title:
UV-induced mutagenesis in Escherichia coli SOS response: A quantitative
  model

abstract:
  Escherichia coli bacteria respond to DNA damage by a highly orchestrated
series of events known as the SOS response, regulated by transcription factors,
protein-protein binding and active protein degradation. We present a dynamical
model of the UV-induced SOS response, incorporating mutagenesis by the
error-prone polymerase, Pol V. In our model, mutagenesis depends on a
combination of two key processes: damage counting by the replication forks and
a long term memory associated with the accumulation of UmuD'. Together, these
provide a tight regulation of mutagenesis resulting, we show, in a "digital"
turn-on and turn-off of Pol V. Our model provides a compact view of the
topology and design of the SOS network, pinpointing the specific functional
role of each of the regulatory processes. In particular, we suggest that the
recently observed second peak in the activity of promoters in the SOS regulon
(Friedman et al., 2005, PLoS Biol. 3, e238) is the result of a positive
feedback from Pol V to RecA filaments.


title: Elastic Correlations in Nucleosomal DNA Structure

abstract:
  The structure of DNA in the nucleosome core particle is studied using an
elastic model that incorporates anisotropy in the bending energetics and
twist-bend coupling. Using the experimentally determined structure of
nucleosomal DNA [T.J. Richmond and C.A. Davey, Nature {\bf 423}, 145 (2003)],
it is shown that elastic correlations exist between twist, roll, tilt, and
stretching of DNA, as well as the distance between phosphate groups. The
twist-bend coupling term is shown to be able to capture these correlations to a
large extent, and a fit to the experimental data yields a new estimate of G=25
nm for the value of the twist-bend coupling constant.


title:
An HIV Feedback-Resistor: Auto-Regulatory Circuit Deactivator and Noise
  Buffer

abstract:
  Many viruses have the cunning ability to enter a hibernative or off state,
termed latency or lysogeny. When in a latent state, the virus is unable to
replicate, and its gene expression program is largely shut down. This facility
for lying dormant typically ensures lifelong persistence of the virus in the
host; it is also a particularly problematic obstacle in the treatment of HIV.
For most viruses, the molecular regulation of entry into latency is not
completely understood, but it is believed that viral gene expression must be
deactivated in some way. In this study, we introduce a new regulatory motif,
the feedback resistor, that enables a genetic circuit to shut off without the
need for an active repressor molecule. We first show that many animal viruses
might encode feedback resistors in their regulatory circuits. Then, by using a
combination of mathematical theory and single-cell real-time imaging
experiments, we show that a feedback resistor in the HIV Tat transcriptional
circuit likely allows the HIV virus to enter into latency. We postulate that
feedback resistors may give increased stability and control in the complex
noisy signaling environment of the cell.

categories: q-bio.MN cond-mat.soft physics.bio-ph q-bio.SC


title: Short-time dynamics of polypeptides

abstract:
  The authors study the short-time dynamics of helix-forming polypeptide chains
using an all-atom representation of the molecules and an implicit solvation
model to approximate the interaction with the surrounding solvent. The results
confirm earlier observations that the helix-coil transition in proteins can be
described by a set of critical exponents. The high statistics of the
simulations allows the authors to determine the exponents values with increased
precision and support universality of the helix-coil transition in homopolymers
and (helical) proteins.


title: Speculations on the emergence of self-awareness in big-brained organisms

abstract:
  This paper argues that self-awareness is a learned behavior that emerges in
organisms whose brains have a sufficiently integrated, complex ability for
associative learning and memory. Continual sensory input of information related
to the organism causes the organism's brain to learn the physical
characteristics of the organism, in the sense that neural pathways are produced
that are reinforced by, and therefore recognize, various features associated
with the organism. This results in the formation of a set of associations that
may be termed an organismal self-image, which provides a mechanistic basis for
the emergence of various behaviors that are associated with self-awareness,
such as self-recognition. In humans, self-awareness includes additional
behaviors such as recognition of self-awareness, the concept of I, and various
existential and religious questions. This paper shows how associative memory
and learning, combined with an organismal self-image and, in the case of
humans, language, leads to the emergence of these various behaviors. This paper
also discusses various tautologies that invariably emerge when discussing
self-awareness, that ultimately prevent an unambiguous resolution to the
various existential issues that arise. We continue with various speculations on
manipulating self-awareness, and discuss how concepts from set and logic may
provide a highly useful set of tools in computational neuroscience for
understanding the emergence of higher cognitive functions in complex organisms.
The existence of other types of awareness, and the role of mirror neurons in
the emergence of self-awareness, are also briefly discussed.


title: Oscillations in I/O monotone systems under negative feedback

abstract:
  Oscillatory behavior is a key property of many biological systems. The
Small-Gain Theorem (SGT) for input/output monotone systems provides a
sufficient condition for global asymptotic stability of an equilibrium and
hence its violation is a necessary condition for the existence of periodic
solutions. One advantage of the use of the monotone SGT technique is its
robustness with respect to all perturbations that preserve monotonicity and
stability properties of a very low-dimensional (in many interesting examples,
just one-dimensional) model reduction. This robustness makes the technique
useful in the analysis of molecular biological models in which there is large
uncertainty regarding the values of kinetic and other parameters. However,
verifying the conditions needed in order to apply the SGT is not always easy.
This paper provides an approach to the verification of the needed properties,
and illustrates the approach through an application to a classical model of
circadian oscillations, as a nontrivial ``case study,'' and also provides a
theorem in the converse direction of predicting oscillations when the SGT
conditions fail.


title:
Comparative analysis of protein structure using multiscale additive
  functionals

abstract:
  This work reports a new methodology aimed at describing characteristics of
protein structural shapes, and suggests a framework in which to resolve or
classify automatically such structures into known families. This new approach
to protein structure characterization is based on elements of integral geometry
using biologically relevant measurements of shape and considering them on a
multi-scale representation which align the proposed methodology to the recently
reported "tube picture" of a protein structure as a minimal representation
model. The method has been applied with good results to a subset of protein
structures known to be especially challenging to revert into families,
confirming the potential of the proposed method for accurate structure
classification.


title:
Dynamical robustness of biological networks with hierarchical
  distribution of time scales

abstract:
  We propose the concepts of distributed robustness and r-robustness, well
adapted to functional genetics. Then we discuss the robustness of the
relaxation time using a chemical reaction description of genetic and signalling
networks. First, we obtain the following result for linear networks: for large
multiscale systems with hierarchical distribution of time scales the variance
of the inverse relaxation time (as well as the variance of the stationary rate)
is much lower than the variance of the separate constants. Moreover, it can
tend to 0 faster than 1/n, where n is the number of reactions. We argue that
similar phenomena are valid in the nonlinear case as well. As a numerical
illustration we use a model of signalling network that can be applied to
important transcription factors such as NFkB.


title: Motif Discovery through Predictive Modeling of Gene Regulation

abstract:
  We present MEDUSA, an integrative method for learning motif models of
transcription factor binding sites by incorporating promoter sequence and gene
expression data. We use a modern large-margin machine learning approach, based
on boosting, to enable feature selection from the high-dimensional search space
of candidate binding sequences while avoiding overfitting. At each iteration of
the algorithm, MEDUSA builds a motif model whose presence in the promoter
region of a gene, coupled with activity of a regulator in an experiment, is
predictive of differential expression. In this way, we learn motifs that are
functional and predictive of regulatory response rather than motifs that are
simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model
of the transcriptional control logic that can predict the expression of any
gene in the organism, given the sequence of the promoter region of the target
gene and the expression state of a set of known or putative transcription
factors and signaling molecules. Each motif model is either a $k$-length
sequence, a dimer, or a PSSM that is built by agglomerative probabilistic
clustering of sequences with similar boosting loss. By applying MEDUSA to a set
of environmental stress response expression data in yeast, we learn motifs
whose ability to predict differential expression of target genes outperforms
motifs from the TRANSFAC dataset and from a previously published candidate set
of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed
binding sites associated with environmental stress response from the
literature.


title: High level modeling of tonic dopamine mechanisms in striatal neurons

abstract:
  The extant versions of many basal ganglia models use a `gating' model of
dopamine function which enhances input to D1 receptor units and attenuates
input to D2 receptor units. There is evidence that this model is unsatisfactory
because (a) there are not sufficient dopaminergic synapses to gate all input
and (b) dopamine's main effect is likely to be on the ion-channels contributing
to the neuron's membrane potential. Thus, an alternative output function-based
model of dopamine's effect is proposed which accounts for the dopamine-mediated
changes in ion-channel based currents. Simulation results show that the
selection and switching properties of the intrinsic and extended models are
retained with the new models. The parameter regimes under which this occurs
leads us to predict that an L-type Ca2+ current is likely to be the major
determinant of striatal neuron output if the basal ganglia is indeed an action
selection mechanism. In addition, the results provide evidence that increasing
dopamine can improve a neuron's signal-to-noise ratio.


title:
The cosmological model of eternal inflation and the transition from
  chance to biological evolution in the history of life: The possibility of
  chance emergence of the replication and translation systems, and the protein
  superfolds

abstract:
  Evolution of life on earth was governed, primarily, by natural selection,
with major contribution of other evolutionary processes, such as neutral
variation, exaptation, and gene duplication. However, for biological evolution
to take off, a certain minimal degree of complexity is required such that a
replicating genome encodes means for its own replication with sufficient rate
and fidelity. In all existing life forms, this is achieved by dedicated
proteins, polymerases (replicases), that are produced by the elaborate
translation system. However, evolution of the coupled system of replication and
translation does not appear possible without pre-existing efficient
replication; hence a chicken-egg type paradox. I argue that the
many-worlds-in-one version of the cosmological model of eternal inflation
implies that emergence of replication and translation, as well as the major
protein folds, by chance alone, as opposed to biological evolution, is a
realistic possibility and could provide for the onset of biological evolution.
Hence an RNA world, as it is currently conceived, might have never existed
although catalytic activities of RNA were, probably, critical for the onset of
biological evolution and its early stages.


title: Solution of an infection model near threshold

abstract:
  We study the Susceptible-Infected-Recovered model of epidemics in the
vicinity of the threshold infectivity. We derive the distribution of total
outbreak size in the limit of large population size $N$. This is accomplished
by mapping the problem to the first passage time of a random walker subject to
a drift that increases linearly with time. We recover the scaling results of
Ben-Naim and Krapivsky that the effective maximal size of the outbreak scales
as $N^{2/3}$, with the average scaling as $N^{1/3}$, with an explicit form for
the scaling function.


title: Evolution of Canalizing Boolean Networks

abstract:
  Boolean networks with canalizing functions are used to model gene regulatory
networks. In order to learn how such networks may behave under evolutionary
forces, we simulate the evolution of a single Boolean network by means of an
adaptive walk, which allows us to explore the fitness landscape. Mutations
change the connections and the functions of the nodes. Our fitness criterion is
the robustness of the dynamical attractors against small perturbations. We find
that with this fitness criterion the global maximum is always reached and that
there is a huge neutral space of 100% fitness. Furthermore, in spite of having
such a high degree of robustness, the evolved networks still share many
features with "chaotic" networks.


title:
The Influence of Photosynthesis on the Number of Metamers per Growth
  Unit in GreenLab Model

abstract:
  GreenLab Model is a functional-structural plant growth model that combines
both organogenesis (at each cycle, new organs are created with respect to
genetic rules) and photosynthesis (organs are filled with the biomass produced
by the leaves photosynthesis). Our new developments of the model concern the
retroaction of photosynthesis on organogenesis. We present here the first step
towards the total representation of this retroaction, where the influence of
available biomass on the number of metamers in new growth units us modelled.
The theory is introduced and applied to a Corner model tree. Different
interesting behaviours are pointed out.


title:
Marker enzyme phenotype ratios in agamospermous sugarbeet progenies as a
  demonstration of multidimensional encoding of inherited information in plants

abstract:
  It has been demonstrated that the observed ratio of phenotypes of marker
enzymes in some sugarbeet plants produced by mitotic agamospermy can be
explained by different degrees of endoreduplication of chromosomes carrying
different alleles of the enzyme loci. In these plants, different patterns of
variability of the enzymes controlled by the linked loci suggest different
degrees of endoreduplication of different chromosomal regions. A concept of
multidimensional encoding of inherited information in eukaryotes has been
proposed.


title: Quantitative Protein Dynamics from Dominant Folding Pathways

abstract:
  We develop a theoretical approach to the protein folding problem based on
out-of-equilibrium stochastic dynamics. Within this framework, the
computational difficulties related to the existence of large time scale gaps in
the protein folding problem are removed and simulating the entire reaction in
atomistic details using existing computers becomes feasible. In addition, this
formalism provides a natural framework to investigate the relationships between
thermodynamical and kinetic aspects of the folding. For example, it is possible
to show that, in order to have a large probability to remain unchanged under
Langevin diffusion, the native state has to be characterized by a small
conformational entropy. We discuss how to determine the most probable folding
pathway, to identify configurations representative of the transition state and
to compute the most probable transition time. We perform an illustrative
application of these ideas, studying the conformational evolution of alanine
di-peptide, within an all-atom model based on the empiric GROMOS96 force field.


title: Analysis and Assembling of Network Structure in Mutualistic Systems

abstract:
  It has been observed that mutualistic bipartite networks have a nested
structure of interactions. In addition, the degree distributions associated
with the two guilds involved in such networks (e.g. plants & pollinators or
plants & seed dispersers) approximately follow a truncated power law. We show
that nestedness and truncated power law distributions are intimately linked,
and that any biological reasons for such truncation are superimposed to finite
size effects . We further explore the internal organization of bipartite
networks by developing a self-organizing network model (SNM) that reproduces
empirical observations of pollination systems of widely different sizes. Since
the only inputs to the SNM are numbers of plant and animal species, and their
interactions (i.e., no data on local abundance of the interacting species are
needed), we suggest that the well-known association between species frequency
of interaction and species degree is a consequence rather than a cause, of the
observed network structure.


title: Molecular chaperones: The modular evolution of cellular networks

abstract:
  Molecular chaperones play a prominent role in signaling and transcriptional
regulatory networks of the cell. Recent advances uncovered that chaperones act
as genetic buffers stabilizing the phenotype of various cells and organisms and
may serve as potential regulators of evolvability. Chaperones have weak links,
connect hubs, are in the overlaps of network modules and may uncouple these
modules during stress, which gives an additional protection for the cell at the
network-level. Moreover, after stress chaperones are essential to re-build
inter-modular contacts by their low affinity sampling of the potential
interaction partners in different modules. This opens the way to the
chaperone-regulated modular evolution of cellular networks, and helps us to
design novel therapeutic and anti-aging strategies.


title: Lotka-Volterra population model of genetic evolution

abstract:
  A deterministic model of an age-structured population with genetics analogous
to the discrete time Penna model of genetic evolution is constructed on the
basis of the Lotka-Volterra scheme. It is shown that if, as in the Penna model,
genetic information is represented by the fraction of defective genes in the
population, the population numbers for each specific individual's age are
represented by exactly the same functions of age in both models. This gives us
a new possibility to consider multi-species evolution without using detailed
microscopic Penna model.
  We discuss a particular case of the predator-prey system representing an
ecosystem consisting of a limited amount of energy resources consumed by the
age-structured species living in this ecosystem. Then, the increase in number
of the individuals in the population under consideration depends on the
available energy resources, the shape of the distribution function of defective
genes in the population and the fertility age. We show that these parameters
determine the trend toward equilibrium of the whole ecosystem.


title: Desynchronization and sustainability of noisy metapopulation cycles

abstract:
  The apparent stability of population oscillations in ecological systems is a
long-standing puzzle. A generic solution for this problem is suggested here.
The stabilizing mechanism involves the combined effect of spatial migration,
amplitude-dependent frequency, and noise: small differences between spatial
patches, induced by the noise, are amplified by the frequency gradient.
Migration among desynchronized regions then stabilizes the oscillations in the
vicinity of the homogenous manifold. A simple model of diffusively coupled
oscillators allows the derivation of quantitative results, like the dependence
of the desynchronization upon diffusion strength and frequency differences. For
an unstable system, a noise-induced transition is demonstrated, from extinction
for small noise to stability if the noise exceeds some threshold. The coupled
oscillator model is shown to reproduce all previously suggested stabilizing
mechanisms. Accordingly, we suggest using this model as a standard tool for
identification and classification of population oscillations on spatial
domains.


title: Noise Correlation Induced Synchronization in a Mutualism Ecosystem

abstract:
  Understanding the cause of the synchronization of population evolution is an
important issue for ecological improvement. Here we present a
Lotka-Volterra-type model driven by two correlated environmental noises and
show, via theoretical analysis and direct simulation, that noise correlation
can induce a synchronization of the mutualists. The time series of mutual
species exhibit a chaotic-like fluctuation, which is independent to the noise
correlation, however, the chaotic fluctuation of mutual species ratio decreases
with the noise correlation. A quantitative parameter defined for characterizing
chaotic fluctuation provides a good approach to measure when the complete
synchronization happens.


title:
A structural and a functional aspect of stable information processing by
  the brain

abstract:
  In this paper a model of neural circuit in the brain has been proposed which
is composed of cyclic sub-circuits. A big loop has been defined to be
consisting of a feed forward path from the sensory neurons to the highest
processing area of the brain and feed back paths from that region back up to
close to the same sensory neurons. It has been mathematically shown how some
smaller cycles can amplify signal. A big loop processes information by contrast
and amplify principle. It has been assumed that the spike train coming out of a
firing neuron encodes all the information produced by it as output. This
information over a period of time can be extracted by a Fourier transform. The
Fourier coefficients arranged in a vector form will uniquely represent the
neural spike train over a period of time. The information emanating out of all
the neurons in a given neural circuit over a period of time will be represented
by a collection of points in a multidimensional vector space. This cluster of
points represents the functional or behavioral form of the neural circuit. It
has been proposed that a particular cluster of vectors as the representation of
a new behavior is chosen by the brain interactively with respect to the memory
stored in that circuit and the synaptic plasticity of the circuit. It has been
proposed that in this situation a Coulomb force like expression governs the
dynamics of functioning of the circuit and stability of the system is reached
at the minimum of all the minima of a potential function derived from the force
like expression. The calculations have been done with respect to a pseudometric
defined in a multidimensional vector space.


title:
Hierarchy and Feedback in the Evolution of the E. coli Transcription
  Network

abstract:
  The E.coli transcription network has an essentially feedforward structure,
with, however, abundant feedback at the level of self-regulations. Here, we
investigate how these properties emerged during evolution. An assessment of the
role of gene duplication based on protein domain architecture shows that (i)
transcriptional autoregulators have mostly arisen through duplication, while
(ii) the expected feedback loops stemming from their initial cross-regulation
are strongly selected against. This requires a divergent coevolution of the
transcription factor DNA-binding sites and their respective DNA cis-regulatory
regions. Moreover, we find that the network tends to grow by expansion of the
existing hierarchical layers of computation, rather than by addition of new
layers. We also argue that rewiring of regulatory links due to
mutation/selection of novel transcription factor/DNA binding interactions
appears not to significantly affect the network global hierarchy, and that
horizontally transferred genes are mainly added at the bottom, as new target
nodes. These findings highlight the important evolutionary roles of both
duplication and selective deletion of crosstalks between autoregulators in the
emergence of the hierarchical transcription network of E.coli.


title: Parametrized Stochastic Grammars for RNA Secondary Structure Prediction

abstract:
  We propose a two-level stochastic context-free grammar (SCFG) architecture
for parametrized stochastic modeling of a family of RNA sequences, including
their secondary structure. A stochastic model of this type can be used for
maximum a posteriori estimation of the secondary structure of any new sequence
in the family. The proposed SCFG architecture models RNA subsequences
comprising paired bases as stochastically weighted Dyck-language words, i.e.,
as weighted balanced-parenthesis expressions. The length of each run of
unpaired bases, forming a loop or a bulge, is taken to have a phase-type
distribution: that of the hitting time in a finite-state Markov chain. Without
loss of generality, each such Markov chain can be taken to have a bounded
complexity. The scheme yields an overall family SCFG with a manageable number
of parameters.


title:
Role of electrostatic interactions in amyloid beta-protein (Abeta)
  oligomer formation: A discrete molecular dynamics study

abstract:
  Pathological folding and oligomer formation of the amyloid beta-protein
(Abeta) are widely perceived as central to Alzheimer's disease (AD).
Experimental approaches to study Abeta self-assembly are problematic, because
most relevant aggregates are quasi-stable and inhomogeneous. We apply a
discrete molecular dynamics (DMD) approach combined with a four-bead protein
model to study oligomer formation of the amyloid beta-protein (Abeta). We
address the differences between the two most common Abeta alloforms, Abeta40
and Abeta42, which oligomerize differently in vitro. We study how the presence
of electrostatic interactions (EIs) between pairs of charged amino acids
affects Abeta40 and Abeta42 oligomer formation. Our results indicate that EIs
promote formation of larger oligomers in both Abeta40 and Abeta42. The Abeta40
size distribution remains unimodal, whereas the Abeta42 distribution is
trimodal, as observed experimentally. Abeta42 folded structure is characterized
by a turn in the C-terminus that is not present in Abeta40. We show that the
same C-terminal region is also responsible for the strongest intermolecular
contacts in Abeta42 pentamers and larger oligomers. Our results suggest that
this C-terminal region plays a key role in the formation of Abeta42 oligomers
and the relative importance of this region increases in the presence of EIs.
These results suggest that inhibitors targeting the C-terminal region of
Abeta42 oligomers may be able to prevent oligomer formation or structurally
modify the assemblies to reduce their toxicity.


title:
Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and
  Containment Interventions

abstract:
  We present a study of the worldwide spread of a pandemic influenza and its
possible containment at a global level taking into account all available
information on air travel. We studied a metapopulation stochastic epidemic
model on a global scale that considers airline travel flow data among urban
areas. We provided a temporal and spatial evolution of the pandemic with a
sensitivity analysis of different levels of infectiousness of the virus and
initial outbreak conditions (both geographical and seasonal). For each
spreading scenario we provided the timeline and the geographical impact of the
pandemic in 3,100 urban areas, located in 220 different countries. We compared
the baseline cases with different containment strategies, including travel
restrictions and the therapeutic use of antiviral (AV) drugs. We show that the
inclusion of air transportation is crucial in the assessment of the occurrence
probability of global outbreaks. The large-scale therapeutic usage of AV drugs
in all hit countries would be able to mitigate a pandemic effect with a
reproductive rate as high as 1.9 during the first year; with AV supply use
sufficient to treat approximately 2% to 6% of the population, in conjunction
with efficient case detection and timely drug distribution. For highly
contagious viruses (i.e., a reproductive rate as high as 2.3), even the
unrealistic use of supplies corresponding to the treatment of approximately 20%
of the population leaves 30%-50% of the population infected. In the case of
limited AV supplies and pandemics with a reproductive rate as high as 1.9, we
demonstrate that the more cooperative the strategy, the more effective are the
containment results in all regions of the world, including those countries that
made part of their resources available for global use.


title: Universally Sloppy Parameter Sensitivities in Systems Biology

abstract:
  Quantitative computational models play an increasingly important role in
modern biology. Such models typically involve many free parameters, and
assigning their values is often a substantial obstacle to model development.
Directly measuring \emph{in vivo} biochemical parameters is difficult, and
collectively fitting them to other data often yields large parameter
uncertainties. Nevertheless, in earlier work we showed in a
growth-factor-signaling model that collective fitting could yield
well-constrained predictions, even when it left individual parameters very
poorly constrained. We also showed that the model had a `sloppy' spectrum of
parameter sensitivities, with eigenvalues roughly evenly distributed over many
decades. Here we use a collection of models from the literature to test whether
such sloppy spectra are common in systems biology. Strikingly, we find that
every model we examine has a sloppy spectrum of sensitivities. We also test
several consequences of this sloppiness for building predictive models. In
particular, sloppiness suggests that collective fits to even large amounts of
ideal time-series data will often leave many parameters poorly constrained.
Tests over our model collection are consistent with this suggestion. This
difficulty with collective fits may seem to argue for direct parameter
measurements, but sloppiness also implies that such measurements must be
formidably precise and complete to usefully constrain many model predictions.
We confirm this implication in our signaling model. Our results suggest that
sloppy sensitivity spectra are universal in systems biology models. The
prevalence of sloppiness highlights the power of collective fits and suggests
that modelers should focus on predictions rather than on parameters.


title:
Meta-analysis of movements in Atlantic leatherback turtles during
  nesting season : conservation implications

abstract:
  Despite decades of conservation efforts on the nesting beaches, the critical
status of leatherback turtles shows that their survival predominantly depends
on our ability to reduce at-sea mortality. Although areas where leatherbacks
meet fisheries have been identified during the long distance movements between
two consecutive nesting seasons, hotspots of lethal interactions are still
poorly defined within the nesting season, when individuals concentrate close to
land. Here we report movements of satellite-tracked gravid leatherback turtles
during the nesting season in Western Central Africa, South America and
Caribbean Sea, accounting for about 70% of the world population. We show that
during, and at the end of, the nesting season leatherback turtles have the
propensity to remain over the continental shelf, yet sometimes perform extended
movements and may even nest in neighbouring countries. Leatherbacks exploit
coastal commercial fishing grounds and face substantial accidental capture by
regional coastal fisheries (e.g. at least 10% in French Guiana). This
emphasises the need for regional conservation strategies to be developed at the
ocean scale, both at sea and on land, to ensure the survival of the last
leatherback turtles.


title:
Community structure and modularity in networks of correlated brain
  activity

abstract:
  We present an approach to study functional segregation and integration in the
living brain based on community structure decomposition determined by maximum
modularity. We demonstrate this method with a network derived from functional
imaging data with nodes defined by individual image pixels, and edges in terms
of correlated signal changes. We found communities whose anatomical
distributions correspond to biologically meaningful structures and include
compelling functional subdivisions between anatomically equivalent brain
regions.


title: Stochastic dynamics of a finite-size spiking neural network

abstract:
  We present a simple Markov model of spiking neural dynamics that can be
analytically solved to characterize the stochastic dynamics of a finite-size
spiking neural network. We give closed-form estimates for the equilibrium
distribution, mean rate, variance and autocorrelation function of the network
activity. The model is applicable to any network where the probability of
firing of a neuron in the network only depends on the number of neurons that
fired in a previous temporal epoch. Networks with statistically homogeneous
connectivity and membrane and synaptic time constants that are not excessively
long could satisfy these conditions. Our model completely accounts for the size
of the network and correlations in the firing activity. It also allows us to
examine how the network dynamics can deviate from mean-field theory. We show
that the model and solutions are applicable to spiking neural networks in
biophysically plausible parameter regimes.


title:
Macroscopic consequences of calcium signaling in microdomains: A first
  passage time approach

abstract:
  Calcium (Ca) plays an important role in regulating various cellular
processes. In a variety of cell types, Ca signaling occurs within microdomains
where channels deliver localized pulses of Ca which activate a nearby
collection of Ca-sensitive receptors. The small number of channels involved
ensures that the signaling process is stochastic. The aggregate response of
several thousand of these microdomains yields a whole-cell response which
dictates the cell behavior. Here, we study analytically the statistical
properties of a population of these microdomains in response to a trigger
signal. We apply these results to understand the relationship between Ca influx
and Ca release in cardiac cells. In this context, we use a first passage time
approach to show analytically how Ca release in the whole cell depends on the
single channel kinetics of Ca channels and the properties of microdomains.
Using these results, we explain the underlying mechanism for the graded
relationship between Ca influx and Ca release in cardiac cells.


title:
Oscillations in two-species models: tying the stochastic and
  deterministic approaches

abstract:
  We analyze general two-species stochastic models, of the kind generally used
for the study of population dynamics. We show that the conditions for the
stochastic (microscopic) model to display approximate sustained oscillatory
behavior are governed by the parameters of the corresponding deterministic
(macroscopic) model. We provide a quantitative criterion for the quality of the
stochastic oscillation, using a dimensionless parameter that depends only on
the deterministic model. When this parameter is small, the oscillations are
clear, and the frequencies of the stochastic and deterministic oscillations are
close, for all stochastic models compatible with the same deterministic one. On
the other hand, when it is large, the oscillations cannot be distinguished from
a noise.


title: Modelling the brain as a n Apollonian network

abstract:
  Networks of living neurons exhibit an avalanche mode of activity,
experimentally found in organotypic cultures. Moreover, experimental studies of
morphology indicate that neurons develop a network of small-world-like
connections, with the possibility of very high connectivity degree. Here we
study a recent model based on self-organized criticality, which consists of an
electrical network with threshold firing and activity-dependent synapse
strengths. We study the model on a scale-free network, the Apollonian network,
which presents many features of neuronal systems. The system exhibits a power
law distributed avalanche activity. The analysis of the power spectra of the
electrical signal reproduces very robustly the power law behaviour with the
exponent 0.8, experimentally measured in electroencephalograms (EEG) spectra.
The exponents are found to be quite stable with respect to initial
configurations and strength of plastic remodelling, indicating that
universality holds for a wide class of brain models.


title:
Mobility of Min-proteins in Escherichia coli measured by fluorescence
  correlation spectroscopy

abstract:
  In the bacterium Escherichia coli, selection of the division site involves
pole-to-pole oscillations of the proteins MinD and MinE. Different oscillation
mechanisms based on cooperative effects between Min-proteins and on the
exchange of Min-proteins between the cytoplasm and the cytoplasmic membrane
have been proposed. The parameters characterizing the dynamics of the
Min-proteins in vivo are not known. It has therefore been difficult to compare
the models quantitatively with experiments. Here, we present in vivo
measurements of the mobility of MinD and MinE using fluorescence correlation
spectroscopy. Two distinct time-scales are clearly visible in the correlation
curves. While the faster time-scale can be attributed to cytoplasmic diffusion,
the slower time-scale could result from diffusion of membrane-bound proteins or
from protein exchange between the cytoplasm and the membrane. We determine the
diffusion constant of cytoplasmic MinD to be approximately 16\mu^{2}/s, while
for MinE we find about 10\mu^{2}/s, independently of the processes responsible
for the slower time-scale. Implications of the measured values for the
oscillation mechanism are discussed.


title:
Many Attractors, Long Chaotic Transients, and Failure in Small-World
  Networks of Excitable Neurons

abstract:
  We study the dynamical states that emerge in a small-world network of
recurrently coupled excitable neurons through both numerical and analytical
methods. These dynamics depend in large part on the fraction of long-range
connections or `short-cuts' and the delay in the neuronal interactions.
Persistent activity arises for a small fraction of `short-cuts', while a
transition to failure occurs at a critical value of the `short-cut' density.
The persistent activity consists of multi-stable periodic attractors, the
number of which is at least on the order of the number of neurons in the
network. For long enough delays, network activity at high `short-cut' densities
is shown to exhibit exceedingly long chaotic transients whose failure-times
averaged over many network configurations follow a stretched exponential. We
show how this functional form arises in the ensemble-averaged activity if each
network realization has a characteristic failure-time which is exponentially
distributed.

categories: q-bio.NC cond-mat.dis-nn nlin.CD physics.bio-ph


title:
Marine animal behaviour: neglecting ocean currents can lead us up the
  wrong track

abstract:
  Tracks of marine animals in the wild, now increasingly acquired by electronic
tagging of individuals, are of prime interest not only to identify habitats and
high-risk areas, but also to gain detailed information about the behaviour of
these animals. Using recent satellite-derived current estimates and leatherback
turtle (Dermochelys coriacea) tracking data, we demonstrate that oceanic
currents, usually neglected when analysing tracking data, can substantially
distort the observed trajectories. Consequently, this will affect several
important results deduced from the analysis of tracking data, such as the
evaluation of the orientation skills and the energy budget of animals or the
identification of foraging areas. We conclude that currents should be
systematically taken into account to ensure the unbiased interpretation of
tracking data, which now play a major role in marine conservation biology.


title: Random RNA under tension

abstract:
  The Laessig-Wiese (LW) field theory for the freezing transition of random RNA
secondary structures is generalized to the situation of an external force. We
find a second-order phase transition at a critical applied force f = f_c. For f
< f_c forces are irrelevant. For f > f_c, the extension L as a function of
pulling force f scales as (f-f_c)^(1/gamma-1). The exponent gamma is calculated
in an epsilon-expansion: At 1-loop order gamma = epsilon/2 = 1/2, equivalent to
the disorder-free case. 2-loop results yielding gamma = 0.6 are briefly
mentioned. Using a locking argument, we speculate that this result extends to
the strong-disorder phase.


title:
A quantum-theoretical approach to the phenomenon of directed mutations
  in bacteria

abstract:
  The Darwinian paradigm of biological evolution is based on the separability
of the variation and selection processes. As a result, the population thinking
had always been an integral part of the Darwinian approach. I propose an
alternative scheme of biological adaptation. It is based on appreciation of
limits of what we can observe considering an individual biological object. This
leads to a possibility for the adaptation process to occur on the level of a
single object, as a 'selection among virtual states of the organism'. I discuss
the application of this idea to the phenomenon of adaptive mutations in
bacteria.


title: Huddling behavior in emperor penguins : dynamics of huddling

abstract:
  Although huddling was shown to be the key by which emperor penguins
(Aptenodytes forsteri) save energy and sustain their breeding fast during the
Antarctic winter, the intricacies of this social behavior have been poorly
studied. We recorded abiotic variables with data loggers glued to the feathers
of eight individually marked emperor penguins to investigate their
thermoregulatory behavior and to estimate their "huddling time budget"
throughout the breeding season (pairing and incubation period). Contrary to the
classic view, huddling episodes were discontinuous and of short and variable
duration, lasting 1.6+/-1.7 (S.D.) h on average. Despite heterogeneous huddling
groups, birds had equal access to the warmth of the huddles. Throughout the
breeding season, males huddled for 38+/-18% (S.D.) of their time, which raised
the ambient temperature that birds were exposed to above 0 degrees C (at
average external temperatures of -17 degrees C). As a consequence of tight
huddles, ambient temperatures were above 20 degrees C during 13+/-12% (S.D.) of
their huddling time. Ambient temperatures increased up to 37.5 degrees C, close
to birds' body temperature. This complex social behavior therefore enables all
breeders to get a regular and equal access to an environment which allows them
to save energy and successfully incubate their eggs during the Antarctic
winter.


title: Attractors in continuous and Boolean networks

abstract:
  We study the stable attractors of a class of continuous dynamical systems
that may be idealized as networks of Boolean elements, with the goal of
determining which Boolean attractors, if any, are good approximations of the
attractors of generic continuous systems. We investigate the dynamics in simple
rings and rings with one additional self-input. An analysis of switching
characteristics and pulse propagation explains the relation between attractors
of the continuous systems and their Boolean approximations. For simple rings,
"reliable" Boolean attractors correspond to stable continuous attractors. For
networks with more complex logic, the qualitative features of continuous
attractors are influenced by inherently non-Boolean characteristics of
switching events.


title: Dissipation and spontaneous symmetry breaking in brain dynamics

abstract:
  We compare the predictions of the dissipative quantum model of brain with
neurophysiological data collected from electroencephalograms resulting from
high-density arrays fixed on the surfaces of primary sensory and limbic areas
of trained rabbits and cats. Functional brain imaging in relation to behavior
reveals the formation of coherent domains of synchronized neuronal oscillatory
activity and phase transitions predicted by the dissipative model.


title: Inhomogeneous maps and mathematical theory of selection

abstract:
  In this paper we develop a theory of general selection systems with discrete
time and explore the evolution of selection systems, in particular,
inhomogeneous populations. We show that the knowledge of the initial
distribution of the selection system allows us to determine explicitly the
system distribution at the entire time interval. All statistical
characteristics of interest, such as mean values of the fitness or any trait
can be predicted effectively for indefinite time and these predictions
dramatically depend on the initial distribution. The Fisher Fundamental theorem
of natural selection (FTNS) and more general the Price equations are the famous
results of the mathematical selection theory. We show that the problem of
dynamic insufficiency for the Price equations and for the FTNS can be resolved
within the framework of selection systems. Effective formulas for solutions of
the Price equations and for the FTNS are derived. Applications of the developed
theory to some other problems of mathematical biology (dynamics of
inhomogeneous logistic and Ricker model, selection in rotifer populations) are
also given. Complex behavior of the total population size, the mean fitness (in
contrast to the plain FTNS) and other traits is possible for inhomogeneous
populations with density-dependent fitness. The temporary dynamics of these
quantities can be investigated with the help of suggested methods.


title:
Comparing Protein Interaction Networks via a Graph Match-and-Split
  Algorithm

abstract:
  We present a method that compares the protein interaction networks of two
species to detect functionally similar (conserved) protein modules between
them. The method is based on an algorithm we developed to identify matching
subgraphs between two graphs. Unlike previous network comparison methods, our
algorithm has provable guarantees on correctness and efficiency. Our algorithm
framework also admits quite general connectivity and local matching criteria
that define when two subgraphs match and constitute a conserved module.
  We apply our method to pairwise comparisons of the yeast protein network with
the human, fruit fly and nematode worm protein networks, using a lenient
criterion based on connectedness and matching edges, coupled with a betweenness
clustering heuristic. We evaluate the detected conserved modules against
reference yeast protein complexes using sensitivity and specificity measures.
In these evaluations, our method performs competitively with and sometimes
better than two previous network comparison methods. Further under some
conditions (proper homolog and species selection), our method performs better
than a popular single-species clustering method. Beyond these evaluations, we
discuss the biology of a couple of conserved modules detected by our method. We
demonstrate the utility of network comparison for transferring annotations from
yeast proteins to human ones, and validate the predicted annotations.


title: Ancestral processes with selection: Branching and Moran models

abstract:
  We consider two versions of stochastic population models with mutation and
selection. The first approach relies on a multitype branching process; here,
individuals reproduce and change type (i.e., mutate) independently of each
other, without restriction on population size. We analyze the equilibrium
behaviour of this model, both in the forward and in the backward direction of
time; the backward point of view emerges if the ancestry of individuals chosen
randomly from the present population is traced back into the past.
  The second approach is the Moran model with selection. Here, the population
has constant size N. Individuals reproduce (at rates depending on their types),
the offspring inherits the parent's type, and replaces a randomly chosen
individual (to keep population size constant). Independently of the
reproduction process, individuals can change type. As in the branching model,
we consider the ancestral lines of single individuals chosen from the
equilibrium population. We use analytical results of Fearnhead (2002) to
determine the explicit properties, and parameter dependence, of the ancestral
distribution of types, and its relationship with the stationary distribution in
forward time.


title: Stochastic Simulations of Genetic Switch Systems

abstract:
  Genetic switch systems with mutual repression of two transcription factors
are studied using deterministic methods (rate equations) and stochastic methods
(the master equation and Monte Carlo simulations). These systems exhibit
bistability, namely two stable states such that spontaneous transitions between
them are rare. Induced transitions may take place as a result of an external
stimulus. We study several variants of the genetic switch and examine the
effects of cooperative binding, exclusive binding, protein-protein interactions
and degradation of bound repressors. We identify the range of parameters in
which bistability takes place, enabling the system to function as a switch.
Numerous studies have concluded that cooperative binding is a necessary
condition for the emergence of bistability in these systems. We show that a
suitable combination of network structure and stochastic effects gives rise to
bistability even without cooperative binding. The average time between
spontaneous transitions is evaluated as a function of the biological
parameters.


title: Brain as Quantum-like Machine for Transferring Time into Mind

abstract:
  We propose a model of processing of information in the brain which has the
following distinguishing features: a). It is quantum-like (QL). The brain uses
the quantum rule (given by von Neumann trace formula) for calculation of
averages for psychological functions. b). Those functions are considered as
self-observations of the brain. c). The QL-representation has the temporal
basis. The brain is a machine transferring time into cognition. d). Any
cognitive process is based on (at least) two time scales: precognitive time
scale (which is very fine) and cognitive time scale (which is essentially
coarser).
  To couple our model to physiology, behavioral science, and psychology, we
consider a number of known fundamental time scales in the brain. Although the
elaboration of those scales was based on advanced experimental research, there
are still many controversial approaches and results. The temporal structure of
the brain functioning is very complex.


title: In search of lost introns

abstract:
  Many fundamental questions concerning the emergence and subsequent evolution
of eukaryotic exon-intron organization are still unsettled. Genome-scale
comparative studies, which can shed light on crucial aspects of eukaryotic
evolution, require adequate computational tools.
  We describe novel computational methods for studying spliceosomal intron
evolution. Our goal is to give a reliable characterization of the dynamics of
intron evolution. Our algorithmic innovations address the identification of
orthologous introns, and the likelihood-based analysis of intron data. We
discuss a compression method for the evaluation of the likelihood function,
which is noteworthy for phylogenetic likelihood problems in general. We prove
that after $O(nL)$ preprocessing time, subsequent evaluations take $O(nL/\log
L)$ time almost surely in the Yule-Harding random model of $n$-taxon
phylogenies, where $L$ is the input sequence length.
  We illustrate the practicality of our methods by compiling and analyzing a
data set involving 18 eukaryotes, more than in any other study to date. The
study yields the surprising result that ancestral eukaryotes were fairly
intron-rich. For example, the bilaterian ancestor is estimated to have had more
than 90% as many introns as vertebrates do now.


title:
Stress-induced rearrangements of cellular networks: consequences for
  protection and drug design

abstract:
  The complexity of the cells can be described and understood by a number of
networks such as protein-protein interaction, cytoskeletal, organelle,
signalling, gene transcription and metabolic networks. All these networks are
highly dynamic producing continuous rearrangements in their links, hubs,
network-skeleton and modules. Here we describe the adaptation of cellular
networks after various forms of stress causing perturbations, congestions and
network damage. Chronic stress decreases link-density, decouples or even
quarantines modules, and induces an increased competition between network hubs
and bridges. Extremely long or strong stress may induce a topological phase
transition in the respective cellular networks, which switches the cell to a
completely different mode of cellular function. We summarize our initial
knowledge on network restoration after stress including the role of molecular
chaperones in this process. Finally, we discuss the implications of
stress-induced network rearrangements in diseases and ageing, and propose
therapeutic approaches both to increase the robustness and help the repair of
cellular networks.


title:
Predicting the size and probability of epidemics in a population with
  heterogeneous infectiousness and susceptibility

abstract:
  We analytically address disease outbreaks in large, random networks with
heterogeneous infectivity and susceptibility. The transmissibility $T_{uv}$
(the probability that infection of $u$ causes infection of $v$) depends on the
infectivity of $u$ and the susceptibility of $v$. Initially a single node is
infected, following which a large-scale epidemic may or may not occur. We use a
generating function approach to study how heterogeneity affects the probability
that an epidemic occurs and, if one occurs, its attack rate (the fraction
infected). For fixed average transmissibility, we find upper and lower bounds
on these. An epidemic is most likely if infectivity is homogeneous and least
likely if the variance of infectivity is maximized. Similarly, the attack rate
is largest if susceptibility is homogeneous and smallest if the variance is
maximized. We further show that heterogeneity in infectious period is
important, contrary to assumptions of previous studies. We confirm our
theoretical predictions by simulation. Our results have implications for
control strategy design and identification of populations at higher risk from
an epidemic.


title: Epitope prediction improved by multitask support vector machines

abstract:
  Motivation: In silico methods for the prediction of antigenic peptides
binding to MHC class I molecules play an increasingly important role in the
identification of T-cell epitopes. Statistical and machine learning methods, in
particular, are widely used to score candidate epitopes based on their
similarity with known epitopes and non epitopes. The genes coding for the MHC
molecules, however, are highly polymorphic, and statistical methods have
difficulties to build models for alleles with few known epitopes. In this case,
recent works have demonstrated the utility of leveraging information across
alleles to improve the performance of the prediction. Results: We design a
support vector machine algorithm that is able to learn epitope models for all
alleles simultaneously, by sharing information across similar alleles. The
sharing of information across alleles is controlled by a user-defined measure
of similarity between alleles. We show that this similarity can be defined in
terms of supertypes, or more directly by comparing key residues known to play a
role in the peptide-MHC binding. We illustrate the potential of this approach
on various benchmark experiments where it outperforms other state-of-the-art
methods.


title:
Intestinal gluconeogenesis and glucose transport according to body fuel
  availability in rats

abstract:
  Intestinal hexose absorption and gluconeogenesis have been studied in
relation to refeeding after two different fasting phases: a long period of
protein sparing during which energy expenditure is derived from lipid oxidation
(phase II), and a later phase characterized by a rise in plasma corticosterone
triggering protein catabolism (phase III). Such a switch in body fuel uses,
leading to changes in body reserves and gluconeogenic precursors, could
modulate intestinal gluconeogenesis and glucose transport. The gene and protein
levels, and the cellular localization of the sodium-glucose cotransporter
SGLT1, and of GLUT5 and GLUT2, as well as that of the key gluconeogenic enzymes
phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (Glc6Pase)
were measured. PEPCK and Glc6Pase activities were also determined. In phase III
fasted rats, SGLT1 was up-regulated and intestinal glucose uptake rates were
higher than in phase II fasted and fed rats. PEPCK and Glc6Pase mRNA, protein
levels and activities also increased in phase III. GLUT5 and GLUT2 were
down-regulated throughout the fast, but increased after refeeding, with GLUT2
recruited to the apical membrane. The increase in SGLT1 expression during phase
III may allow glucose absorption at low concentrations as soon as food is
available. Furthermore, an increased epithelial permeability due to fasting may
induce a paracellular movement of glucose. In the absence of intestinal GLUT2
during fasting, Glc6Pase could be involved in glucose release to the
bloodstream via membrane trafficking. Finally, refeeding triggered GLUT2 and
GLUT5 synthesis and apical recruitment of GLUT2, to absorb larger amounts of
hexoses.


title:
The electrophysiology of the betacell based on single transmembrane
  protein characteristics

abstract:
  The electrophysiology of betacells is at the origin of insulin secretion.
Betacells exhibit a complex behaviour upon stimulation with glucose including
repeated and uninterrupted bursting. Mathematical modelling is most suitable to
improve knowledge about the function of various transmembrane currents provided
the model is based on reliable data. This is the first attempt to build a
mathematical model for the betacell-electrophysiology in a bottom-up approach
which relies on single protein conductivity data. The results of previous
whole-cell-based models are reconsidered. The full simulation including all
prominent transmembrane proteins in betacells is used to provide a functional
interpretation of their role in betacell-bursting and an updated vantage point
of betacell-electrophysiology. As a result of a number of in silico knock-out-
and block-experiments the novel model makes some unexpected predictions:
Single-channel conductivity data imply that calcium-gated potassium currents
are rather small. Thus, their role in burst interruption has to be revisited.
An alternative role in high calcium level oscillations is proposed and an
alternative burst interruption model is presented. It also turns out that
sodium currents are more relevant than expected so far. Experiments are
proposed to verify these predictions.


title: Bistability preserving model reduction in apoptosis

abstract:
  Biological systems are typically very complex and need to be reduced before
they are amenable to a thorough analysis. Also, they often possess functionally
important dynamic features like bistability. In model reduction, it is
sometimes more desirable to preserve the dynamic features only than to recover
a good quantitative approximation. We present an approach to reduce the order
of a bistable dynamical system significantly while preserving bistability and
the switching threshold. These properties are important for the operation of
the system in the context of a larger network. As an application example, a
bistable model for caspase activation in apoptosis is considered.


title:
Internal strain regulates the nucleotide binding site of the kinesin
  leading head

abstract:
  In the presence of ATP, kinesin proceeds along the protofilament of
microtubule by alternated binding of two motor domains on the tubulin binding
sites. Since the processivity of kinesin is much higher than other motor
proteins, it has been speculated that there exists a mechanism for allosteric
regulation between the two monomers. Recent experiments suggest that ATP
binding to the leading head domain in kinesin is regulated by the rearward
strain built on the neck-linker. We test this hypothesis by explicitly modeling
a $C_{\alpha}$-based kinesin structure whose both motor domains are bound on
the tubulin binding sites. The equilibrium structures of kinesin on the
microtubule show disordered and ordered neck-linker configurations for the
leading and the trailing head, respectively. The comparison of the structures
between the two heads shows that several native contacts present at the
nucleotide binding site in the leading head are less intact than those in the
binding site of the rear head. The network of native contacts obtained from
this comparison provides the internal tension propagation pathway, which leads
to the disruption of the nucleotide binding site in the leading head. Also,
using an argument based on polymer theory, we estimate the internal tension
built on the neck-linker to be f~(12-15) pN. Both of these conclusions support
the experimental hypothesis.


title: Influence of Temperature on Neuronal Excitability in Cochlear Nucleus

abstract:
  The influence of temperature on neuronal excitability is studied by numerical
simulations on the spiking threshold characteristics of bushy cells in cochlear
nucleus periodically stimulated by synaptic currents. The results reveal that
there is a cut-off frequency for the spiking of bushy cell in a specific
temperature environment, corresponding to the existence of a critical
temperature for the neuron to respond with real spikes to the synaptic stimulus
of a given frequency, due to the finiteness of spike width. An optimal
temperature range for neuronal spiking is also found for a specific stimulus
frequency, and the temperature range span decreases with increasing stimulus
frequency. These findings imply that there is a physiological temperature range
which is beneficial for the information processing in auditory system.


title: Stochastic Models for Phylogenetic Trees on Higher-order Taxa

abstract:
  Simple stochastic models for phylogenetic trees on species have been well
studied. But much paleontology data concerns time series or trees on
higher-order taxa, and any broad picture of relationships between extant groups
requires use of higher-order taxa. A coherent model for trees on (say) genera
should involve both a species-level model and a model for the classification
scheme by which species are assigned to genera. We present a general framework
for such models, and describe three alternate classification schemes. Combining
with the species-level model of Aldous-Popovic (2005), one gets models for
higher-order trees, and we initiate analytic study of such models. In
particular we derive formulas for the lifetime of genera, for the distribution
of number of species per genus, and for the offspring structure of the tree on
genera.


title: Biology's next revolution

abstract:
  The interpretation of recent environmental genomics data exposes the
far-reaching influence of horizontal gene transfer, and is changing our basic
concepts of organism, species and evolution itself.


title:
Compositionality, stochasticity and cooperativity in dynamic models of
  gene regulation

abstract:
  We present an approach for constructing dynamic models for the simulation of
gene regulatory networks from simple computational elements. Each element is
called a ``gene gate'' and defines an input/output-relationship corresponding
to the binding and production of transcription factors. The proposed reaction
kinetics of the gene gates can be mapped onto stochastic processes and the
standard ode-description. While the ode-approach requires fixing the system's
topology before its correct implementation, expressing them in stochastic
pi-calculus leads to a fully compositional scheme: network elements become
autonomous and only the input/output relationships fix their wiring. The
modularity of our approach allows to pass easily from a basic first-level
description to refined models which capture more details of the biological
system. As an illustrative application we present the stochastic repressilator,
an artificial cellular clock, which oscillates readily without any cooperative
effects.


title:
Corticosterone selectively decreases humoral immunity in female eiders
  during incubation

abstract:
  Immunity is hypothesized to share limited resources with other physiological
functions and this may partly account for the fitness costs of reproduction.
Previous studies have shown that the acquired immunity of female common eider
ducks (Somateria mollissima) is suppressed during their incubation, during
which they entirely fast. Corticosterone was proposed to be an underlying
physiological mechanism for such immunosuppression. Therefore, the current
study aimed to assess the effects of exogenous corticosterone on acquired
immunity in captive eiders. To this end, females were implanted with
corticosterone pellets at different stages of their incubation fast. We
measured total immunoglobulin levels, T-cell-mediated immune response, body
mass and corticosterone levels in these females and compared them with those of
control females prior to and after manipulation (i.e. corticosterone pellet
implantation). To mimic corticosterone effects on body mass, we experimentally
extended fasting duration in a group of females termed ;late fasters'...


title:
Intestinal apoptotic changes linked to metabolic status in fasted and
  refed rats

abstract:
  Intestinal apoptosis and expression of apoptosis inducers - the cytokines
TNFalpha, TGFbeta1 - and the intestinal transcription factor Cdx2, were studied
according to two different metabolic and hormonal phases which characterize
long-term fasting: the long period of protein sparing during which energy
expenditure is derived from lipid oxidation (phase II), and the later phase
characterized by a rise in body protein utilization and plasma corticosterone
(phase III). Apoptosis was further studied in 2, 6, and 24 h refed rats.
Morphological apoptotic events were observed by environmental and conventional
scanning electron microscopy and a TUNEL test was used to characterize the
final stages of apoptotic death. The gene and protein expressions of TNFalpha,
TGFbeta1, and Cdx2 were measured. Apoptotic events and TNFalpha, TGFbeta1, and
Cdx2 gene and protein expressions did not vary significantly during phase II as
compared to the normally fed animals. However, a phase III fasting induced a
delay in intestinal epithelial apoptosis, along with a 92, 58, and 25% decrease
in TNFalpha, TGFbeta1, and Cdx2 mRNAs, respectively. The amounts of TNFalpha,
TGFbeta1, and Cdx2 proteins decreased by 70, 36, and 25%, respectively.
Apoptosis was restored rapidly after a 2 h refeeding following the phase III,
accompanied by a significant increase in TNFalpha, TGFbeta1, and Cdx2 mRNA and
the protein levels, compared to the phase III fasting values. The concomitant
decreases in cytokines and Cdx2 and in apoptotic cells during phase III suggest
the preservation of enterocytes during this critical fasting period in order to
optimize nutrient absorption as soon as food is available and thus, to rapidly
restore body mass.


title:
Corticothalamic projections control synchronization in locally coupled
  bistable thalamic oscillators

abstract:
  Thalamic circuits are able to generate state-dependent oscillations of
different frequencies and degrees of synchronization. However, only little is
known how synchronous oscillations, like spindle oscillations in the thalamus,
are organized in the intact brain. Experimental findings suggest that the
simultaneous occurrence of spindle oscillations over widespread territories of
the thalamus is due to the corticothalamic projections, as the synchrony is
lost in the decorticated thalamus. Here we study the influence of
corticothalamic projections on the synchrony in a thalamic network, and uncover
the underlying control mechanism, leading to a control method which is
applicable in wide range of stochastic driven excitable units.


title:
Face processing limitation to own species in primates: a comparative
  study in brown capuchins, Tonkean macaques and humans

abstract:
  Most primates live in social groups which survival and stability depend on
individuals' abilities to create strong social relationships with other group
members. The existence of those groups requires to identify individuals and to
assign to each of them a social status. Individual recognition can be achieved
through vocalizations but also through faces. In humans, an efficient system
for the processing of own species faces exists. This specialization is achieved
through experience with faces of conspecifics during development and leads to
the loss of ability to process faces from other primate species. We hypothesize
that a similar mechanism exists in social primates. We investigated face
processing in one Old World species (genus Macaca) and in one New World species
(genus Cebus). Our results show the same advantage for own species face
recognition for all tested subjects. This work suggests in all species tested
the existence of a common trait inherited from the primate ancestor: an
efficient system to identify individual faces of own species only.


title:
Duality, thermodynamics, and the linear programming problem in
  constraint-based models of metabolism

abstract:
  It is shown that the dual to the linear programming problem that arises in
constraint-based models of metabolism can be given a thermodynamic
interpretation in which the shadow prices are chemical potential analogues, and
the objective is to minimise free energy consumption given a free energy drain
corresponding to growth. The interpretation is distinct from conventional
non-equilibrium thermodynamics, although it does satisfy a minimum entropy
production principle. It can be used to motivate extensions of constraint-based
modelling, for example to microbial ecosystems.


title: Finding Sequence Features in Tissue-specific Sequences

abstract:
  The discovery of motifs underlying gene expression is a challenging one. Some
of these motifs are known transcription factors, but sequence inspection often
provides valuable clues, even discovery of novel motifs with uncharacterized
function in gene expression. Coupled with the complexity underlying
tissue-specific gene expression, there are several motifs that are putatively
responsible for expression in a certain cell type. This has important
implications in understanding fundamental biological processes, such as
development and disease progression. In this work, we present an approach to
the principled selection of motifs (not necessarily transcription factor sites)
and examine its application to several questions in current bioinformatics
research.
  There are two main contributions of this work: Firstly, we introduce a new
metric for variable selection during classification, and secondly, we
investigate a problem of finding specific sequence motifs that underlie tissue
specific gene expression. In conjunction with the SVM classifier we find these
motifs and discover several novel motifs which have not yet been attributed
with any particular functional role (eg: TFBS binding motifs). We hypothesize
that the discovery of these motifs would enable the large-scale investigation
for the tissue specific regulatory potential of any conserved sequence element
identified from genome-wide studies.
  Finally, we propose the utility of this developed framework to not only aid
discovery of discriminatory motifs, but also to examine the role of any motif
of choice in co-regulation or co-expression of gene groups.


title:
Group movement decisions in capuchin monkeys : the utility of an
  experimental study and a mathematical model to explore the relationship
  between individual and collective behaviours

abstract:
  In primate groups, collective movements are typically described as processes
dependent on leadership mechanisms. However, in some species, decision-making
includes negotiations and distributed leadership. These facts suggest that
simple underlying processes may explain certain decision mechanisms during
collective movements. To study such processes, we have designed experiments on
white-faced capuchin monkeys (Cebus capucinus) during which we provoked
collective movements involving a binary choice. These experiments enabled us to
analyse the spatial decisions of individuals in the group.We found that the
underlying process includes anonymous mimetism, which means that each
individual may influence all members of the group. To support this result, we
created a mathematical model issued from our experimental data. A totally
anonymous model does not fit perfectly with our experimental distribution. A
more individualised model, which takes into account the specific behaviour of
social peripheral individuals, revealed the validity of the mimetism
hypothesis. Even though white-faced capuchins have complex cognitive abilities,
a coexistence of anonymous and social mechanisms appears to influence their
choice of direction during collective movements. The present approach may offer
vital insights into the relationships between individual behaviours and their
emergent collective acts.


title:
Coincident Frequencies and Relative Phases Among Female-Brain Signals
  and Progesterone-Estrogen levels

abstract:
  Fourier transform has become a basic tool for analyzing biological signals
1,2,3. Mostly a fast Fourier transform is computed for a finite sequence of
data sample 4. This is the standard way apparatuses and modern computerized
technology provide information, according with their frequency range, of the
well known brain signals Delta, Theta, Alpha 1, Alpha 2, Beta 1 and Beta 2
furnishing experts with electroencephalographic (EEG) profile of clinical use
obtained from these short periods 5,6.
  For long periods, an analogous novel procedure is established as follows:
Assigning certain numerical value, i.e., the absolute power, to each brain
signal at certain sampling times, generates data that can be interpolated and
extrapolated through a long period, yielding an absolute power function of time
for each signal 7. A further Fourier transform is then performed8,9, to analyze
these new functions, finding typical frequencies and their corresponding
periods for each one of these signals and, also, relative phases for coincident
periods between two or more signals. Our procedure of analysis presented here
can be applied, in principle, to any biological signal of interest.


title: Stochastic Synchronization of Genetic Oscillator Networks

abstract:
  The study of synchronization among genetic oscillators is essential for the
understanding of the rhythmic phenomena of living organisms at both molecular
and cellular levels. Genetic networks are intrinsically noisy due to natural
random intra- and inter-cellular fluctuations. Therefore, it is important to
study the effects of noise perturbation on the synchronous dynamics of genetic
oscillators. From the synthetic biology viewpoint, it is also important to
implement biological systems that minimizing the negative influence of the
perturbations. In this paper, based on systems biology approach, we provide a
general theoretical result on the synchronization of genetic oscillators with
stochastic perturbations. By exploiting the specific properties of many genetic
oscillator models, we provide an easy-verified sufficient condition for the
stochastic synchronization of coupled genetic oscillators, based on the Lur'e
system approach in control theory. A design principle for minimizing the
influence of noise is also presented. To demonstrate the effectiveness of our
theoretical results, a population of coupled repressillators is adopted as a
numerical example. In summary, we present an efficient theoretical method for
analyzing the synchronization of genetic oscillator networks, which is helpful
for understanding and testing the synchronization phenomena in biological
organisms. Besides, the results are actually applicable to general oscillator
networks.


title: Effective Potential Energy Expression for Membrane Transport

abstract:
  All living cells transport molecules and ions across membranes, often against
concentration gradients. This active transport requires continual energy
expenditure and is clearly a nonequilibrium process for which standard
equilibrium thermodynamics is not rigorously applicable. Here we derive a
nonequilibrium effective potential that evaluates the per particle transport
energy invested by the membrane. A novel method is used whereby a Hamiltonian
function is constructed using particle concentrations as generalized
coordinates. The associated generalized momenta are simply related to the
individual particle energy from which we identify the effective potential.
Examples are given and the formalism is compared with the equilibrium Gibb's
free energy.


title:
Network-based analysis of stochastic SIR epidemic models with random and
  proportionate mixing

abstract:
  In this paper, we outline the theory of epidemic percolation networks and
their use in the analysis of stochastic SIR epidemic models on undirected
contact networks. We then show how the same theory can be used to analyze
stochastic SIR models with random and proportionate mixing. The epidemic
percolation networks for these models are purely directed because undirected
edges disappear in the limit of a large population. In a series of simulations,
we show that epidemic percolation networks accurately predict the mean outbreak
size and probability and final size of an epidemic for a variety of epidemic
models in homogeneous and heterogeneous populations. Finally, we show that
epidemic percolation networks can be used to re-derive classical results from
several different areas of infectious disease epidemiology. In an appendix, we
show that an epidemic percolation network can be defined for any
time-homogeneous stochastic SIR model in a closed population and prove that the
distribution of outbreak sizes given the infection of any given node in the SIR
model is identical to the distribution of its out-component sizes in the
corresponding probability space of epidemic percolation networks. We conclude
that the theory of percolation on semi-directed networks provides a very
general framework for the analysis of stochastic SIR models in closed
populations.


title: The multifractal fly: a dynamically multilayered visual system

abstract:
  We dynamically analyze our experimental results on the motion sensitive
spiking H1 neuron of the fly's visual system. We find that the fly uses an
alphabet composed of a few letters to encode the information contained in the
stimulus. The {\em alphabet dynamics} is multifractal both with and without
stimulus, though the multifractality increases with the stimulus entropy. This
is in sharp contrast to models generating independent spike-intervals, whose
dynamics is monofractal.


title: Graph animals, subgraph sampling and motif search in large networks

abstract:
  We generalize a sampling algorithm for lattice animals (connected clusters on
a regular lattice) to a Monte Carlo algorithm for `graph animals', i.e.
connected subgraphs in arbitrary networks. As with the algorithm in [N. Kashtan
et al., Bioinformatics 20, 1746 (2004)], it provides a weighted sample, but the
computation of the weights is much faster (linear in the size of subgraphs,
instead of super-exponential). This allows subgraphs with up to ten or more
nodes to be sampled with very high statistics, from arbitrarily large networks.
Using this together with a heuristic algorithm for rapidly classifying
isomorphic graphs, we present results for two protein interaction networks
obtained using the TAP high throughput method: one of Escherichia coli with 230
nodes and 695 links, and one for yeast (Saccharomyces cerevisiae) with roughly
ten times more nodes and links. We find in both cases that most connected
subgraphs are strong motifs (Z-scores >10) or anti-motifs (Z-scores <-10) when
the null model is the ensemble of networks with fixed degree sequence. Strong
differences appear between the two networks, with dominant motifs in E. coli
being (nearly) bipartite graphs and having many pairs of nodes which connect to
the same neighbors, while dominant motifs in yeast tend towards completeness or
contain large cliques. We also explore a number of methods that do not rely on
measurements of Z-scores or comparisons with null models. For instance, we
discuss the influence of specific complexes like the 26S proteasome in yeast,
where a small number of complexes dominate the $k$-cores with large k and have
a decisive effect on the strongest motifs with 6 to 8 nodes. We also present
Zipf plots of counts versus rank. They show broad distributions that are not
power laws, in contrast to the case when disconnected subgraphs are included.


title: The Role of Lipid Bilayer Mechanics in Mechanosensation

abstract:
  Mechanosensation is a key part of the sensory repertoire of a vast array of
different cells and organisms. The molecular dissection of the origins of
mechanosensation is rapidly advancing as a result of both structural and
functional studies. One intriguing mode of mechanosensation results from
tension in the membrane of the cell (or vesicle) of interest. The aim of this
review is to catalogue recent work that uses a mix of continuum and statistical
mechanics to explore the role of the lipid bilayer in the function of
mechanosensitive channels that respond to membrane tension. The role of bilayer
deformation will be explored in the context of the well known mechanosensitive
channel MscL. Additionally, we make suggestions for bridging gaps between our
current theoretical understanding and common experimental techniques.


title:
Cooperative Gating and Spatial Organization of Membrane Proteins through
  Elastic Interactions

abstract:
  Biological membranes are elastic media in which the presence of a
transmembrane protein leads to local bilayer deformation. The energetics of
deformation allow two membrane proteins in close proximity to influence each
other's equilibrium conformation via their local deformations, and spatially
organize the proteins based on their geometry. We use the mechanosensitive
channel of large conductance (MscL) as a case study to examine the implications
of bilayer-mediated elastic interactions on protein conformational statistics
and clustering. The deformations around MscL cost energy on the order of 10 kT
and extend ~3nm from the protein edge, as such elastic forces induce
cooperative gating and we propose experiments to measure these effects.
Additionally, since elastic interactions are coupled to protein conformation,
we find that conformational changes can severely alter the average separation
between two proteins. This has important implications for how conformational
changes organize membrane proteins into functional groups within membranes.


title: Bacteria are not Lamarckian

abstract:
  Instructive influence of environment on heredity has been a debated topic for
centuries. Darwin's identification of natural selection coupled to chance
variation as the driving force for evolution, against a formal interpretation
proposed by Lamarck, convinced most scientists that environment does not
specifically instruct evolution in an oriented direction. This is true for
multicellular organisms. In contrast, bacteria were long thought of as prone to
receive oriented influences from their environment, although much was in favour
of the Darwinian route (1). In this context Cairns et al. raised a passionate
debate by suggesting that bacteria generate mutations oriented by the
environmental conditions (2). Several independent pieces of work subsequently
demonstrated that mutations overcoming specific defects arised as a consequence
of cultivation on specific media (3-7). Two diametrically opposed
interpretations were proposed to explain these observations : either induction
of mutations instructed by the environment (e.g. by a process involving a
putative reverse transcription) or selection of variants among a large set of
mutant bacteria generated when stress conditions are present. The experiments
presented below indicate that the Darwinian paradigm is the most plausible.


title: A Framework for Conscious Information Processing

abstract:
  This paper exploits the fact that the variability in the inter-spike
intervals, in the spike train issuing from a neuron, carries substantial
information regarding the input to the neuron. A framework for neuronal
information processing is proposed which utilizes the above fact to distinguish
phenomenal from non-phenomenal mental representation. In the process, an
explanation is offered as to what it is, in the nature of conscious mental
states, that imparts them a subjective feeling: there is something it is like
to be in those mental states. To give empirical support, it is shown how the
proposed framework can neatly explain, the delay in eliciting conscious
awareness as observed by Libet and the related backwards referral in time.


title:
On the relationships between kinetic schemes and two-state single
  molecule trajectories

abstract:
  Trajectories of a signal that fluctuates between two states which originate
from single molecule activities have become ubiquitous. Common examples are
trajectories of ionic flux through individual membrane-channels, and of photon
counts collected from diffusion, activity, and conformational changes of
biopolymers. By analyzing the trajectory, one wishes to deduce the underlying
mechanism, which is usually described by a multi-substate kinetic scheme. In
previous works, we divided kinetic schemes that generate two-state trajectories
into two types: reducible schemes and irreducible schemes. We showed that all
the information in trajectories generated from reducible schemes is contained
in the waiting time probability density functions (PDFs) of the two states. It
follows that reducible schemes with the same waiting time PDFs are not
distinguishable. In this work, we further characterize the topologies of
kinetic schemes, now of irreducible schemes, and further study two-state
trajectories from the two types of scheme. We suggest various methods for
extracting information about the underlying kinetic scheme from the trajectory
(e. g., calculate the binned successive waiting times PDF and analyze the
ordered waiting times trajectory), and point out the advantages and
disadvantages of each. We show that the binned successive waiting times PDF is
not only more robust than other functions when analyzing finite trajectories,
but contains, in most cases, more information about the underlying kinetic
scheme than other functions in the limit of infinitely long trajectories. For
some cases however, analyzing the ordered waiting times trajectory may supply
unique information about the underlying kinetic scheme.


title: Species clustering in competitive Lotka-Volterra models

abstract:
  We study the properties of Lotka-Volterra competitive models in which the
intensity of the interaction among species depends on their position along an
abstract niche space through a competition kernel. We show analytically and
numerically that the properties of these models change dramatically when the
Fourier transform of this kernel is not positive definite, due to a pattern
forming instability. We estimate properties of the species distributions, such
as the steady number of species and their spacings, for different types of
kernels.


title: Nucleotide Distribution Patterns in Insect Genomes

abstract:
  This work analyzed genome-wide nucleotide distribution patterns in ten insect
genomes. Two internal measures were applied: (i) GC variation and (ii) third
codon nucleotide preference. Although the genome size and overall GC level did
not show any correlation with insect order, the internal measures usually
displayed higher levels of consistency. GC variations in genomes of
hymenopteran insects, honeybee and wasp, ranked highest among all eukaryotic
genomes analyzed by us. Genomes of honeybee and beetle, insects of different
orders with similar overall GC levels, showed significant internal differences.
Honeybee genome stood out as unusual due to its high GC variation and
'left-handed' gene locations.


title:
Ultraconserved Sequences in the Honeybee Genome - Are GC-rich Regions
  Preferred?

abstract:
  Among all insect genomes, honeybee displays one of the most unusual patterns
with interspersed long AT and GC-rich segments. Nearly 75% of the
protein-coding genes are located in the AT-rich segments of the genome, but the
biological significance of the GC-rich regions is not well understood. Based on
an observation that the bee miRNAs, actins and tubulins are located in the
GC-rich segments, this work investigated whether other highly conserved genomic
regions show similar preferences. Sequences ultraconserved between the genomes
of honeybee and Nasonia, another hymenopteran insect, were determined. They
showed strong preferences towards locating in the GC-rich regions of the bee
genome.


title:
Modelling Sex Ratio and Numbers for Translocation in Meta-Population
  Management

abstract:
  The management of endangered species as metapopulations is becoming
increasingly common. Diverse aspects of metapopulation dynamics and management
have received attention in recent years. In particular, translocation of
individuals between subpopulations of a metapopulation is practiced, or
envisaged, for a variety of reasons and requires careful consideration.
Linklater (2003) proposed that the number of individuals of each sex
translocated into a target population for the purposes of maintaining genetic
diversity could be chosen on the basis of parental investment theory. In this
paper, following basic ideas in the parental investment literature, I propose a
simple model to capture Linklater's proposal and provide a thorough
mathematical analysis of the model. Granted the necessary species-specific
biological information which would determine the model parameters in any
instance of application, the analysis indicates that a practical algorithm can
be constructed to generate the model's predictions for the optimal
translocation.


title: Effect of DNA looping on the induction kinetics of the lac operon

abstract:
  The induction of the lac operon follows cooperative kinetics.The first
mechanistic model of these kinetics is the de facto standard in the modeling
literature (Yagil & Yagil, Biophys J, 11, 11-27, 1971). Yet, subsequent studies
have shown that the model is based on incorrect assumptions. Specifically, the
repressor is a tetramerwith four (not two) inducer-binding sites, and the
operon contains two auxiliary operators (in addition to the main operator).
Furthermore, these structural features are crucial for the formation of DNA
loops, the key determinants of lac repression and induction. Indeed, the
repression is determined almost entirely (>95%) by the looped complexes (Oehler
et al, EMBO J, 13, 3348, 1990), and the pronounced cooperativity of the
induction curve hinges upon the existence of the looped complexes (Oehler et
al, Nucleic Acids Res, 34, 606, 2006). Here, we formulate a model of lac
induction taking due account of the tetrameric structure of the repressor and
the existence of looped complexes. We show that: (1) The kinetics are
significantly more cooperative than those predicted by the Yagil & Yagil model.
(2) The model provides good fits to the repression data for cells containing
tetrameric (or mutant dimeric) repressor, as well as the induction curves for 6
different strains of E. coli. It also implies that the ratios of certain looped
and non-looped complexes are independent of inducer and repressor levels, a
conclusion that can be rigorously tested by gel electrophoresis. (3) Repressor
overexpression dramatically increases the cooperativity of the induction curve.
This suggests that repressor overexpression can induce bistability in systems,
such as growth of E. coli on lactose, that are otherwise monostable.


title:
A physical model for competition between biological speciation and
  extinction

abstract:
  Time evolution of number of species (genera, families, and others),
population of them, and size distribution of present ones and life times are
studied in terms of a new model, where population of each genetic taxon
increases by a (random) rate and decreases by (random) division
(fragmentation). Each of log-normal, exponential and power law distributions in
various empirical data about mentioned events are considered for gradual,
intermittent and abrupt evolution cases. Results come out in good agreement
with several theoretical literatures and with available empirical data. Sum of
exponential functions with independent random amplitude and exponent are shown
to be useful to express many aspects of the present competition, and they are
shown here to involve power law with power -1.


title:
Inbreeding and outbreeding depressions in the Penna model as a result of
  crossover frequency

abstract:
  The population in the sexual Penna ageing model is first separated into
several reproductively isolated groups. Then, after equilibration, sexual
mixing between the groups is allowed. We study the changes in the population
size due to this mixing and interpret them through a counterplay of purifying
selection and of haplotype complementarity.


title:
Satisfiability, sequence niches, and molecular codes in cellular
  signaling

abstract:
  Biological information processing as implemented by regulatory and signaling
networks in living cells requires sufficient specificity of molecular
interaction to distinguish signals from one another, but much of regulation and
signaling involves somewhat fuzzy and promiscuous recognition of molecular
sequences and structures, which can leave systems vulnerable to crosstalk. This
paper examines a simple computational model of protein-protein interactions
which reveals both a sharp onset of crosstalk and a fragmentation of the
neutral network of viable solutions as more proteins compete for regions of
sequence space, revealing intrinsic limits to reliable signaling in the face of
promiscuity. These results suggest connections to both phase transitions in
constraint satisfaction problems and coding theory bounds on the size of
communication codes.


title:
Spatial Constraint Corrections to the Elasticity of dsDNA Measured with
  Magnetic Tweezers

abstract:
  In this paper, we have studied, within a discrete WLC model, the spatial
constraints in magnetic tweezers used in single molecule experiments. Two
elements are involved: first, the fixed plastic slab on which is stuck the
initial strand, second, the magnetic bead which pulls (or twists) the attached
molecule free end. We have shown that the bead surface can be replaced by its
tangent plane at the anchoring point, when it is close to the bead south pole
relative to the force. We are led to a model with two parallel repulsive
plates: the fixed anchoring plate and a fluctuating plate, simulating the bead,
in thermal equilibrium with the system. The bead effect is a slight upper shift
of the elongation, about four times smaller than the similar effect induced by
the fixed plate. This rather unexpected result, has been qualitatively
confirmed within the soluble Gaussian model. A study of the molecule elongation
versus the countour length exhibits a significant non-extensive behaviour. The
curve for short molecules (with less than 2 kbp) is well fitted by a straight
line, with a slope given by the WLC model, but it does not go through the
origin. The non-extensive offset gives a 15% upward shift to the elongation of
a 2 kbp molecule stretched by a 0.3 pN force.


title:
A windowed local fdr estimator providing higher resolution and robust
  thresholds

abstract:
  Motivation: In microarray analysis, special consideration must be given to
the issues of multiple statistical tests and typically p-values are adjusted to
control family-wise error rate (FWER) or false discovery rate (FDR). FDR
metrics have been suggested for controlling false positives, however, genes
with p-values close to the threshold typically have a higher chance of being
false positives than genes with very low p-values. The local FDR (fdr) metric
gives the number of false positives in the vicinity of a test statistic. We
propose a new fdr estimator that uses windows instead of binsand define
heuristics that use the fluctuations in the estimator to determine robust
thresholds for classifying differential expression.
  Results: Our fdr approach estimates the false discovery rate within a window
of p-values. We present heuristics that derive robust fdr thresholds such that
a significant change in the fdr threshold yields a small change in the number
of rejected hypotheses. We compare these thresholds with thresholds from other
approaches using two simulated datasets and one cancer microarray dataset. In
the latter, our estimator finds two robust thresholds. Since our fdr estimator
is an extension of the FDR metric, it can be used with many FDR estimation
methods.
  Availability: An R function implementing the proposed estimator is available
at http://www.dbi.tju.edu/dbi/tools/fdr
  Contact: james.schwaber@jefferson.edu
  Supplementary Information: Supplementary figures and code are available at
http://www.dbi.tju.edu/dbi/tools/fdr