Although the principles of synthetic biology were initially established in model bacteria, microbial producers, extremophiles and gut microbes have now emerged as valuable prokaryotic chassis for biological engineering. Extending the host range in which designed circuits can function reliably and predictably presents a major challenge for the concept of synthetic biology to materialize. In this work, we systematically characterized the cross-species universality of two transcriptional regulatory modules—the T7 RNA polymerase activator module and the repressors module—in three non-model microbes. We found striking linear relationships in circuit activities among different organisms for both modules. Parametrized model fitting revealed host non-specific parameters defining the universality of both modules. Lastly, a genetic NOT gate and a band-pass filter circuit were constructed from these modules and tested in non-model organisms. Combined models employing host non-specific parameters were successful in quantitatively predicting circuit behaviors, underscoring the potential of universal biological parts and predictive modeling in synthetic bioengineering.

It is increasingly clear that cancer is a complex systemic disease and one of the most fatal diseases in humans. Complex systems, including cancer, exhibit critical transitions in which the system abruptly shifts from one state to another. However, predicting these critical transitions is difficult as the system may show little change before the tipping point is reached. Models for predicting cancer are generally not accurate enough to reliably predict where these critical transitions will occur. Additionally, there is often a gap between theoretical results and clinical practice. To address these issues, we conducted a study using gastric cancer as a representative to reveal the tipping point of cancer and develop a feasible method for clinical monitoring. We used gene regulatory networks and a landscape framework to quantify the formation of gastric cancer. Since the dissipation cost of cancer cells is different from that of normal cells, we calculated the entropy product rate (EPR) and mean flux to quantify the thermodynamic cost and dynamical driving force in predicting critical transitions of cancer, which can serve as early warning signals. Both the EPR and mean flux change sharply near the point when the cancer state is about to emerge and/or the normal state is about to disappear. Moreover, the peak or sharp upward trends of the signals occur much earlier than critical slowdown and flickering frequency. These significant variations can be used as early warning signals for cancer. To further explore early warning signals in clinical and experimental trials, we calculated the difference in cross correlations (ΔC) forward and backward in time for the stochastic gene expression time series. This time-irreversible measure gives a rise to peak before the bifurcation points, which can help detect precancerous and metastatic early warning signals in clinical practice rather than just theoretical calculation. This study is crucial for effectively identifying early warning signals for cancer in clinical and experimental settings.

Cell senescence has attracted much attention in the long history of human beings, and telomere shortening (TS) is one of the main concerns in the study of cell senescence. To reveal the microscopic mechanism of TS process, we model it based on molecular stochastic process from the perspective of nonequilibrium statistical physics. We associate the TS process with the continuous time random walk and derive the Fokker–Planck equation to describe the length distribution of the TS. We further modify the model describing the TS process, similar to the anomalous tempered diffusion, and derive the Feynman–Kac equation characterizing the functional distribution of the TS process. Finally, we study the statistics related to the critical telomere length I_c, including the occupation time and first passage time. These two kinds of statistics help us understand the time scale of cell senescence.

Gene transcription is a stochastic process characterized by fluctuations in mRNA levels of the same gene in isogenic cell populations. A central question in single-cell studies is how to map transcriptional variability to phenotypic differences between isogenic cells. We introduced a measurable and statistical transcription threshold I for critical genes that determine the entry level of Waddington’s canal toward a specific cell fate. Subsequently, J_I, which is the probability that a cell has at least I mRNA molecules of a given gene, approximates the likelihood of a cell committing to the corresponding fate. In this study, we extended the previous results of J_I of the classical telegraph model by considering more complex models with different gene activation frameworks. We showed that (a) the upregulation of the critical gene may significantly suppress cell fate change and (b) increasing transcription noise performs a bidirectional role that can either enhance or suppress the cell fate change. These observations matched accurately with the data from bacterial, yeast, and mammalian cells. We estimated the threshold I from these data and predicted that (a) the traditional human immunodeficiency virus (HIV) activators that modulate gene activation frequency at high doses may largely suppress HIV reactivation and (b) the cells may favor noisier (or less noisy) regulation of stress genes under high (or low) environmental pressures to maintain cell viability.

In recent years, exploring the physical mechanisms of brain functions has been a hot topic in the fields of nonlinear dynamics and complex networks, and many important achievements have been made, mainly based on the characteristic features of time series of human brain. To speed up the further study of this problem, herein we make a brief review on these important achievements, which includes the aspects of explaining: (i) the mechanism of brain rhythms by network synchronization, (ii) the mechanism of unihemispheric sleep by chimera states, (iii) the fundamental difference between the structural and functional brain networks by remote synchronization, (iv) the mechanism of stronger detection ability of human brain to weak signals by remote firing propagation, and (v) the mechanism of dementia patterns by eigen-microstate analysis. As a brief review, we will mainly focus on the aspects of basic ideas, research histories, and key results but ignore the tedious mathematical derivations. Moreover, some outlooks will be discussed for future studies.

Living systems operate within physical constraints imposed by nonequilibrium thermodynamics. This review explores recent advancements in applying these principles to understand the fundamental limits of biological functions. We introduce the framework of stochastic thermodynamics and its recent developments, followed by its application to various biological systems. We emphasize the interconnectedness of kinetics and energetics within this framework, focusing on how network topology, kinetics, and energetics influence functions in thermodynamically consistent models. We discuss examples in the areas of molecular machine, error correction, biological sensing, and collective behaviors. This review aims to bridge physics and biology by fostering a quantitative understanding of biological functions.

Critical transitions and tipping phenomena between two meta-stable states in stochastic dynamical systems are a scientific issue. In this work, we expand the methodology of identifying the most probable transition pathway between two meta-stable states with Onsager-Machlup action functional, to investigate the evolutionary transition dynamics between two meta-stable invariant sets with Schrödinger bridge. In contrast to existing methodologies such as statistical analysis, bifurcation theory, information theory, statistical physics, topology, and graph theory for early warning indicators, we introduce a novel framework on Early Warning Signals (EWS) within the realm of probability measures that align with the entropy production rate. To validate our framework, we apply it to the Morris-Lecar model and investigate the transition dynamics between a meta-stable state and a stable invariant set (the limit cycle or homoclinic orbit) under various conditions. Additionally, we analyze real Alzheimer’s data from the Alzheimer’s Disease Neuroimaging Initiative database to explore EWS indicating the transition from healthy to pre-AD states. This framework not only expands the transition pathway to encompass measures between two specified densities on invariant sets, but also demonstrates the potential of our early warning indicators for complex diseases.

Self-organized pattern formation is common in biological systems. Microbial populations can generate spatiotemporal patterns through various mechanisms, such as chemotaxis, quorum sensing, and mechanical interactions. When their motile behavior is coupled to a gravitational potential field, swimming microorganisms display a phenomenon known as bioconvection, which is characterized by the pattern formation of active cellular plumes that enhance material mixing in the fluid. While bioconvection patterns have been characterized in various organisms, including eukaryotic and bacterial microswimmers, the dynamics of bioconvection pattern formation in bacteria is less explored. Here, we study this phenomenon using suspensions of a chemotactic bacterium Bacillus subtilis confined in closed three-dimensional (3D) fluid chambers. We discovered an active plume lattice pattern that displays hexagonal order and emerges via a self-organization process. By flow field measurement, we revealed a toroidal flow structure associated with individual plumes. We also uncovered a power-law scaling relation between the lattice pattern’s wavelength and the dimensionless Rayleigh number that characterizes the ratio of buoyancy-driven convection to diffusion. Taken together, this study highlights that coupling between chemotaxis and external potential fields can promote the self-assembly of regular spatial structures in bacterial populations. The findings are also relevant to material transport in surface water environments populated by swimming microorganisms.

Explaining biodiversity is the central focus in theoretical ecology. A significant obstacle arises from the competitive exclusion principle (CEP), which states that two species competing for the same type of resources cannot coexist at constant population densities, or more generally, the number of consumer species cannot exceed that of resource species at steady states. The conflict between CEP and biodiversity is exemplified by the paradox of the plankton, where a few types of limiting resources support a plethora of plankton species. In this review, we introduce mechanisms proposed over the years for promoting biodiversity in ecosystems, with a special focus on those that alleviate the constraints imposed by the CEP, including mechanisms that challenge the CEP in well-mixed systems at a steady state or those that circumvent its limitations through contextual differences.

Asymmetry between outer and inner leaflets of cell membrane, such as variations in phospholipid composition, cholesterol (CHOL) distribution, stress levels, and ion environments, could significantly influence the biophysical properties of membranes, including the lateral organization of lipids and the formation of membrane nanodomains. To elucidate the effects of lipid component, lipid number mismatch, CHOL concentration asymmetry, and ionic conditions on membrane properties, we constructed several sets of all-atom, multi-component lipid bilayer models. Using molecular dynamics (MD) simulations, we investigated how membrane asymmetry modulates its biological characteristics. Our results indicate that CHOL concentration, whether symmetric or asymmetric between the leaflets, is the primary factor affecting membrane thickness, order parameters of the lipid tail, tilting angles of lipid molecules, water permeability, lateral pressure profiles, and transmembrane potential. Both low and high CHOL concentrations significantly alter lipid bilayer properties. Inducing cross-leaflet stress by mismatching lipid numbers can modify lipid order parameters and the tilting angles but has only mild effect on lateral pressure profiles and membrane thickness. Additionally, we found that transmembrane potential, generated by ions concentration differences across the membrane, can influence water permeability. Our findings expand the current understanding of lipid membrane properties and underscore the importance of considering CHOL and phospholipid asymmetry in membrane biophysics. The membrane models developed in our study also provide more physiological conditions for studying membrane proteins using MD simulations.

Intrinsically disordered proteins (IDP) are highly dynamic, and the effective characterization of IDP conformations is still a challenge. Here, we analyze the chain topology of IDPs and focus on the physical link of the IDP chain, that is, the entanglement between two segments along the IDP chain. The Gauss linking number of two segments throughout the IDP chain is systematically calculated to analyze the physical link. The crossing points of physical links are identified and denoted as link nodes. We notice that the residues involved in link nodes tend to have lower root mean square fluctuation (RMSF), that is, the entanglement of the IDP chain may affect its conformation fluctuation. Moreover, the evolution of the physical link is considerably slow with a timescale of hundreds of nanoseconds. The essential conformation evolution may be depicted on the basis of chain topology.