2,227 research outputs found
A type system for components
In modern distributed systems, dynamic reconfiguration, i.e.,
changing at runtime the communication pattern of a program, is chal-
lenging. Generally, it is difficult to guarantee that such modifications will
not disrupt ongoing computations. In a previous paper, a solution to this
problem was proposed by extending the object-oriented language ABS
with a component model allowing the programmer to: i) perform up-
dates on objects by means of communication ports and their rebinding;
and ii) precisely specify when such updates can safely occur in an object
by means of critical sections. However, improper rebind operations could
still occur and lead to runtime errors. The present paper introduces a
type system for this component model that extends the ABS type system
with the notion of ports and a precise analysis that statically enforces
that no object will attempt illegal rebinding
Biomarkers in emergency medicine
Researchers navigate the ocean of biomarkers searching for proper targets and optimal utilization of them. Emergency medicine builds up the front line to maximize the utility of clinically validated biomarkers and is the cutting edge field to test the applicability of promising biomarkers emerging from thorough translational researches. The role of biomarkers in clinical decision making would be of greater significance for identification, risk stratification, monitoring, and prognostication of the patients in the critical- and acute-care settings. No doubt basic research to explore novel biomarkers in relation to the pathogenesis
is as important as its clinical counterpart. This special issue includes five selected research papers that cover a variety of biomarker- and disease-related topics
Syntactic Markovian Bisimulation for Chemical Reaction Networks
In chemical reaction networks (CRNs) with stochastic semantics based on
continuous-time Markov chains (CTMCs), the typically large populations of
species cause combinatorially large state spaces. This makes the analysis very
difficult in practice and represents the major bottleneck for the applicability
of minimization techniques based, for instance, on lumpability. In this paper
we present syntactic Markovian bisimulation (SMB), a notion of bisimulation
developed in the Larsen-Skou style of probabilistic bisimulation, defined over
the structure of a CRN rather than over its underlying CTMC. SMB identifies a
lumpable partition of the CTMC state space a priori, in the sense that it is an
equivalence relation over species implying that two CTMC states are lumpable
when they are invariant with respect to the total population of species within
the same equivalence class. We develop an efficient partition-refinement
algorithm which computes the largest SMB of a CRN in polynomial time in the
number of species and reactions. We also provide an algorithm for obtaining a
quotient network from an SMB that induces the lumped CTMC directly, thus
avoiding the generation of the state space of the original CRN altogether. In
practice, we show that SMB allows significant reductions in a number of models
from the literature. Finally, we study SMB with respect to the deterministic
semantics of CRNs based on ordinary differential equations (ODEs), where each
equation gives the time-course evolution of the concentration of a species. SMB
implies forward CRN bisimulation, a recently developed behavioral notion of
equivalence for the ODE semantics, in an analogous sense: it yields a smaller
ODE system that keeps track of the sums of the solutions for equivalent
species.Comment: Extended version (with proofs), of the corresponding paper published
at KimFest 2017 (http://kimfest.cs.aau.dk/
Experimental Biological Protocols with Formal Semantics
Both experimental and computational biology is becoming increasingly
automated. Laboratory experiments are now performed automatically on
high-throughput machinery, while computational models are synthesized or
inferred automatically from data. However, integration between automated tasks
in the process of biological discovery is still lacking, largely due to
incompatible or missing formal representations. While theories are expressed
formally as computational models, existing languages for encoding and
automating experimental protocols often lack formal semantics. This makes it
challenging to extract novel understanding by identifying when theory and
experimental evidence disagree due to errors in the models or the protocols
used to validate them. To address this, we formalize the syntax of a core
protocol language, which provides a unified description for the models of
biochemical systems being experimented on, together with the discrete events
representing the liquid-handling steps of biological protocols. We present both
a deterministic and a stochastic semantics to this language, both defined in
terms of hybrid processes. In particular, the stochastic semantics captures
uncertainties in equipment tolerances, making it a suitable tool for both
experimental and computational biologists. We illustrate how the proposed
protocol language can be used for automated verification and synthesis of
laboratory experiments on case studies from the fields of chemistry and
molecular programming
Process algebra modelling styles for biomolecular processes
We investigate how biomolecular processes are modelled in process algebras, focussing on chemical reactions. We consider various modelling styles and how design decisions made in the definition of the process algebra have an impact on how a modelling style can be applied. Our goal is to highlight the often implicit choices that modellers make in choosing a formalism, and illustrate, through the use of examples, how this can affect expressability as well as the type and complexity of the analysis that can be performed
Computational Modeling for the Activation Cycle of G-proteins by G-protein-coupled Receptors
In this paper, we survey five different computational modeling methods. For
comparison, we use the activation cycle of G-proteins that regulate cellular
signaling events downstream of G-protein-coupled receptors (GPCRs) as a driving
example. Starting from an existing Ordinary Differential Equations (ODEs)
model, we implement the G-protein cycle in the stochastic Pi-calculus using
SPiM, as Petri-nets using Cell Illustrator, in the Kappa Language using
Cellucidate, and in Bio-PEPA using the Bio-PEPA eclipse plug in. We also
provide a high-level notation to abstract away from communication primitives
that may be unfamiliar to the average biologist, and we show how to translate
high-level programs into stochastic Pi-calculus processes and chemical
reactions.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
Design and analysis of DNA strand displacement devices using probabilistic model checking
Designing correct, robust DNA devices is difficult because of the many possibilities for unwanted interference between molecules in the system. DNA strand displacement has been proposed as a design paradigm for DNA devices, and the DNA strand displacement (DSD) programming language has been developed as a means of formally programming and analysing these devices to check for unwanted interference. We demonstrate, for the first time, the use of probabilistic verification techniques to analyse the correctness, reliability and performance of DNA devices during the design phase. We use the probabilistic model checker prism, in combination with the DSD language, to design and debug DNA strand displacement components and to investigate their kinetics. We show how our techniques can be used to identify design flaws and to evaluate the merits of contrasting design decisions, even on devices comprising relatively few inputs. We then demonstrate the use of these components to construct a DNA strand displacement device for approximate majority voting. Finally, we discuss some of the challenges and possible directions for applying these methods to more complex designs
A Photometric Investigation of the GRB970228 Afterglow and the Associated Nebulosity
We carefully analyze the WFPC2 and STIS images of GRB970228. We measure
magnitudes for the GRB970228 point source component in the WFPC2 images of
, and
, on March 26 and April 7,
respectively; and on September 4 in the STIS image.
For the extended component, we measure magnitudes of
in the combined WFPC2 images and
in the STIS image, which are consistent with no
variation. This value is fainter than previously reported (Galama et al. 98)
and modifies the previously assumed magnitudes for the optical transient when
it faded to a level where the extended source component contribution was not
negligible, alleviating the discrepancy to a power-law temporal behavior. We
also measure a color of for the
extended source component. Taking into account the extinction measured in this
field (Castander & Lamb 1998), this color implies that the extended source is
most likely a galaxy with ongoing star formation.Comment: 21 pages, including 8 figures. Submitted to Ap
Formal lumping of polynomial differential equations through approximate equivalences
It is well known that exact notions of model abstraction and reduction for dynamical systems may not be robust enough in practice because they are highly sensitive to the specific choice of parameters. In this paper we consider this problem for nonlinear ordinary differential equations (ODEs) with polynomial derivatives. We introduce a model reduction technique based on approximate differential equivalence, i.e., a partition of the set of ODE variables that performs an aggregation when the variables are governed by nearby derivatives. We develop algorithms to (i) compute the largest approximate differential equivalence; (ii) construct an approximately reduced model from the original one via an appropriate perturbation of the coefficients of the polynomials; and (iii) provide a formal certificate on the quality of the approximation as an error bound, computed as an over-approximation of the reachable set of the reduced model. Finally, we apply approximate differential equivalences to case studies on electric circuits, biological models, and polymerization reaction networks
The importance of root interactions in field bean/triticale intercrops
To highlight the contribution of belowground interactions to biomass and N and P yields,
field bean and triticale were grown in a P-poor soil as sole crops and as replacement intercrops at two
N levels. The shoots were always in contact, while the roots of adjacent rows were free to interact
or were completely separated. This allowed simultaneous testing the intraspecific and interspecific
competition between rows, which to our knowledge has not been studied before. Root biomass,
distribution in soil, morphometry, and functional traits were determined, together with the nodule
number and biomass. The Land Equivalent Ratio for shoot biomass and N and P yield were higher
than 1 when roots were in contact, and markedly lower when they were separated. This demonstrates
the positive contribution of root interactions, which in field bean, consisted of increased root elongation
without changes in biomass and nutrient status; in triticale, of increased N and P uptake eciency and
reduced biomass partitioning to roots. The soil-plant processes underlying intercrop advantage led to
complementarity in N sources with low N inputs and facilitated N and P uptake with high N inputs,
which demonstrates that intercropping could be profitable in both low and high input agriculture
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