Sean Campbell, Courtney C. White, Amanda M. Alexander
et al.
Delay is an inherent feature of genetic regulatory networks. It represents the time required for the assembly of functional regulator proteins. The protein production process is complex, as it includes transcription, translocation, translation, folding, and oligomerization. Because these steps are noisy, the resulting delay associated with protein production is distributed (random). We here consider how distributed delay impacts the dynamics of bistable genetic circuits. We show that for a variety of genetic circuits that exhibit bistability, increasing the noise level in the delay distribution dramatically stabilizes the metastable states. By this we mean that mean residence times in the metastable states dramatically increase. Relevance to Life Sciences. Bistable genetic regulatory networks are ubiquitous in living organisms. Evolutionary processes seem to have tuned such networks so that they switch between metastable states when it is important to do so, but small fluctuations do not cause unwanted switching. Understanding how evolution has tuned the stability of biological switches is an important problem. In particular, such understanding can guide the design of forward-engineered synthetic bistable genetic regulatory networks. Mathematical Content. We use two methods to explain this stabilization phenomenon. First, we introduce and simulate stochastic hybrid models that depend on a switching-rate parameter. These stochastic hybrid models allow us to unfold the distributed-delay models in the sense that, in certain cases, the distributed-delay model can be viewed as a fast-switching limit of the corresponding stochastic hybrid model. Second, we generalize the three-states model, a symbolic model of bistability, and analyze this extension.
Valentin Wössner, Falko Ziebert, Ulrich S. Schwarz
Cellular actin structures are continuously turned over while keeping similar sizes. Since they all compete for a shared pool of actin monomers, the question arises how they can coexist in these dynamic steady states. Recently, the coexistence of branched actin networks with different densities growing in a shared and finite pool of purified proteins has been demonstrated in a biomimetic bead assay. However, theoretical work in the context of organelle size regulation has mainly been focused on linear architectures, such as single filaments and bundles, and thus is not able to explain this observation. Here we show theoretically that the local depletion of actin monomers caused by the growth of a branched network naturally gives rise to a negative feedback loop between network density and growth rate, and that this competition is captured by one central equation. A comprehensive bifurcation analysis shows that the theory leads to well-defined steady states even in the case of multiple networks sharing the same pool of monomers, without any need for specific molecular processes. Under increasing competition strength, coexistence is replaced by selection. We also show that our theory is in excellent agreement with spatiotemporal simulations implemented in a finite element framework. In summary, our work suggests that local monomer depletion is the decisive and universal factor controlling growth of branched actin networks.
Hélène Peyret, Céline Konecki, Christine Terryn
et al.
Methylglyoxal (MGO) is an endogenous, highly reactive dicarbonyl metabolite generated under hyperglycaemic conditions. MGO plays a role in developing pathophysiological conditions, including diabetic cardiomyopathy. However, the mechanisms involved and the molecular targets of MGO in the heart have not been elucidated. In this work, we studied the exposure-related effects of MGO on cardiac function in an isolated perfused rat heart ex vivo model. The effect of MGO on calcium homeostasis in cardiomyocytes was studied in vitro by the fluorescence indicator of intracellular calcium Fluo-4. We demonstrated that MGO induced cardiac dysfunction, both in contractility and diastolic function. In rat heart, the effects of MGO treatment were significantly limited by aminoguanidine, a scavenger of MGO, ruthenium red, a general cation channel blocker, and verapamil, an L-type voltage-dependent calcium channel blocker, demonstrating that this dysfunction involved alteration of calcium regulation. MGO induced a significant concentration-dependent increase of intracellular calcium in neonatal rat cardiomyocytes, which was limited by aminoguanidine and verapamil. These results suggest that the functionality of various calcium channels is altered by MGO, particularly the L-type calcium channel, thus explaining its cardiac toxicity. Therefore, MGO could participate in the development of diabetic cardiomyopathy through its impact on calcium homeostasis in cardiac cells.
Ansgar Gruber, Cedar McKay, Gabrielle Rocap
et al.
Plastid targeted proteins of diatoms and related algae can be predicted with high sensitivity and specificity using the ASAFind method published in 2015. ASAFind predictions rely on SignalP predictions of endoplasmic reticulum (ER) targeting signal peptides. Recently (in 2019), a new version of SignalP was released, SignalP 5.0. We tested the ability of SignalP 5.0 to recognize signal peptides of nucleus-encoded, plastid-targeted diatom pre-proteins, and to identify the signal peptide cleavage site. The results were compared to manual predictions of the characteristic cleavage site motif, and to previous versions of SignalP. SignalP 5.0 is less sensitive than the previous versions of SignalP in this specific task, and also in the detection of signal peptides of non-plastid proteins in diatoms. However, in combination with ASAFind, the resulting prediction performance for plastid proteins is high. In addition, we tested the multi-location prediction tool TargetP for its suitability to provide signal peptide information to ASAFind. The newest version, TargetP 2.0, had the highest prediction performances for diatom signal peptides and mitochondrial transit peptides compared to other versions of SignalP and TargetP, thus it provides a good basis for ASAFind predictions.
Vincent Ouazan-Reboul, Jaime Agudo-Canalejo, Ramin Golestanian
Biomolecular condensates in cells are often rich in catalytically-active enzymes. This is particularly true in the case of the large enzymatic complexes known as metabolons, which contain different enzymes that participate in the same catalytic pathway. One possible explanation for this self-organization is the combination of the catalytic activity of the enzymes and a chemotactic response to gradients of their substrate, which leads to a substrate-mediated effective interaction between enzymes. These interactions constitute a purely non-equilibrium effect and show exotic features such as non-reciprocity. Here, we analytically study a model describing the phase separation of a mixture of such catalytically-active particles. We show that a Michaelis-Menten-like dependence of the particles' activities manifests itself as a screening of the interactions, and that a mixture of two differently-sized active species can exhibit phase separation with transient oscillations. We also derive a rich stability phase diagram for a mixture of two species with both concentration-dependent activity and size dispersity. This work highlights the variety of possible phase separation behaviours in mixtures of chemically-active particles, which provides an alternative pathway to the passive interactions more commonly associated with phase separation in cells. Our results highlight non-equilibrium organizing principles that can be important for biologically relevant liquid-liquid phase separation.
The role of rotational molecular motors of the ATP synthase class is integral to the metabolism of cells. Yet the function of FliI6-FliJ complex - homologous to the F1 ATPase motor - within the flagellar export apparatus remains unclear. We use a simple two-state model adapted from studies of linear molecular motors to identify key features of this motor. The two states are the 'locked' ground state where the FliJ coiled coil filament experiences fluctuations in an asymmetric torsional potential, and a 'free' excited state in which FliJ undergoes rotational diffusion. Michaelis-Menten kinetics was used to treat transitions between these two states, and obtain the average angular velocity of the FliJ filament within the FliI6 stator: ~9.0 rps. The motor was then studied under external counter torque conditions in order to ascertain its maximal power output: Pmax ~42 kBT/s, and the stall torque: ~3 kBT/rad. Two modes of action within the flagellar export apparatus are proposed, in which the motor performs useful work either by continuously 'grinding' through the resistive environment, or by exerting equal and opposite stall force on it. In both cases, the resistance is provided by flagellin subunits entering the flagellar export channel prior to their unfolding. We therefore propose that the function of the FliI6-FliJ complex is to lower the energy barrier and therefore assist in unfolding of the flagellar proteins before feeding them into the transport channel.
Although the key promoter elements necessary to drive transcription in Escherichia coli have long been understood, we still cannot predict the behavior of arbitrary novel promoters, hampering our ability to characterize the myriad of sequenced regulatory architectures as well as to design novel synthetic circuits. This work builds upon a beautiful recent experiment by Urtecho et al. who measured the gene expression of over 10,000 promoters spanning all possible combinations of a small set of regulatory elements. Using this data, we demonstrate that a central claim in energy matrix models of gene expression - that each promoter element contributes independently and additively to gene expression - contradicts experimental measurements. We propose that a key missing ingredient from such models is the avidity between the -35 and -10 RNA polymerase binding sites and develop what we call a refined energy matrix model that incorporates this effect. We show that this the refined energy matrix model can characterize the full suite of gene expression data and explore several applications of this framework, namely, how multivalent binding at the -35 and -10 sites can buffer RNAP kinetics against mutations and how promoters that bind overly tightly to RNA polymerase can inhibit gene expression. The success of our approach suggests that avidity represents a key physical principle governing the interaction of RNA polymerase to its promoter.
Radix Bupleuri is a hepatoprotective traditional Chinese medicine (TCM) used for thousands of years in clinical, which was reported to be linked with liver damage. Previous studies have revealed that saikosaponins are the major types of components that contribute to the hepatotoxicity of Radix Bupleuri. However the underlying molecular mechanism is far from being understood. In order to clarify whether these structural analogues exert toxicity effects through the same molecular targets, a systematic comparison study was done in this paper. The effects of SSa, b2, c, and d on isolated rat liver mitochondria and human hepatocyte L02 cells were explored, respectively. The collective results indicated that although saikosaponins share the similar structures but they have quite different mechanisms. SSb2 and SSd showed most serious damage on the function of mitochondria and survival rate of cell, respectively. SSb2 could cause mitochondrial permeability transition pore (mPTP) opening and collapse of mitochondrial membrane potential (ΔΨm) by impairing the mitochondrial respiratory chain complex III. While SSd destroyed plasma membrane and led to the release of lactate dehydrogenase (LDH) mainly through activating caspase-1. Furthermore, the combine index (CI) demonstrated that the combined hepatotoxicity of SSb2 and SSd could be additive. This finding might yield more in depth understanding of hepatotoxicity of Radix Bupleuri possess many different saikosaponins.
PEBPs (PhosphatidylEthanolamine Binding Proteins) form a protein family widely present in the living world since they are encountered in microorganisms, plants and animals. In all organisms PEBPs appear to regulate important mechanisms that govern cell cycle, proliferation, differentiation and motility. In humans, three PEBPs have been identified, namely PEBP1, PEBP2 and PEBP4. PEBP1 and PEBP4 are the most studied as they are implicated in the development of various cancers. PEBP2 is specific of testes in mammals and was essentially studied in rats and mice where it is very abundant. A lot of information has been gained on PEBP1 also named RKIP (Raf Kinase Inhibitory protein) due to its role as a metastasis suppressor in cancer. PEBP1 was also demonstrated to be implicated in Alzheimers disease, diabetes and nephropathies. Furthermore, PEBP1 was described to be involved in many cellular processes, among them are signal transduction, inflammation, cell cycle, proliferation, adhesion, differentiation, apoptosis, autophagy, circadian rhythm and mitotic spindle checkpoint. On the molecular level, PEBP1 was shown to regulate several signaling pathways such as Raf/MEK/ERK, NFkB, PI3K/Akt/mTOR, p38, Notch and Wnt. PEBP1 acts by inhibiting most of the kinases of these signaling cascades. Moreover, PEBP1 is able to bind to a variety of small ligands such as ATP, phospholipids, nucleotides, flavonoids or drugs. Considering PEBP1 is a small cytoplasmic protein (21kDa), its involvement in so many diseases and cellular mechanisms is amazing. The aim of this review is to highlight the molecular systems that are common to all these cellular mechanisms in order to decipher the specific role of PEBP1. Recent discoveries enable us to propose that PEBP1 is a modulator of molecular interactions that control signal transduction during membrane and cytoskeleton reorganization.
Telomeres, the nucleoprotein complexes at the termini of linear chromosomes, are essential for the processes of end replication, end-protection, and chromatin segregation. The Mre11 complex is involved in multiple cellular roles in DNA repair and structure in the regulation and function of telomere size homeostasis. In this study, we characterize yeast telomere chromatin structure, phenotypic heritability, and chromatin segregation in both wild-type [MRE11] and A470 motif alleles. MRE11 strains confer a telomere size of 300 base pairs of G+T irregular simple sequence repeats. This DNA and a portion of subtelomeric DNA is embedded in a telosome: an MNase-resistant non-nucleosomal particle. Chromatin immunoprecipitation shows a three to four-fold lower occupancy of Mre11A470T proteins than wild-type proteins in telosomes. Telosomes containing the Mre11A470T protein confer a greater resistance to MNase digestion than wild-type telosomes. The integration of a wild-type MRE11 allele into an ectopic locus in the genome of a mre11A470T mutant and the introduction of a mre11A470T allele at an ectopic site in a wild-type strain lead to unexpectedly differing results. In each case, the replicated sister chromatids inherit telosomes containing only the protein encoded by the genomic mre11 locus, even in the presence of protein encoded by the opposing ectopic allele. We hypothesize that the telosome segregates by a conservative mechanism. These data support a mechanism for the linkage between sister chromatid replication and maintenance of either identical mutant or identical wild-type telosomes after replication of sister chromatids. These data suggest the presence of an active mechanism for chromatin segregation in yeast.
A new class of formal latent-variable stochastic processes called hidden quantum models (HQM's) is defined in order to clarify the theoretical foundations of ion channel signal processing. HQM's are based on quantum stochastic processes which formalize time-dependent observation. They allow the calculation of autocovariance functions which are essential for frequency-domain signal processing. HQM's based on a particular type of observation protocol called independent activated measurements are shown to to be distributionally equivalent to hidden Markov models yet without an underlying physical Markov process. Since the formal Markov processes are non-physical, the theory of activated measurement allows merging energy-based Eyring rate theories of ion channel behavior with the more common phenomenological Markov kinetic schemes to form energy-modulated quantum channels. Using the simplest quantum channel model consistent with neuronal membrane voltage-clamp experiments, activation eigenenergies are calculated for the Hodgkin-Huxley K+ and Na+ ion channels. It is also shown that maximizing entropy under constrained activation energy yields noise spectral densities approximating $S(f) \sim 1/f^α$, thus offering a biophysical explanation for the ubiquitous $1/f$-type in neurological signals.
The aim of this paper is to give an introductory review of the cryoelectron microscopy as a complex data source for the most of the system biology branches, including the most perspective non-local approaches known as "localomics" and "dynamomics". A brief summary of various cryoelectron mi-croscopy methods and corresponding system biological ap-proaches is given in the text. The above classification can be considered as a useful framework for the primary comprehen-sions about cryoelectron microscopy aims and instrumental tools. We do not discuss any of these concepts in details, but merely point out that their methodological complexity follows only from the structure-functional complexity of biological systems which are investigated in this manner. We also postu-late that one can employ some of the cryoelectron microscopic techniques not only for observation, but also for modification and structural refunctionalization of some biological and similar soft matter objects and microscopic samples. In other worlds, we start with the cryoelectron microscopy as a tool for the sys-tem biology and progress to its applying as an instrument for system biology and functional biomimetics; i.e. "system cryobi-ology" goes over into "synthetic cryobiology" or "cryogenic biomimetics". All these conclusions can be deduced from the most recent works of the latest years, including just submitted foreign papers. This article provides an up-to-date description of the conceptual basis for the novel view on the computational cryoelectron microscopy (in silico) approaches and the data mining principles which lie at the very foundation of modern structural analysis and reconstruction.
Khem Raj Ghusinga, Cesar A. Vargas-Garcia, Abhyudai Singh
How exponentially growing cells maintain size homeostasis is an important fundamental problem. Recent single-cell studies in prokaryotes have uncovered the adder principle, where cells on average, add a fixed size (volume) from birth to division. Interestingly, this added volume differs considerably among genetically-identical newborn cells with similar sizes suggesting a stochastic component in the timing of cell-division. To mechanistically explain the adder principle, we consider a time-keeper protein that begins to get stochastically expressed after cell birth at a rate proportional to the volume. Cell-division time is formulated as the first-passage time for protein copy numbers to hit a fixed threshold. Consistent with data, the model predicts that while the mean cell-division time decreases with increasing size of newborns, the noise in timing increases with size at birth. Intriguingly, our results show that the distribution of the volume added between successive cell-division events is independent of the newborn cell size. This was dramatically seen in experimental studies, where histograms of the added volume corresponding to different newborn sizes collapsed on top of each other. The model provides further insights consistent with experimental observations: the distributions of the added volume and the cell-division time when scaled by their respective means become invariant of the growth rate. Finally, we discuss various modifications to the proposed model that lead to deviations from the adder principle. In summary, our simple yet elegant model explains key experimental findings and suggests a mechanism for regulating both the mean and fluctuations in cell-division timing for size control.
Gabriele Micali, Gerardo Aquino, David M. Richards
et al.
Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms.
The disappearance of the guanosine triphosphate (GTP)-tubulin cap is widely believed to be the forerunner event for the growth-shrinkage transition (`catastrophe') in microtubule filaments in eukaryotic cells. We study a discrete version of a stochastic model of the GTP cap dynamics, originally proposed by Flyvbjerg, Holy and Leibler (Flyvbjerg, Holy and Leibler, Phys. Rev. Lett. 73, 2372, 1994). Our model includes both spontaneous and vectorial hydrolysis, as well as dissociation of a non-hydrolyzed dimer from the filament after incorporation. In the first part of the paper, we apply this model to a single protofilament of a microtubule. A catastrophe transition is defined for each protofilament, similar to the earlier one-dimensional models, the frequency of occurrence of which is then calculated under various conditions, but without explicit assumption of steady state conditions. Using a perturbative approach, we show that the leading asymptotic behavior of the protofilament catastrophe in the limit of large growth velocities is remarkably similar across different models. In the second part of the paper, we extend our analysis to the entire filament by making a conjecture that a minimum number of such transitions are required to occur for the onset of microtubule catastrophe. The frequency of microtubule catastrophe is then determined using numerical simulations, and compared with analytical/semi-analytical estimates made under steady state/quasi-steady state assumptions respectively for the protofilament dynamics. A few relevant experimental results are analyzed in detail, and compared with predictions from the model. Our results indicate that loss of GTP cap in 2-3 protofilaments is necessary to trigger catastrophe in a microtubule.
We develop an agent-based model of the motion and pattern formation of vesicles. These intracellular particles can be found in four different modes of (undirected and directed) motion and can fuse with other vesicles. While the size of vesicles follows a log-normal distribution that changes over time due to fusion processes, their spatial distribution gives rise to distinct patterns. Their occurrence depends on the concentration of proteins which are synthesized based on the transcriptional activities of some genes. Hence, differences in these spatio-temporal vesicle patterns allow indirect conclusions about the (unknown) impact of these genes. By means of agent-based computer simulations we are able to reproduce such patterns on real temporal and spatial scales. Our modeling approach is based on Brownian agents with an internal degree of freedom, $θ$, that represents the different modes of motion. Conditions inside the cell are modeled by an effective potential that differs for agents dependent on their value $θ$. Agent's motion in this effective potential is modeled by an overdampted Langevin equation, changes of $θ$ are modeled as stochastic transitions with values obtained from experiments, and fusion events are modeled as space-dependent stochastic transitions. Our results for the spatio-temporal vesicle patterns can be used for a statistical comparison with experiments. We also derive hypotheses of how the silencing of some genes may affect the intracellular transport, and point to generalizations of the model.