform. Its molecular structure and unique properties exhibited remarkable self-assembly, forming a homogeneous nanovaccine with ovalbumin (OVA) as the model antigen. When used as an adjuvant, NAPSL.p significantly increased OVA uptake by dendritic cells. In vivo imaging revealed prolonged pharmacokinetics of NAPSL. p-delivered OVA compared to OVA alone. Notably, NAPSL.p induced elevated levels of specific serum IgG and isotype titers, enhancing rejection of B16-OVA melanoma xenografts in vaccinated mice. Additionally, NAPSL.p formulation improved therapeutic effects, inhibiting tumor growth, and increasing animal survival rates. The nanovaccine elicited CD4+ and CD8+ T cell-based immune responses, demonstrating the potential for melanoma prevention. Furthermore, NAPSL.p-based vaccination showed stronger protective effects against influenza compared to Al (OH)3 adjuvant. Our findings suggest NAPSL.p as a promising, natural self-adjuvanting delivery platform to enhance vaccine design across applications.

Multistep optimization for the electrodeposited mixed perovskite FA1-yCsyPbBrxI3-xsolar cells.

The electrodeposition method has recently been developed for the fabrication of perovskite solar cells due to its potential advantages in commercial preparation. However, there is few studies on the preparation of perovskite solar cells by the electrodeposition method, especially on the perovskite FAPbI3-based solar cells. Herein, we fabricated the mixed perovskite FA1-yCsyPbBrxI3-xsolar cells by an optimized electrodeposition method, in which the electrodeposited PbO2reacts directly with FAI and an appropriate amount of CsBr dopants. The corresponding solar cells display the best PCE of 4.97%. By regulating the growth temperature in the reaction between PbO2and FAI/CsBr, the efficiency of the mixed perovskite solar cells can be promoted to 10.18%. These results illustrate that the element doping and growth environment regulation can optimize the quality of the perovskite films, thus promoting the efficiency of the perovskite solar cells. With further optimizing the growth process in the electrodeposition method, it is expected to open up a new commercial preparation route for the perovskite solar cells in the near future.

An improved approach for calculating energy landscape of gene networks from moment equations.

The energy landscape theory has widely been applied to study the stochastic dynamics of biological systems. Different methods have been developed to quantify the energy landscape for gene networks, e.g., using Gaussian approximation (GA) approach to calculate the landscape by solving the diffusion equation approximately from the first two moments. However, how high-order moments influence the landscape construction remains to be elucidated. Also, multistability exists extensively in biological networks. So, how to quantify the landscape for a multistable dynamical system accurately, is a paramount problem. In this work, we prove that the weighted summation from GA (WSGA), provides an effective way to calculate the landscape for multistable systems and limit cycle systems. Meanwhile, we proposed an extended Gaussian approximation (EGA) approach by considering the effects of the third moments, which provides a more accurate way to obtain probability distribution and corresponding landscape. By applying our generalized EGA approach to two specific biological systems: multistable genetic circuit and synthetic oscillatory network, we compared EGA with WSGA by calculating the KL divergence of the probability distribution between these two approaches and simulations, which demonstrated that the EGA provides a more accurate approach to calculate the energy landscape.

Unraveling the stochastic transition mechanism between oscillation states by the landscape and the minimum action path theory.

Cell fate transitions have been studied from various perspectives, such as the transition between stable states, or the transition between stable states and oscillation states. However, there is a lack of study on the stochastic transition between different oscillation states. Here, we aim to explore the stochastic transition mechanism for the switching between oscillations. By employing a landscape and flux theory for a simplified two-dimensional model, we quantify the stochastic dynamics and the global stability of the double oscillation system, and find that the system will escape the starting limit cycle at the position where the flux is large, and cross the barrier between oscillations at the position where the barrier is lower. We also calculate the transition path between limit cycle states based on the minimum action path (MAP) theory. So, the barrier height based on landscape topography and probability flux govern the stochastic transition process between limit cycles, which is further supported by the analysis of mean first passage time (MFPT). We provide a way to calculate the critical points where the switching behavior most likely occurs along a cycle. We validate these conclusions in a realistic biological system; the NF-κB gene regulatory system. The results for the potential landscape, flux and transition path further our understanding of the underlying mechanism of stochastic transitions between different oscillation states.

Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition.

Epithelial-mesenchymal transition (EMT), a basic developmental process that might promote cancer metastasis, has been studied from various perspectives. Recently, the early warning theory has been used to anticipate critical transitions in EMT from mathematical modeling. However, the underlying mechanisms of EMT involving complex molecular networks remain to be clarified. Especially, how to quantify the global stability and stochastic transition dynamics of EMT and what the underlying mechanism for early warning theory in EMT is remain to be fully clarified. To address these issues, we constructed a comprehensive gene regulatory network model for EMT and quantified the corresponding potential landscape. The landscape for EMT displays multiple stable attractors, which correspond to E, M, and some other intermediate states. Based on the path-integral approach, we identified the most probable transition paths of EMT, which are supported by experimental data. Correspondingly, the results of transition actions demonstrated that intermediate states can accelerate EMT, consistent with recent studies. By integrating the landscape and path with early warning concept, we identified the potential barrier height from the landscape as a global and more accurate measure for early warning signals to predict critical transitions in EMT. The landscape results also provide an intuitive and quantitative explanation for the early warning theory. Overall, the landscape and path results advance our mechanistic understanding of dynamical transitions and roles of intermediate states in EMT, and the potential barrier height provides a new, to our knowledge, measure for critical transitions and quantitative explanations for the early warning theory.

Cannabisin D from Sinomenium Acutum Inhibits Proliferation and Migration of Glioblastoma Cells through MAPKs Signaling.

Glioblastoma is the most common and malignant tumor in human central nervous system with poor prognosis. From the dried stem of Sinomenium acutum, an herbal medicine, five compounds (sinomenine, syringin, corchoionoside C, protocatechuic acid and cannabisin D) were isolated, characterized and subjected to cytotoxicity screening on U-87 and U-251 glioblastoma cells. Cannabisin D presented effective inhibitory effects on the proliferation and migration of glioblastoma cells. By flow cytometry, real-time PCR and Western blotting, cell apoptosis and cell cycle arrest were proved to contribute to the anti-glioblastoma effects. Further, the activation of MAPKs signaling (p38 MAPK, p42/p44 MAPK and SAPK/JNK) was observed in glioblastoma cells upon cannabisin D treatment by Western blotting, indicating the involvement of MAPKs signaling in the inhibitory effects of cannabisin D. These data suggested that S. acutum is a novel natural source of cannabisin D and cannabisin D is a novel anti-glioblastoma agent candidate.

Landscape and flux quantify the stochastic transition dynamics for p53 cell fate decision.

The p53 transcription factor is a key mediator in cellular responses to various stress signals including DNA repair, cell cycle arrest, and apoptosis. In this work, we employ landscape and flux theory to investigate underlying mechanisms of p53-regulated cell fate decisions. Based on a p53 regulatory network, we quantified the potential landscape and probabilistic flux for the p53 system. The landscape topography unifies and quantifies three cell fate states, including the limit cycle oscillations (representing cell cycle arrest), high p53 state (characterizing apoptosis), and low p53 state (characterizing the normal proliferative state). Landscape and flux results provide a quantitative explanation for the biphasic dynamics of the p53 system. In the oscillatory phase (first phase), the landscape attracts the system into the ring valley and flux drives the system cyclically moving, leading to cell cycle arrest. In the fate decision-making phase (second phase), the ring valley shape of the landscape provides an efficient way for cells to return to the normal proliferative state once DNA damage is repaired. If the damage is unrepairable with larger flux, the system may cross the barrier between two states and switch to the apoptotic state with a high p53 level. By landscape-flux decomposition, we revealed a trade-off between stability (guaranteed by landscape) and function (driven by flux) in cellular systems. Cells need to keep a balance between appropriate speed to repair DNA damage and appropriate stability to survive. This is further supported by flux landscape analysis showing that flux may provide the dynamical origin of phase transition in a non-equilibrium system by changing landscape topography.

Uncovering the cell fate decision in lysis-lysogeny transition and stem cell development via Markov state modeling.

Understanding the behavior of a complex gene regulatory network is a fundamental but challenging task in systems biology. How to reduce the large number of degrees of freedom of a specific network and identify its main biological pathway is the key issue. In this paper, we utilized the transition path theory (TPT) and Markov state modeling (MSM) framework to numerically study two typical cell fate decision processes: the lysis-lysogeny transition and stem cell development. The application of TPT to the lysis-lysogeny decision-making system reveals that the competitions of CI and Cro dimer binding play the major role in determining the cell fates. We also quantified the transition rates from the lysogeny to lysis state under different conditions. The overall computational results are consistent with biological intuitions but with more detailed information. For the stem cell developmental system, we applied the MSM to reduce the original dynamics to a moderate-size Markov chain. Further spectral analysis showed that the reduced system exhibits nine metastable states, which correspond to the refinement of the five known typical cell types in development. We further investigated the dominant transition pathways corresponding to the cell differentiation, reprogramming, and trans-differentiation. A similar approach can be applied to study other biological systems.

An enriched network motif family regulates multistep cell fate transitions with restricted reversibility.

Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.

Landscape and flux govern cellular mode-hopping between oscillations.

Recently, a "mode-hopping" phenomenon has been observed in a NF-κB gene regulatory network with oscillatory tumor necrosis factor (TNF) inputs. It was suggested that noise facilitates the switch between different oscillation modes. However, the underlying mechanism of this noise-induced "cellular mode-hopping" behavior remains elusive. We employed a landscape and flux approach to study the stochastic dynamics and global stability of the NF-κB regulatory system. We used a truncated moment equation approach to calculate the probability distribution and potential landscape for gene regulatory systems. The potential landscape of the NF-κB system exhibits a "double ring valley" shape. Barrier heights from landscape topography provide quantitative measures of the global stability and transition feasibility of the double oscillation system. We found that the landscape and flux jointly govern the dynamical "mode-hopping" behavior of the NF-κB regulatory system. The landscape attracts the system into a "double ring valley," and the flux drives the system to move cyclically. As the external noise increases, relevant barrier heights decrease, and the flux increases. As the amplitude of the TNF input increases, the flux contribution, from the total driving force, increases and the system behavior changes from one to two cycles and ultimately to chaotic dynamics. Therefore, the probabilistic flux may provide an origin of chaotic behavior. We found that the height of the peak of the power spectrum of autocorrelation functions and phase coherence is correlated with barrier heights of the landscape and provides quantitative measures of global stability of the system under intrinsic fluctuations.

Enhanced Photocatalytic Activity of Directly Assembled Fe2O3/Gd2O3@TiO2-Based Nanosheets.

TiO2-based nanosheets (TNSs) modified with surface-enriched Fe2O3 and Gd2O3 nanoparticles (NPs) have been synthesized via a direct interfacial assembly strategy. The TNSs with a unique two-dimensional structure are favorable for supporting Fe2O3 and Gd2O3 NPs for photocatalytic applications. The prepared samples were characterized using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), Raman spectroscopy (Raman), BET, X-ray photoelectron spectroscopy (XPS), UV-Vis diffuse reflectance spectra (DRS), photoluminescence (PL) spectroscopy and the surface photocurrent (SPC) technique. The photocatalysts exhibited large specific surface area (160-260 m²/g). The co-modification with Fe2O3 and Gd2O3 NPs influenced the crystallinity and surface area of the TNSs, and improved visible-light absorption. Surface photocurrent and PL studies revealed that the photogenerated charge carrier separation efficiency could be improved by an appropriate amount of NPs. The optimized nanostructure exhibited photocatalytic efficiency for rhodamine B (RhB) degradation and H2 production is 5.66-fold and 2.99-fold respectively than those of TNSs under visible-light irradiation. The enhancement is attributed to the combined effect of Gd2O3 and Fe2O3 NPs in the Fe2O3/Gd2O3@TNSs composites. The simultaneous use of two different types of NPs led to a fast separation and slow recombination of photoinduced electron-hole pairs. A mechanism is proposed to explain the enhanced visible-light photocatalytic activity.

Synthesis, properties, and optoelectronic applications of two-dimensional MoS2 and MoS2-based heterostructures.

As a two-dimensional (2D) material, molybdenum disulfide (MoS2) exhibits unique electronic and optical properties useful for a variety of optoelectronic applications including light harvesting. In this article, we review recent progress in the synthesis, properties and applications of MoS2 and related heterostructures. Heterostructured materials are developed to add more functionality or flexibility compared to single component materials. Our focus is on their novel properties and functionalities as well as emerging applications, especially in the areas of light energy harvesting or conversion. We highlight the correlation between structural properties and other properties including electronic, optical, and dynamic. Whenever appropriate, we also try to provide fundamental insight gained from experimental as well as theoretical studies. Finally, we discuss some current challenges and opportunities in technological applications of MoS2.

Identifying the optimal anticancer targets from the landscape of a cancer-immunity interaction network.

Cancer immunotherapy, an approach of targeting immune cells to attack tumor cells, has been suggested to be a promising way for cancer treatment recently. However, the successful application of this approach warrants a deeper understanding of the intricate interplay between cancer cells and the immune system. Especially, the mechanisms of immunotherapy remain elusive. In this work, we constructed a cancer-immunity interplay network by incorporating interactions among cancer cells and some representative immune cells, and uncovered the potential landscape of the cancer-immunity network. Three attractors emerge on the landscape, representing the cancer state, the immune state, and the hybrid state, which can correspond to escape, elimination, and equilibrium phases in the immunoediting theory, respectively. We quantified the transition processes between the cancer state and the immune state by calculating transition actions and identifying the corresponding minimum action paths (MAPs) between these two attractors. The transition actions, directly calculated from the high dimensional system, are correlated with the barrier heights from the landscape, but provide a more precise description of the dynamics of a system. By optimizing the transition actions from the cancer state to the immune state, we identified some optimal combinations of anticancer targets. Our combined approach of the landscape and optimization of transition actions offers a framework to study the stochastic dynamics and identify the optimal combination of targets for the cancer-immunity interplay, and can be applied to other cell communication networks or gene regulatory networks.

Correction: Identifying the optimal anticancer targets from the landscape of a cancer-immunity interaction network.

Correction for 'Identifying the optimal anticancer targets from the landscape of a cancer-immunity interaction network' by Chunhe Li et al., Phys. Chem. Chem. Phys., 2017, 19, 7642-7651.

Landscape and flux reveal a new global view and physical quantification of mammalian cell cycle.

Cell cycles, essential for biological function, have been investigated extensively. However, enabling a global understanding and defining a physical quantification of the stability and function of the cell cycle remains challenging. Based upon a mammalian cell cycle gene network, we uncovered the underlying Mexican hat landscape of the cell cycle. We found the emergence of three local basins of attraction and two major potential barriers along the cell cycle trajectory. The three local basins of attraction characterize the G1, S/G2, and M phases. The barriers characterize the G1 and S/G2 checkpoints, respectively, of the cell cycle, thus providing an explanation of the checkpoint mechanism for the cell cycle from the physical perspective. We found that the progression of a cell cycle is determined by two driving forces: curl flux for acceleration and potential barriers for deceleration along the cycle path. Therefore, the cell cycle can be promoted (suppressed), either by enhancing (suppressing) the flux (representing the energy input) or by lowering (increasing) the barrier along the cell cycle path. We found that both the entropy production rate and energy per cell cycle increase as the growth factor increases. This reflects that cell growth and division are driven by energy or nutrition supply. More energy input increases flux and decreases barrier along the cell cycle path, leading to faster oscillations. We also identified certain key genes and regulations for stability and progression of the cell cycle. Some of these findings were evidenced from experiments whereas others lead to predictions and potential anticancer strategies.

Quantifying the landscape and kinetic paths for epithelial-mesenchymal transition from a core circuit.

Epithelial-mesenchymal transition (EMT), as a crucial process in embryonic development and cancer metastasis, has been investigated extensively. However, how to quantify the global stability and transition dynamics for EMT under fluctuations remains to be elucidated. Starting from a core EMT genetic circuit composed of three key proteins or microRNAs (microRNA-200, ZEB and SNAIL), we uncovered the potential landscape for the EMT process. Three attractors emerge from the landscape, which correspond to epithelial, mesenchymal and partial EMT states respectively. Based on the landscape, we analyzed two important quantities of the EMT system: the barrier heights between different basins of attraction that describe the degree of difficulty for EMT or backward transition, and the mean first passage time (MFPT) that characterizes the kinetic transition rate. These quantities can be harnessed as measurements for the stability of cell types and the degree of difficulty of transitions between different cell types. We also calculated the minimum action paths (MAPs) by path integral approaches. The MAP delineates the transition processes between different cell types quantitatively. We propose two different EMT processes: a direct EMT from E to P, and a step-wise EMT going through an intermediate state, depending on different extracellular environments. The landscape and kinetic paths we acquired offer a new physical and quantitative way for understanding the mechanisms of EMT processes, and indicate the possible roles for the intermediate states.