87,615 research outputs found
Parallel Function Application on a DNA Substrate
In this paper I present a new model that employs a biological (specifically DNA -based) substrate for performing computation. Specifically, I describe strategies for performing parallel function application in the DNA-computing models described by Adelman, Cai et. al., and Liu et. al. Employing only DNA operations which can presently be performed, I discuss some direct algorithms for computing a variety of useful mathematical functions on DNA, culminating in an algorithm for minimizing an arbitrary continuous function. In addition, computing genetic algorithms on a DNA substrate is briefly discussed
Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism
Dangerous damage to mitochondrial DNA (mtDNA) can be ameliorated during
mammalian development through a highly debated mechanism called the mtDNA
bottleneck. Uncertainty surrounding this process limits our ability to address
inherited mtDNA diseases. We produce a new, physically motivated, generalisable
theoretical model for mtDNA populations during development, allowing the first
statistical comparison of proposed bottleneck mechanisms. Using approximate
Bayesian computation and mouse data, we find most statistical support for a
combination of binomial partitioning of mtDNAs at cell divisions and random
mtDNA turnover, meaning that the debated exact magnitude of mtDNA copy number
depletion is flexible. New experimental measurements from a wild-derived mtDNA
pairing in mice confirm the theoretical predictions of this model. We
analytically solve a mathematical description of this mechanism, computing
probabilities of mtDNA disease onset, efficacy of clinical sampling strategies,
and effects of potential dynamic interventions, thus developing a quantitative
and experimentally-supported stochastic theory of the bottleneck.Comment: Main text: 14 pages, 5 figures; Supplement: 17 pages, 4 figures;
Total: 31 pages, 9 figure
Applications and Challenges of Real-time Mobile DNA Analysis
The DNA sequencing is the process of identifying the exact order of
nucleotides within a given DNA molecule. The new portable and relatively
inexpensive DNA sequencers, such as Oxford Nanopore MinION, have the potential
to move DNA sequencing outside of laboratory, leading to faster and more
accessible DNA-based diagnostics. However, portable DNA sequencing and analysis
are challenging for mobile systems, owing to high data throughputs and
computationally intensive processing performed in environments with unreliable
connectivity and power.
In this paper, we provide an analysis of the challenges that mobile systems
and mobile computing must address to maximize the potential of portable DNA
sequencing, and in situ DNA analysis. We explain the DNA sequencing process and
highlight the main differences between traditional and portable DNA sequencing
in the context of the actual and envisioned applications. We look at the
identified challenges from the perspective of both algorithms and systems
design, showing the need for careful co-design
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Synthetic biology: advancing biological frontiers by building synthetic systems
Advances in synthetic biology are contributing
to diverse research areas, from basic biology to
biomanufacturing and disease therapy. We discuss the
theoretical foundation, applications, and potential of
this emerging field
Robust execution of service workflows using redundancy and advance reservations
In this paper, we develop a novel algorithm that allows service consumers to execute business processes (or workflows) of interdependent services in a dependable manner within tight time-constraints. In particular, we consider large inter-organisational service-oriented systems, where services are offered by external organisations that demand financial remuneration and where their use has to be negotiated in advance using explicit service-level agreements (as is common in Grids and cloud computing). Here, different providers often offer the same type of service at varying levels of quality and price. Furthermore, some providers may be less trustworthy than others, possibly failing to meet their agreements. To control this unreliability and ensure end-to-end dependability while maximising the profit obtained from completing a business process, our algorithm automatically selects the most suitable providers. Moreover, unlike existing work, it reasons about the dependability properties of a workflow, and it controls these by using service redundancy for critical tasks and by planning for contingencies. Finally, our algorithm reserves services for only parts of its workflow at any time, in order to retain flexibility when failures occur. We show empirically that our algorithm consistently outperforms existing approaches, achieving up to a 35-fold increase in profit and successfully completing most workflows, even when the majority of providers fail
Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system
Biology has taken strong steps towards becoming a computer science aiming at
reprogramming nature after the realisation that nature herself has reprogrammed
organisms by harnessing the power of natural selection and the digital
prescriptive nature of replicating DNA. Here we further unpack ideas related to
computability, algorithmic information theory and software engineering, in the
context of the extent to which biology can be (re)programmed, and with how we
may go about doing so in a more systematic way with all the tools and concepts
offered by theoretical computer science in a translation exercise from
computing to molecular biology and back. These concepts provide a means to a
hierarchical organization thereby blurring previously clear-cut lines between
concepts like matter and life, or between tumour types that are otherwise taken
as different and may not have however a different cause. This does not diminish
the properties of life or make its components and functions less interesting.
On the contrary, this approach makes for a more encompassing and integrated
view of nature, one that subsumes observer and observed within the same system,
and can generate new perspectives and tools with which to view complex diseases
like cancer, approaching them afresh from a software-engineering viewpoint that
casts evolution in the role of programmer, cells as computing machines, DNA and
genes as instructions and computer programs, viruses as hacking devices, the
immune system as a software debugging tool, and diseases as an
information-theoretic battlefield where all these forces deploy. We show how
information theory and algorithmic programming may explain fundamental
mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and
Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George
Ellis (eds.), Cambridge University Pres
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