Showing papers in "Physical Biology in 2012"
TL;DR: This paper builds on research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models.
Abstract: Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology.
406 citations
TL;DR: This initial validation of an enrichment-free assay demonstrates the ability to identify significant numbers of HD-CTCs in a majority of patients with prostate, breast and pancreatic cancers.
Abstract: Hematologic spread of carcinoma results in incurable metastasis; yet, the basic characteristics and travel mechanisms of cancer cells in the bloodstream are unknown. We have established a fluid phase biopsy approach that identifies circulating tumor cells (CTCs) without using surface protein-based enrichment and presents them in sufficiently high definition (HD) to satisfy diagnostic pathology image quality requirements. This 'HD-CTC' assay finds >5 HD-CTCs mL?1 of blood in 80% of patients with metastatic prostate cancer (n = 20), in 70% of patients with metastatic breast cancer (n = 30), in 50% of patients with metastatic pancreatic cancer (n = 18), and in 0% of normal controls (n = 15). Additionally, it finds HD-CTC clusters ranging from 2 HD-CTCs to greater than 30 HD-CTCs in the majority of these cancer patients. This initial validation of an enrichment-free assay demonstrates our ability to identify significant numbers of HD-CTCs in a majority of patients with prostate, breast and pancreatic cancers.
271 citations
TL;DR: An enrichment-free immunofluorescence detection method is reported that can reproducibly detect and enumerate homotypic CTC aggregates in patient samples and may assist future studies in determining which population of cells is more physically likely to contribute to metastasis and VTE.
Abstract: Circulating tumor cells (CTCs) have been implicated as a population of cells that may seed metastasis and venous thromboembolism (VTE), two major causes of mortality in cancer patients. Thus far, existing CTC detection technologies have been unable to reproducibly detect CTC aggregates in order to address what contribution CTC aggregates may make to metastasis or VTE. We report here an enrichment-free immunofluorescence detection method that can reproducibly detect and enumerate homotypic CTC aggregates in patient samples. We identified CTC aggregates in 43% of 86 patient samples. The fraction of CTC aggregation was investigated in blood draws from 24 breast, 14 non-small cell lung, 18 pancreatic, 15 prostate stage IV cancer patients and 15 normal blood donors. Both single CTCs and CTC aggregates were measured to determine whether differences exist in the physical characteristics of these two populations. Cells contained in CTC aggregates had less area and length, on average, than single CTCs. Nuclear to cytoplasmic ratios between single CTCs and CTC aggregates were similar. This detection method may assist future studies in determining which population of cells is more physically likely to contribute to metastasis and VTE.
190 citations
TL;DR: A simple deterministic reaction-diffusion model is formulated, which successfully predicts the spatial patterns created by two competing species during colony expansion and connects the microscopic parameters like growth rates and diffusion constants with macroscopic spatial patterns and predicts the relationship between fitness in liquid cultures and on Petri dishes, which is confirmed experimentally.
Abstract: Evolutionary experiments with microbes are a powerful tool to study mutations and natural selection. These experiments, however, are often limited to the well-mixed environments of a test tube or a chemostat. Since spatial organization can significantly affect evolutionary dynamics, the need is growing for evolutionary experiments in spatially structured environments. The surface of a Petri dish provides such an environment, but a more detailed understanding of microbial growth on Petri dishes is necessary to interpret such experiments. We formulate a simple deterministic reaction–diffusion model, which successfully predicts the spatial patterns created by two competing species during colony expansion. We also derive the shape of these patterns analytically without relying on microscopic details of the model. In particular, we find that the relative fitness of two microbial strains can be estimated from the logarithmic spirals created by selective sweeps. The theory is tested with strains of the budding yeast Saccharomyces cerevisiae for spatial competitions with different initial conditions and for a range of relative fitnesses. The reaction–diffusion model also connects the microscopic parameters like growth rates and diffusion constants with macroscopic spatial patterns and predicts the relationship between fitness in liquid cultures and on Petri dishes, which we confirmed experimentally. Spatial sector patterns therefore provide an alternative fitness assay to the commonly used liquid culture fitness assays.
169 citations
TL;DR: The melanoma-like phenotype arising from serotonergic signaling by 'instructor' cells-a cell population that is able to induce a metastatic phenotype in normal melanocytes is investigated and the bioelectrical properties of tumor-like structures induced by canonical oncogenes and cancer-causing compounds are investigated.
Abstract: Cancer may result from localized failure of instructive cues that normally orchestrate cell behaviors toward the patterning needs of the organism. Steady-state gradients of transmembrane voltage (Vmem) in non-neural cells are instructive, epigenetic signals that regulate pattern formation during embryogenesis and morphostatic repair. Here, we review molecular data on the role of bioelectric cues in cancer and present new findings in the Xenopus laevis model on how the microenvironment's biophysical properties contribute to cancer in vivo. First, we investigated the melanoma-like phenotype arising from serotonergic signaling by ‘instructor’ cells—a cell population that is able to induce a metastatic phenotype in normal melanocytes. We show that when these instructor cells are depolarized, blood vessel patterning is disrupted in addition to the metastatic phenotype induced in melanocytes. Surprisingly, very few instructor cells need to be depolarized for the hyperpigmentation phenotype to occur; we present a model of antagonistic signaling by serotonin receptors that explains the unusual all-or-none nature of this effect. In addition to the body-wide depolarization-induced metastatic phenotype, we investigated the bioelectrical properties of tumor-like structures induced by canonical oncogenes and cancer-causing compounds. Exposure to carcinogen 4-nitroquinoline 1-oxide (4NQO) induces localized tumors, but has a broad (and variable) effect on the bioelectric properties of the whole body. Tumors induced by oncogenes show aberrantly high sodium content, representing a non-invasive diagnostic modality. Importantly, depolarized transmembrane potential is not only a marker of cancer but is functionally instructive: susceptibility to oncogene-induced tumorigenesis is significantly reduced by forced prior expression of hyperpolarizing ion channels. Importantly, the same effect can be achieved by pharmacological manipulation of endogenous chloride channels, suggesting a strategy for cancer suppression that does not require gene therapy. Together, these data extend our understanding of the recently demonstrated role of transmembrane potential in tumor formation and metastatic cell behavior. Vmem is an important non-genetic biophysical aspect of the microenvironment that regulates the balance between normally patterned growth and carcinogenesis.
132 citations
TL;DR: This work demonstrates features of metabolism at each of several levels of hierarchy, beginning with the small-molecule metabolic substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.
Abstract: Metabolism is built on a foundation of organic chemistry, and employs structures and interactions at many scales. Despite these sources of complexity, metabolism also displays striking and robust regularities in the forms of modularity and hierarchy, which may be described compactly in terms of relatively few principles of composition. These regularities render metabolic architecture comprehensible as a system, and also suggests the order in which layers of that system came into existence. In addition metabolism also serves as a foundational layer in other hierarchies, up to at least the levels of cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, motivates us to interpret metabolism as a source of causation or constraint on many forms of organization in the biosphere. Many of the forms of modularity and hierarchy exhibited by metabolism are readily interpreted as stages in the emergence of catalytic control by living systems over organic chemistry, sometimes recapitulating or incorporating geochemical mechanisms.We identify as modules, either subsets of chemicals and reactions, or subsets of functions, that are re-used in many contexts with a conserved internal structure. At the small molecule substrate level, module boundaries are often associated with the most complex reaction mechanisms, catalyzed by highly conserved enzymes. Cofactors form a biosynthetically and functionally distinctive control layer over the small-molecule substrate. The most complex members among the cofactors are often associated with the reactions at module boundaries in the substrate networks, while simpler cofactors participate in widely generalized reactions. The highly tuned chemical structures of cofactors (sometimes exploiting distinctive properties of the elements of the periodic table) thereby act as 'keys' that incorporate classes of organic reactions within biochemistry.Module boundaries provide the interfaces where change is concentrated, when we catalogue extant diversity of metabolic phenotypes. The same modules that organize the compositional diversity of metabolism are argued, with many explicit examples, to have governed long-term evolution. Early evolution of core metabolism, and especially of carbon-fixation, appears to have required very few innovations, and to have used few rules of composition of conserved modules, to produce adaptations to simple chemical or energetic differences of environment without diverse solutions and without historical contingency. We demonstrate these features of metabolism at each of several levels of hierarchy, beginning with the small-molecule metabolic substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.
132 citations
TL;DR: The results demonstrate that despite stringent morphologic inclusion criteria for the definition of HD-CTCs, theHD-CTC assay shows high sensitivity in the detection and characterization of both early- and late-stage lung cancer CTCs.
Abstract: Circulating tumor cell (CTC) counts are an established prognostic marker in metastatic prostate, breast and colorectal cancer, and recent data suggest a similar role in late stage non-small cell lung cancer (NSCLC). However, due to sensitivity constraints in current enrichment-based CTC detection technologies, there are few published data about CTC prevalence rates and morphologic heterogeneity in early-stage NSCLC, or the correlation of CTCs with disease progression and their usability for clinical staging. We investigated CTC counts, morphology and aggregation in early stage, locally advanced and metastatic NSCLC patients by using a fluid-phase biopsy approach that identifies CTCs without relying on surface-receptor-based enrichment and presents them in sufficiently high definition (HD) to satisfy diagnostic pathology image quality requirements. HD-CTCs were analyzed in blood samples from 78 chemotherapy-naive NSCLC patients. 73% of the total population had a positive HD-CTC count (>0 CTC in 1 mL of blood) with a median of 4.4 HD-CTCs mL⁻¹ (range 0-515.6) and a mean of 44.7 (±95.2) HD-CTCs mL⁻¹. No significant difference in the medians of HD-CTC counts was detected between stage IV (n = 31, range 0-178.2), stage III (n = 34, range 0-515.6) and stages I/II (n = 13, range 0-442.3). Furthermore, HD-CTCs exhibited a uniformity in terms of molecular and physical characteristics such as fluorescent cytokeratin intensity, nuclear size, frequency of apoptosis and aggregate formation across the spectrum of staging. Our results demonstrate that despite stringent morphologic inclusion criteria for the definition of HD-CTCs, the HD-CTC assay shows high sensitivity in the detection and characterization of both early- and late-stage lung cancer CTCs. Extensive studies are warranted to investigate the prognostic value of CTC profiling in early-stage lung cancer. This finding has implications for the design of extensive studies examining screening, therapy and surveillance in lung cancer patients.
129 citations
TL;DR: A primer for quantitative biologists is provided that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.
Abstract: Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.
106 citations
TL;DR: This study evaluated a method using enrichment free fluorescent labeling of CTCs followed by automated digital microscopy in patients with non-small cell lung cancer and found a propensity for increased CTC detection as the disease progressed in individual patients.
Abstract: Sampling circulating tumor cells (CTCs) from peripheral blood is ideally accomplished using assays that detect high numbers of cells and preserve them for downstream characterization We sought to evaluate a method using enrichment free fluorescent labeling of CTCs followed by automated digital microscopy in patients with non-small cell lung cancer Twenty-eight patients with non-small cell lung cancer and hematogenously seeded metastasis were analyzed with multiple blood draws We detected CTCs in 68% of analyzed samples and found a propensity for increased CTC detection as the disease progressed in individual patients CTCs were present at a median concentration of 16 CTCs ml⁻¹ of analyzed blood in the patient population Higher numbers of detected CTCs were associated with an unfavorable prognosis
100 citations
TL;DR: The motion of a suspension of red blood cells (RBCs) flowing in a Y-shaped bifurcating microfluidic channel is investigated using a validated low-dimensional RBC model based on dissipative particle dynamics, which leads to efficient simulations of blood flow in microcirculation over a wide range of hematocrits.
Abstract: The motion of a suspension of red blood cells (RBCs) flowing in a Y-shaped bifurcating microfluidic channel is investigated using a validated low-dimensional RBC model based on dissipative particle dynamics. Specifically, the RBC is represented as a closed torus-like ring of ten colloidal particles, which leads to efficient simulations of blood flow in microcirculation over a wide range of hematocrits. Adaptive no-slip wall boundary conditions were implemented to model hydrodynamic flow within a specific wall structure of diverging three-dimensional microfluidic channels, paying attention to controlling density fluctuations. Plasma skimming and the all-or-nothing phenomenon of RBCs in a bifurcating microfluidic channel have been investigated in our simulations for healthy and diseased blood, including the size of a cell-free layer on the daughter branches. The feed hematocrit level in the parent channel has considerable influence on blood‐plasma separation. Compared to the blood‐plasma separation efficiencies of healthy RBCs, malaria-infected stiff RBCs (iRBCs) have a tendency to travel into the low flow-rate daughter branch because of their different initial distribution in the parent channel. Our simulation results are consistent with previously published experimental results and theoretical predictions.
90 citations
TL;DR: It is concluded that knowledge of both evolutionary tradeoffs and drug interactions is crucial in planning optimal chemotherapy schedules for individual patients.
Abstract: Chemotherapy for metastatic cancer commonly fails due to evolution of drug resistance in tumor cells. Here, we view cancer treatment as a game in which the oncologists choose a therapy and tumors 'choose' an adaptive strategy. We propose the oncologist can gain an upper hand in the game by choosing treatment strategies that anticipate the adaptations of the tumor. In particular, we examine the potential benefit of exploiting evolutionary tradeoffs in tumor adaptations to therapy. We analyze a math model where cancer cells face tradeoffs in allocation of resistance to two drugs. The tumor 'chooses' its strategy by natural selection and the oncologist chooses her strategy by solving a control problem. We find that when tumor cells perform best by investing resources to maximize response to one drug the optimal therapy is a time-invariant delivery of both drugs simultaneously. However, if cancer cells perform better using a generalist strategy allowing resistance to both drugs simultaneously, then the optimal protocol is a time varying solution in which the two drug concentrations negatively covary. However, drug interactions can significantly alter these results. We conclude that knowledge of both evolutionary tradeoffs and drug interactions is crucial in planning optimal chemotherapy schedules for individual patients.
TL;DR: An alternative biologically-motivated model for F-actin-NPF interaction based on properties of GTPases acting as NPFs is presented, which explains distinct types of pattern initiation mechanisms, and identifies parameter regimes corresponding to distinct behaviours.
Abstract: Patterns of waves, patches, and peaks of actin are observed experimentally in many living cells. Models of this phenomenon have been based on the interplay between filamentous actin (F-actin) and its nucleation promoting factors (NPFs) that activate the Arp2/3 complex. Here we present an alternative biologically-motivated model for F-actin-NPF interaction based on properties of GTPases acting as NPFs. GTPases (such as Cdc42, Rac) are known to promote actin nucleation, and to have active membrane-bound and inactive cytosolic forms. The model is a natural extension of a previous mathematical mini-model of small GTPases that generates static cell polarization. Like other modellers, we assume that F-actin negative feedback shapes the observed patterns by suppressing the trailing edge of NPF-generated wave-fronts, hence localizing the activity spatially. We find that our NPF-actin model generates a rich set of behaviours, spanning a transition from static polarization to single pulses, reflecting waves, wave trains, and oscillations localized at the cell edge. The model is developed with simplicity in mind to investigate the interaction between nucleation promoting factor kinetics and negative feedback. It explains distinct types of pattern initiation mechanisms, and identifies parameter regimes corresponding to distinct behaviours. We show that weak actin feedback yields static patterning, moderate feedback yields dynamical behaviour such as travelling waves, and strong feedback can lead to wave trains or total suppression of patterning. We use a recently introduced nonlinear bifurcation analysis to explore the parameter space of this model and predict its behaviour with simulations validating those results.
TL;DR: An overview of logic modeling formalisms in the context of training logic models to data is presented, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction are presented.
Abstract: Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.
TL;DR: The emergence of intense sources of terahertz radiation based on lasers and electron accelerators has considerable potential for research on biological systems but this potential will only be realized if they are accompanied by the development of sophisticated pump-probe and multidimensional experimental techniques and by the study of biological systems in the controlled environments necessary for their maintenance and viability.
Abstract: The emergence of intense sources of terahertz radiation based on lasers and electron accelerators has considerable potential for research on biological systems. This perspective gives a brief survey of theoretical work and the results of experiments on biological molecules and more complex biological systems. Evidence is accumulating that terahertz radiation influences biological systems and this needs to be clarified in order to establish safe levels of human exposure to this radiation. The use of strong sources of terahertz radiation may contribute to the resolution of controversies over the mechanism of biological organization. However the potential of these sources will only be realized if they are accompanied by the development of sophisticated pump-probe and multidimensional experimental techniques and by the study of biological systems in the controlled environments necessary for their maintenance and viability.
TL;DR: This study uses immunocytochemistry to compare the protein expression levels of total cytokeratin (CK) and androgen receptor (AR) in CTCs and cell lines from patients with prostate cancer to determine what translational insights might be gained through the use of cell line data.
Abstract: Many important experiments in cancer research are initiated with cell line data analysis due to the ease of accessibility and utilization. Recently, the ability to capture and characterize circulating tumor cells (CTCs) has become more prevalent in the research setting. This ability to detect, isolate and analyze CTCs allows us to directly compare specific protein expression levels found in patient CTCs to cell lines. In this study, we use immunocytochemistry to compare the protein expression levels of total cytokeratin (CK) and androgen receptor (AR) in CTCs and cell lines from patients with prostate cancer to determine what translational insights might be gained through the use of cell line data. A non-enrichment CTC detection assay enables us to compare cytometric features and relative expression levels of CK and AR by indirect immunofluorescence from prostate cancer patients against the prostate cancer cell line LNCaP. We measured physical characteristics of these two groups and observed significant differences in cell size, fluorescence intensity and nuclear to cytoplasmic ratio. We hope that these experiments will initiate a foundation to allow cell line data to be compared against characteristics of primary cells from patients.
TL;DR: In this paper, the stochastic mutual repressor model is analyzed using perturbation methods and Monte Carlo simulations in the bistable regime, where the deterministic limit exhibits two stable fixed points and an unstable saddle, and the case of small noise is considered.
Abstract: The stochastic mutual repressor model is analysed using perturbation methods. This simple model of a gene circuit consists of two genes and three promotor states. Either of the two protein products can dimerize, forming a repressor molecule that binds to the promotor of the other gene. When the repressor is bound to a promotor, the corresponding gene is not transcribed and no protein is produced. Either one of the promotors can be repressed at any given time or both can be unrepressed, leaving three possible promotor states. This model is analysed in its bistable regime in which the deterministic limit exhibits two stable fixed points and an unstable saddle, and the case of small noise is considered. On small timescales, the stochastic process fluctuates near one of the stable fixed points, and on large timescales, a metastable transition can occur, where fluctuations drive the system past the unstable saddle to the other stable fixed point. To explore how different intrinsic noise sources affect these transitions, fluctuations in protein production and degradation are eliminated, leaving fluctuations in the promotor state as the only source of noise in the system. The process without protein noise is then compared to the process with weak protein noise using perturbation methods and Monte Carlo simulations. It is found that some significant differences in the random process emerge when the intrinsic noise source is removed.
TL;DR: This work considers an active cell motility process in which internal polarity is governed by a positive feedback from cell displacements, a mechanism that can result in highly persistent motion when constrained by an oriented ECM structure.
Abstract: Cell invasion from an aggregate into a surrounding extracellular matrix (ECM) is an important process during development disease, e.g., vascular network assembly or tumor progression. To describe the behavior emerging from autonomous cell motility, cell?cell adhesion and contact guidance by ECM filaments, we propose a suitably modified cellular Potts model. We consider an active cell motility process in which internal polarity is governed by a positive feedback from cell displacements, a mechanism that can result in highly persistent motion when constrained by an oriented ECM structure. The model allows us to explore the interplay between haptotaxis, matrix degradation and active cell movement. We show that for certain conditions the cells are able to both invade the ECM and follow the ECM tracks. Furthermore, we argue that enforcing mechanical equilibrium within a bulk cell mass is of key importance in multicellular simulations.
TL;DR: It is shown that a network's bistability range is positively correlated with its maximum open-loop gain and that both quantities depend on the sign of the feedback loops and the type of feedback coupling.
Abstract: Biochemical regulatory networks governing diverse cellular processes such as stress-response, differentiation and cell cycle often contain coupled feedback loops. We aim at understanding how features of feedback architecture, such as the number of loops, the sign of the loops and the type of their coupling, affect network dynamical performance. Specifically, we investigate how bistability range, maximum open-loop gain and switching times of a network with transcriptional positive feedback are affected by additive or multiplicative coupling with another positive- or negative-feedback loop. We show that a network's bistability range is positively correlated with its maximum open-loop gain and that both quantities depend on the sign of the feedback loops and the type of feedback coupling. Moreover, we find that the addition of positive feedback could decrease the bistability range if we control the basal level in the signal-response curves of the two systems. Furthermore, the addition of negative feedback has the capacity to increase the bistability range if its dissociation constant is much lower than that of the positive feedback. We also find that the addition of a positive feedback to a bistable network increases the robustness of its bistability range, whereas the addition of a negative feedback decreases it. Finally, we show that the switching time for a transition from a high to a low steady state increases with the effective fold change in gene regulation. In summary, we show that the effect of coupled feedback loops on the bistability range and switching times depends on the underlying mechanistic details.
TL;DR: The Torquato approximation provides the most accurate estimates of D(e) for all collagen concentrations among all of the analytical approximations the authors consider, and rigorous cross-property relations are applied to estimate the effective bulk modulus of collagen networks from a knowledge of the effective diffusion coefficient computed.
Abstract: Biopolymer networks are of fundamental importance to many biological processes in normal and tumorous tissues. In this paper, we employ the panoply of theoretical and simulation techniques developed for characterizing heterogeneous materials to quantify the microstructure and effective diffusive transport properties (diffusion coefficient De and mean survival time τ ) of collagen type I networks at various collagen concentrations. In particular, we compute the pore-size probability density function P(δ) for the networks and present a variety of analytical estimates of the effective diffusion coefficient De for finite-sized diffusing particles, including the low-density approximation, the Ogston approximation and the Torquato approximation. The Hashin‐Strikman upper bound on the effective diffusion coefficient De and the pore-size lower bound on the mean survival time τ are used as benchmarks to test our analytical approximations and numerical results. Moreover, we generalize the efficient first-passage-time techniques for Brownian-motion simulations in suspensions of spheres to the case of fiber networks and compute the associated effective diffusion coefficient De as well as the mean survival time τ , which is related to nuclear magnetic resonance relaxation times. Our numerical results for De are in excellent agreement with analytical results for simple network microstructures, such as periodic arrays of parallel cylinders. Specifically, the Torquato approximation provides the most accurate estimates of De for all collagen concentrations among all of the analytical approximations we consider. We formulate a universal curve for τ for the networks at different collagen concentrations, extending the work of Torquato and Yeong (1997 J. Chem. Phys. 106 8814). We apply rigorous cross-property relations to estimate the effective bulk modulus of collagen networks from a knowledge of the effective diffusion coefficient computed here. The use of cross-property relations to link other physical properties to the transport properties of collagen networks is also discussed.
TL;DR: This paper reports on the combination of three computationally inexpensive simulation methods--normal mode analysis using the elastic network model, rigidity analysisUsing the pebble game algorithm, and geometric simulation of protein motion--to explore conformational change along normal mode eigenvectors.
Abstract: Protein function frequently involves conformational changes with large amplitude on timescales which are difficult and computationally expensive to access using molecular dynamics. In this paper, we report on the combination of three computationally inexpensive simulation methods—normal mode analysis using the elastic network model, rigidity analysis using the pebble game algorithm, and geometric simulation of protein motion—to explore conformational change along normal mode eigenvectors. Using a combination of ElNemo and First/Froda software, large-amplitude motions in proteins with hundreds or thousands of residues can be rapidly explored within minutes using desktop computing resources. We apply the method to a representative set of six proteins covering a range of sizes and structural characteristics and show that the method identifies specific types of motion in each case and determines their amplitude limits.
TL;DR: New 2D data on interaction of the T cell receptor with pathogen-derived peptide presented by the major histocompatibility complex (pMHC) molecule is reviewed and how it may impact previously developed models of T cell discrimination between pMHCs of different potencies is discussed.
Abstract: Interaction of the T cell receptor (TCR) with pathogen-derived peptide presented by the major histocompatibility complex (pMHC) molecule is central to adaptive immunity as it initiates intracellular signaling to trigger T cell response to infection. Kinetic parameters of this interaction have been under intensive investigation for more than two decades using soluble pMHCs and/or TCRs with at least one of them in the solution (three-dimensional (3D) methods). Recently, several techniques have been developed to enable kinetic analysis on live T cells with pMHCs presented by surrogate antigen presenting cells (APCs) or supported planar lipid bilayers (two-dimensional (2D) methods). Comparison of 2D versus 3D parameters reveals drastic differences with broader ranges of 2D affinities and on-rates and orders of magnitude faster 2D off-rates for functionally distinct pMHCs. Here we review new 2D data and discuss how it may impact previously developed models of T cell discrimination between pMHCs of different potencies.
TL;DR: It is found that a sequential two-step model of transcription initiation at the promoter region explains the results well and the distributions of intervals between consecutive productions of RNAs are found to be sub-exponential, and the process of RNA production is found toBe sub-Poissonian.
Abstract: We measured the in vivo production of RNA molecules tagged with MS2d-GFP in Escherichia coli, driven by the lar promoter, under weak and medium induction. The distributions of intervals between consecutive productions of RNAs are found to be sub-exponential, and the process of RNA production is found to be sub-Poissonian. We discuss possible models of transcription initiation and, based on our results and previous in vitro measurements, find that a sequential two-step model of transcription initiation at the promoter region explains the results well.
TL;DR: An overview of classic and contemporary models of early lineage development in the mouse is provided and the emerging body of work that highlights similarities and differences between blastocyst development inThe mouse and other mammalian species are discussed.
Abstract: Preimplantation development in mammals encompasses a period from fertilization to implantation and results in formation of a blastocyst composed of three distinct cell lineages: epiblast, trophectoderm and primitive endoderm. The epiblast gives rise to the organism, while the trophectoderm and the primitive endoderm contribute to extraembryonic tissues that support embryo development after implantation. In many vertebrates, such as frog or fish, maternally supplied lineage determinants are partitioned within the egg. Cell cleavage that follows fertilization results in polarization of these factors between the individual blastomeres, which become restricted in their developmental fate. In contrast, the mouse oocyte and zygote lack clear polarity and, until the eight-cell stage, individual blastomeres retain the potential to form all lineages. How are cell lineages specified in the absence of a maternally supplied blueprint? This is a fundamental question in the field of developmental biology. The answer to this question lies in understanding the cell‐cell interactions and gene networks involved in embryonic development prior to implantation and using this knowledge to create testable models of the developmental processes that govern cell fates. We provide an overview of classic and contemporary models of early lineage development in the mouse and discuss the emerging body of work that highlights similarities and differences between blastocyst development in the mouse and other mammalian species.
TL;DR: The applications of AFM to the study of DNA transcription in reductionist single-molecule bottom-up approaches are reviewed, allowing new insights into the interactions between ribonucleic acid (RNA) polymerase to be gained and enabled quantification of some aspects of the transcription process.
Abstract: Atomic force microscopy (AFM) can detect single biomacromolecules with a high signal-to-noise ratio on atomically flat biocompatible support surfaces, such as mica. Contrast arises from the innate forces and therefore AFM does not require imaging contrast agents, leading to sample preparation that is relatively straightforward. The ability of AFM to operate in hydrated environments, including humid air and aqueous buffers, allows structure and function of biological and biomolecular systems to be retained. These traits of the AFM are ensuring that it is being increasingly used to study deoxyribonucleic acid (DNA) structure and DNA-protein interactions down to the secondary structure level. This report focuses in particular on reviewing the applications of AFM to the study of DNA transcription in reductionist single-molecule bottom-up approaches. The technique has allowed new insights into the interactions between ribonucleic acid (RNA) polymerase to be gained and enabled quantification of some aspects of the transcription process, such as promoter location, DNA wrapping and elongation. More recently, the trend is towards studying the interactions of more than one enzyme operating on a single DNA template. These methods begin to reveal the mechanics of gene expression at the single-molecule level and will enable us to gain greater understanding of how the genome is transcribed and translated into the proteome.
TL;DR: The results shed light on the mechanisms involved in processes such as cytokinesis and cellular contractility, where myosin motors organized in clusters operate cooperatively to induce the structural organization of cytoskeletal networks.
Abstract: The structural reorganization of the actin cytoskeleton is facilitated through the action of motor proteins that crosslink the actin filaments and transport them relative to each other. Here, we present a combined experimental-computational study that probes the dynamic evolution of mixtures of actin filaments and clusters of myosin motors. While on small spatial and temporal scales the system behaves in a very noisy manner, on larger scales it evolves into several well distinct patterns such as bundles, asters and networks. These patterns are characterized by junctions with high connectivity, whose formation is possible due to the organization of the motors in ‘oligoclusters’ (intermediate-size aggregates). The simulations reveal that the self-organization process proceeds through a series of hierarchical steps, starting from local microscopic moves and ranging up to the macroscopic large scales where the steady-state structures are formed. Our results shed light on the mechanisms involved in processes such as cytokinesis and cellular contractility, where myosin motors organized in clusters operate cooperatively to induce the structural organization of cytoskeletal networks.
TL;DR: This work implements Pareto optimization in the context of gene network evolution to evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse ends.
Abstract: The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that can’t all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into the categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad-hoc combination of the design criteria.
TL;DR: The dynamics of QS-regulated gene expression induced by acylhomoserine lactones (AHLs) in Pseudomonas putida IsoF containing a green fluorescent protein-based AHL reporter is investigated to find that without supplying external AHL gene expression is induced without flow while flow suppresses the induction.
Abstract: Quorum sensing (QS) describes the capability of microbes to communicate with each other by the aid of small molecules. Here we investigate the dynamics of QS-regulated gene expression induced by acylhomoserine lactones (AHLs) in Pseudomonas putida IsoF containing a green fluorescent protein-based AHL reporter. The fluorescence time course of individual colonies is monitored following the external addition of a defined AHL concentration to cells which had previously reached the QS-inactive state in AHL-free medium. Using a microfluidic setup the experiment is performed both under flow and non-flow conditions. We find that without supplying external AHL gene expression is induced without flow while flow suppresses the induction. Both without and with flow, at a low AHL concentration the fluorescence onset is significantly delayed while fluorescence starts to increase directly upon the addition of AHL at a high concentration. The differences between no flow and flow can be accounted for using a two-compartment model. This indicates AHL accumulation in a volume which is not affected by the flow. The experiments furthermore show significant cell-to-cell and colony-to-colony variability which is discussed in the context of a compartmentalized QS mechanism.
TL;DR: Quasi-elastic light scattering studies of solutions of methylated and unmethylated DNA support the view that a significant fraction of the meDNA changes into the stiffer A form as the more hydrophobic meDNA is dehydrated at the interface.
Abstract: AFM images show that chromatin reconstituted on methylated DNA (meDNA) is compacted when imaged under water. Chromatin reconstituted on unmethylated DNA is less compacted and less sensitive to hydration. These differences must reflect changes in the physical properties of DNA on methylation, but prior studies have not revealed large differences between methylated and unmethylated DNA. Quasi-elastic light scattering studies of solutions of methylated and unmethylated DNA support this view. In contrast, AFM images of molecules at a water/solid interface yield a persistence length that nearly doubles (to 92.5 ± 4 nm) when 9% of the total DNA is methylated. This increase in persistence length is accompanied by a decrease in contour length, suggesting that a significant fraction of the meDNA changes into the stiffer A form as the more hydrophobic meDNA is dehydrated at the interface. This suggests a simple mechanism for gene silencing as the stiffer meDNA is more difficult to remove from nucleosomes.
TL;DR: In this paper, a model is proposed to explain long-range coordination of dynamics of multiple cells in epithelial spreading, which may be induced, under different conditions, by a chemical signal, or mechanically induced strain, or both.
Abstract: We propose a minimal mathematical model to explain long-range coordination of dynamics of multiple cells in epithelial spreading, which may be induced, under different conditions, by a chemical signal, or mechanically induced strain, or both. The model is based on chemo-mechanical interactions including a chemical effect of strain, chemically induced polarization and active traction, and interaction between polarized cells. The results, showing kinase concentration distribution and cell displacement, velocity, and stress fields, allow us to reproduce qualitatively available experimental data and distinguish between distinct dynamical patterns observed under conditions of injury or unconstraining.
TL;DR: Applications demonstrate that this computational method allows for realistic modeling of protein association kinetics under crowding.
Abstract: In cellular environments, two protein molecules on their way to form a specific complex encounter many bystander macromolecules. The latter molecules, or crowders, affect both the energetics of the interaction between the test molecules and the dynamics of their relative motion. In earlier work (Zhou and Szabo 1991 J. Chem. Phys. 95 5948–52), it has been shown that, in modeling the association kinetics of the test molecules, the presence of crowders can be accounted for by their energetic and dynamic effects. The recent development of the transient-complex theory for protein association in dilute solutions makes it possible to easily incorporate the energetic and dynamic effects of crowders. The transient complex refers to a late on-pathway intermediate, in which the two protein molecules have near-native relative separation and orientation, but have yet to form the many short-range specific interactions of the native complex. The transient-complex theory predicts the association rate constant as ka = ka0exp( − ΔG*el/kBT), where ka0 is the ‘basal’ rate constant for reaching the transient complex by unbiased diffusion, and the Boltzmann factors captures the influence of long-range electrostatic interactions between the protein molecules. Crowders slow down the diffusion, therefore reducing the basal rate constant (to kac0), and induce an effective interaction energy ΔGc. We show that the latter interaction energy for atomistic proteins in the presence of spherical crowders is ‘long’-ranged, allowing the association rate constant under crowding to be computed as kac = kac0exp[ − (ΔG*el + ΔG*c)/kBT]. Applications demonstrate that this computational method allows for realistic modeling of protein association kinetics under crowding.