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At least 325 records · Page 18Linked to original sources

arXiv2016

Effects of initial telomere length distribution on senescence onset and heterogeneity

Replicative senescence, induced by telomere shortening, exhibits considerable asynchrony and heterogeneity, the origins of which remain unclear. Here, we formally study how telomere shortening mechanisms impact on senescence kinetics and define two regimes of senescence, depending on the initial telomere length variance. We provide analytical solutions to the model, highlighting a non-linear relationship between senescence onset and initial telomere length distribution. This study reveals the complexity of the collective behavior of telomeres as they shorten, leading to senescence heterogeneity.

q-bio.GN↗

arXiv2016

Stochastic models of the binding kinetics of VEGF-A to VEGFR1 and VEGFR2 in endothelial cells

Vascular endothelial growth factor receptors (VEGFRs) are receptor tyrosine kinases (RTKs) that regulate proliferation, migration, angiogenesis and vascular permeability of endothelial cells. VEGFR1 and VEGFR2 bind vascular endothelial growth factors (VEGFs), inducing receptor dimerisation and activation, characterised by phosphorylation of tyrosine residues in their cytoplasmic domain. Although experimental evidence suggests that RTK signalling occurs both on the plasma membrane and intra-cellularly, and reveals the role of endocytosis in RTK signal transduction, we still lack knowledge of VEGFR phosphorylation-site use and of the spatiotemporal regulation of VEGFR signalling. In this paper, we introduce four stochastic mathematical models to study the binding kinetics of vascular endothelial growth factor VEGF-A to VEGFR1 and VEGFR2, and phosphorylation. The formation of phosphorylated dimers on the cell surface is a two-step process: diffusive transport and binding. The first two of our models only consider VEGFR2, which allows us to introduce new stochastic descriptors making use of a matrix-analytic approach. The two remaining models describe the competition of VEGFR1 and VEGFR2 for ligand availability, and are analysed making use of Gillespie simulations and the van Kampen approximation. Under the hypothesis that bound phosphorylated receptor dimers are the signalling units, we study the time to reach a threshold number of such complexes. Our results indicate that the presence of VEGFR1 does not only affect the timescale to reach a given signalling threshold, but it also affects the maximum attainable threshold. This result is consistent with the conjectured role of VEGFR1 as a decoy receptor, that prevents VEGF-A binding to VEGFR2, and thus, VEGFR2 attaining suitable phosphorylation levels. We identify an optimum range of ligand concentration for sustained dimer phosphorylation.

q-bio.BM↗

arXiv2016

Narrow escape to small windows on a small ball modeling the viral entry into the cell nucleus

A certain class of viruses replicates inside a cell if they can enter the nucleus through one of many small target pores, before being permanently trapped or degraded. We adopt for viral motion a switching stochastic process model and we estimate here the probability and the conditional mean first passage time for a viral particle to attain alive the nucleus. The cell nucleus is covered with thousands of small absorbing nuclear pores and the minimum distance between them defines the smallest spatial scale that limits the efficiency of stochastic simulations. Using the Neuman-Green's function method to solve the steady-state Fokker-Planck equation, we derive asymptotic formula for the probability and mean arrival time to a small window for various pores' distributions, that agree with stochastic simulations. These formulas reveal how key geometrical parameters defines the cytoplasmic stage of viral infection.

q-bio.SC↗

arXiv2016

Mechanosensing in myosin filament solves a 60 years old conflict in skeletal muscle modeling between high power output and slow rise in tension

Almost 60 years ago Andrew Huxley with his seminal paper \cite{Huxley1957} laid the foundation of modern muscle modeling, linking chemical events to mechanical performance. He described mechanics and energetics of muscle contraction through the cyclical attachment and detachment of myosin motors to the actin filament with ad hoc assumptions on the dependence of the rate constants on the strain of the myosin motors. That relatively simple hypothesis is still present in recent models, even though with several modifications to adapt the model to the different experimental constraints which became subsequently available. However, already in that paper, one controversial aspect of the model became clear. Relatively high attachment and detachment rates of myosin to the actin filament were needed to simulate the high power output at intermediate velocity of contraction. However, these rates were incompatible with the relatively slow rise in tension after activation, despite the rise should be generated by the same rate functions. This discrepancy has not been fully solved till today, despite several hypotheses have been forwarded to reconcile the two aspects. Here, using a conventional muscle model, we show that the recently revealed mechanosensing mechanism of recruitment of myosin motors \cite{Linarietal2015} can solve this long standing problem without any further ad-hoc hypotheses.

physics.bio-ph↗

arXiv2016

Allosteric Regulation by a Critical Membrane

Many of the processes that underly neural computation are carried out by ion channels embedded in the plasma membrane, a two-dimensional liquid that surrounds all cells. Recent experiments have demonstrated that this membrane is poised close to a liquid-liquid critical point in the Ising universality class. Here we use both exact and stochastic techniques on the lattice Ising model to explore the ramifications of proximity to criticality for proteins that are allosterically coupled to Ising composition modes. Owing to diverging generalized susceptibilities, such a protein's activity becomes strongly influenced by perturbations that influence the two relevant parameters of the critical point, especially the critical temperature. In addition, the protein's kinetics acquire a range of time scales from its surrounding membrane, naturally leading to non-Markovian dynamics.

physics.bio-ph↗

arXiv2016

Genoautotomy (Genome 'Self-Injury') in Eukaryotic Cells: A Cellular Defence Response to Genotoxic Stress

This paper proposes that eukaryotic cells, under severe genotoxic stress, can commit genoautotomy (genome 'self-injury') that involves cutting and releasing single-stranded DNA (ssDNA) fragments from double-stranded DNA and leaving ssDNA gaps in the genome. The ssDNA gaps could be easily and precisely repaired later. The released ssDNA fragments may play some role in the regulation of cell cycle progression. Taken together, genoautotomy causes limited nonlethal DNA damage, but prevents the whole genome from lethal damage, and thus should be deemed as a eukaryotic cellular defence response to genotoxic stress.

q-bio.SC↗

arXiv2016

Computational Limitations of First-Order Repressor Systems

Almost all current approaches for engineering modular logic components in synthetic biology use first-order regulators, including most CRISPR/CAS, TAL, zinc finger, and RNA interference systems. Many practitioners understand intuitively that second and higher order binding is necessary for scalability, and this is easy to show for single-input single-output systems. However, no study to date has analysed whether a more complex system, utilizing e.g. feedback or error correction, can produce scalable computation from first-order regulators. We prove here that first order repressor systems cannot support bistability. In the process, we introduce a function G to measure signal quality in molecular systems, and we show that G always decreases in dynamic feedback systems as well as static feed-forward logic cascades of first-order repressors. As a result, first order repressors cannot build memory or signal buffering elements. Finally, we suggest G as a potential new property for characterization of standard biological parts.

q-bio.MN↗

arXiv2016

Effect of gene-expression bursts on stochastic timing of cellular events

Gene expression is inherently a noisy process which manifests as cell-to-cell variability in time evolution of proteins. Consequently, events that trigger at critical threshold levels of regulatory proteins exhibit stochasticity in their timing. An important contributor to the noise in gene expression is translation bursts which correspond to randomness in number of proteins produced in a single mRNA lifetime. Modeling timing of an event as a first-passage time (FPT) problem, we explore the effect of burst size distribution on event timing. Towards this end, the probability density function of FPT is computed for a gene expression model with burst size drawn from a generic non-negative distribution. Analytical formulas for FPT moments are provided in terms of known vectors and inverse of a matrix. The effect of burst size distribution is investigated by looking at how the feedback regulation strategy that minimizes noise in timing around a given time deviates from the case when burst is deterministic. Interestingly, results show that the feedback strategy for deterministic burst case is quite robust to change in burst size distribution, and deviations from it are confined to about 20% of the optimal value. These findings facilitate an improved understanding of noise regulation in event timing.

q-bio.SC↗

arXiv2016

Variance-corrected Michaelis-Menten equation predicts transient rates of single-enzyme reactions and response times in bacterial gene-regulation

Many chemical reactions in biological cells occur at very low concentrations of constituent molecules. Thus, transcriptional gene-regulation is often controlled by poorly expressed transcription-factors, such as E.coli lac repressor with few tens of copies. Here we study the effects of inherent concentration fluctuations of substrate-molecules on the seminal Michaelis-Menten scheme of biochemical reactions. We present a universal correction to the Michaelis-Menten equation for the reaction-rates. The relevance and validity of this correction for enzymatic reactions and intracellular gene-regulation is demonstrated. Our analytical theory and simulation results confirm that the proposed variance-corrected Michaelis-Menten equation predicts the rate of reactions with remarkable accuracy even in the presence of large non-equilibrium concentration fluctuations. The major advantage of our approach is that it involves only the mean and variance of the substrate-molecule concentration. Our theory is therefore accessible to experiments and not specific to the exact source of the concentration fluctuations.

q-bio.QM↗

arXiv2017

A model of autophagy size selectivity by receptor clustering on peroxisomes

Selective autophagy must not only select the correct type of organelle, but also must discriminate between individual organelles of the same kind so that some but not all of the organelles are removed. We propose that physical clustering of autophagy receptor proteins on the organelle surface can provide an appropriate all-or-none signal for organelle degradation. We explore this proposal using a computational model restricted to peroxisomes and the relatively well characterized pexophagy receptor proteins NBR1 and p62. We find that larger peroxisomes nucleate NBR1 clusters first and lose them last through competitive coarsening. This results in significant size-selectivity that favors large peroxisomes, and can explain the increased catalase signal that results from siRNA inhibition of p62. Excess ubiquitin, resulting from damaged organelles, suppresses size-selectivity but not cluster formation. Our proposed selectivity mechanism thus allows all damaged organelles to be degraded, while otherwise selecting only a portion of organelles for degradation.

physics.bio-ph↗

arXiv2016

Robustness analysis of spatiotemporal models in the presence of extrinsic fluctuations

We analyze the governing partial differential equations of a model of pole-to-pole oscillations of the MinD protein in a bacterial cell. The sensitivity to extrinsic noise in the parameters of the model is explored. Our analysis shows that overall, the oscillations are robust to extrinsic perturbations in the sense that small perturbations in reaction coefficients result in small differences in the frequency and in the amplitude. However, a combination of analysis and simulation also reveals that the oscillations are more sensitive to some extrinsic time-scales than to others.

math.NA↗

arXiv2016

Geometric stability considerations of the ribosome flow model with pool

In order to better understand the process of gene translation, the ribosome flow model (RFM) with pool was introduced recently. This model describes the movement of several ribosomes along an mRNA template and simultaneously captures the dynamics of the finite pool of ribosomes. Studying this system with respect to the number and stability of its equilibria was so far based on monotone systems theory (Margaliot and Tuller, 2012). We extend the results obtained therein by using a geometric approach, showing that the equilibria of the system constitute a normally hyperbolic invariant submanifold. Subsequently, we analyze the Jacobi linearization of the system evaluated at the equilibria in order to show that the equilibria are asymptotically stable relative to certain affine subspaces. As this approach does not require any monotonicity features of the system, it may also be applied for more complex systems of the same kind such as bi-directional ribosome flows or time-varying template numbers.

q-bio.SC↗

arXiv2016

A resource dependent protein synthesis model for evaluating synthetic circuits

Reliable in-silico design of synthetic gene networks necessitates novel approaches to model the process of protein synthesis under the influence of limited resources. We present such a novel protein synthesis model which originates from the Ribosome Flow Model and among other things describes the movement of RNA-polymerase and Ribosomes on mRNA and DNA templates respectively. By analyzing the convergence properties of this model based upon geometric considerations we present additional insights into the dynamic mechanisms of the process of protein synthesis. Further, we exemplarily show how this model can be used to evaluate the performance of synthetic gene circuits under different loading scenarios.

q-bio.MN↗

arXiv2016

Theory on the mechanisms of combinatorial binding of transcription factors with DNA

We develop a theoretical framework on the mechanism of combinatorial binding of transcription factors (TFs) with their specific binding sites on DNA. We consider three possible mechanisms viz. monomer, hetero-oligomer and coordinated recruitment pathways. In the monomer pathway, combinatorial TFs search for their targets in an independent manner and the protein-protein interactions among them will be insignificant. The protein-protein interactions are very strong so that the hetero-oligomer complex of TFs as a whole searches for the cognate sites in case of hetero-oligomer pathway. The TF which arrived first will recruit the adjacent TFs in a sequential manner in the recruitment pathway. The free energy released from the protein-protein interactions among TFs will be in turn utilized to stabilize the TFs-DNA complex. Such coordinated binding of TFs in fact emerges as the cooperative effect. Monomer and hetero-oligomer pathways are efficient only when few TFs are involved in the combinatorial regulation. Detailed random walk simulations suggest that when the number of TFs in a combination increases then the searching efficiency of TFs in these pathways decreases with the increasing number of TFs in a power law manner. The power law exponent associated with the monomer pathway seems to be strongly dependent on the number of TFs, distance between the initial position of TFs from their specific binding sites and the hop size associated with the dynamics of TFs on DNA.

q-bio.SC↗

arXiv2024

Nonequilibrium and nonlinear kinetics as key determinants for bistability in fission yeast G2-M transition

A living cell is an open, nonequilibrium biochemical system where ATP hydrolysis serves as the energy source for a wide range of intracellular processes, possibly including the assurance for decision-making. In the fission yeast cell cycle, the transition from G2 to M phase is driven by the activation of Cdc13/Cdc2 and Cdc25 and the deactivation of Wee1 through phosphorylation-dephosphorylation cycles with feedback loops. Here, we present a kinetic description of the G2-M circuit which reveals that both cellular ATP level and ATP hydrolysis free energy critically control Cdc2 activation. Using fission yeast nucleoplasmic extract (YNPE), we experimentally verify that increased ATP level drives the activation of Cdc2 which exhibits bistability and hysteresis in response to changes in cellular ATP level and ATP hydrolysis energy. These findings suggest that cellular ATP level and ATP hydrolysis energy are determinants of the bistability and robustness of Cdc2 activation during G2-M transition.

q-bio.MN↗

arXiv2017

Automated scalable segmentation of neurons from multispectral images

Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.

q-bio.NC↗

arXiv2016

Dynamic instabilities in the kinetics of growth and disassembly of microtubules

Dynamic instability of microtubules is considered using frameworks of non-linear thermodynamics and non-equilibrium reaction-diffusion systems. Stochastic assembly/disassembly phases in the polymerization dynamics of microtubules are treated as a result of collective clusterization of microdefects (holes in structure). The model explains experimentally observed power law dependence of catastrophe frequency from the microtubule growth rate. Additional reaction-diffusion-precipitation model is developed to account for kinetic limitations in microtubule dynamics. It is shown that large scale periodic microtubules length fluctuations are accompanied by concentration autowaves. We built corresponding parametric diagram mapping areas of stationary, non-stationary and metastable solutions. The loss of stability for the stationary solutions happens through bifurcation of Andronov-Hopf. Using parametric diagram we classify cytostatic effect of microtubule stabilizing drugs in four major classes and analyze their compatibility and possible synergistic effect for cancer treatment therapy.

q-bio.SC↗

arXiv2016

An alternative approach to Michaelis-Menten kinetics that is based on the Renormalization Group: Comparison with the perturbation expansion beyond the sQSSA

We recall the perturbation expansion for Michaelis-Menten kinetics, beyond the standard quasi-steady-state approximation (sQSSA). Against this background, we are able to appropriately apply the alternative approach to the study of singularly perturbed differential equations that is based on the renormalization group (SPDERG), by clarifying similarities and differences. In the present demanding situation, we directly renormalize the bare initial condition value for the substrate. Our main results are: i) the 2nd order SPDERG uniform approximations to the correct solutions contain, up to 1st order, the same outer components as the known perturbation expansion ones; ii) the differential equation to be solved for the derivation of the 1st order outer substrate component is simpler within the SPDERG approach; iii) the approximations better reproduce the numerical solutions of the original problem in a region encompassing the matching one, because of the 2nd order terms in the inner components, calculated here for the first time to our knowledge: iv) the refined SPDERG uniform approximations, that we propose, give the correct asymptotically vanishing solutions, too, and allow to obtain results nearly indistinguishable from the solutions of the original problem in a large part of the whole relevant time window, even in the studied unfavourable kinetic constant case, for an expansion parameter value as large as ε = 0.5.

physics.chem-ph↗