title:
Comment on ``Critical branching captures activity in living neural
  networks and maximizes the number of metastable states''

abstract:
  It is shown that, contrary to the claims in a recent letter by Haldeman and
Beggs (PRL, 94, 058101, 2005), the branching ratio in epileptic cortical
cultures is smaller than one. In addition, and also in contrast to claims made
in that paper, the number of metastable states is not significantly different
between cortical cultures in the critical state and cultures made epileptic
using picrotoxin.


title:
Self-organization of gene regulatory network motifs enriched with short
  transcript's half-life transcription factors

abstract:
  Network motifs, the recurring regulatory structural patterns in networks, are
able to self-organize to produce networks. Three major motifs, feedforward
loop, single input modules and bi-fan are found in gene regulatory networks.
The large ratio of genes to transcription factors (TFs) in genomes leads to a
sharing of TFs by motifs and is sufficient to result in network
self-organization. We find a common design principle of these motifs: short
transcript's half-life (THL) TFs are significantly enriched in motifs and hubs.
This enrichment becomes one of the driving forces for the emergence of the
network scale-free topology and allows the network to quickly adapt to
environmental changes. Most feedforward loops and bi-fans contain at least one
short THL TF, which can be seen as a criterion for self-assembling these
motifs. We have classified the motifs according to their short THL TF content.
We show that the percentage of the different motif subtypes varies in different
cellular conditions.


title:
Low Degree Metabolites Explain Essential Reactions and Enhance
  Modularity in Biological Networks

abstract:
  Recently there has been a lot of interest in identifying modules at the level
of genetic and metabolic networks of organisms, as well as in identifying
single genes and reactions that are essential for the organism. A goal of
computational and systems biology is to go beyond identification towards an
explanation of specific modules and essential genes and reactions in terms of
specific structural or evolutionary constraints. In the metabolic networks of
E. coli, S. cerevisiae and S. aureus, we identified metabolites with a low
degree of connectivity, particularly those that are produced and/or consumed in
just a single reaction. Using FBA we also determined reactions essential for
growth in these metabolic networks. We find that most reactions identified as
essential in these networks turn out to be those involving the production or
consumption of low degree metabolites. Applying graph theoretic methods to
these metabolic networks, we identified connected clusters of these low degree
metabolites. The genes involved in several operons in E. coli are correctly
predicted as those of enzymes catalyzing the reactions of these clusters. We
independently identified clusters of reactions whose fluxes are perfectly
correlated. We find that the composition of the latter `functional clusters' is
also largely explained in terms of clusters of low degree metabolites in each
of these organisms. Our findings mean that most metabolic reactions that are
essential can be tagged by one or more low degree metabolites. Those reactions
are essential because they are the only ways of producing or consuming their
respective tagged metabolites. Furthermore, reactions whose fluxes are strongly
correlated can be thought of as `glued together' by these low degree
metabolites.


title:
Extraclassical receptive field phenomena & short-range connectivity in
  V1

abstract:
  Neural mechanisms of extraclassical receptive field phenomena in V1 are
commonly assumed to result from long-range lateral connections and/or
extrastriate feedback. We address two such phenomena: surround suppression and
contrast dependent receptive field size. We present rigorous computational
support for the hypothesis that the phenomena largely result from local
short-range (< 0.5 mm) cortical connections and LGN input. Surround suppression
in our simulations results from (A) direct cortical inhibition or (B)
suppression of recurrent cortical excitation, or (C) action of both these
mechanisms simultaneously. Mechanisms B and C are substantially more prevalent
than A. We observe an average growth in the range of spatial summation of
excitatory and inhibitory synaptic inputs for low contrast. However, we find
this is neither sufficient nor necessary to explain contrast dependent
receptive field size, which usually involves additional changes in the relative
gain of these inputs.


title: Modeling multi-cellular systems using sub-cellular elements

abstract:
  We introduce a model for describing the dynamics of large numbers of
interacting cells. The fundamental dynamical variables in the model are
sub-cellular elements, which interact with each other through phenomenological
intra- and inter-cellular potentials. Advantages of the model include i)
adaptive cell-shape dynamics, ii) flexible accommodation of additional
intra-cellular biology, and iii) the absence of an underlying grid. We present
here a detailed description of the model, and use successive mean-field
approximations to connect it to more coarse-grained approaches, such as
discrete cell-based algorithms and coupled partial differential equations. We
also discuss efficient algorithms for encoding the model, and give an example
of a simulation of an epithelial sheet. Given the biological flexibility of the
model, we propose that it can be used effectively for modeling a range of
multi-cellular processes, such as tumor dynamics and embryogenesis.


title:
A physically meaningful method for the comparison of potential energy
  functions

abstract:
  In the study of the conformational behavior of complex systems, such as
proteins, several related statistical measures are commonly used to compare two
different potential energy functions. Among them, the Pearson's correlation
coefficient r has no units and allows only semi-quantitative statements to be
made. Those that do have units of energy and whose value may be compared to a
physically relevant scale, such as the root mean square deviation (RMSD), the
mean error of the energies (ER), the standard deviation of the error (SDER) or
the mean absolute error (AER), overestimate the distance between potentials.
Moreover, their precise statistical meaning is far from clear. In this article,
a new measure of the distance between potential energy functions is defined
which overcomes the aforementioned difficulties. In addition, its precise
physical meaning is discussed, the important issue of its additivity is
investigated and some possible applications are proposed. Finally, two of these
applications are illustrated with practical examples: the study of the van der
Waals energy, as implemented in CHARMM, in the Trp-Cage protein (PDB code 1L2Y)
and the comparison of different levels of the theory in the ab initio study of
the Ramachandran map of the model peptide HCO-L-Ala-NH2.


title: Modeling of negative autoregulated genetic networks in single cells

abstract:
  We discuss recent developments in the modeling of negative autoregulated
genetic networks. In particular, we consider the temporal evolution of the
population of mRNA and proteins in simple networks using rate equations. In the
limit of low copy numbers, fluctuation effects become significant and more
adequate modeling is then achieved using the master equation formalism. The
analogy between regulatory gene networks and chemical reaction networks on dust
grains in the interstellar medium is discussed. The analysis and simulation of
complex reaction networks are also considered.


title:
Parametric Resonance May Explain Virologic Failure to HIV Treatment
  Interruptions

abstract:
  Pilot studies of structured treatment interruptions (STI) in HIV therapy have
shown that patients can maintain low viral loads whilst benefiting from reduced
treatment toxicity. However, a recent STI clinical trial reported a high degree
of virologic failure. Here we present a novel hypothesis that could explain
virologic failure to STI and provides new insights of great clinical relevance.
We analyze a classic mathematical model of HIV within-host viral dynamics and
find that nonlinear parametric resonance occurs when STI are added to the
model; resonance is observed as virologic failure. We use the model to simulate
clinical trial data and to calculate patient-specific resonant spectra. We gain
two important insights. Firstly, within an STI trial, we determine that
patients who begin with similar viral loads can be expected to show extremely
different virologic responses as a result of resonance. Thus, high
heterogeneity of patient response within a STI clinical trial is to be
expected. Secondly and more importantly, we determine that virologic failure is
not simply due to STI or patient characteristics; rather it is the result of a
complex dynamic interaction between STI and patient viral dynamics. Hence, our
analyses demonstrate that no universal regimen with periodic interruptions will
be effective for all patients. On the basis of our results, we suggest that
immunologic and virologic parameters should be used to design patient-specific
STI regimens.


title:
Evolutionary origin of power-laws in Biochemical Reaction Network;
  embedding abundance distribution into topology

abstract:
  The evolutionary origin of universal statistics in biochemical reaction
network is studied, to explain the power-law distribution of reaction links and
the power-law distributions of chemical abundances. Using cell models with
catalytic reaction network, we find evidence that the power-law distribution in
abundances of chemicals emerges by the selection of cells with higher growth
speeds. Through the further evolution, this inhomogeneity in chemical
abundances is shown to be embedded in the distribution of links, leading to the
power-law distribution. These findings provide novel insights into the nature
of network evolution in living cells.


title: Geometric and physical considerations for realistic protein models

abstract:
  Protein structure is generally conceptualized as the global arrangement or of
smaller, local motifs of helices, sheets, and loops. These regular, recurring
secondary structural elements have well-understood and standardized definitions
in terms of amino acid backbone geometry and the manner in which hydrogen
bonding requirements are satisfied. Recently, "tube" models have been proposed
to explain protein secondary structure in terms of the geometrically optimal
packing of a featureless cylinder. However, atomically detailed simulations
demonstrate that such packing considerations alone are insufficient for
defining secondary structure; both excluded volume and hydrogen bonding must be
explicitly modeled for helix formation. These results have fundamental
implications for the construction and interpretation of realistic and
meaningful biomacromolecular models.


title: tRNA-alike in Nanoarchaeum equitans ?

abstract:
  The recent algorithm for five split tRNA-genes in N.equitans is new . It
locates missing tRNA-trp, tRNA-imet, tRNA-glu and tRNA-his . But the split
tRNA-trp(CCA) solution is anomalous ; the tRNA-imet lacks cognition elements
for aminoacylation . In view therefore we present here alternate non-split
composite solutions for tRNA-trp, tRNA-imet, tRNA-glu and tRNA-his .


title:
Study of Arbitrary Nonlinearities in Convective Population Dynamics with
  Small Diffusion

abstract:
  Convective counterparts of variants of the nonlinear Fisher equation which
describes reaction diffusion systems in population dynamics are studied with
the help of an analytic prescription and shown to lead to interesting
consequences for the evolution of population densities. The initial value
problem is solved explicitly for some cases and for others it is shown how to
find traveling wave solutions analytically. The effect of adding diffusion to
the convective equations is first studied through exact analysis through a
piecewise linear representation of the nonlinearity. Using an appropriate small
parameter suggested by that analysis, a perturbative treatment is developed to
treat the case in which the convective evolution is augmented by a small amount
of diffusion.


title:
Limitations of Markov chain Monte Carlo algorithms for Bayesian
  Inference of phylogeny

abstract:
  Markov chain Monte Carlo algorithms play a key role in the Bayesian approach
to phylogenetic inference. In this paper, we present the first theoretical work
analyzing the rate of convergence of several Markov chains widely used in
phylogenetic inference. We analyze simple, realistic examples where these
Markov chains fail to converge quickly. In particular, the data studied are
generated from a pair of trees, under a standard evolutionary model. We prove
that many of the popular Markov chains take exponentially long to reach their
stationary distribution. Our construction is pertinent since it is well known
that phylogenetic trees for genes may differ within a single organism. Our
results shed a cautionary light on phylogenetic analysis using Bayesian
inference and highlight future directions for potential theoretical work.


title: Features and dimensions: Motion estimation in fly vision

abstract:
  We characterize the computation of motion in the fly visual system as a
mapping from the high dimensional space of signals in the retinal photodetector
array to the probability of generating an action potential in a motion
sensitive neuron. Our approach to this problem identifies a low dimensional
subspace of signals within which the neuron is most sensitive, and then samples
this subspace to visualize the nonlinear structure of the mapping. The results
illustrate the computational strategies predicted for a system that makes
optimal motion estimates given the physical noise sources in the detector
array. More generally, the hypothesis that neurons are sensitive to low
dimensional subspaces of their inputs formalizes the intuitive notion of
feature selectivity and suggests a strategy for characterizing the neural
processing of complex, naturalistic sensory inputs.


title: Strong-coupling dynamics of a multi-cellular chemotactic system

abstract:
  Chemical signaling is one of the ubiquitous mechanisms by which
inter-cellular communication takes place at the microscopic level, particularly
via chemotaxis. Such multi-cellular systems are popularly studied using
continuum, mean-field equations. In this letter we study a stochastic model of
chemotactic signaling. The Langevin formalism of the model makes it amenable to
calculation via non-perturbative analysis, which enables a quantification of
the effect of fluctuations on both the weak and strongly-coupled biological
dynamics. In particular we show that the (i) self-localization due to
auto-chemotaxis is impossible. (ii) when aggregation occurs, the aggregate
performs a random walk with a renormalized diffusion coefficient $D_R \propto
\epsilon^{-2} N^{-3}$. (iii) the stochastic model exhibits sharp transitions in
cell motile behavior for negative chemotaxis, behavior which has no parallel in
the mean-field Keller-Segel equations.


title:
Ab initio identification of putative human transcription factor binding
  sites by comparative genomics

abstract:
  We discuss a simple and powerful approach for the ab initio identification of
cis-regulatory motifs involved in transcriptional regulation. The method we
present integrates several elements: human-mouse comparison, statistical
analysis of genomic sequences and the concept of coregulation. We apply it to a
complete scan of the human genome. By using the catalogue of conserved upstream
sequences collected in the CORG database we construct sets of genes sharing the
same overrepresented motif (short DNA sequence) in their upstream regions both
in human and in mouse. We perform this construction for all possible motifs
from 5 to 8 nucleotides in length and then filter the resulting sets looking
for two types of evidence of coregulation: first, we analyze the Gene Ontology
annotation of the genes in the set, searching for statistically significant
common annotations; second, we analyze the expression profiles of the genes in
the set as measured by microarray experiments, searching for evidence of
coexpression. The sets which pass one or both filters are conjectured to
contain a significant fraction of coregulated genes, and the upstream motifs
characterizing the sets are thus good candidates to be the binding sites of the
TF's involved in such regulation. In this way we find various known motifs and
also some new candidate binding sites.


title:
Yeast Protein Interactome Topology Provides Framework for
  Coordinated-Functionality

abstract:
  The architecture of the network of protein-protein physical interactions in
Saccharomyces cerevisiae is exposed through the combination of two
complementary theoretical network measures, betweenness centrality and
`Q-modularity'. The yeast interactome is characterized by well-defined
topological modules connected via a small number of inter-module protein
interactions. Should such topological inter-module connections turn out to
constitute a form of functional coordination between the modules, we speculate
that this coordination is occurring typically in a pair-wise fashion, rather
than by way of high-degree hub proteins responsible for coordinating multiple
modules. The unique non-hub-centric hierarchical organization of the
interactome is not reproduced by gene duplication-and-divergence stochastic
growth models that disregard global selective pressures.

categories: q-bio.MN cond-mat.other cond-mat.stat-mech q-bio.OT


title:
Virtual Identification of Essential Proteins Within the Protein
  Interaction Network of Yeast

abstract:
  Topological analysis of large scale protein-protein interaction networks
(PINs) is important for understanding the organisational and functional
principles of individual proteins. The number of interactions that a protein
has in a PIN has been observed to be correlated with its indispensability.
Essential proteins generally have more interactions than the non-essential
ones. We show here that the lethality associated with removal of a protein from
the yeast proteome correlates with different centrality measures of the nodes
in the PIN, such as the closeness of a protein to many other proteins, or the
number of pairs of proteins which need a specific protein as an intermediary in
their communications, or the participation of a protein in different protein
clusters in the PIN. These measures are significantly better than random
selection in identifying essential proteins in a PIN. Centrality measures based
on graph spectral properties of the network, in particular the subgraph
centrality, show the best performance in identifying essential proteins in the
yeast PIN. Subgraph centrality gives important structural information about the
role of individual proteins and permits the selection of possible targets for
rational drug discovery through the identification of essential proteins in the
PIN.


title: Noise-enhanced computation in a model of a cortical column

abstract:
  Varied sensory systems use noise in order to enhance detection of weak
signals. It has been conjectured in the literature that this effect, known as
stochastic resonance, may take place in central cognitive processes such as the
memory retrieval of arithmetical multiplication. We show in a simplified model
of cortical tissue, that complex arithmetical calculations can be carried out
and are enhanced in the presence of a stochastic background. The performance is
shown to be positively correlated to the susceptibility of the network, defined
as its sensitivity to a variation of the mean of its inputs. For nontrivial
arithmetic tasks such as multiplication, stochastic resonance is an emergent
property of the microcircuitry of the model network.


title: Wiring cost in the organization of a biological network

abstract:
  To find out the role of the wiring cost in the organization of the neural
network of the nematode \textit{Caenorhapditis elegans} (\textit{C. elegans}),
we build the neuronal map of \textit{C. elegans} based on geometrical positions
of neurons and define the cost as inter-neuronal Euclidean distance \textit{d}.
We show that the wiring probability decays exponentially as a function of
\textit{d}. Using the edge exchanging method and the component placement
optimization scheme, we show that positions of neurons are not randomly
distributed but organized to reduce the total wiring cost. Furthermore, we
numerically study the trade-off between the wiring cost and the performance of
the Hopfield model on the neural network.


title:
The Dynamics of Image Processing by Feature Maps in the Primary Visual
  Cortex

abstract:
  The operational characteristics of a linear neural network image processing
system based on the brain's vision system are investigated. The final stage of
the network consists of edge detectors of various orienations arranged in a
feature map, corresponding to the primary visual cortex, or V1. The lateral
geniculate nucleus is modeled as a preprocessing stage. Excitatory forward and
inhibitory backward connections exist between the LGN and V1. By a method of
reconstructing the input images in terms of V1 activities, the simulations show
that images can be faithfully represented in V1 by the proposed network. The
signal-to-noise ratio of the image is improved by the representation, and
compression ratios of well over two-hundred are possible. Lateral interacations
between V1 neurons sharpen their orientational tuning. We further study the
dynamics of the processing, showing that the rate of decrease of the error of
the representation is maximized for the receptive fields used, and we develop a
Fokker-Planck equation for a more detailed prediction of the error value vs.
time. Finally, we show how the eigenvalues of the covariance matrix of the
inputs can be employed to predict the rate of error decrease.


title:
Receptive Field and Feature Map Formation in the Primary Visual Cortex
  via Hebbian Learning with Inhibitory Feedback

abstract:
  A linear neural network is proposed for mamalian vision system in which
backward connections from the primary visual cortex (V1) to the lateral
geniculate nucleus play a key role. The backward connections control the flow
of information from the LGN to V1 in such a way as to maximize the rate of
transfer of information from the LGN to V1. The application of hebbian learning
to the forward and backward connections causes the formation of receptive
fields which are sensitive to edges, bars, and spatial frequencies of preferred
orientations. Receptive field types in V1 are shown to depend on the density of
the afferent connections in the LGN. Orientational preferences are organized in
the primary visual cortex by the application of lateral interactions during the
learning phase. Change in the size of the eye between the immature and mature
animal is shown be an important factor in the development of V1 organization.
The orgainization of the mature network is compared to that found in the
macaque monkey by several analytical tests.


title:
Dynamics of learning in coupled oscillators tutored with delayed
  reinforcements

abstract:
  In this work we analyze the solutions of a simple system of coupled phase
oscillators in which the connectivity is learned dynamically. The model is
inspired in the process of learning of birdsong by oscine birds. An oscillator
acts as the generator of a basic rhythm, and drives slave oscillators which are
responsible for different motor actions. The driving signal arrives to each
driven oscillator through two different pathways. One of them is a "direct"
pathway. The other one is a "reinforcement" pathway, through which the signal
arrives delayed. The coupling coefficients between the driving oscillator and
the slave ones evolve in time following a Hebbian-like rule. We discuss the
conditions under which a driven oscillator is capable of learning to lock to
the driver. The resulting phase difference and connectivity is a function of
the delay of the reinforcement. Around some specific delays, the system is
capable to generate dramatic changes in the phase difference between the driver
and the driven systems. We discuss the dynamical mechanism responsible for this
effect, and possible applications of this learning scheme.


title: Environmental effects on the spread of the Neolithic

abstract:
  The causes and implications of the regional variations in the spread of the
incipient agriculture in Europe remain poorly understood. We apply population
dynamics models to study the dispersal of the Neolithic in Europe from a
localized area in the Near East, solving the two-dimensional reaction-diffusion
equation on a spherical surface. We focus on the role of major river paths and
coastlines in the advance of farming to model the rapid advances of the Linear
Pottery (LBK) and the Impressed Ware traditions along the Danube-Rhine corridor
and the Mediterranean coastline respectively. We argue that the random walk of
individuals, which results in diffusion of the population, can be anisotropic
in those areas. The standard reaction-diffusion equation is thus supplemented
with advection-like terms confined to the proximity of major rivers and
coastlines. The model allows for the spatial variation in both the human
mobility (diffusivity) and the carrying capacity of landscapes, reflecting the
local altitude and latitude. This approach can easily be generalised to include
other environmental factors, such as the bioproductivity of landscapes. Our
model successfully accounts for the regional variations in the spread of the
Neolithic, consistent with the radiocarbon dated data, and reproduces a time
delay in the spread of farming to the Eastern Europe and Scandinavia.


title:
Structure calculation strategies for helical membrane proteins; a
  comparison study

abstract:
  Structure predictions of helical membrane proteins have been designed to take
advantage of the structural autonomy of secondary structure elements, as
postulated by the two-stage model of Engelman and Popot. In this context, we
investigate structure calculation strategies for two membrane proteins with
different functions, sizes, aminoacid compositions, and topologies: the
glycophorin A homodimer (a paradigm for close inter-helical packing in membrane
proteins) and aquaporin (a channel protein). Our structure calculations are
based on two alternative folding schemes: a one-step simulated annealing from
an extended chain conformation, and a two-step procedure inspired by the
grid-search methods traditionally used in membrane protein predictions. In this
framework, we investigate rationales for the utilization of sparse NMR data
such as distance-based restraints and residual dipolar couplings in structure
calculations of helical membrane proteins.


title: Nonlinear Modeling Approach to Human Promoter Sequences

abstract:
  By means of the nonlinear modeling technique (NM technique), we find the
nonlinear deterministic structures in the promoter regions (PPRs) of DNA
sequences, called deterministic valleys (DVs) in this paper. These DVs prefer
to occur much more outside of a special region around TSS. The number,
positions and shapes of the DVs are basically different for different PPRs.
Generally, these DVs do not occur in the CpG islands, which tells us that they
should be special structures with new biological functions rather than the CpG
islands. PACS numbers: 87.14.Gg, 87.10.1e, 05.45.2a


title: Simulation of geographical trends in Chowdhury ecosystem model

abstract:
  A computer simulation based on individual births and deaths gives a
biodiversity increasing from cold to warm climates, in agreement with reality.
Complexity of foodwebs increases with time and at a higher rate at low
latitudes, and there is a higher rate of species creation at low latitudes.
Keeping many niches empty makes the results correspond more closely to natural
gradients.


title:
Evolution of polymorphism and sympatric speciation through competition
  in a unimodal distribution of resources

abstract:
  A microscopic agent dynamical model for diploid age-structured populations is
used to study evolution of polymorphism and sympatric speciation. The
underlying ecology is represented by a unimodal distribution of resources of
some width. Competition among individuals is also described by a similar
distribution, and its strength is maximum for individuals with the same
phenotype and decreases with distance in phenotype space as a gaussian, with
some width. These two widths define the model's phase space, in which we
identify the regions where an autonomous emergence of stable polymorphism or
speciation is more likely.


title: Inferring interactions from combinatorial protein libraries

abstract:
  Proteins created by combinatorial methods in vitro are an important source of
information for understanding sequence-structure-function relationships.
Alignments of folded proteins from combinatorial libraries can be analyzed
using methods developed for naturally occurring proteins, but this neglects the
information contained in the unfolded sequences of the library. We introduce
two algorithms, logistic regression and excess information analysis, that use
both the folded and unfolded sequences and compare them against contingency
table and statistical coupling analysis, which only use the former. The test
set for this benchmark study is a library of fictitious proteins that fold
according to a hypothetical energy model. Of the four methods studied, only
logistic regression is able to correctly recapitulate the energy model from the
sequence alignment. The other algorithms predict spurious interactions between
alignment positions with strong but individual influences on protein stability.
When present in the same protein, stabilizing amino acids tend to lower the
energy below the threshold needed for folding. As a result, their frequencies
in the alignment can be correlated even if the positions do not interact. We
believe any algorithm that neglects the nonlinear relationship between folding
and energy is susceptible to this error.


title: Diversity as a product of interspecial interactions

abstract:
  We demonstrate diversification rather than optimisation for highly
interacting organisms in a well mixed biological system by means of a simple
model and reference to experiment, and find the cause to be the complex network
of interactions formed, allowing species less well adapted to an environment to
flourish by co-interaction over the `best' species. This diversification can be
considered as the construction of many co-evolutionary niches by the network of
interactions between species. Evidence for this comes from work with the
bacteria Escherichia coli, which may coexist with their own mutants under
certain conditions. Diversification only occurs above a certain threshold
interaction strength, below which competitive exclusion occurs.


title: The contact network of patients in a regional healthcare system

abstract:
  Yet in spite of advances in hospital treatment, hospitals continue to be a
breeding ground for several airborne diseases and for diseases that are
transmitted through close contacts like SARS, methicillin-resistant
Staphylococcus aureus (MRSA), norovirus infections and tuberculosis (TB). Here
we extract contact networks for up to 295,108 inpatients for durations up to
two years from a database used for administrating a local public healthcare
system serving a population of 1.9 million individuals. Structural and
dynamical properties of the network of importance for the transmission of
contagious diseases are then analyzed by methods from network epidemiology. The
contact networks are found to be very much determined by an extreme (age
independent) variation in duration of hospital stays and the hospital
structure. We find that that the structure of contacts between in-patients
exhibit structural properties, such as a high level of transitivity,
assortativity and variation in number of contacts, that are likely to be of
importance for the transmission of less contagious diseases. If these
properties are considered when designing prevention programs the risk for and
the effect of epidemic outbreaks may be decreased.


title:
Characterizing Self-Developing Biological Neural Networks: A First Step
  Towards their Application To Computing Systems

abstract:
  Carbon nanotubes are often seen as the only alternative technology to silicon
transistors. While they are the most likely short-term one, other longer-term
alternatives should be studied as well. While contemplating biological neurons
as an alternative component may seem preposterous at first sight, significant
recent progress in CMOS-neuron interface suggests this direction may not be
unrealistic; moreover, biological neurons are known to self-assemble into very
large networks capable of complex information processing tasks, something that
has yet to be achieved with other emerging technologies. The first step to
designing computing systems on top of biological neurons is to build an
abstract model of self-assembled biological neural networks, much like computer
architects manipulate abstract models of transistors and circuits. In this
article, we propose a first model of the structure of biological neural
networks. We provide empirical evidence that this model matches the biological
neural networks found in living organisms, and exhibits the small-world graph
structure properties commonly found in many large and self-organized systems,
including biological neural networks. More importantly, we extract the simple
local rules and characteristics governing the growth of such networks, enabling
the development of potentially large but realistic biological neural networks,
as would be needed for complex information processing/computing tasks. Based on
this model, future work will be targeted to understanding the evolution and
learning properties of such networks, and how they can be used to build
computing systems.


title: Modelization of Thermal Fluctuations in G Protein-Coupled Receptors

abstract:
  We simulate the electrical properties of a device realized by a G protein
coupled receptor (GPCR), embedded in its membrane and in contact with two
metallic electrodes through which an external voltage is applied. To this
purpose, recently, we have proposed a model based on a coarse graining
description, which describes the protein as a network of elementary impedances.
The network is built from the knowledge of the positions of the C-alpha atoms
of the amino acids, which represent the nodes of the network. Since the
elementary impedances are taken depending of the inter-nodes distance, the
conformational change of the receptor induced by the capture of the ligand
results in a variation of the network impedance. On the other hand, the
fluctuations of the atomic positions due to thermal motion imply an impedance
noise, whose level is crucial to the purpose of an electrical detection of the
ligand capture by the GPCR. Here, in particular, we address this issue by
presenting a computational study of the impedance noise due to thermal
fluctuations of the atomic positions within a rhodopsin molecule. In our model,
the C-alpha atoms are treated as independent, isotropic, harmonic oscillators,
with amplitude depending on the temperature and on the position within the
protein (alpha-helix or loop). The relative fluctuation of the impedance is
then calculated for different temperatures.


title: On the Complexity of Several Haplotyping Problems

abstract:
  In this paper we present a collection of results pertaining to haplotyping.
The first set of results concerns the combinatorial problem of reconstructing
haplotypes from incomplete and/or imperfectly sequenced haplotype data. More
specifically, we show that an interesting, restricted case of Minimum Error
Correction (MEC) is NP-hard, point out problems in earlier claims about a
related problem, and present a polynomial-time algorithm for the ungapped case
of Longest Haplotype Reconstruction (LHR). Secondly, we present a polynomial
time algorithm for the problem of resolving genotype data using as few
haplotypes as possible (the Pure Parsimony Haplotyping Problem, PPH) where each
genotype has at most two ambiguous positions, thus solving an open problem
posed by Lancia et al in "Haplotyping Populations by Pure Parsimony: Complexity
of Exact and Approximation Algorithms."


title:
Search for optimal measure for discriminating spike trains with
  different randomness

abstract:
  We wish to discriminate spike sequences based on the degree of irregularity.
For this purpose, we search for a rational expressions of quadratic functions
of consecutive interspike intervals that efficiently measures spiking
irregularity. Under natural assumptions, the functional form of the coefficient
can be parameterized by a single parameter. The parameter is determined so as
to maximize the mutual information between the distributions of coefficients
computed for spike sequences derived from different renewal point processes. We
find that the local variation of interspike intervals, LV (Neural Comput. Vol.
15, pp. 2823-42, 2003), is nearly optimal for whose intrinsic irregularity is
close to that of experimental data.


title:
Prediction of transcription factor binding to DNA using rule induction
  methods

abstract:
  The transcription of DNA into mRNA is initiated and aided by a number of
transcription factors (TFs), proteins with DNA-binding regions that attach
themselves to binding sites in the DNA (transcription factor binding sites,
TFBSs). As it has become apparent that both TFs and TFBSs are highly variable,
tools are needed to quantify the strength of the interaction resulting from a
certain TF variant binding to a certain TFBS. We used a simple way to predict
interactions between protein and DNA: given experimental cases from the
literature where the interaction strength between two sequences has been
quantified, we created training vectors for rule induction by regarding each
amino acid and nucleotide position as a single feature in the example vector.
The resulting interaction strength was used as the target class or value. These
training vectors were then used to build a rule induction model. We applied the
rule induction method to three protein families - transcription factors from
the NF-kappaB, the early-growth-response (EGR), and the paired domain groups -
and their corresponding DNA targets. These three prediction problems increase
in complexity with regard to model building, and thus gave us a good range for
validation. The main focus of the study was the most complex problem: paired
domain-DNA binding. For this problem, we also found sequence/binding strength
correlations using measures from information theory. Prediction results were
uniformly good: the rule induction approach was able to correctly order all of
nine unseen examples for one NF-kappaB protein, achieved a correlation
coefficient of 0.52 on unseen (and noisy) examples in the EGR case, and reached
a classification accuracy of 69.7% for the Paired domain as evaluated by
cross-validation.


title: A Predation Behavior Model Based on Game Theory

abstract:
  This article adopts game theory to build a model for explaining the predation
behavior of animals.We assume that both the prey and the preydator have two
stratigies in this game,the active one and the passive one.By calculating the
outcome and the income of energy in different stratigies, we find the solution
to analyze the different evolution path of both the prey and the predator.A
simulation result approximately represents the correctness of our model.


title: Combinatorial rules of icosahedral capsid growth

abstract:
  A model of growth of icosahedral viral capsids is proposed. It takes into
account the diversity of hexamers' compositions, leading to definite capsid
size. We show that the observed yield of capsid production implies a very high
level of self-organization of elementary building blocks. The exact number of
different protein dimers composing hexamers is related to the size of a given
capsid, labeled by its T-number. Simple rules determining these numbers for
each value of T are deduced and certain consequences are discussed.


title: Discretization of Time Series Data

abstract:
  Data discretization, also known as binning, is a frequently used technique in
computer science, statistics, and their applications to biological data
analysis. We present a new method for the discretization of real-valued data
into a finite number of discrete values. Novel aspects of the method are the
incorporation of an information-theoretic criterion and a criterion to
determine the optimal number of values. While the method can be used for data
clustering, the motivation for its development is the need for a discretization
algorithm for several multivariate time series of heterogeneous data, such as
transcript, protein, and metabolite concentration measurements. As several
modeling methods for biochemical networks employ discrete variable states, the
method needs to preserve correlations between variables as well as the dynamic
features of the time series. A C++ implementation of the algorithm is available
from the authors at http://polymath.vbi.vt.edu/discretization .


title:
Postsynaptic mechanisms for the induction of long-term potentiation and
  long-term depression of synaptic transmission in CA1 pyramidal neurons of the
  hippocampus

abstract:
  The studies described in this dissertation have attempted to address the
cellular mechanisms of information storage by the brain. My work has focused
primarily on the postsynaptic events that occur during and after induction of
long-term potentiation (LTP) and long-term depression (LTD) at the Schaffer
collateral-CA1 synapse of the hippocampus. I have explored various aspects of
the role of postsynaptic Ca$^{2+}$ in the induction of LTP and LTD using both
extracellular and whole-cell recording techniques, as well as fluorescence
imaging of Ca$^{2+}$. I have also examined the possible role of modulation of
dendritic K$^{+}$ channels in the induction of LTP.


title: Design of amino acid sequences to fold into C_alpha-model proteins

abstract:
  In order to extend the results obtained with minimal lattice models to more
realistic systems, we study a model where proteins are described as a chain of
20 kinds of structureless amino acids moving in a continuum space and
interacting through a contact potential controlled by a 20x20 quenched random
matrix. The goal of the present work is to design and characterize amino acid
sequences folding to the SH3 conformation, a 60-residues recognition domain
common to many regulatory proteins. We show that a number of sequences can
fold, starting from a random conformation, to within a distance root mean
square deviation (dRMSD) of 2.6A from the native state. Good folders are those
sequences displaying in the native conformation an energy lower than a
sequence--independent threshold energy.


title: Traveling wave solutions of Fitzhugh model with cross-diffusion

abstract:
  The Fitzhugh-Nagumo equations have been used as a caricature of the
Hodgkin-Huxley equations of neuron firing to better understand the essential
dynamics of the interaction of the membrane potential and the restoring force
and to capture, qualitatively, the general properties of an excitable membrane.
Even though its simplicity allows very valuable insight to be gained, the
accuracy of reproducing real experimental results is limited. In this paper, we
utilize a modified version of the Fitzhugh-Nagumo equations to model the
spatial propagation of neuron firing; we assume that this propagation is (at
least, partially) caused by the cross-diffusion connection between the
potential and recovery variables. We show that the cross-diffusion version of
the model, besides giving rise to the typical fast traveling wave solution
exhibited in the original diffusion Fitzhugh-Nagumo equations, also gives rise
to a slow traveling wave solution. We analyze all possible traveling wave
solutions of the Fitzhugh-Nagumo equations with this cross-diffusion term and
show that there exists a threshold of the cross-diffusion coefficient (the
maximum value for a given speed of propagation), which bounds the area where
normal impulse propagation is possible.


title: Geometrical model for the native-state folds of proteins

abstract:
  We recently introduced a physical model [Hoang et al., P. Natl. Acad. Sci.
USA (2004), Banavar et al., Phys. Rev. E (2004)] for proteins which
incorporates, in an approximate manner, several key features such as the
inherent anisotropy of a chain molecule, the geometrical and energetic
constraints placed by the hydrogen bonds and sterics, and the role played by
hydrophobicity. Within this framework, marginally compact conformations
resembling the native state folds of proteins emerge as broad competing minima
in the free energy landscape even for a homopolymer. Here we show how the
introduction of sequence heterogeneity using a simple scheme of just two types
of amino acids, hydrophobic (H) and polar (P), and sequence design allows a
selected putative native fold to become the free energy minimum at low
temperature. The folding transition exhibits thermodynamic cooperativity, if
one neglects the degeneracy between two different low energy conformations
sharing the same fold topology.


title:
Contact-inhibited chemotaxis in de novo and sprouting blood-vessel
  growth

abstract:
  Blood vessels form either when dispersed endothelial cells (the cells lining
the inner walls of fully-formed blood vessels) organize into a vessel network
(vasculogenesis), or by sprouting or splitting of existing blood vessels
(angiogenesis). Although they are closely related biologically, no current
model explains both phenomena with a single biophysical mechanism. Most
computational models describe sprouting at the level of the blood vessel,
ignoring how cell behavior drives branch splitting during sprouting. We present
a cell-based, Glazier-Graner-Hogeweg-model simulation of the initial patterning
before the vascular cords form lumens, based on plausible behaviors of
endothelial cells. The endothelial cells secrete a chemoattractant, which
attracts other endothelial cells. As in the classic Keller-Segel model,
chemotaxis by itself causes cells to aggregate into isolated clusters. However,
including experimentally-observed adhesion-driven contact inhibition of
chemotaxis in the simulation causes randomly-distributed cells to organize into
networks and cell aggregates to sprout, reproducing aspects of both de novo and
sprouting blood-vessel growth. We discuss two branching instabilities
responsible for our results. Cells at the surfaces of cell clusters attempting
to migrate to the centers of the clusters produce a buckling instability. In a
model variant that eliminates the surface-normal force, a dissipative mechanism
drives sprouting, with the secreted chemical acting both as a chemoattractant
and as an inhibitor of pseudopod extension. The branching instabilities
responsible for our results, which result from contact inhibition of
chemotaxis, are both generic developmental mechanisms and interesting examples
of unusual patterning instabilities.


title:
The histone octamer influences the wrapping direction of DNA on it:
  Brownian dynamics simulation of the nucleosome chirality

abstract:
  In eukaryote nucleosome, DNA wraps around a histone octamer in a left-handed
way. We study the process of chirality formation of nucleosome with Brownian
dynamics simulation. We model the histone octamer with a quantitatively
adjustable chirality: left-handed, right-handed or non-chiral, and simulate the
dynamical wrapping process of a DNA molecule on it. We find that the chirality
of a nucleosome formed is strongly dependent on that of the histone octamer,
and different chiralities of the histone octamer induce its different rotation
directions in the wrapping process of DNA. In addition, a very weak chirality
of the histone octamer is quite enough for sustaining the correct chirality of
the nucleosome formed. We also show that the chirality of a nucleosome may be
broken at elevated temperature.


title: A minimal stochastic model for influenza evolution

abstract:
  We introduce and discuss a minimal individual-based model for influenza
dynamics. The model takes into account the effects of specific immunization
against viral strains, but also infectivity randomness and the presence of a
short-lived strain transcending immunity recently suggested in the literature.
We show by simulations that the resulting model exhibits substitution of viral
strains along the years, but that their divergence remains bounded. We also
show that dropping any of these features results in a drastically different
behavior, leading either to the extinction of the disease, to the proliferation
of the viral strains, or to their divergence.

categories: q-bio.PE cond-mat.dis-nn cond-mat.other q-bio.OT


title:
Hydromechanical considerations on the evolution and diversification of
  the echinoderm bauplans

abstract:
  The pentaradial organisation of echinoderms is postulated to have evolved as
the result of the reorganisation of the internal U-shaped mesentery of the
intestinal tract during inflation of the trunk of a pterobranch-like ancestor.
Under this scenario, loops of the mesentery developed between five hydraulic
bulges by three different mechanisms: (1) by the early formation of two
additional loops resulting directly in five hydraulic bulges, (2) by the
subsequent formation of two additional loops resulting in the formation of
initially three and then five hydraulic bulges or (3) by the inflation of the
body without formation of loops. Accordingly there are at least three main
evolutionary pathways within the echinoderms. The anatomical structures, such
as the ambulacral system or the skeletal capsule, which characterize the
echinoderms, developed independently in both lineages. From all the three
pathways various body structures can be derived matching those found in fossil
and extant echinoderms. The eleutherozoans and the earliest pentaradial
echinoderms most likely evolved from the direct-pentaradial pathway whereas
triradiate echinoderms and those which show a pentaradial organisation
superimposed on triradial symmetry, likely evolved along the indirect pathway.
The asymmetric or bilateral symmetric echinoderms evolved from the third
paythway. The most important morphological transformations leading to the
directly and indirectly pentaradial echinoderms are discussed and described.


title:
Specificity of Trypsin and Chymotrypsin: Loop Motion Controlled Dynamic
  Correlation as a Determinant

abstract:
  Trypsin and chymotrypsin are both serine proteases with high sequence and
structural similarities, but with different substrate specificity. Previous
experiments have demonstrated the critical role of the two loops outside the
binding pocket in controlling the specificity of the two enzymes. To understand
the mechanism of such a control of specificity by distant loops, we have used
the Gaussian Network Model to study the dynamic properties of trypsin and
chymotrypsin and the roles played by the two loops. A clustering method was
introduced to analyze the correlated motions of residues. We have found that
trypsin and chymotrypsin have distinct dynamic signatures in the two loop
regions which are in turn highly correlated with motions of certain residues in
the binding pockets. Interestingly, replacing the two loops of trypsin with
those of chymotrypsin changes the motion style of trypsin to chymotrypsin-like,
whereas the same experimental replacement was shown necessary to make trypsin
have chymotrypsin's enzyme specificity and activity. These results suggest that
the cooperative motions of the two loops and the substrate-binding sites
contribute to the activity and substrate specificity of trypsin and
chymotrypsin.


title:
Hydromechanical considerations on the origin of the pentaradial body
  structure of echinoderms

abstract:
  When echinoderms are conceptualized as hydraulic entities, the early
evolution of this group can be presented in a scenario which describes how a
bilateral ancestor (an enteropneust-like organism) gradually evolved into a
pentaradial echinoderm. According to this scenario, the arms are outgrowths
from the anterior/posterior body axis of the bilateral pterobranchia-like
intermediate. These outgrowths developed when the originally U-shaped mesentery
of the intestinal tract formed loops, and correspondingly, the tensile chords
of the mesentery were attached to the body wall in five loops. The wall faces
between these regions of tensile chords could bulge out under the hydraulic
pressure of the body coelom. The originally more or less round body cavity was
deformed into a pneu with five bulges. The loops of the gut forced a roughly
symmetric arrangement, which was enhanced by a physical fact: five pneus as
well as one pneu with five internal tethers, naturally adopt a pentaradial
pattern of "minimum contact surfaces", as the most economic arrangement. These
evolutionary transformations were accompanied by certain histological
modifications, such as the development of mutable connective tissues and
skeletal elements that fused to ossicles and provided shape stabilization in
the form of a calcareous skeleton in the tissues of the body wall. The
resultant organism was an ancestral eleutherozoan echinoderm (Ur-Echinoderm).


title: Dynamics of inhomogeneous populations and global demography models

abstract:
  The dynamic theory of inhomogeneous populations developed during the last
decade predicts several essential new dynamic regimes applicable even to the
well-known, simple population models. We show that, in an inhomogeneous
population with a distributed reproduction coefficient, the entire initial
distribution of the coefficient should be used to investigate real population
dynamics. In the general case, neither the average rate of growth nor the
variance or any finite number of moments of the initial distribution is
sufficient to predict the overall population growth. We developed methods for
solving the heterogeneous models and explored the dynamics of the total
population size together with the reproduction coefficient distribution. We
show that, typically, there exists a phase of hyper-exponential growth that
precedes the well-known exponential phase of population growth in a free
regime. The developed formalism is applied to models of global demography and
the problem of population explosion predicted by the known hyperbolic formula
of world population growth. We prove here that the hyperbolic formula presents
an exact solution to the Malthus model with an exponentially distributed
reproduction coefficient and that population explosion is a corollary of
certain implicit unrealistic assumptions. Alternative models of world
population growth are derived; they show a notable phenomenon, a transition
from protracted hyperbolical growth (the phase of hyper-exponential
development) to the brief transitional phase of exponential growth and,
subsequently, to stabilization. The model solutions are consistent with real
data and produce relatively accurate forecasts.


title: Spatial dynamics of homochiralization

abstract:
  The emergence and spreading of chirality on the early Earth is considered by
studying a set of reaction-diffusion equations based on a polymerization model.
It is found that effective mixing of the early oceans is necessary to reach the
present homochiral state. The possibility of introducing mass extinctions and
modifying the emergence rate of life is discussed.


title: Uni-directional polymerization leading to homochirality in the RNA world

abstract:
  The differences between uni-directional and bi-directional polymerization are
considered. The uni-directional case is discussed in the framework of the RNA
world. Similar to earlier models of this type, where polymerization was assumed
to proceed in a bi-directional fashion (presumed to be relevant to peptide
nucleic acids), left-handed and right-handed monomers are produced via an
autocatalysis from an achiral substrate. The details of the bifurcation from a
racemic solution to a homochiral state of either handedness is shown to be
remarkably independent of whether the polymerization in uni-directional or
bi-directional. Slightly larger differences are seen when dissociation is
allowed and the dissociation fragments are being recycled into the achiral
substrate.


title: The effect of genome length on ejection forces in bacteriophage lambda

abstract:
  A variety of viruses tightly pack their genetic material into protein capsids
that are barely large enough to enclose the genome. In particular, in
bacteriophages, forces as high as 60 pN are encountered during packaging and
ejection, produced by DNA bending elasticity and self-interactions. The high
forces are believed to be important for the ejection process, though the extent
of their involvement is not yet clear. As a result, there is a need for
quantitative models and experiments that reveal the nature of the forces
relevant to DNA ejection. Here we report measurements of the ejection forces
for two different mutants of bacteriophage lambda, lambda b221cI26 and lambda
cI60, which differ in genome length by ~30%. As expected for a force-driven
ejection mechanism, the osmotic pressure at which DNA release is completely
inhibited varies with the genome length: we find inhibition pressures of 15 atm
and 25 atm, respectively, values that are in agreement with our theoretical
calculations.


title:
Probing the Effects of the Well-mixed Assumption on Viral Infection
  Dynamics

abstract:
  Viral kinetics have been extensively studied in the past through the use of
spatially well-mixed ordinary differential equations describing the time
evolution of the diseased state. However, emerging spatial structures such as
localized populations of dead cells might adversely affect the spread of
infection, similar to the manner in which a counter-fire can stop a forest fire
from spreading. In a previous publication (Beauchemin et al., 2005), a simple
2-D cellular automaton model was introduced and shown to be accurate enough to
model an uncomplicated infection with influenza A. Here, this model is used to
investigate the effects of relaxing the well-mixed assumption. Particularly,
the effects of the initial distribution of infected cells, the regeneration
rule for dead epithelial cells, and the proliferation rule for immune cells are
explored and shown to have an important impact on the development and outcome
of the viral infection in our model.


title:
The effect of travel restrictions on the spread of a highly contagious
  disease in Sweden

abstract:
  Travel restrictions may reduce the spread of a contagious disease that
threatens public health. In this study we investigate what effect different
levels of travel restrictions may have on the speed and geographical spread of
an outbreak of a disease similar to SARS. We use a stochastic simulation model
of the Swedish population, calibrated with survey data of travel patterns
between municipalities in Sweden collected over three years. We find that a ban
on journeys longer than 50 km drastically reduces the speed and the
geographical spread of outbreaks, even with when compliance is less than 100%.
The result is found to be robust for different rates of inter-municipality
transmission intensities. Travel restrictions may therefore be an effective way
to mitigate the effect of a future outbreak.


title:
Modeling the dynamics of natural rotifer populations: phase-parametric
  analysis

abstract:
  A model of the dynamics of natural rotifer populations is described as a
discrete nonlinear map depending on three parameters, which reflect
characteristics of the population and environment. Model dynamics and their
change by variation of these parameters were investigated by methods of
bifurcation theory. A phase-parametric portrait of the model was constructed
and domains of population persistence (stable equilibrium, periodic and
a-periodic oscillations of population size) as well as population extinction
were identified and investigated. The criteria for population persistence and
approaches to determining critical parameter values are described. The results
identify parameter values that lead to population extinction under various
environmental conditions. They further illustrate that the likelihood of
extinction can be substantially increased by small changes in environmental
quality, which shifts populations into new dynamical regimes.


title:
Correlations of Amino Acids with Secondary Structure Types: Connection
  with Amino Acid Structure

abstract:
  The correlations of primary and secondary structures were analyzed using
proteins with known structure from Protein Data Bank. The correlation values of
amino acid type and the eight secondary structure types at distant position
were calculated for distances between -25 and 25. Shapes of the diagrams
indicate that amino acids polarity and capability for hydrogen bonding have
influence on the secondary structure at some distances. Clear preference of
most of the amino acids towards certain secondary structure type classifies
amino acids into four groups: alpha-helix admirers, strand admirers, turn and
bend admirers and the others. Group four consists of His and Cis, the amino
acids that do not show clear preference for any secondary structure. Amino
acids from a group have similar physicochemical properties, and the same
structural characteristics. The results suggest that amino acid preference for
secondary structure type is based on the structural characteristics at Cb and
Cg atoms of amino acid. alpha-helix admirers do not have polar heteroatoms on
Cb and Cg atoms, nor branching or aromatic group on Cb atom. Amino acids that
have aromatic groups or branching on Cb atom are strand admirers. Turn and bend
admirers have polar heteroatom on Cb or Cg atoms or do not have Cb atom at all.
Our results indicate that polarity and capability for hydrogen bonding have
influence on the secondary structure at some distance, and that amino acid
preference for secondary structure is caused by structural properties at Cb or
Cg atoms.


title:
Deconstructing the energy landscape: new algorithms for folding
  heteropolymers

abstract:
  We apply the computational methodology of phase retrieval to the problem of
folding heteropolymers. The ground state fold of the polymer is defined by the
intersection of two sets in the configuration space of its constituent
monomers: a geometrical chain constraint and a threshold constraint on the
contact energy. A dynamical system is then defined in terms of the projections
to these constraint sets, such that its fixed points solve the set intersection
problem. We present results for two off-lattice HP models: one with only
rotameric degrees of freedom, and one proposed by Stillinger et al. with
flexible bond angles. Our phase retrieval inspired algorithm is competitive
with more established algorithms and even finds lower energy folds for one of
the longer polymer chains.


title: Template coexistence in prebiotic vesicle models

abstract:
  The coexistence of distinct templates is a common feature of the diverse
proposals advanced to resolve the information crisis of prebiotic evolution.
However, achieving robust template coexistence turned out to be such a
difficult demand that only a class of models, the so-called package models,
seems to have met it so far. Here we apply Wright's Island formulation of group
selection to study the conditions for the coexistence of two distinct template
types confined in packages (vesicles) of finite capacity. In particular, we
show how selection acting at the level of the vesicles can neutralize the
pressures towards the fixation of any one of the template types (random drift)
and of the type with higher replication rate (deterministic competition). We
give emphasis to the role of the distinct generation times of templates and
vesicles as yet another obstacle to coexistence.


title: Gene & Genome Duplication in Acanthamoeba Polyphaga Mimivirus

abstract:
  Gene duplication is key to molecular evolution in all three domains of life
and may be the first step in the emergence of new gene function. It is a well
recognized feature in large DNA viruses, but has not been studied extensively
in the largest known virus to date, the recently discovered Acanthamoeba
Polyphaga Mimivirus. Here we present a systematic analysis of gene and genome
duplication events in the Mimivirus genome. We find that one third of the
Mimivirus genes are related to at least one other gene in the Mimivirus genome,
either through a large segmental genome duplication event that occurred in the
more remote past, either through more recent gene duplication events, which
often occur in tandem. This shows that gene and genome duplication played a
major role in shaping the Mimivirus genome. Using multiple alignments together
with remote homology detection methods based on Hidden Markov Model comparison,
we assign putative functions to some of the paralogous gene families. We
suggest that a large part of the duplicated Mimivirus gene families are likely
to interfere with important host cell processes, such as transcription control,
protein degradation, and cell regulatory processes. Our findings support the
view that large DNA viruses are complex evolving organisms, possibly deeply
rooted within the tree of life, and oppose the paradigm that viral evolution is
dominated by lateral gene acquisition, at least in what concerns large DNA
viruses.


title:
HLA and HIV Infection Progression: Application of the Minimum
  Description Length Principle to Statistical Genetics

abstract:
  The minimum description length (MDL) principle states that the best model to
account for some data minimizes the sum of the lengths, in bits, of the
descriptions of the model and the residual error. The description length is
thus a criterion for model selection. Description-length analysis of HLA
alleles from the Chicago MACS cohort enables classification of alleles
associated with plasma HIV RNA, an indicator of infection progression.
Progression variation is most strongly associated with HLA-B. Individuals
without B58s supertype alleles average viral RNA levels 3.6-fold greater than
individuals with them.


title:
Min-protein oscillations in Escherichia coli with spontaneous formation
  of two-stranded filaments in a three-dimensional stochastic
  reaction-diffusion model

abstract:
  We introduce a three-dimensional stochastic reaction-diffusion model to
describe MinD/MinE dynamical structures in Escherichia coli. This model
spontaneously generates pole-to-pole oscillations of the membrane-associated
MinD proteins, MinE ring, as well as filaments of the membrane-associated MinD
proteins. Experimental data suggest MinD filaments are two-stranded. In order
to model them we assume that each membrane-associated MinD protein can form up
to three bonds with adjacent membrane associated MinD molecules and that MinE
induced hydrolysis strongly depends on the number of bonds MinD has
established.


title:
Geometry of proteins: hydrogen bonding, sterics and marginally compact
  tubes

abstract:
  The functionality of proteins is related to their structure in the native
state. Protein structures are made up of emergent building blocks of helices
and almost planar sheets. A simple coarse-grained geometrical model of a
flexible tube barely subject to compaction provides a unified framework for
understanding the common character of globular proteins.We argue that a recent
critique of the tube idea is not well founded.


title:
Efficient spike-sorting of multi-state neurons using inter-spike
  intervals information

abstract:
  We demonstrate the efficacy of a new spike-sorting method based on a Markov
Chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from
Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique
in its capability to estimate and make use of the firing statistics as well as
the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple
discharge states, giving rise to multimodal interspike interval (ISI)
histograms and to correlations between successive ISIs. The amplitude of the
spikes generated by a PC in an "active" state decreases, a feature typical of
many neurons from both vertebrates and invertebrates. These two features
constitute a major and recurrent problem for all the presently available
spike-sorting methods. We first show that a Hidden Markov Model with 3
log-Normal states provides a flexible and satisfying description of the complex
firing of single PCs. We then incorporate this model into our previous MCMC
based spike-sorting algorithm (Pouzat et al, 2004, J. Neurophys. 91, 2910-2928)
and test this new algorithm on multi-unit recordings of bursting PCs. We show
that our method successfully classifies the bursty spike trains fired by PCs by
using an independent single unit recording from a patch-clamp pipette.

categories: q-bio.QM math.ST physics.bio-ph physics.data-an stat.TH


title: Maximum Likelihood Jukes-Cantor Triplets: Analytic Solutions

abstract:
  Complex systems of polynomial equations have to be set up and solved
algebraically in order to obtain analytic solutions for maximum likelihood on
phylogenetic trees. This has restricted the types of systems previously
resolved to the simplest models - three and four taxa under a molecular clock,
with just two state characters. In this work we give, for the first time,
analytic solutions for a family of trees with four state characters, like
normal DNA or RNA. The model of substitution we use is the Jukes-Cantor model,
and the trees are on three taxa under molecular clock, namely rooted triplets.
  We employ a number of approaches and tools to solve this system: Spectral
methods (Hadamard conjugation), a new representation of variables (the path-set
spectrum), and algebraic geometry tools (the resultant of two polynomials). All
these, combined with heavy application of computer algebra packages (Maple),
let us derive the desired solution.


title:
Hadamard Conjugation for the Kimura 3ST Model: Combinatorial Proof using
  Pathsets

abstract:
  In most stochastic models of molecular sequence evolution the probability of
each possible pattern of homologous characters at a site is estimated
numerically. However in the case of Kimura's three-substitution-types (K3ST)
model, these probabilities can be expressed analytically by Hadamard
conjugation as a function of the phylogeny T and the substitution probabilities
on each edge of T, together with an analytic inverse function. In this paper we
produce a direct proof of these results, using pathset distances which
generalise pairwise distances between sequences. This interpretation allows us
to apply Hadamard conjugation to a number of topical problems in the
mathematical analysis of sequence evolution.


title:
Error thresholds in a mutation-selection model with Hopfield-type
  fitness

abstract:
  A deterministic mutation-selection model in the sequence space approach is
investigated. Genotypes are identified with two-letter sequences. Mutation is
modelled as a Markov process, fitness functions are of Hopfield type, where the
fitness of a sequence is determined by the Hamming distances to a number of
predefined patterns. Using a maximum principle for the population mean fitness
in equilibrium, the error threshold phenomenon is studied for quadratic
Hopfield-type fitness functions with small numbers of patterns. Different from
previous investigations of the Hopfield model, the system shows error threshold
behaviour not for all fitness functions, but only for certain parameter values.


title: Mesoscopic modeling for nucleic acid chain dynamics

abstract:
  To gain a deeper insight into cellular processes such as transcription and
translation, one needs to uncover the mechanisms controlling the
configurational changes of nucleic acids. As a step toward this aim, we present
here a novel mesoscopic-level computational model that provides a new window
into nucleic acid dynamics. We model a single-stranded nucleic as a polymer
chain whose monomers are the nucleosides. Each monomer comprises a bead
representing the sugar molecule and a pin representing the base. The bead-pin
complex can rotate about the backbone of the chain. We consider pairwise
stacking and hydrogen-bonding interactions. We use a modified Monte Carlo
dynamics that splits the dynamics into translational bead motion and rotational
pin motion. By performing a number of tests we first show that our model is
physically sound. We then focus on the study of a the kinetics of a DNA
hairpin--a single-stranded molecule comprising two complementary segments
joined by a non-complementary loop--studied experimentally. We find that
results from our simulations agree with experimental observations,
demonstrating that our model is a suitable tool for the investigation of the
hybridization of single strands.


title: Why highly expressed proteins evolve slowly

abstract:
  Much recent work has explored molecular and population-genetic constraints on
the rate of protein sequence evolution. The best predictor of evolutionary rate
is expression level, for reasons which have remained unexplained. Here, we
hypothesize that selection to reduce the burden of protein misfolding will
favor protein sequences with increased robustness to translational missense
errors. Pressure for translational robustness increases with expression level
and constrains sequence evolution. Using several sequenced yeast genomes,
global expression and protein abundance data, and sets of paralogs traceable to
an ancient whole-genome duplication in yeast, we rule out several confounding
effects and show that expression level explains roughly half the variation in
Saccharomyces cerevisiae protein evolutionary rates. We examine causes for
expression's dominant role and find that genome-wide tests favor the
translational robustness explanation over existing hypotheses that invoke
constraints on function or translational efficiency. Our results suggest that
proteins evolve at rates largely unrelated to their functions, and can explain
why highly expressed proteins evolve slowly across the tree of life.


title: Weighted-Codon-Usage Based Phylogeny In Ectocarpales

abstract:
  We analyse forty seven chloroplastid genes of the large subunit of RuBisCO,
from the Algal order Ectocarpales, sourced from GenBank. Codon-usage weighted
by the nucleotide base bias defines our score called the
Codon-Impact-Parameter. This score is used to obtain phylogenetic relations
amongst the 47 Ectocarpales. We compare our classification with the ones done
earlier.


title: Robustness and Evolvability of the B Cell Mutator Mechanism

abstract:
  We present a model that considers the maturation of the antibody population
following primary antigen presentation as a global optimization problem. The
trade-off that emerges from our model describes the balance between the safety
of mutations that lead to local improvements in affinity and the necessity of
the system to undergo global reconfigurations in the antibody's shape in order
to achieve its goals, in this example of fast-paced evolution. The parameter p
which quantifies this trade-off appears to be itself both robust and evolvable.
This parallels the rapidity and consistency of the optimization operating
during the biologic response. In this paper, we explore the robust qualities
and evolvability of this tunable control parameter, p.


title:
Asymptotic construction of pulses in the Hodgkin Huxley model for
  myelinated nerves

abstract:
  A quantitative description of pulses and wave trains in the spatially
discrete Hodgkin-Huxley model for myelinated nerves is given. Predictions of
the shape and speed of the waves and the thresholds for propagation failure are
obtained. Our asymptotic predictions agree quite well with numerical solutions
of the model and describe wave patterns generated by repeated firing at a
boundary.


title:
Modeling of anti-tumor immune response: immunocorrective effect of weak
  centimeter electromagnetic waves

abstract:
  We formulate the dynamical model for the anti-tumor immune response based on
intercellular cytokine-mediated interactions with the interleukin-2 (IL-2)
taken into account. The analysis shows that the expression level of tumor
antigens on antigen presenting cells has a distinct influence on the tumor
dynamics. At low antigen presentation a progressive tumor growth takes place to
the highest possible value. At high antigen presentation there is a decrease in
tumor size to some value when the dynamical equilibrium between the tumor and
the immune system is reached. In the case of the medium antigen presentation
both these regimes can be realized depending on the initial tumor size and the
condition of the immune system. A pronounced immunomodulating effect (the
suppression of tumor growth and the normalization of IL-2 concentration) is
established by considering the influence of low-intensity electromagnetic
microwaves as a parametric perturbation of the dynamical system. This finding
is in qualitative agreement with the recent experimental results on
immunocorrective effects of centimeter electromagnetic waves in tumor-bearing
mice.


title: Mimivirus and the emerging concept of "giant" virus

abstract:
  The recently discovered Acanthamoeba polyphaga Mimivirus is the largest known
DNA virus. Its particle size (>400 nm), genome length (1.2 million bp) and
large gene repertoire (911 protein coding genes) blur the established
boundaries between viruses and parasitic cellular organisms. In addition, the
analysis of its genome sequence identified new types of genes not expected to
be seen in a virus, such as aminoacyl-tRNA synthetases and other central
components of the translation machinery. In this article, we examine how the
finding of a giant virus for the first time overlapping with the world of
cellular organisms in terms of size and genome complexity might durably
influence the way we look at microbial biodiversity, and force us to
fundamentally revise our classification of life forms. We propose to introduce
the word "girus" to recognize the intermediate status of these giant DNA
viruses, the genome complexity of which make them closer to small parasitic
prokaryotes than to regular viruses.


title: Giant viruses in the oceans : the 4th Algal Virus Workshop

abstract:
  Giant double-stranded DNA viruses (such as record breaking Acanthamoeba
polyphaga Mimivirus), with particle sizes of 0.2 to 0.6 micron, genomes of 300
kbp to 1.200 kbp, and commensurate complex gene contents, constitute an
evolutionary mystery. They challenge the common vision of viruses,
traditionally seen as highly streamlined genomes optimally fitted to the
smallest possible -filterable- package. Such giant viruses are now discovered
in increasing numbers through the systematic sampling of ocean waters as well
as freshwater aquatic environments, where they play a significant role in
controlling phyto- and bacterio- plankton populations. The 4th algal virus
workshop showed that the study of these ecologically important viruses is now
massively entering the genomic era, promising a better understanding of their
diversity and, hopefully, some insights on their origin and the evolutionary
forces that shaped their genomes.


title:
Measuring Shared Information and Coordinated Activity in Neuronal
  Networks

abstract:
  Most nervous systems encode information about stimuli in the responding
activity of large neuronal networks. This activity often manifests itself as
dynamically coordinated sequences of action potentials. Since multiple
electrode recordings are now a standard tool in neuroscience research, it is
important to have a measure of such network-wide behavioral coordination and
information sharing, applicable to multiple neural spike train data. We propose
a new statistic, informational coherence, which measures how much better one
unit can be predicted by knowing the dynamical state of another. We argue
informational coherence is a measure of association and shared information
which is superior to traditional pairwise measures of synchronization and
correlation. To find the dynamical states, we use a recently-introduced
algorithm which reconstructs effective state spaces from stochastic time
series. We then extend the pairwise measure to a multivariate analysis of the
network by estimating the network multi-information. We illustrate our method
by testing it on a detailed model of the transition from gamma to beta rhythms.

categories: q-bio.NC math.ST nlin.CD q-bio.QM stat.TH


title: Mutation model for nucleotide sequences based on crystal basis

abstract:
  A nucleotides sequence is identified, in the two (four) letters alphabet, by
the the labels of a vector state of an irreducible representation of U_q(sl(2))
(U_q(sl(2) + sl(2))), in the limit q -> 0. A master equation for the
distribution function is written, where the intensity of the one-spin flip is
assumed to depend from the variation of the labels of the state. In the two
letters approximation, the numerically computed equilibrium distribution for
short sequences is nicely fitted by a Yule distribution, which is the observed
distribution of the ranked short oligonucleotides frequency in DNA. The four
letter alphabet description, applied to the codons, is able to reproduce the
form of the fitted rank ordered usage frequencies distribution.


title: A single determinant for the rate of yeast protein evolution

abstract:
  A gene's rate of sequence evolution is among the most fundamental
evolutionary quantities in common use, but what determines evolutionary rates
has remained unclear. Here, we show that the two most commonly used methods to
disentangle the determinants of evolutionary rate, partial correlation analysis
and ordinary multivariate regression, produce misleading or spurious results
when applied to noisy biological data. To overcome these difficulties, we
employ an alternative method, principal component regression, which is a
multivariate regression of evolutionary rate against the principal components
of the predictor variables. We carry out the first combined analysis of seven
predictors (gene expression level, dispensability, protein abundance, codon
adaptation index, gene length, number of protein-protein interactions, and the
gene's centrality in the interaction network). Strikingly, our analysis reveals
a single dominant component which explains 40-fold more variation in
evolutionary rate than any other, suggesting that protein evolutionary rate has
a single determinant among the seven predictors. The dominant component
explains nearly half the variation in the rate of synonymous and protein
evolution. Our results support the hypothesis that selection against the cost
of translation-error-induced protein misfolding governs the rate of synonymous
and protein sequence evolution in yeast.


title: The prion-like folding behavior in aggregated proteins

abstract:
  We investigate the folding behavior of protein sequences by numerically
studying all sequences with maximally compact lattice model through exhaustive
enumeration. We get the prion-like behavior of protein folding. Individual
proteins remaining stable in the isolated native state may change their
conformations when they aggregate. We observe the folding properties as the
interfacial interaction strength changes, and find that the strength must be
strong enough before the propagation of the most stable structures happens.


title: Statistical model selection methods applied to biological networks

abstract:
  Many biological networks have been labelled scale-free as their degree
distribution can be approximately described by a powerlaw distribution. While
the degree distribution does not summarize all aspects of a network it has
often been suggested that its functional form contains important clues as to
underlying evolutionary processes that have shaped the network. Generally
determining the appropriate functional form for the degree distribution has
been fitted in an ad-hoc fashion.
  Here we apply formal statistical model selection methods to determine which
functional form best describes degree distributions of protein interaction and
metabolic networks. We interpret the degree distribution as belonging to a
class of probability models and determine which of these models provides the
best description for the empirical data using maximum likelihood inference,
composite likelihood methods, the Akaike information criterion and
goodness-of-fit tests. The whole data is used in order to determine the
parameter that best explains the data under a given model (e.g. scale-free or
random graph). As we will show, present protein interaction and metabolic
network data from different organisms suggests that simple scale-free models do
not provide an adequate description of real network data.


title: Folding of small proteins: A matter of geometry?

abstract:
  We review some of our recent results obtained within the scope of simple
lattice models and Monte Carlo simulations that illustrate the role of native
geometry in the folding kinetics of two state folders.


title: Nonlinear aspects of the EEG during sleep in children

abstract:
  Electroencephalograph (EEG) analysis enables the neuronal behavior of a
section of the brain to be examined. If the behavior is nonlinear then
nonlinear tools can be used to glean information on brain behavior, and aid in
the diagnosis of sleep abnormalities such as obstructive sleep apnea syndrome
(OSAS). In this paper the sleep EEGs of a set of normal and mild OSAS children
are evaluated for nonlinear behaviour. We consider how the behaviour of the
brain changes with sleep stage and between normal and OSAS children.


title: Ions in Fluctuating Channels: Transistors Alive

abstract:
  Ion channels are proteins with a hole down the middle embedded in cell
membranes. Membranes form insulating structures and the channels through them
allow and control the movement of charged particles, spherical ions, mostly
Na+, K+, Ca++, and Cl-. Membranes contain hundreds or thousands of types of
channels, fluctuating between open conducting, and closed insulating states.
Channels control an enormous range of biological function by opening and
closing in response to specific stimuli using mechanisms that are not yet
understood in physical language. Open channels conduct current of charged
particles following laws of Brownian movement of charged spheres rather like
the laws of electrodiffusion of quasi-particles in semiconductors. Open
channels select between similar ions using a combination of electrostatic and
'crowded charge' (Lennard-Jones) forces. The specific location of atoms and the
exact atomic structure of the channel protein seems much less important than
certain properties of the structure, namely the volume accessible to ions and
the effective density of fixed and polarization charge. There is no sign of
other chemical effects like delocalization of electron orbitals between ions
and the channel protein. Channels play a role in biology as important as
transistors in computers, and they use rather similar physics to perform part
of that role. Understanding their fluctuations awaits physical insight into the
source of the variance and mathematical analysis of the coupling of the
fluctuations to the other components and forces of the system.


title: Built to evolve

abstract:
  We study the probabilities of evolution based on random mutations and natural
selection. We conclude that evolution to multicellular eukaryots, or even
prokaryots, is unlikely to be the result of only random mutations. Complex
organisms have evolved through several mechanisms besides random mutations,
namely DNA recombination, adaptive mutations, and acquisition of foreign DNA.
We conclude that all living organisms, in addition to being self-organizing and
reproducing (autopoyetic), have built-in mechanisms of evolution, some of which
respond in very specific ways to environmental stress.


title:
Spontaneous natural selection in a model for spatially distributed
  interacting populations

abstract:
  We present an individual-based model for two interacting populations
diffusing on lattices in which a strong natural selection develops
spontaneously. The models combine traditional local predator-prey dynamics with
random walks. Individual's mobility is considered as an inherited trait. Small
variations upon inheritance, mimicking mutations, provide variability on which
natural selection may act. Although the dynamic rules defining the models do
not explicitly favor any mobility values, we found that the average mobility of
both populations tend to be maximized in various situations. In some situations
there is evidence of polymorphism, indicated by an adaptive landscape with many
local maxima. We provide evidence relating selective pressure for high mobility
with pattern formation.


title:
Hierarchy, Fractality, Small-World and Resilience of Haversian Bone
  Structure: A Complex Network Study

abstract:
  This article describes the application of recently introduced complex
networks concepts and methods to the characterization and analysis of cortical
bone structure. Three-dimensional reconstructions of the system of channels
underlying bone structure are obtained by using histological and computer
graphics methods and then represented in terms of complex networks.
Conﬂuences of two or more channels are represented as nodes, while the
interconnecting channels are expressed as edges. The hierarchical backbone (the
tree with maximum depth) of such a network is obtained and understood to
correspond to the main structure underlying the channel system. The remainder
of the network is shown to correspond to geographical communities, suggesting
that the bone channel structure involves a number of regular communities
appended along the hierarchical backbone. It is shown that such additional
edges play a crucial role in enhancing the network resilience and in reducing
the shortest paths in both topology and geometry.The recently introduced
concept of fractal dimension of a network (cond-mat/0503078) is then correlated
with the resilience of the several components of the bone channel structure to
obstruction and failure, with important implications for the understanding of
the organization and robustness of cortical bone structure.


title: Identity Elements of Archaeal tRNA

abstract:
  Features unique to a transfer-RNA are recognized by the corresponding
tRNA-synthetase. Keeping this in view we isolate the discriminating features of
all archaeal tRNA. These are our identity elements. Further, we investigate
tRNA-characteristics that delineate the different orders of archaea.


title:
Evidence for abundant transcription of non-coding regions in the
  Saccharomyces cerevisiae genome

abstract:
  Background: Recent studies in a growing number of organisms have yielded
accumulating evidence that a significant portion of the non-coding region in
the genome is transcribed. We address this issue in the yeast Saccharomyces
cerevisiae.
  Results: Taking into account the absence of a significantly large yeast EST
database, we use microarray expression data collected for genomic regions
erroneously believed to be coding to study the expression pattern of non-coding
regions in the Saccharomyces cerevisiae genome. We find that at least 164 out
of 589 (28%) such regions are expressed under specific biological conditions.
In particular, looking at the probes that are located opposing other known
genes at the same genomic locus, we find that 88 out of 341 (26%) of these
genes support antisense transcription. The expression patterns of these
antisense genes are positively correlated. We validate these results using
RT-PCR on a sample of 6 non-coding transcripts.
  Conclusions: 1. The yeast genome is transcribed on a scale larger than
previously assumed. 2. Correlated transcription of antisense genes is abundant
in the yeast genome. 3. Antisense genes in yeast are non-coding.


title: Is there any sense in antisense editing?

abstract:
  A number of recent studies have hypothesized that sense-antisense RNA
transcript pairs create dsRNA duplexes that undergo extensive A-to-I RNA
editing. Here we studied human and mouse genomic antisense regions, and found
that the editing level in these areas is negligible. This observation puts in
question the scope of sense-antisense duplexes formation in-vivo, which is the
basis for a number of proposed regulatory mechanisms.


title:
Biological remodelling: Stationary energy, configurational change,
  internal variables and dissipation

abstract:
  Remodelling is defined as an evolution of microstructure or variations in the
configuration of the underlying manifold. The manner in which a biological
tissue and its subsystems remodel their structure is treated in a continuum
mechanical setting. While some examples of remodelling are conveniently
modelled as evolution of the reference configuration (Case I), others are more
suited to an internal variable description (Case II). In this paper we explore
the applicability of stationary energy states to remodelled systems. A
variational treatment is introduced by assuming that stationary energy states
are attained by changes in microstructure via one of the two mechanisms--Cases
I and II. An example is presented to illustrate each case. The example
illustrating Case II is further studied in the context of the thermodynamic
dissipation inequality.


title:
Transitions in Microtubule C-termini conformations as a possible
  Dendritic signaling phenomenon

abstract:
  We model the dynamical states of the C-termini of tubulin dimers that
comprise neuronal microtubules. We use molecular dynamics and other
computational tools to explore the time-dependent behavior of conformational
states of a C-terminus of tubulin within a microtubule and assume that each
C-terminus interacts via screened Coulomb forces with the surface of a tubulin
dimer, with neighboring C-termini and also with any adjacent
microtubule-associated protein 2 (MAP2). Each C-terminus can either bind to the
tubulin surface via one of several positively charged regions or can be allowed
to explore the space available in the solution surrounding the dimer. We find
that the preferential orientation of each C-terminus is away from the tubulin
surface but binding to the surface may also take place, albeit at a lower
probability. The results of our model suggest that perturbations generated by
the C-termini interactions with counter-ions surrounding a MAP2 may propagate
over distances greater than those between adjacent microtubules. Thus, the MAP2
structure is able to act as a kind of biological wire (or a cable) transmitting
local electrostatic perturbations resulting in ionic concentration gradients
from one microtubule to another. We briefly discuss implications the current
dynamic modeling may have on synaptic activation and potentiation.

categories: q-bio.SC physics.bio-ph physics.comp-ph q-bio.NC


title: Stochastic approach to DNA breathing dynamics

abstract:
  We propose a stochastic Gillespie scheme to describe the temporal
fluctuations of local denaturation zones in double-stranded DNA as a single
molecule time series. It is demonstrated that the model recovers the
equilibrium properties. We also study measurable dynamical quantities such as
the bubble size autocorrelation function. This efficient computational approach
will be useful to analyse in detail recent single molecule experiments on
clamped homopolymer breathing domains, to probe the parameter values of the
underlying Poland-Scheraga model, as well as to design experimental conditions
for similar setups.


title: DNA as a programmable viscoelastic nanoelement

abstract:
  The two strands of a DNA molecule with a repetitive sequence can pair into
many different basepairing patterns. For perfectly periodic sequences, early
bulk experiments of Poerschke indicate the existence of a sliding process,
permitting the rapid transition between different relative strand positions
[Biophys. Chem. 2 (1974) 83]. Here, we use a detailed theoretical model to
study the basepairing dynamics of periodic and nearly periodic DNA. As
suggested by Poerschke, DNA sliding is mediated by basepairing defects (bulge
loops), which can diffuse along the DNA. Moreover, a shear force f on opposite
ends of the two strands yields a characteristic dynamic response: An outward
average sliding velocity v~1/N is induced in a double strand of length N,
provided f is larger than a threshold f_c. Conversely, if the strands are
initially misaligned, they realign even against an external force less than
f_c. These dynamics effectively result in a viscoelastic behavior of DNA under
shear forces, with properties that are programmable through the choice of the
DNA sequence. We find that a small number of mutations in periodic sequences
does not prevent DNA sliding, but introduces a time delay in the dynamic
response. We clarify the mechanism for the time delay and describe it
quantitatively within a phenomenological model. Based on our findings, we
suggest new dynamical roles for DNA in artificial nanoscale devices. The
basepairing dynamics described here is also relevant for the extension of
repetitive sequences inside genomic DNA.


title: Statistical Mechanics of Two-player Iterated Games

abstract:
  We construct a statistical ensemble of games, where in each independent
subensemble we have two players playing the same game.
  We derive the mean payoffs per move of the representative players of the
game, and we evaluate all the deterministic policies with finite memory. In
particular, we show that if one of the players has a generalized tit-for-tat
policy, the mean payoff per move of both players is the same, forcing the
equalization of the mean payoffs per move of both players. In the case of
symmetric, non-cooperative and dilemmatic games, we show that generalized
tit-for-tat policies together with the condition of not being the first to
defect, leads to the highest mean payoffs per move for the players.