Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach.

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally determined from experiments, augmented with dynamical network computations involving endpoint objective functions, mutual information, change-point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method, based on the strategies described above. The cybernetic-inspired method is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

Investigating the unparalleled effects of economic growth and high-quality economic development on energy insecurity in China: A provincial perspective.

China has changed its focus from traditional high-speed economic growth to high-quality economic development (HQED) and the implementation of environmentally friendly practices. This transition can have parallel or unparallel impacts on energy insecurity (EIS). In this regards, HQED, inter Alia, is crucial in mitigating EIS and combating the energy crisis. Our study explores the impact of economic growth (EG) and HQED on EIS using the provincial panel data of China for the period 2011-2017. From the perspective of comparative analysis, the results reveal that HQED reduces EIS while EG increases it. The robustness checks indicate that industrial structure (IS) has a negative impact on EIS, whereas industrial structure upgrading (ISU) and green innovation (GI) have a positive influence. This implies that IS contributes to an increase in EIS, whereas ISU and GI result in a decrease in EIS. In addition, the analysis reveals that digital financial inclusion (DFI) exhibits a significant positive relation with EIS, albeit occasionally a negative but insignificant link. The policy implication is that the government should stimulate policies to promote HQED which reduces the EIS.

How diversification of products impact emissions in China: a provincial perspective.

Currently, global warming and air pollution are the world's most urgent issues partly caused by carbon dioxide (CO2) and sulfur dioxide (SO2) emissions, and prompt actions are needed to address these global concerns. Sustainable development cannot be attained until we reverse the negative impact of economic factors on the quality of the environment. It is noteworthy to offer a new indication on whether and how the empirical liaison between product diversification and environmental degradation evolved in China from 2008 to 2019. Product diversification (PD) is a remedy for reducing environmental degradation (ED). It is a crucial component of energy demand, which a significant impact on reducing energy consumption and ED. The purpose of this study is to investigate the impact of PD on ED in China using the provincial panel dataset. Employing the fixed effects-Driscoll-Kraay standard errors (FE-DKSE) and feasible generalized least squares (FGLS) methods, we discover an inverted U-shaped link between PD and ED. The control variable urbanization (URB) and technological innovation (TI) reduce ED significantly. However, industry value added (IVA) and energy consumption (EC) promote ED. Our results are robust with the addition of various controls in all models. The policy implication from our findings is that, to achieve a target of carbon neutrality, countries should adopt the product diversification strategy.

Does economic fitness matter in carbon emissions mitigation in BRICS countries?

The economic fitness of a country shows its capacity to address complex issues, such as climate change and environmental degradation, which are urgent global concerns. Its key function is given less importance in empirical research and has been neglected by existing empirical studies. Concerning this neglect, our study investigates the influence of economic fitness on CO2 emissions in the context of the environmental Kuznets curve (EKC) for the BRICS nations between 1995 and 2015. The Feasible Generalized Least Squares (FGLS) and Panel-Corrected Standard Error (PCSE) techniques are used to estimate the empirical association. The results suggest that economic fitness and CO2 emissions have an inverted N-shaped relationship. Furthermore, after accounting for major contributing factor of CO2 emissions like GDP per capita, financial development, urbanization, and foreign direct investment, our robustness checks produce robust and significant results.

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach.

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present an elegant modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding entirely novel strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally-determined from experiments, augmented with dynamical network computations involving end point objective functions, mutual information, change point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method (CIM), utilizing the strategies described above. The CIM is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this state-of-the-art framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights. STATEMENT OF SIGNIFICANCE: Cellular processes like cell cycle are overly complex, involving multiple players interacting at multiple levels, and explicit modeling of such systems is challenging. The availability of longitudinal RNA measurements provides an opportunity to "reverse-engineer" for novel regulatory models. We develop a novel framework, inspired using goal-oriented cybernetic model, to implicitly model transcriptional regulation by constraining the system using inferred temporal goals. A preliminary causal network based on information-theory is used as a starting point, and our framework is used to distill the network to temporally-based networks containing essential molecular players. The strength of this approach is its ability to dynamically model the RNA temporal measurements. The approach developed paves the way for inferring regulatory processes in many complex cellular processes.