Bio-switches: what makes them robust?

A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity

The pathophysiological process of Alzheimer's disease (AD) is believed to begin many years before the formal diagnosis of AD dementia. This protracted preclinical phase offers a crucial window for potential therapeutic interventions, yet its comprehensive characterization remains elusive. Accumulating evidence suggests that amyloid-β (Aβ) may mediate neuronal hyperactivity in circuit dysfunction in the early stages of AD. At the same time, neural activity can also facilitate Aβ accumulation through intricate feed-forward interactions, complicating elucidating the conditions governing Aβ-dependent hyperactivity and its diagnostic utility. In this study, we use biophysical modeling to shed light on such conditions. Our analysis reveals that the inherently nonlinear nature of the underlying molecular interactions can give rise to the emergence of various modes of hyperactivity. This diversity in the mechanisms of hyperactivity may ultimately account for a spectrum of AD manifestations.

Applications of synthetic biology in medical and pharmaceutical fields

Synthetic biology aims to design or assemble existing bioparts or bio-components for useful bioproperties. During the past decades, progresses have been made to build delicate biocircuits, standardized biological building blocks and to develop various genomic/metabolic engineering tools and approaches. Medical and pharmaceutical demands have also pushed the development of synthetic biology, including integration of heterologous pathways into designer cells to efficiently produce medical agents, enhanced yields of natural products in cell growth media to equal or higher than that of the extracts from plants or fungi, constructions of novel genetic circuits for tumor targeting, controllable releases of therapeutic agents in response to specific biomarkers to fight diseases such as diabetes and cancers. Besides, new strategies are developed to treat complex immune diseases, infectious diseases and metabolic disorders that are hard to cure via traditional approaches. In general, synthetic biology brings new capabilities to medical and pharmaceutical researches. This review summarizes the timeline of synthetic biology developments, the past and present of synthetic biology for microbial productions of pharmaceutics, engineered cells equipped with synthetic DNA circuits for diagnosis and therapies, live and auto-assemblied biomaterials for medical treatments, cell-free synthetic biology in medical and pharmaceutical fields, and DNA engineering approaches with potentials for biomedical applications.

Dual Feedforward Loops Modulate Type I Interferon Responses and Induce Selective Gene Expression during TLR4 Activation

Although the dynamic features of type I coherent feedforward loop (C1-FFL) has been well studied, how C1-FFL shapes cell-to-cell heterogeneity remains unclear. Here, we found that C1-FFL with OR logic serves as "noise reducer," whereas C1-FFL with AND logic functions as "noise propagator" to fine-tune the heterogeneity of signaling molecule's activation. Within Toll-like receptor 4 (TLR4) signaling pathway, we demonstrated that MyD88 together with TRIF generates a C1-FFL to control TBK1 phosphorylation and reduce its cell-to-cell heterogeneity, whereas noisy TRIF activation induced high heterogeneity of IRF3 activation through another C1-FFL. We further developed a mathematical model with dual C1-FFLs to uncover how MyD88 and TRIF encoded differential dynamics for TBK1 and IRF3 activation. Integration of dual FFLs drives MyD88-TBK1 axis to determine the specificity of IFN-stimulated genes transcription. Collectively, our work elucidates a paradigm that tunable TLR4-mediated type I IFN responses are subtly controlled by dual FFLs.

Chimera state in complex networks of bistable Hodgkin-Huxley neurons

In this paper we study a chimera state in complex networks of bistable Hodgkin-Huxley neurons with excitatory coupling, which manifests as a termination of spiking activity of a part of interacting neurons. We provide a detailed investigation of this phenomenon in scale-free, small-world, and random networks and show that the chimera state is robust to the network topology. Nevertheless, network topological properties determine the stability of spatiotemporal states and therefore affect the excitability of the chimera state in the whole network. In particular, the scale-free network whose higher degree nodes are more stable to small perturbations is least exposed to chimera formation and exhibits an abrupt transition from a spiking to a silent regime. On the other hand, small-world and random networks are more likely to provide transitions to the chimera state.

Statistical mechanics of the Huxley-Simmons model

The chemomechanical model of Huxley and Simmons (HS) [A. F. Huxley and R. M. Simmons, Nature 233, 533 (1971)NATUAS0028-083610.1038/233533a0] provides a paradigmatic description of mechanically induced collective conformational changes relevant in a variety of biological contexts, from muscles power stroke and hair cell gating to integrin binding and hairpin unzipping. We develop a statistical mechanical perspective on the HS model by exploiting a formal analogy with a paramagnetic Ising model. We first study the equilibrium HS model with a finite number of elements and compute explicitly its mechanical and thermal properties. To model kinetics, we derive a master equation and solve it for several loading protocols. The developed formalism is applicable to a broad range of allosteric systems with mean-field interactions.

Optimal homeostasis necessitates bistable control

Bistability is a fundamental phenomenon in nature. In biology, a number of fine properties of bistability have been identified. However, these properties are only consequences of bistability at the physiological level, which do not explain why it had to emerge during evolution. Using optimal homeostasis as the first principle, I find that bistability emerges as an indispensable control mechanism. It is the only solution to a dilemma in glucose homeostasis: high insulin efficiency is required to confer rapidness in plasma glucose clearance, whereas an insulin sparing state is required to guarantee the brain's safety during fasting. The optimality consideration renders a clear correspondence between the molecular and physiological levels. This new perspective can illuminate studies on the twin epidemics of obesity and diabetes and the corresponding intervening strategies. For example, overnutrition and sedentary lifestyle may represent sudden environmental changes that cause the lose of optimality, which may contribute to the marked rise of obesity and diabetes in our generation. Because this bistability result is independent of the parameters of the mathematical model (for which the result is quite general), some other biological systems may also use bistability to control homeostasis.

Quantitative analysis of cellular metabolic dissipative, self-organized structures

One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life.

Use of virtual cell in studies of cellular dynamics

The Virtual Cell (VCell) is a unique computational environment for modeling and simulation of cell biology. It has been specifically designed to be a tool for a wide range of scientists, from experimental cell biologists to theoretical biophysicists. The models created with VCell can range from the simple, to evaluate hypotheses or to interpret experimental data, to complex multilayered models used to probe the predicted behavior of spatially resolved, highly nonlinear systems. In this chapter, we discuss modeling capabilities of VCell and demonstrate representative examples of the models published by the VCell users.

Positive feedback in cellular control systems

Feedback loops have been identified in a variety of regulatory systems and organisms. While feedback loops of the same type (negative or positive) tend to have properties in common, they can play distinctively diverse roles in different regulatory systems, where they can affect virulence in a pathogenic bacterium, maturation patterns of vertebrate oocytes and transitions through cell cycle phases in eukaryotic cells. This review focuses on the properties and functions of positive feedback in biological systems, including bistability, hysteresis and activation surges.

Necrosis induction in glioblastoma cells reveals a new "bioswitch" function for the MT1-MMP/G6PT signaling axis in proMMP-2 activation versus cell death decision

Cytoskeleton disorganization is an early step in the activation process of matrix metalloproteinase 2 (MMP-2) by membrane type 1 MMP (MT1-MMP) but is also associated with endoplasmic reticulum (ER) dysfunction and subsequent cell death. Given evidence that the ER-embedded glucose-6-phosphate transporter (G6PT) regulates glioblastoma cell survival and that MT1-MMP is a key enzyme in the cancer cell invasive phenotype, we explored the molecular link between G6PT and MT1-MMP. Cytoskeleton-disrupting agents such as concanavalin A (ConA) and cytochalasin D triggered proMMP-2 activation and cell death in U87 glioma cells. ConA decreased G6PT gene expression, an event that was also observed in cells overexpressing the full-length recombinant MT1-MMP protein. Overexpression of a membrane-bound catalytically active but cytoplasmic domain-deleted MT1-MMP was unable to downregulate G6PT gene expression or to trigger necrosis. Gene silencing of MT1-MMP with small interfering RNA prevented proMMP-2 activation and induced G6PT gene expression. ConA inhibited Akt phosphorylation, whereas overexpression of recombinant G6PT rescued the cells from ConA-induced proMMP-2 activation and increased Akt phosphorylation. Altogether, new functions of MT1-MMP in cell death signaling may be linked to those of G6PT. Our study indicates a molecular signaling axis regulating the invasive phenotype of brain tumor cells and highlights a new "bioswitch" function for G6PT in cell survival.

Origin of bistability in the lac Operon

Multistability is an emergent dynamic property that has been invoked to explain multiple coexisting biological states. In this work, we investigate the origin of bistability in the lac operon. To do this, we develop a mathematical model for the regulatory pathway in this system and compare the model predictions with other experimental results in which a nonmetabolizable inducer was employed. We investigate the effect of lactose metabolism using this model, and show that it greatly modifies the bistable region in the external lactose (Le) versus external glucose (Ge) parameter space. The model also predicts that lactose metabolism can cause bistability to disappear for very low Ge. We have also carried out stochastic numerical simulations of the model for several values of Ge and Le. Our results indicate that bistability can help guarantee that Escherichia coli consumes glucose and lactose in the most efficient possible way. Namely, the lac operon is induced only when there is almost no glucose in the growing medium, but if Le is high, the operon induction level increases abruptly when the levels of glucose in the environment decrease to very low values. We demonstrate that this behavior could not be obtained without bistability if the stability of the induced and uninduced states is to be preserved. Finally, we point out that the present methods and results may be useful to study the emergence of multistability in biological systems other than the lac operon.

Evaluation of the Function of the Granzyme-Mediated Antiviral System through a Mathematical Model

ABSTRACT Granzymes (gzms), the major components of cytolytic lymphocyte granules, are predominantly associated with immune regulatory processes against primary viral infections in vertebrates. By releasing into the infected cell cytosol, they initiate a rapid apoptotic process to eliminate infected cells. However, the kinetics and control mechanisms of gzms in antiviral processes have not yet been fully described. Based on earlier experimental data of ectromelia virus infection, we developed a mathematical model to evaluate the function of the gzm-mediated antiviral system. The results of simulation runs for the infections corresponded well to the experimental data, suggesting that the computational methods could be applied in researching such field. Furthermore, by altering the parameters of the model and stability analysis, we found that for viruses with various virulence, gzm-mediated defense systems cannot completely suppress the first infections in most cases, unless the virulence is limited in a narrow range. Depending on the differences of viral virulence, gzms cause oscillations of viral level in different patterns. By adding secondary immune response to the model, we found that the oscillation evoked by the gzm-mediated antiviral system has crucial significance. Consequently, we bring forward a hypothesis that most of these oscillations may allow ancillary immune responses to effectively suppress a virus as it oscillates through a minimum level.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the lambda phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.