Observations on the larval hatching success of dungeness crab, Cancer magister, from the San Francisco and Eureka-Crescent City region

Ovigerous Dungeness crabs were collected from the San Francisco and Eureka-Crescent City regions and maintained at the Department's Marine Culture Laboratory near Monterey. Hatching success, expressed as viable larvae released, was measured and compared by region. Larval counts were made from 15 Dungeness crabs, 5 from the San Francisco region, 7 from the Eureka-Crescent City area and 3 of unknown origin. Mean hatching success exceeded 80% in the San Francisco region, and averaged more than 90% in the Eureka-Crescent City area. However, a Student's t-Test showed this difference in hatching success was not significant. (16pp.

Parallel-plate Flow Chamber and Continuous Flow Circuit to Evaluate Endothelial Progenitor Cells under Laminar Flow Shear Stress

The overall goal of this method is to describe a technique to subject adherent cells to laminar flow conditions and evaluate their response to well quantifiable fluid shear stresses1. Our flow chamber design and flow circuit (Fig. 1) contains a transparent viewing region that enables testing of cell adhesion and imaging of cell morphology immediately before flow (Fig. 11A, B), at various time points during flow (Fig. 11C), and after flow (Fig. 11D). These experiments are illustrated with human umbilical cord blood-derived endothelial progenitor cells (EPCs) and porcine EPCs2,3. This method is also applicable to other adherent cell types, e.g. smooth muscle cells (SMCs) or fibroblasts. The chamber and all parts of the circuit are easily sterilized with steam autoclaving In contrast to other chambers, e.g. microfluidic chambers, large numbers of cells (> 1 million depending on cell size) can be recovered after the flow experiment under sterile conditions for cell culture or other experiments, e.g. DNA or RNA extraction, or immunohistochemistry (Fig. 11E), or scanning electron microscopy5. The shear stress can be adjusted by varying the flow rate of the perfusate, the fluid viscosity, or the channel height and width. The latter can reduce fluid volume or cell needs while ensuring that one-dimensional flow is maintained. It is not necessary to measure chamber height between experiments, since the chamber height does not depend on the use of gaskets, which greatly increases the ease of multiple experiments. Furthermore, the circuit design easily enables the collection of perfusate samples for analysis and/or quantification of metabolites secreted by cells under fluid shear stress exposure, e.g. nitric oxide (Fig. 12)6

Autologous Endothelial Progenitor Cell-Seeding Technology and Biocompatibility Testing For Cardiovascular Devices in Large Animal Model

Implantable cardiovascular devices are manufactured from artificial materials (e.g. titanium (Ti), expanded polytetrafluoroethylene), which pose the risk of thromboemboli formation1,2,3. We have developed a method to line the inside surface of Ti tubes with autologous blood-derived human or porcine endothelial progenitor cells (EPCs)4. By implanting Ti tubes containing a confluent layer of porcine EPCs in the inferior vena cava (IVC) of pigs, we tested the improved biocompatibility of the cell-seeded surface in the prothrombotic environment of a large animal model and compared it to unmodified bare metal surfaces5,6,7 (Figure 1). This method can be used to endothelialize devices within minutes of implantation and test their antithrombotic function in vivo

The Nondeterministic Waiting Time Algorithm: A Review

We present briefly the Nondeterministic Waiting Time algorithm. Our technique for the simulation of biochemical reaction networks has the ability to mimic the Gillespie Algorithm for some networks and solutions to ordinary differential equations for other networks, depending on the rules of the system, the kinetic rates and numbers of molecules. We provide a full description of the algorithm as well as specifics on its implementation. Some results for two well-known models are reported. We have used the algorithm to explore Fas-mediated apoptosis models in cancerous and HIV-1 infected T cells

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Markovian Dynamics on Complex Reaction Networks

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm

Norovirus Recombination in ORF1/ORF2 Overlap

Norovirus (NoV) genogroups I and II (GI and GII) are now recognized as the predominant worldwide cause of outbreaks of acute gastroenteritis in humans. Three recombinant NoV GII isolates were identified and characterized, 2 of which are unrelated to any previously published recombinant NoV. Using data from the current study, published sequences, database searches, and molecular techniques, we identified 23 recombinant NoV GII and 1 recombinant NoV GI isolates. Analysis of the genetic relationships among the recombinant NoV GII isolates identified 9 independent recombinant sequences; the other 14 strains were close relatives. Two of the 9 independent recombinant NoV were closely related to other recombinants only in the polymerase region, and in a similar fashion 1 recombinant NoV was closely related to another only in the capsid region. Breakpoint analysis of recombinant NoV showed that recombination occurred in the open reading frame (ORF)1/ORF2 overlap. We provide evidence to support the theory of the role of subgenomic RNA promoters as recombination hotspots and describe a simple mechanism of how recombination might occur in NoV

State estimation of chemical engineering systems tending to multiple solutions

A well-evaluated state covariance matrix avoids error propagation due to divergence issues and, thereby, it is crucial for a successful state estimator design. In this paper we investigate the performance of the state covariance matrices used in three unconstrained Extended Kalman Filter (EKF) formulations and one constrained EKF formulation (CEKF). As benchmark case studies we have chosen: a) a batch chemical reactor with reversible reactions whose system model and measurement are such that multiple states satisfy the equilibrium condition and b) a CSTR with exothermic irreversible reactions and cooling jacket energy balance whose nonlinear behavior includes multiple steady-states and limit cycles. The results have shown that CEKF is in general the best choice of EKF formulations (even if they are constrained with an ad hoc clipping strategy which avoids undesired states) for such case studies. Contrary to a clipped EKF formulation, CEKF incorporates constraints into an optimization problem, which minimizes the noise in a least square sense preventing a bad noise distribution. It is also shown that, although the Moving Horizon Estimation (MHE) provides greater robustness to a poor guess of the initial state, converging in less steps to the actual states, it is not justified for our examples due to the high additional computational effort

Models for synthetic biology

Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification. The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules. What is new is the emphasis on system behavior