321 research outputs found
Noise and information transmission in promoters with multiple internal states
Based on the measurements of noise in gene expression performed during the
last decade, it has become customary to think of gene regulation in terms of a
two-state model, where the promoter of a gene can stochastically switch between
an ON and an OFF state. As experiments are becoming increasingly precise and
the deviations from the two-state model start to be observable, we ask about
the experimental signatures of complex multi-state promoters, as well as the
functional consequences of this additional complexity. In detail, we (i) extend
the calculations for noise in gene expression to promoters described by state
transition diagrams with multiple states, (ii) systematically compute the
experimentally accessible noise characteristics for these complex promoters,
and (iii) use information theory to evaluate the channel capacities of complex
promoter architectures and compare them to the baseline provided by the
two-state model. We find that adding internal states to the promoter
generically decreases channel capacity, except in certain cases, three of which
(cooperativity, dual-role regulation, promoter cycling) we analyze in detail.Comment: 16 pages, 9 figure
Discrete modes of social information processing predict individual behavior of fish in a group
Individual computations and social interactions underlying collective
behavior in groups of animals are of great ethological, behavioral, and
theoretical interest. While complex individual behaviors have successfully been
parsed into small dictionaries of stereotyped behavioral modes, studies of
collective behavior largely ignored these findings; instead, their focus was on
inferring single, mode-independent social interaction rules that reproduced
macroscopic and often qualitative features of group behavior. Here we bring
these two approaches together to predict individual swimming patterns of adult
zebrafish in a group. We show that fish alternate between an active mode in
which they are sensitive to the swimming patterns of conspecifics, and a
passive mode where they ignore them. Using a model that accounts for these two
modes explicitly, we predict behaviors of individual fish with high accuracy,
outperforming previous approaches that assumed a single continuous computation
by individuals and simple metric or topological weighing of neighbors behavior.
At the group level, switching between active and passive modes is uncorrelated
among fish, yet correlated directional swimming behavior still emerges. Our
quantitative approach for studying complex, multi-modal individual behavior
jointly with emergent group behavior is readily extensible to additional
behavioral modes and their neural correlates, as well as to other species
Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation
Gene expression is controlled primarily by interactions between transcription
factor proteins (TFs) and the regulatory DNA sequence, a process that can be
captured well by thermodynamic models of regulation. These models, however,
neglect regulatory crosstalk: the possibility that non-cognate TFs could
initiate transcription, with potentially disastrous effects for the cell. Here
we estimate the importance of crosstalk, suggest that its avoidance strongly
constrains equilibrium models of TF binding, and propose an alternative
non-equilibrium scheme that implements kinetic proofreading to suppress
erroneous initiation. This proposal is consistent with the observed covalent
modifications of the transcriptional apparatus and would predict increased
noise in gene expression as a tradeoff for improved specificity. Using
information theory, we quantify this tradeoff to find when optimal proofreading
architectures are favored over their equilibrium counterparts.Comment: 5 pages, 3 figure
Dynamics of transcription factor binding site evolution
Evolution of gene regulation is crucial for our understanding of the
phenotypic differences between species, populations and individuals.
Sequence-specific binding of transcription factors to the regulatory regions on
the DNA is a key regulatory mechanism that determines gene expression and hence
heritable phenotypic variation. We use a biophysical model for directional
selection on gene expression to estimate the rates of gain and loss of
transcription factor binding sites (TFBS) in finite populations under both
point and insertion/deletion mutations. Our results show that these rates are
typically slow for a single TFBS in an isolated DNA region, unless the
selection is extremely strong. These rates decrease drastically with increasing
TFBS length or increasingly specific protein-DNA interactions, making the
evolution of sites longer than ~10 bp unlikely on typical eukaryotic speciation
timescales. Similarly, evolution converges to the stationary distribution of
binding sequences very slowly, making the equilibrium assumption questionable.
The availability of longer regulatory sequences in which multiple binding sites
can evolve simultaneously, the presence of "pre-sites" or partially decayed old
sites in the initial sequence, and biophysical cooperativity between
transcription factors, can all facilitate gain of TFBS and reconcile
theoretical calculations with timescales inferred from comparative genetics.Comment: 28 pages, 15 figure
Evolution of new regulatory functions on biophysically realistic fitness landscapes
Regulatory networks consist of interacting molecules with a high degree of
mutual chemical specificity. How can these molecules evolve when their function
depends on maintenance of interactions with cognate partners and simultaneous
avoidance of deleterious "crosstalk" with non-cognate molecules? Although
physical models of molecular interactions provide a framework in which
co-evolution of network components can be analyzed, most theoretical studies
have focused on the evolution of individual alleles, neglecting the network. In
contrast, we study the elementary step in the evolution of gene regulatory
networks: duplication of a transcription factor followed by selection for TFs
to specialize their inputs as well as the regulation of their downstream genes.
We show how to coarse grain the complete, biophysically realistic
genotype-phenotype map for this process into macroscopic functional outcomes
and quantify the probability of attaining each. We determine which evolutionary
and biophysical parameters bias evolutionary trajectories towards fast
emergence of new functions and show that this can be greatly facilitated by the
availability of "promiscuity-promoting" mutations that affect TF specificity
Extending the dynamic range of transcription factor action by translational regulation
A crucial step in the regulation of gene expression is binding of
transcription factor (TF) proteins to regulatory sites along the DNA. But
transcription factors act at nanomolar concentrations, and noise due to random
arrival of these molecules at their binding sites can severely limit the
precision of regulation. Recent work on the optimization of information flow
through regulatory networks indicates that the lower end of the dynamic range
of concentrations is simply inaccessible, overwhelmed by the impact of this
noise. Motivated by the behavior of homeodomain proteins, such as the maternal
morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which
transcription factors also act as indirect translational regulators, binding to
the mRNA of other transcription factors. Intuitively, each mRNA molecule acts
as an independent sensor of the TF concentration, and averaging over these
multiple sensors reduces the noise. We analyze information flow through this
new scheme and identify conditions under which it outperforms direct
transcriptional regulation. Our results suggest that the dual role of
homeodomain proteins is not just a historical accident, but a solution to a
crucial physics problem in the regulation of gene expression.Comment: 14 pages, 5 figure
Positional information, positional error, and read-out precision in morphogenesis: a mathematical framework
The concept of positional information is central to our understanding of how
cells in a multicellular structure determine their developmental fates.
Nevertheless, positional information has neither been defined mathematically
nor quantified in a principled way. Here we provide an information-theoretic
definition in the context of developmental gene expression patterns and examine
which features of expression patterns increase or decrease positional
information. We connect positional information with the concept of positional
error and develop tools to directly measure information and error from
experimental data. We illustrate our framework for the case of gap gene
expression patterns in the early Drosophila embryo and show how information
that is distributed among only four genes is sufficient to determine
developmental fates with single cell resolution. Our approach can be
generalized to a variety of different model systems; procedures and examples
are discussed in detail
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