Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.

Nonmodular oscillator and switch based on RNA decay drive regeneration of multimodal gene expression

Periodic gene expression dynamics are key to cell and organism physiology. Studies of oscillatory expression have focused on networks with intuitive regulatory negative feedback loops, leaving unknown whether other common biochemical reactions can produce oscillations. Oscillation and noise have been proposed to support the capacity of mammalian progenitor cells to restore heterogenous, multimodal expression from extreme subpopulations, but underlying networks and specific roles of noise remained elusive. We use mass-action-based models to show that regulated RNA degradation involving as few as two RNA species, applicable to nearly half of human protein-coding genes, can generate sustained oscillations without imposed feedback. Diverging oscillation periods synergize with noise to robustly restore bimodal expression in cell populations. The global bifurcation organizing this divergence relies on an oscillator and bistable switch which cannot be decomposed into two structural modules. Our work reveals surprisingly rich dynamics of post-transcriptional reactions and a potentially widespread mechanism useful for development and regeneration.

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Background Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. Results In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. Conclusions We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

Roles of mTOR in thoracic aortopathy understood by complex intracellular signaling interactions

Thoracic aortopathy–aneurysm, dissection, and rupture–is increasingly responsible for significant morbidity and mortality. Advances in medical genetics and imaging have improved diagnosis and thus enabled earlier prophylactic surgical intervention in many cases. There remains a pressing need, however, to understand better the underlying molecular and cellular mechanisms with the hope of finding robust pharmacotherapies. Diverse studies in patients and mouse models of aortopathy have revealed critical changes in multiple smooth muscle cell signaling pathways that associate with disease, yet integrating information across studies and models has remained challenging. We present a new quantitative network model that includes many of the key smooth muscle cell signaling pathways and validate the model using a detailed data set that focuses on hyperactivation of the mechanistic target of rapamycin (mTOR) pathway and its inhibition using rapamycin. We show that the model can be parameterized to capture the primary experimental findings both qualitatively and quantitatively. We further show that simulating a population of cells by varying receptor reaction weights leads to distinct proteomic clusters within the population, and that these clusters emerge due to a bistable switch driven by positive feedback in the PI3K/AKT/mTOR signaling pathway.

Tailoring Optical Bistability In Nonlinear Photonic Crystals Based on Metallic Nanoparticles And Graphene

In this work, we revisit the optical response of a one-dimensional photonic crystal consisting of graphene monolayers and a plasmonic nanocomposite as a defect layer in the structure. By taking advantage of the modified transfer matrix approach, the analytical solution of the light transmission and field distribution of the photonic crystal are evaluated. Besides, by considering one of the layers as a Kerr-nonlinear medium, we delve into optical bistability phenomenon in the model for two different cases. Our numerical results reveal that the proposed photonic crystal can enhance the field distribution and reduce the optical bistability’s threshold in comparison to the conventional photonic crystals. Furthermore, the optical bistable switch-up and switch-down thresholds of the proposed resonator can be tailored flexibly by plasmon-plasmon interactions in the defect layer. Finally, the electric field distribution amelioration and optical bistability by means of graphene layers in the structure are attainable. The influences of the parameters such as the graphene and the nanocomposite on the performance of OB are analyzed and compared in the two different cases. Therefore, present approach can lay the groundwork for designing highly sensitive surface plasmon resonance biosensors and switches where the proposed technique may find unprecedented capabilities.

Modular, robust and extendible multicellular circuit design in yeast

Division of labor between cells is ubiquitous in biology but the use of multi-cellular consortia for engineering applications is only beginning to be explored. A significant advantage of multi-cellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three- and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.

Circadian Proteins Cry and Rev-erb Deepen Cellular Quiescence by Down-regulating Cyclin D and Cdk4,6

The proper balance and transition between cellular quiescence and proliferation are critical to tissue homeostasis, and their deregulations are commonly found in many human diseases, including cancer and aging. Recent studies showed that the reentry of quiescent cells to the cell cycle is subjected to circadian regulation. However, the underlying mechanisms are largely unknown. Here, we report that two circadian proteins, Cryptochrome (Cry) and Rev-erb, deepen cellular quiescence in rat embryonic fibroblasts, resulting in stronger serum stimulation required for cells to exit quiescence and reenter the cell cycle. This finding was opposite from what we expected from the literature. By modeling a library of possible regulatory topologies linking Cry and Rev-erb to a bistable Rb-E2f gene network switch that controls the quiescence-to-proliferation transition and by experimentally testing model predictions, we found Cry and Rev-erb converge to downregulate Cyclin D/Cdk4,6 activity, leading to an ultrasensitive increase of the serum threshold to activate the Rb-E2f bistable switch. Our findings suggest a mechanistic role of circadian proteins in modulating the depth of cellular quiescence, which may have implications in the varying potentials of tissue repair and regeneration at different times of the day.

An ABA-GA bistable switch can account for natural variation in the variability of Arabidopsis seed germination time

Genetically identical plants growing in the same conditions can display heterogeneous phenotypes. Here we use Arabidopsis seed germination time as a model system to examine phenotypic variability and its underlying mechanisms. We show extensive variation in seed germination time variability between Arabidopsis accessions and use a multiparent recombinant inbred population to identify two genetic loci involved in this trait. Both loci include genes implicated in modulating abscisic acid (ABA) sensitivity. Mutually antagonistic regulation between ABA, which represses germination, and gibberellic acid (GA), which promotes germination, underlies the decision to germinate and can act as a bistable switch. A simple stochastic model of the ABA-GA network shows that modulating ABA sensitivity can generate the range of germination time distributions we observe experimentally. We validate the model by testing its predictions on the effects of exogenous hormone addition. Our work provides a foundation for understanding the mechanism and functional role of phenotypic variability in germination time.

Bacteriophage λ RexA and RexB Functions Assist the Transition from Lysogeny to Lytic Growth

SummaryThe CI and Cro repressors of bacteriophage λ create a bistable switch between lysogenic and lytic growth. In λ lysogens, CI repressor expressed from thePRMpromoter blocks expression of the lytic promotersPLandPRto allow stable maintenance of the lysogenic state. When lysogens are induced, CI repressor is inactivated and Cro repressor is expressed from the lyticPRpromoter. Cro repressor blocksPRMtranscription and CI repressor synthesis to ensure that the lytic state proceeds. RexA and RexB proteins, like CI, are expressed from thePRMpromoter in λ lysogens; RexB is also expressed from a second promoter,PLIT, embedded inrexA.Here we show that RexA binds CI repressor and assists the transition from lysogenic to lytic growth, using both intact lysogens and defective prophages with reporter genes under control of the lyticPLandPRpromoters. Once lytic growth begins, if the bistable switch does return to the immune state, RexA expression lessens the probability that it will remain there, thus stabilizing the lytic state and activation of the lyticPLandPRpromoters. RexB modulates the effect of RexA and may also help establish phage DNA replication as lytic growth ensues.

Characteristic Analysis and Optimization of a Four-bar Compliant Mechanism with Single Flexible Member

The characteristics and optimization analysis of a four-bar compliant mechanism with one flexible member (or flexible joint) are carried out. Firstly, based on the pseudo-rigid body model theory of the compliant mechanism, the kinematics relationship, system kinetic energy and potential energy of the general four-bar compliant mechanism are analyzed, and the dynamic model of the four-bar compliant mechanism is established by using Lagrange's equation. Secondly, through the creation of the energy equation of the four-bar compliant mechanism and the analysis of the first and second derivatives of the input variables, the parametric conditions for the existence of the bistable characteristics of the four-bar compliant mechanism with one flexible member are proposed. Then, taking the compliant bistable switch as an example, the internal relations between the driving characteristics of the four-bar compliant mechanism, the initial motion position of the mechanism and the parameters of the flexible member are explored. Finally, based on the improvement of the performance of the compliant bistable switch, the optimization analysis of the maximization of the motion range of the driving link and the maximization of the deformation energy of the flexible member are carried out. The research provides a theoretical basis for the product development and control of a bistable four-bar compliant mechanism.