7,223 research outputs found
Fixed points and limit cycles in the population dynamics of lysogenic viruses and their hosts
Starting with stochastic rate equations for the fundamental interactions
between microbes and their viruses, we derive a mean field theory for the
population dynamics of microbe-virus systems, including the effects of
lysogeny. In the absence of lysogeny, our model is a generalization of that
proposed phenomenologically by Weitz and Dushoff. In the presence of lysogeny,
we analyze the possible states of the system, identifying a novel limit cycle,
which we interpret physically. To test the robustness of our mean field
calculations to demographic fluctuations, we have compared our results with
stochastic simulations using the Gillespie algorithm. Finally, we estimate the
range of parameters that delineate the various steady states of our model.Comment: 20 pages, 16 figures, 4 table
Gene autoregulation via intronic microRNAs and its functions
Background: MicroRNAs, post-transcriptional repressors of gene expression,
play a pivotal role in gene regulatory networks. They are involved in core
cellular processes and their dysregulation is associated to a broad range of
human diseases. This paper focus on a minimal microRNA-mediated regulatory
circuit, in which a protein-coding gene (host gene) is targeted by a microRNA
located inside one of its introns. Results: Autoregulation via intronic
microRNAs is widespread in the human regulatory network, as confirmed by our
bioinformatic analysis, and can perform several regulatory tasks despite its
simple topology. Our analysis, based on analytical calculations and
simulations, indicates that this circuitry alters the dynamics of the host gene
expression, can induce complex responses implementing adaptation and Weber's
law, and efficiently filters fluctuations propagating from the upstream network
to the host gene. A fine-tuning of the circuit parameters can optimize each of
these functions. Interestingly, they are all related to gene expression
homeostasis, in agreement with the increasing evidence suggesting a role of
microRNA regulation in conferring robustness to biological processes. In
addition to model analysis, we present a list of bioinformatically predicted
candidate circuits in human for future experimental tests. Conclusions: The
results presented here suggest a potentially relevant functional role for
negative self-regulation via intronic microRNAs, in particular as a homeostatic
control mechanism of gene expression. Moreover, the map of circuit functions in
terms of experimentally measurable parameters, resulting from our analysis, can
be a useful guideline for possible applications in synthetic biology.Comment: 29 pages and 7 figures in the main text, 18 pages of Supporting
Informatio
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
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