title: Optimized parallel tempering simulations of proteins

abstract:
  We apply a recently developed adaptive algorithm that systematically improves
the efficiency of parallel tempering or replica exchange methods in the
numerical simulation of small proteins. Feedback iterations allow us to
identify an optimal set of temperatures/replicas which are found to concentrate
at the bottlenecks of the simulations. A measure of convergence for the
equilibration of the parallel tempering algorithm is discussed. We test our
algorithm by simulating the 36-residue villin headpiece sub-domain HP-36
wherewe find a lowest-energy configuration with a root-mean-square-deviation of
less than 4 Angstroem to the experimentally determined structure.


title: Stochastic model of transcription factor-regulated gene expression

abstract:
  We consider a stochastic model of transcription factor (TF)-regulated gene
expression. The model describes two genes: Gene A and Gene B which synthesize
the TFs and the target gene proteins respectively. We show through analytic
calculations that the TF fluctuations have a significant effect on the
distribution of the target gene protein levels when the mean TF level falls in
the highest sensitive region of the dose-response curve. We further study the
effect of reducing the copy number of Gene A from two to one. The enhanced TF
fluctuations yield results different from those in the deterministic case. The
probability that the target gene protein level exceeds a threshold value is
calculated with a knowledge of the probability density functions associated
with the TF and target gene protein levels. Numerical simulation results for a
more detailed stochastic model are shown to be in agreement with those obtained
through analytic calculations. The relevance of these results in the context of
the genetic disorder haploinsufficiency is pointed out. Some experimental
observations on the haploinsufficiency of the tumour suppressor gene, Nkx3.1,
are explained with the help of the stochastic model of TF-regulated gene
expression.


title:
Realization of features of immune response by dynamical system models
  and a possible mechanism of memory of antigen invasion

abstract:
  Among features of real immune responses which occur when antigens invade a
body,there are two remarkable features. One is that the amount of antibodies
produced in the secondary invasion by the same antigens is more than 10 times
larger than that in the primary invasion. The other is that more effective
antibodies which can neutralize the antigens more quickly are produced by
somatic hypermutation during the immune response. This phenomenon is named as
'affinity maturation'. In this paper, we try to reproduce these features by
dynamical system models and present possible factors to realize them. Further,
we present a model in which the memory of the invasion by antigens is realized
without immune memory cells.


title: Wringing out DNA

abstract:
  The chiral nature of DNA plays a crucial role in cellular processes. Here we
use magnetic tweezers to explore one of the signatures of this chirality, the
coupling between stretch and twist deformations. We show that the extension of
a stretched DNA molecule increases linearly by 0.42 nm per excess turn applied
to the double helix. This result contradicts the intuition that DNA should
lengthen as it is unwound and get shorter with overwinding. We then present
numerical results of energy minimizations of torsionally restrained DNA that
display a behaviour similar to the experimental data and shed light on the
molecular details of this surprising effect.


title: Intracellular delay limits cyclic changes in gene expression

abstract:
  Based on previously published experimental observations and mathematical
models for Hes1, p53 and NF-kB gene expression, we improve these models through
a distributed delay formulation of the time lag between transcription factor
binding and mRNA production. This description of natural variability for delays
introduces a transition from a stable steady state to limit cycle oscillations
and then a second transition back to a stable steady state which has not been
observed in previously published models. On the basis of our results and
following recent discussions about the role of delay-induced oscillations in
gene transcription we establish the hypothesis that the period of the
delay-induced cyclic changes should be characterized by an upper bound so that
it cannot be greater then the period of fundamental biological cycles. We
demonstrate our approach for two models. The first model describes Hes1
autorepression with equations for Hes1 mRNA production and Hes1 protein
translation. The second model describes Hes1 repression by the protein complex
Gro/TLE1/Hes1, where Gro/TLE1 is activated by Hes1 phosphorylation.


title:
On the use of analytic expressions for the voltage distribution to
  analyze intracellular recordings

abstract:
  Different analytic expressions for the membrane potential distribution of
membranes subject to synaptic noise have been proposed, and can be very helpful
to analyze experimental data. However, all of these expressions are either
approximations or limit cases, and it is not clear how they compare, and which
expression should be used in a given situation. In this note, we provide a
comparison of the different approximations available, with an aim to delineate
which expression is most suitable for analyzing experimental data.


title: Dynamics of an inchworm nano-walker

abstract:
  An inchworm processive mechanism is proposed to explain the motion of dimeric
molecular motors such as kinesin. We present here preliminary results for this
mechanism focusing on observables like mean velocity, coupling ratio and
efficiency versus ATP concentration and the external load F.


title: In vivo evidence of alternative loop geometries in DNA-protein complexes

abstract:
  The in vivo free energy of looping double-stranded DNA by the lac repressor
has a remarkable behavior whose origins are not fully understood. In addition
to the intrinsic periodicity of the DNA double helix, the in vivo free energy
has an oscillatory component of about half the helical period and oscillates
asymmetrically with an amplitude significantly smaller than predicted by
current theories. Here, we show that the in vivo behavior is accurately
accounted for by the simultaneous presence of two distinct conformations of
looped DNA. Our analysis reveals that these two conformations have different
optimal free energies and phases and that they behave distinctly in the
presence of key architectural proteins.


title:
The evaluation of protein folding rate constant is improved by
  predicting the folding kinetic order with a SVM-based method

abstract:
  Protein folding is a problem of large interest since it concerns the
mechanism by which the genetic information is translated into proteins with
well defined three-dimensional (3D) structures and functions. Recently
theoretical models have been developed to predict the protein folding rate
considering the relationships of the process with tolopological parameters
derived from the native (atomic-solved) protein structures. Previous works
classified proteins in two different groups exhibiting either a
single-exponential or a multi-exponential folding kinetics. It is well known
that these two classes of proteins are related to different protein structural
features. The increasing number of available experimental kinetic data allows
the application to the problem of a machine learning approach, in order to
predict the kinetic order of the folding process starting from the experimental
data so far collected. This information can be used to improve the prediction
of the folding rate. In this work first we describe a support vector
machine-based method (SVM-KO) to predict for a given protein the kinetic order
of the folding process. Using this method we can classify correctly 78% of the
folding mechanisms over a set of 63 experimental data. Secondly we focus on the
prediction of the logarithm of the folding rate. This value can be obtained as
a linear regression task with a SVM-based method. In this paper we show that
linear correlation of the predicted with experimental data can improve when the
regression task is computed over two different sets, instead of one, each of
them composed by the proteins with a correctly predicted two state or
multistate kinetic order.


title: Self-Organized Criticality model for Brain Plasticity

abstract:
  Networks of living neurons exhibit an avalanche mode of activity,
experimentally found in organotypic cultures. Here we present a model based on
self-organized criticality and taking into account brain plasticity, which is
able to reproduce the spectrum of electroencephalograms (EEG). The model
consists in an electrical network with threshold firing and activity-dependent
synapse strenghts. The system exhibits an avalanche activity power law
distributed. The analysis of the power spectra of the electrical signal
reproduces very robustly the power law behaviour with the exponent 0.8,
experimentally measured in EEG spectra. The same value of the exponent is found
on small-world lattices and for leaky neurons, indicating that universality
holds for a wide class of brain models.


title:
Co-occurrence of resonant activation and noise-enhanced stability in a
  model of cancer growth in the presence of immune response

abstract:
  We investigate a stochastic version of a simple enzymatic reaction which
follows the generic Michaelis-Menten kinetics. At sufficiently high
concentrations of reacting species, the molecular fluctuations can be
approximated as a realization of a Brownian dynamics for which the model
reaction kinetics takes on the form of a stochastic differential equation.
After eliminating a fast kinetics, the model can be rephrased into a form of a
one-dimensional overdamped Langevin equation. We discuss physical aspects of
environmental noises acting in such a reduced system, pointing out the
possibility of coexistence of dynamical regimes where noise-enhanced stability
and resonant activation phenomena can be observed together.


title:
Coexistence of resonant activation and noise enhanced stability in a
  model of tumor-host interaction: Statistics of extinction times

abstract:
  We study a Langevin equation derived from the Michaelis-Menten (MM)
phenomenological scheme for catalysis accompanying a spontaneous replication of
molecules, which may serve as a simple model of cell-mediated immune
surveillance against cancer. We examine how two different and statistically
independent sources of noise - dichotomous multiplicative noise and additive
Gaussian white noise - influence the population's extinction time. This
quantity is identified as the mean first passage time of the system across the
zero population state. We observe the effects of resonant activation (RA) and
noise-enhanced stability (NES) and we report the evidence for competitive
co-occurrence of both phenomena in a given regime of noise parameters. We
discuss the statistics of first passage times in this regime and the role of
different pseudo-potential profiles on the RA and NES phenomena. The RA/NES
coexistence region brings an interesting interpretation for the growth kinetics
of cancer cells population, as the NES effect enhancing the stability of the
tumoral state becomes strongly reduced by the RA phenomenon.


title: Straub tail, the deprivation effect and addiction to aggression

abstract:
  It has been observed that male mice who are consistently winning fights with
conspecifics can raise their tail, which is very similar to a morphine-induced
Straub tail response. Since this response is a typical index of opiate
activation, it has been proposed that the opioidergic systems of such mice are
chronically activated. This activation appeared to be a potent factor, which
leads to addiction to aggression. To check this hypothesis, we exposed the mice
who had won 20 fights in succession with conspecifics to a behavioral
sensitization procedure. The effects of the mu-opioid receptor agonist morphine
(10 mg/kg, i.p.) on the behavior of winners were examined in an open-field test
before and after 5- and 14-day deprivation of aggression. Morphine had a much
stronger stimulating effect on the open-field behavior of 60 % of the winners
deprived of aggression for 14 days than on that of the control mice. Morphine
did not stimulate behavioral activity in the winners before or after
deprivation for 5 days. The aggression level in the winners was higher after
than before deprivation. It has been concluded that, in the winners, the
mu-opioid receptors became tolerant to the effects of morphine and became
sensitized after long aggression deprivation. The development of addiction to
aggression due to repeated victories is discussed in the light of the theory of
addiction (Robinson,Berridge, 2003).


title:
Determinism, Noise, and Spurious Estimations in a Generalised Model of
  Population Growth

abstract:
  We study a generalised model of population growth in which the state variable
is population growth rate instead of population size. Stochastic parametric
perturbations, modelling phenotypic variability, lead to a Langevin system with
two sources of multiplicative noise. The stationary probability distributions
have two characteristic power-law scales. Numerical simulations show that noise
suppresses the explosion of the growth rate which occurs in the deterministic
counterpart. Instead, in different parameter regimes populations will grow with
``anomalous'' stochastic rates and (i) stabilise at ``random carrying
capacities'', or (ii) go extinct in random times. Using logistic fits to
reconstruct the simulated data, we find that even highly significant
estimations do not recover or reflect information about the deterministic part
of the process. Therefore, the logistic interpretation is not biologically
meaningful. These results have implications for distinct model-aided
calculations in biological situations because these kinds of estimations could
lead to spurious conclusions.


title:
Salerno's model of DNA reanalysed: could solitons have biological
  significance?

abstract:
  We investigate the sequence-dependent behaviour of localised excitations in a
toy, nonlinear model of DNA base-pair opening originally proposed by Salerno.
Specifically we ask whether ``breather'' solitons could play a role in the
facilitated location of promoters by RNA polymerase. In an effective potential
formalism, we find excellent correlation between potential minima and {\em
Escherichia coli} promoter recognition sites in the T7 bacteriophage genome.
Evidence for a similar relationship between phage promoters and downstream
coding regions is found and alternative reasons for links between AT richness
and transcriptionally-significant sites are discussed. Consideration of the
soliton energy of translocation provides a novel dynamical picture of sliding:
steep potential gradients correspond to deterministic motion, while ``flat''
regions, corresponding to homogeneous AT or GC content, are governed by random,
thermal motion. Finally we demonstrate an interesting equivalence between
planar, breather solitons and the helical motion of a sliding protein
``particle'' about a bent DNA axis.


title: Demographic Homeostasis and the Evolution of Senescence

abstract:
  Existing theories for the evolution of aging and death treat senescence as a
side-effect of strong selection for fertility. These theories are
well-developed mathematically, but fit poorly with emerging experimental data.
The data suggest that aging is an adaptation, selected for its own sake. But
aging contributes only negatively to fitness of the individual. What kind of
selection model would permit aging to emerge as a population-level adaptation?
I explore the thesis that population dynamics is inherently chaotic, and that
aging is selected for its role in smoothing demographic fluctuations. The
logistic equation provides a natural vehicle for this model because it has
played a central role in two sciences: Population growth in a resource-limited
niche has long been modeled by the differential LE; and, as a difference
equation, the LE is a canonical example of the emergence of chaos. Suppose that
feedback about depleted resources generally arrives too late to avoid a wave of
unsupportable population growth; then logistic population dynamics is subject
to chaotic fluctuations. It is my thesis that aging is an evolutionary
adaptation selected for its stabilizing effect on chaotic population dynamics.


title:
Structured thalamocortical connectivity revealed by random walks on
  complex networks

abstract:
  The segregated regions of the mammalian cerebral cortex and thalamus form an
extensive and complex network, whose structure and function are still only
incompletely understood. The present article describes an application of the
concepts of complex networks and random walks that allows the identification of
non-random, highly structured features of thalamocortical connections, and
their potential effects on dynamic interactions between cortical areas in the
cat brain. Utilizing large-scale anatomical data sets of this thalamocortical
system, we investigate uniform random walks in such a network by considering
the steady state eigenvector of the respective stochastic matrix. It is shown
that thalamocortical connections are organized in such a way as to guarantee
strong correlation between the outdegree and occupancy rate (a stochastic
measure potentially related to activation) of each cortical area. Possible
organizational principles underlying this effect are identified and discussed.


title:
Effects of constraints in general branched molecules: A quantitative ab
  initio study in HCO-L-Ala-NH2

abstract:
  A general approach to the design of accurate classical potentials for protein
folding is described. It includes the introduction of a meaningful statistical
measure of the differences between approximations of the same potential energy,
the definition of a set of Systematic and Approximately Separable and Modular
Internal Coordinates (SASMIC), much convenient for the simulation of general
branched molecules, and the imposition of constraints on the most rapidly
oscillating degrees of freedom. All these tools are used to study the effects
of constraints in the Conformational Equilibrium Distribution (CED) of the
model dipeptide HCO-L-Ala-NH2. We use ab initio Quantum Mechanics calculations
including electron correlation at the MP2 level to describe the system, and we
measure the conformational dependence of the correcting terms to the naive CED
based in the Potential Energy Surface (PES) without any simplifying assumption.
These terms are related to mass-metric tensors determinants and also occur in
the Fixman's compensating potential. We show that some of the corrections are
non-negligible if one is interested in the whole Ramachandran space. On the
other hand, if only the energetically lower region, containing the principal
secondary structure elements, is assumed to be relevant, then, all correcting
terms may be neglected up to peptides of considerable length. This is the first
time, as far as we know, that the analysis of the conformational dependence of
these correcting terms is performed in a relevant biomolecule with a realistic
potential energy function.


title: Invasive advance of an advantageous mutation: nucleation theory

abstract:
  For most organisms with viscous population structure, spatially localized
growth drives the invasive advance of a favorable mutation. We model a
two-allele competition where recurrent mutation introduces a genotype with a
rate of local propagation exceeding the resident's rate. We capture
ecologically important properties of the rare invader's stochastic dynamics by
assuming discrete individuals and neighborhood interactions. To understand how
individual-level processes may govern population patterns, we invoke the
physical theory for nucleation of spatial systems. Nucleation theory
discriminates between single-cluster and multi-cluster dynamics. A sufficiently
low mutation rate, or a sufficiently small environment, generates
single-cluster dynamics, an inherently stochastic process; a favorable mutation
advances only if the invader cluster reaches a critical radius. For this mode
of invasion we identify the probability distribution of waiting times until the
favored allele advances to competitive dominance, and we ask how the critical
cluster size varies as propagation or mortality rates vary. Increasing the
mutation rate or system size generates multi-cluster invasion, where spatial
averaging produces nearly deterministic global dynamics. For this process, an
analytical approximation from nucleation theory, called Avrami's Law, describes
the time-dependent behavior of the genotype densities with remarkable accuracy.


title: Towards a model for protein production rates

abstract:
  In the process of translation, ribosomes read the genetic code on an mRNA and
assemble the corresponding polypeptide chain. The ribosomes perform discrete
directed motion which is well modeled by a totally asymmetric simple exclusion
process (TASEP) with open boundaries. Using Monte Carlo simulations and a
simple mean-field theory, we discuss the effect of one or two ``bottlenecks''
(i.e., slow codons) on the production rate of the final protein. Confirming and
extending previous work by Chou and Lakatos, we find that the location and
spacing of the slow codons can affect the production rate quite dramatically.
In particular, we observe a novel ``edge'' effect, i.e., an interaction of a
single slow codon with the system boundary. We focus in detail on ribosome
density profiles and provide a simple explanation for the length scale which
controls the range of these interactions.


title:
Concurrent encoding of frequency and amplitude modulation in human
  auditory cortex: MEG evidence

abstract:
  A natural sound can be described by dynamic changes in envelope (amplitude)
and carrier (frequency), corresponding to amplitude modulation (AM) and
frequency modulation (FM) respectively. Although the neural responses to both
AM and FM sounds are extensively studied in both animals and humans, it is
uncertain how they are co-represented when changed simultaneously but
independently, as is typical for ecologically natural signals. This study
elucidates the neural coding of such sounds in human auditory cortex using
magnetoencephalography (MEG). Using stimuli with both sinusoidal modulated
envelope (f_{AM}, 37 Hz) and carrier frequency (f_{FM}, 0.3 - 8 Hz), it is
demonstrated that AM and FM stimulus dynamics are co-represented in the neural
code of human auditory cortex. The stimulus AM dynamics are represented
neurally with AM encoding, by the auditory Steady State Response (aSSR) at
f_{AM}. For sounds with slowly changing carrier frequency ((f_{FM} < 5 Hz), it
is shown that the stimulus FM dynamics are tracked by the phase of the aSSR,
demonstrating neural phase modulation (PM) encoding of the stimulus carrier
frequency. For sounds with faster carrier frequency change ((f_{FM} >= 5 Hz),
it is shown that modulation encoding of stimulus FM dynamics persists, but the
neural encoding is no longer purely PM. This result is consistent with the
recruitment of additional neural AM encoding over and above the original neural
PM encoding, indicating that both the amplitude and phase of the aSSR at f_{AM}
track the stimulus FM dynamics. A neural model is suggested to account for
these observations.


title:
Instability of synchronized motion in nonlocally coupled neural
  oscillators

abstract:
  We study nonlocally coupled Hodgkin-Huxley equations with excitatory and
inhibitory synaptic coupling. We investigate the linear stability of the
synchronized solution, and find numerically various nonuniform oscillatory
states such as chimera states, wavy states, clustering states, and
spatiotemporal chaos as a result of the instability.


title: Anderson Localization, Non-linearity and Stable Genetic Diversity

abstract:
  In many models of genotypic evolution, the vector of genotype populations
satisfies a system of linear ordinary differential equations. This system of
equations models a competition between differential replication rates (fitness)
and mutation. Mutation operates as a generalized diffusion process on genotype
space. In the large time asymptotics, the replication term tends to produce a
single dominant quasispecies, unless the mutation rate is too high, in which
case the populations of different genotypes becomes de-localized. We introduce
a more macroscopic picture of genotypic evolution wherein a random replication
term in the linear model displays features analogous to Anderson localization.
When coupled with non-linearities that limit the population of any given
genotype, we obtain a model whose large time asymptotics display stable
genotypic diversity

categories: q-bio.PE cond-mat.stat-mech math.DS math.SP nlin.AO q-bio.QM


title:
A quantitative model for refilling of the sarcoplasmic reticulum during
  vascular smooth muscle asynchronous [Ca2+] oscillations

abstract:
  We have developed a quantitative model for the creation of cytoplasmic Ca2+
gradients near the inner surface of the plasma membrane (PM). In particular we
simulated the refilling of the sarcoplasmic reticulum (SR) via PM-SR junctions
during asynchronous [Ca2+] oscillations in smooth muscle cells of the rabbit
inferior vena cava. We have combined confocal microscopy data on the [Ca2+]
oscillations, force transduction data from cell contraction studies and
electron microscopic images to build a basis for computational simulations that
model the transport of calcium ions from Na+/Ca2+ exchangers (NCX) on the PM to
sarcoplasmic/endoplasmic reticulum Ca2+ ATPase (SERCA) pumps on the SR as a
three-dimensional random walk through the PM-SR junctional cytoplasmic spaces.
Electron microscopic ultrastructural images of the smooth muscle cells were
elaborated with software algorithms to produce a very clear and dimensionally
accurate picture of the PM-SR junctions. From this study, we conclude that it
is plausible and possible for enough Ca2+ to pass through the PM-SR junctions
to replete the SR during the regenerative Ca2+ release, which underlies agonist
induced asynchronous Ca2+ oscillations in vascular smooth muscle.


title: Mining Mass Spectra: Metric Embeddings and Fast Near Neighbor Search

abstract:
  Mining large-scale high-throughput tandem mass spectrometry data sets is a
very important problem in mass spectrometry based protein identification. One
of the fundamental problems in large scale mining of spectra is to design
appropriate metrics and algorithms to avoid all-pair-wise comparisons of
spectra. In this paper, we present a general framework based on vector spaces
to avoid pair-wise comparisons. We first robustly embed spectra in a high
dimensional space in a novel fashion and then apply fast approximate near
neighbor algorithms for tasks such as constructing filters for database search,
indexing and similarity searching. We formally prove that our embedding has low
distortion compared to the cosine similarity, and, along with locality
sensitive hashing (LSH), we design filters for database search that can filter
out more than 989% of peptides (118 times less) while missing at most 0.29% of
the correct sequences. We then show how our framework can be used in similarity
searching, which can then be used to detect tight clusters or replicates. On an
average, for a cluster size of 16 spectra, LSH only misses 1 spectrum and
admits only 1 false spectrum. In addition, our framework in conjunction with
dimension reduction techniques allow us to visualize large datasets in 2D
space. Our framework also has the potential to embed and compare datasets with
post translation modifications (PTM).


title: Mathematical Model of HIV superinfection dynamics and R5 to X4 switch

abstract:
  During the HIV infection several quasispecies of the virus arise, which are
able to use different coreceptors, in particular the CCR5 and CXCR4 coreceptors
(R5 and X4 phenotypes, respectively). The switch in coreceptor usage has been
correlated with a faster progression of the disease to the AIDS phase. As
several pharmaceutical companies are starting large phase III trials for R5 and
X4 drugs, models are needed to predict the co-evolutionary and competitive
dynamics of virus strains. We present a model of HIV early infection which
describes the dynamics of R5 quasispecies and a model of HIV late infection
which describes the R5 to X4 switch. We report the following findings: after
superinfection or coinfection, quasispecies dynamics has time scales of several
months and becomes even slower at low number of CD4+ T cells. The curve of CD4+
T cells decreases, during AIDS late stage, and can be described taking into
account the X4 related Tumor Necrosis Factor dynamics. Phylogenetic inference
of chemokine receptors suggests that viral mutational pathway may generate R5
variants able to interact with chemokine receptors different from CXCR4. This
may explain the massive signaling disruptions in the immune system observed
during AIDS late stages and may have relevance for vaccination and therapy.


title: A two stage model for quantitative PCR

abstract:
  PCR (Polymerase Chain Reaction), a method which replicates a selected
sequence of DNA, has revolutionized the study of genomic material, but
mathematical study of the process has been limited to simple deterministic
models or descriptions relying on stochastic processes. In this paper we
develop a suite of deterministic models for the reactions of quantitative PCR
(Polymerase Chain Reaction) based on the law of mass action. Maps are created
from DNA copy number in one cycle to the next, with ordinary differential
equations describing the evolution of difference molecular species during each
cycle. Qualitative analysis is preformed at each stage and parameters are
estimated by fitting each model to data from Roche LightCycler (TM) runs.


title: The Modern Theory of Evolution from the Viewpoint of Statistical Physics

abstract:
  The problem of the rate and mechanisms of biological evolution was
considered. It was shown that species could not be formed due to undirected
mutations in characteristic times of about one million years. A mechanism of
deterministic molecular evolution assuming a directed change of the genome was
proposed.


title:
The pharmacophore kernel for virtual screening with support vector
  machines

abstract:
  We introduce a family of positive definite kernels specifically optimized for
the manipulation of 3D structures of molecules with kernel methods. The kernels
are based on the comparison of the three-points pharmacophores present in the
3D structures of molecul es, a set of molecular features known to be
particularly relevant for virtual screening applications. We present a
computationally demanding exact implementation of these kernels, as well as
fast approximations related to the classical fingerprint-based approa ches.
Experimental results suggest that this new approach outperforms
state-of-the-art algorithms based on the 2D structure of mol ecules for the
detection of inhibitors of several drug targets.


title:
Compression ratios based on the Universal Similarity Metric still yield
  protein distances far from CATH distances

abstract:
  Kolmogorov complexity has inspired several alignment-free distance measures,
based on the comparison of lengths of compressions, which have been applied
successfully in many areas. One of these measures, the so-called Universal
Similarity Metric (USM), has been used by Krasnogor and Pelta to compare simple
protein contact maps, showing that it yielded good clustering on four small
datasets. We report an extensive test of this metric using a much larger and
representative protein dataset: the domain dataset used by Sierk and Pearson to
evaluate seven protein structure comparison methods and two protein sequence
comparison methods. One result is that Krasnogor-Pelta method has less domain
discriminant power than any one of the methods considered by Sierk and Pearson
when using these simple contact maps. In another test, we found that the USM
based distance has low agreement with the CATH tree structure for the same
benchmark of Sierk and Pearson. In any case, its agreement is lower than the
one of a standard sequential alignment method, SSEARCH. Finally, we manually
found lots of small subsets of the database that are better clustered using
SSEARCH than USM, to confirm that Krasnogor-Pelta's conclusions were based on
datasets that were too small.


title:
Development of a Three-Dimensional Multiscale Agent-Based Tumor Model:
  Simulating Gene-Protein Interaction Profiles, Cell Phenotypes & Multicellular
  Patterns in Brain Cancer

abstract:
  Experimental evidence suggests that epidermal growth factor receptor
(EGFR)-mediated activation of the signaling protein phospholipase C gamma plays
a critical role in a cancer cell's phenotypic decision to either proliferate or
to migrate at a given point in time. Here, we present a novel three-dimensional
multiscale agent-based model to simulate this cellular decision process in the
context of a virtual brain tumor. Each tumor cell is equipped with an EGFR
gene-protein interaction network module that also connects to a simplified cell
cycle description. The simulation results show that over time proliferative and
migratory cell populations not only oscillate but also directly impact the
spatio-temporal expansion patterns of the entire cancer system. The percentage
change in the concentration of the sub-cellular interaction network's molecular
components fluctuates, and, for the proliferation-to-migration switch we find
that the phenotype triggering molecular profile to some degree varies as the
tumor system grows and the microenvironment changes. We discuss potential
implications of these findings for experimental and clinical cancer research.


title: Stochastic models for tumoral growth

abstract:
  Strong experimental evidence has indicated that tumor growth belongs to the
molecular beam epitaxy universality class. This type of growth is characterized
by the constraint of cell proliferation to the tumor border, and surface
diffusion of cells at the growing edge. Tumor growth is thus conceived as a
competition for space between the tumor and the host, and cell diffusion at the
tumor border is an optimal strategy adopted for minimizing the pressure and
helping tumor development. Two stochastic partial differential equations are
introduced in this work in order to correctly model the physical properties of
tumoral growth in (1+1) and (2+1) dimensions. The advantages of these models is
that they reproduce the correct geometry of the tumor and are defined in terms
of polar variables. Analysis of these models allow us to quantitatively
estimate the response of the tumor to an unfavorable perturbation during the
growth.


title:
Spreading dynamics on small-world networks with connectivity
  fluctuations and correlations

abstract:
  Infectious diseases and computer malwares spread among humans and computers
through the network of contacts among them. These networks are characterized by
wide connectivity fluctuations, connectivity correlations and the small-world
property. In a previous work [A. Vazquez, Phys. Rev. Lett. 96, 038702 (2006)] I
have shown that the connectivity fluctuations together with the small-world
property lead to a novel spreading law, characterized by an initial power law
growth with an exponent determined by the average node distance on the network.
Here I extend these results to consider the influence of connectivity
correlations which are generally observed in real networks. I show that
assortative and disassortative connectivity correlations enhance and diminish,
respectively, the range of validity of this spreading law. As a corollary I
obtain the region of connectivity fluctuations and degree correlations
characterized by the absence of an epidemic threshold. These results are
relevant for the spreading of infectious diseases, rumors, and information
among humans and the spreading of computer viruses, email worms and hoaxes
among computer users.

categories: q-bio.PE cond-mat.dis-nn physics.soc-ph q-bio.QM


title: PFMFind: a system for discovery of peptide homology and function

abstract:
  Protein Fragment Motif Finder (PFMFind) is a system that enables efficient
discovery of relationships between short fragments of protein sequences using
similarity search. It supports queries based on score matrices and PSSMs
obtained through an iterative procedure similar to PSI-BLAST. PSSM construction
is customisable through plugins written in Python. PFMFind consists of a GUI
client, an algorithm using an index for fast similarity search and a relational
database for storing search results and sequence annotations. It is written
mostly in Python. All components communicate between themselves using TCP/IP
sockets and can be located on different physical machines.


title: A Chemical Kinetic Model of Transcriptional Elongation

abstract:
  A chemical kinetic model of the elongation dynamics of RNA polymerase along a
DNA sequence is introduced. The proposed model governs the discrete movement of
the RNA polymerase along a DNA template, with no consideration given to elastic
effects. The model's novel concept is a ``look-ahead'' feature, in which
nucleotides bind reversibly to the DNA prior to being incorporated covalently
into the nascent RNA chain. Results are presented for specific DNA sequences
that have been used in single-molecule experiments of the random walk of RNA
polymerase along DNA. By replicating the data analysis algorithm from the
experimental procedure, the model produces velocity histograms, enabling direct
comparison with these published results.


title:
Fisher Waves and the Velocity of Front Propagation in a Two-Species
  Invasion Model with Preemptive Competition

abstract:
  We consider an individual-based two-dimensional spatial model with
nearest-neighbor preemptive competition to study front propagation between an
invader and a resident species. In particular, we investigate the asymptotic
front velocity and compare it with mean-field predictions.


title: Grandmother cells and the storage capacity of the human brain

abstract:
  Quian Quiroga et al. [Nature 435, 1102 (2005)] have recently discovered
neurons that appear to have the characteristics of grandmother (GM) cells. Here
we quantitatively assess the compatibility of their data with the GM-cell
hypothesis. We show that, contrary to the general impression, a GM-cell
representation can be information-theoretically efficient, but that it must be
accompanied by cells giving a distributed coding of the input. We present a
general method to deduce the sparsity distribution of the whole neuronal
population from a sample, and use it to show there are two populations of
cells: a distributed-code population of less than about 5% of the cells, and a
much more sparsely responding population of putative GM cells. With an
allowance for the number of undetected silent cells, we find that the putative
GM cells can code for 10^5 or more categories, sufficient for them to be
classic GM cells, or to be GM-like cells coding for memories. We quantify the
strong biases against detection of GM cells, and show consistency of our
results with previous measurements that find only distributed coding. We
discuss the consequences for the architecture of neural systems and synaptic
connectivity, and for the statistics of neural firing.


title: Complex networks theory for analyzing metabolic networks

abstract:
  One of the main tasks of post-genomic informatics is to systematically
investigate all molecules and their interactions within a living cell so as to
understand how these molecules and the interactions between them relate to the
function of the organism, while networks are appropriate abstract description
of all kinds of interactions. In the past few years, great achievement has been
made in developing theory of complex networks for revealing the organizing
principles that govern the formation and evolution of various complex
biological, technological and social networks. This paper reviews the
accomplishments in constructing genome-based metabolic networks and describes
how the theory of complex networks is applied to analyze metabolic networks.


title:
Influence of stochastic delays in Seidel-Herzel model of human
  cardiorespiratory system

abstract:
  Experiments that discuss influence of noise to H. Seidel and H. Herzel
dynamics model of human cardiovascular system are presented. Noise is
introduced by considering stochastic delays in response to the sympathetic
system. It appears that in the presence of the noise 10 s heart rate
oscillations connected with Mayer waves are preserved. Moreover the heart rate
becomes approximately normally distributed (even in unstable phase of original
Seidel Herzel model), similarly like the real RR intervals data.


title: Enhanced self-attraction of proteins and its evolutionary implications

abstract:
  Statistical analysis of protein-protein interactions shows anomalously high
frequency of homodimers [Ispolatov, I., et al. (2005) Nucleic Acids Res 33,
3629-35]. Furthermore, recent findings [Wright, C.F., et al. (2005) Nature 438,
878-81] demonstrate that maintaining low sequence identity is a key
evolutionary mechanism that inhibits protein aggregation. Here, we study
statistical properties of interacting protein-like surfaces and predict the
effect of universal, enhanced self-attraction of proteins. The effect
originates in the fact that a pattern self-match between two identical, even
randomly organized interacting protein surfaces is always stronger compared to
the pattern match between two different, promiscuous protein surfaces. This
finding implies an increased probability of homodimer selection in the course
of early evolution. Our simple model of early evolutionary selection of
interacting proteins accurately reproduces the experimental data on homodimer
interface aminoacid compositions. In addition, we predict that heterodimers
evolved from homodimers with the negative design evolutionary pressure applied
against promiscuous homodimer formation. We predict that the anti-homodimer
negative design evolutionary signal is conveyed through the enrichment of
heterodimeric interfaces in polar residues, and most profoundly in glutamic
acid and lysine, which is consistent with experimental findings. We predict
therefore that the negative design against homodimers is the


title: Protein folding in a force-clamp

abstract:
  Kinetics of folding of a protein held in a force-clamp are compared to an
unconstrained folding. The comparison is made within a simple topology-based
dynamical model of ubiquitin. We demonstrate that the experimentally observed
variations in the end-to-end distance reflect microscopic events during
folding. However, the folding scenarios in and out of the force-clamp are
distinct.


title: The Water Circuit of the Plants - Do Plants have Hearts ?

abstract:
  There is a correspondence between the circulation of blood in all higher
animals and the circulation of sap in all higher plants - up to heights h of
140 m - through the xylem and phloem vessels. Plants suck in water from the
soil, osmotically through the roothair zone, and subsequently lift it
osmotically again, and by capillary suction (via their buds, leaves, and
fruits) into their crowns. In between happens a reverse osmosis - the
endodermis jump - realized by two layers of subcellular mechanical pumps in the
endodermis walls which are powered by ATP, or in addition by two analogous
layers of such pumps in the exodermis. The thus established root pressure helps
forcing the absorbed ground water upward, through the whole plant, and often
out again, in the form of guttation, or exudation.


title: Multiprotein DNA looping

abstract:
  DNA looping plays a fundamental role in a wide variety of biological
processes, providing the backbone for long range interactions on DNA. Here we
develop the first model for DNA looping by an arbitrarily large number of
proteins and solve it analytically in the case of identical binding. We uncover
a switch-like transition between looped and unlooped phases and identify the
key parameters that control this transition. Our results establish the basis
for the quantitative understanding of fundamental cellular processes like DNA
recombination, gene silencing, and telomere maintenance.

categories: q-bio.SC cond-mat.stat-mech physics.bio-ph q-bio.BM


title:
A model for regulated fatty acid metabolism in liver; equilibria and
  their changes

abstract:
  We build a model for the hepatic fatty acid metabolism and its metabolic and
genetic regulations. The model has two functioning modes: synthesis and
oxidation of fatty acids. We provide a sufficient condition (the strong
lipolytic condition) for the uniqueness of its equilibrium. Under this
condition, modifications of the glucose input produce equilibrium shifts, which
are gradual changes from one functioning mode to the other. We also discuss the
concentration variations of various metabolites during equilibrium shifts. The
model can explain a certain amount of experimental observations, assess the
role of poly-unsaturated fatty acids in genetic regulation, and predict the
behavior of mutants. The analysis of the model is based on block elimination of
variables and uses a modular decomposition of the system dictated by
mathematical global univalence conditions.


title:
Ecosystems in the Mind: Fuzzy Cognitive Maps of the Kizilirmak Delta
  Wetlands in Turkey

abstract:
  Sustainability of ecosystems & ecosystem management are increasingly accepted
societal goals. Can conservation programs be improved by incorporating local
people's understanding of ecosystems? The Kizilirmak Delta is one of Turkey's
most important wetland complexes. It is also one of the most productive
agricultural deltas in Turkey. We obtained 31 cognitive models of the social &
ecological system. These models were converted to adjacency matrices, analyzed
using graph theoretical methods, & augmented into social cognitive maps. Causal
"What-if" scenarios were run to determine the trajectory of the ecosystem based
on models defined by stakeholders. Villagers had significantly larger numbers
of variables, more complex maps, a broader understanding of all the variables
that affect the Kizilirmak Delta, & mentioned more variables that control the
ecosystem than did NGO and government officials. Villagers adapting to changing
ecological & social conditions actively changed & challenged conditions through
the political process. Villagers were faced with many important forcing
functions that they could not control. Most of the variables defined by
villagers were related to agriculture and animal husbandry. Conservation
policies & ecosystem management must encompass larger environmental issues &
villagers' cognitive maps must be reconciled with that of NGOs & government
officials. Cognitive maps can serve as a basis for discussion when policies &
management options are formulated. A villager-centered cognitive mapping
approach is not only necessary because villagers resist conservation projects,
or because top down projects that do not take local knowledge systems into
account fail, but because it is the ethical and responsible way of doing
ecosystem management.


title: Diffusion and interfaces in pattern formation

abstract:
  We discuss several qualitative properties of the solutions of
reaction-diffusion systems and equations of the form $u_t = \epsilon^2 D \Delta
u + f(u,x,\epsilon t)$, that are used in modeling pattern formation. We analyze
the diffusion neutral and the diffusion dependent situations that, in the time
autonomous case, are distinguished by considering the attractors of the shorted
equation $u_t = f(u,x)$. We discuss the consequences of being in one or in the
other of the two situations and present examples from developmental biology and
from fluid mechanics.


title: Thermodynamics of aggregation of two proteins

abstract:
  We investigate aggregation mechanism of two proteins in a thermodynamically
unambiguous manner by considering the finite size effect of free energy
landscape of HP lattice protein model. Multi-Self-Overlap-Ensemble Monte Carlo
method is used for numerical calculations. We find that a dimer can be formed
spontaneously as a thermodynamically stable state when the system is small
enough. It implies the possibility that the aggregation of proteins in a cell
is triggered when they are confined in a small region by, for example, being
surrounded by other macromolecules.We also find that the dimer exhibits a
transition between unstable state and metastable state in the infinite system.


title: Systems theory of Smad signaling

abstract:
  Transforming Growth Factor-beta (TGF-beta) signalling is an important
regulator of cellular growth and differentiation. The principal intracellular
mediators of TGF-beta signalling are the Smad proteins, which upon TGF-beta
stimulation accumulate in the nucleus and regulate transcription of target
genes. To investigate the mechanisms of Smad nuclear accumulation, we developed
a simple mathematical model of canonical Smad signalling. The model was built
using both published data and our experimentally determined cellular Smad
concentrations (isoforms 2, 3, and 4). We found in mink lung epithelial cells
that Smad2 (8.5-12 x 10^4 molecules/cell) was present in similar amounts to
Smad4 (9.3-12 x 10^4 molecules/cell), while both were in excess of Smad3
(1.1-2.0 x 10^4 molecules/cell). Variation of the model parameters and
statistical analysis showed that Smad nuclear accumulation is most sensitive to
parameters affecting the rates of RSmad phosphorylation and dephosphorylation
and Smad complex formation/dissociation in the nucleus. Deleting Smad4 from the
model revealed that rate-limiting phospho-R-Smad dephosphorylation could be an
important mechanism for Smad nuclear accumulation. Furthermore, we observed
that binding factors constitutively localised to the nucleus do not efficiently
mediate Smad nuclear accumulation if dephosphorylation is rapid. We therefore
conclude that an imbalance in the rates of R-Smad phosphorylation and
dephosphorylation is likely an important mechanism of Smad nuclear accumulation
during TGF-beta signalling.


title: Genetic Toggle Switch Without Cooperative Binding

abstract:
  Genetic switch systems with mutual repression of two transcription factors
are studied using deterministic and stochastic methods. Numerous studies have
concluded that cooperative binding is a necessary condition for the emergence
of bistability in these systems. Here we show that for a range of biologically
relevant conditions, a suitable combination of network structure and stochastic
effects gives rise to bistability even without cooperative binding.


title: Ligand Binding, Protein Fluctuations, and Allosteric Free Energy

abstract:
  Although the importance of protein dynamics in protein function is generally
recognized, the role of protein fluctuations in allosteric effects scarcely has
been considered. To address this gap, the Kullback-Leibler divergence (Dx)
between protein conformational distributions before and after ligand binding
was proposed as a means of quantifying allosteric effects in proteins. Here,
previous applications of Dx to methods for analysis and simulation of proteins
are first reviewed, and their implications for understanding aspects of protein
function and protein evolution are discussed. Next, equations for Dx suggest
that k_{B}TDx should be interpreted as an allosteric free energy -- the free
energy associated with changing the ligand-free protein conformational
distribution to the ligand-bound conformational distribution. This
interpretation leads to a thermodynamic model of allosteric transitions that
unifies existing perspectives on the relation between ligand binding and
changes in protein conformational distributions. The definition of Dx is used
to explore some interesting mathematical relations among commonly recognized
thermodynamic and biophysical quantities, such as the total free energy change
upon ligand binding, and ligand-binding affinities for individual protein
conformations. These results represent the beginnings of a theoretical
framework for considering the full protein conformational distribution in
modeling allosteric transitions. Early applications of the framework have
produced results with implications both for methods for coarsed-grained
modeling of proteins, and for understanding the relation between ligand binding
and protein dynamics.


title: A Structure-Centric View of Protein Evolution, Design and Adaptation

abstract:
  Proteins, by virtue of their central role in most biological processes,
represent one of the key subjects of the study of molecular evolution. Inherent
to the indispensability of proteins for living cells is the fact that a given
protein can adopt a specific three-dimensional shape that is specified solely
by the proteins sequence of amino acids. Over the past several decades,
structural biologists have demonstrated that the array of structures that
proteins may adopt is quite astounding, and this has lead to a strong interest
in understanding how protein structures change and evolve over time. In this
review we consider a large body of recent work that attempts to illuminate this
structure-centric picture of protein evolution. Much of this work has focused
on the question of how completely new protein structures (i.e. new folds or
topologies) are discovered by protein sequences as they evolve. Pursuant to
this question of structural innovation has been a desire to describe and
understand the observation that certain types of protein structures are far
more abundant than others and how this uneven distribution of proteins
implicates on the process through which new shapes are discovered. We consider
a number of theoretical models that have been successful at explaining this
heterogeneity in protein populations and discuss the increasing amount of
evidence that indicates that the process of structural evolution involves the
divergence of protein sequences and structures from one another.


title:
Thermodynamic behavior of a phase transition in a model for sympatric
  speciation

abstract:
  We investigate the macroscopic effects of the ingredients that drive the
origin of species through sympatric speciation. In our model, sympatric
speciation is obtained as we tune up the strength of competition between
individuals with different phenotypes. As a function of this control parameter,
we can characterize, through the behavior of a macroscopic order parameter, a
phase transition from a non-speciation to a speciation state of the system. The
behavior of the first derivative of the order parameter with respect to the
control parameter is consistent with a phase transition and exhibits a sharp
peak at the transition point. For different resources distribution, the
transition point is shifted, an effect similar to pressure in PVT system. The
inverse of the parameter related to sexual selection strength behaves like an
external field in the system and, as thus, is also a control parameter. The
macroscopic effects of the biological parameters used in our model reveal thus
fingerprints typical of thermodynamic quantities in a phase transition of an
equilibrium physical system.


title: Spectral analysis of gene expression profiles using gene networks

abstract:
  Microarrays have become extremely useful for analysing genetic phenomena, but
establishing a relation between microarray analysis results (typically a list
of genes) and their biological significance is often difficult. Currently, the
standard approach is to map a posteriori the results onto gene networks to
elucidate the functions perturbed at the level of pathways. However,
integrating a priori knowledge of the gene networks could help in the
statistical analysis of gene expression data and in their biological
interpretation. Here we propose a method to integrate a priori the knowledge of
a gene network in the analysis of gene expression data. The approach is based
on the spectral decomposition of gene expression profiles with respect to the
eigenfunctions of the graph, resulting in an attenuation of the high-frequency
components of the expression profiles with respect to the topology of the
graph. We show how to derive unsupervised and supervised classification
algorithms of expression profiles, resulting in classifiers with biological
relevance. We applied the method to the analysis of a set of expression
profiles from irradiated and non-irradiated yeast strains. It performed at
least as well as the usual classification but provides much more biologically
relevant results and allows a direct biological interpretation.


title:
A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV
  Sequence Data

abstract:
  RNA viruses provide prominent examples of measurably evolving populations. In
HIV infection, the development of drug resistance is of particular interest,
because precise predictions of the outcome of this evolutionary process are a
prerequisite for the rational design of antiretroviral treatment protocols. We
present a mutagenetic tree hidden Markov model for the analysis of longitudinal
clonal sequence data. Using HIV mutation data from clinical trials, we estimate
the order and rate of occurrence of seven amino acid changes that are
associated with resistance to the reverse transcriptase inhibitor efavirenz.


title:
Feedback Medicine: Control Systems Concepts in Personalised, Predictive
  Medicine and Combinatorial Intervention

abstract:
  In its broadest definition, systems biology is the application of a `systems'
way of thinking about and doing cell biology. By implication, this also invites
us to consider a systems approach in the context of medicine and the treatment
of disease. In particular, the idea that systems biology can form the basis of
a personalised, predictive medicine will require that much closer attention is
paid to the analytic properties of the feedback loops which will be set up by a
personalised approach to healthcare. To emphasize the role that feedback theory
will play in understanding personalised medicine, we use the term feedback
medicine to describe the issues outlined.In these notes we consider feedback
and control systems concepts applied to two important themes in medical systems
biology - personalised medicine and combinatorial intervention. In particular,
we formulate a feedback control interpretation for the administration of
medicine, and relate them to various forms of medical treatment.


title:
Integrity of H1 helix in prion protein revealed by molecular dynamic
  simulations to be especially vulnerable to changes in the relative
  orientation of H1 and its S1 flank

abstract:
  In the template-assistance model, normal prion protein (PrPC), the pathogenic
cause of prion diseases such as Creutzfeldt-Jakob (CJD) in human, Bovine
Spongiform Encephalopathy (BSE) in cow, and scrapie in sheep, converts to
infectious prion (PrPSc) through an autocatalytic process triggered by a
transient interaction between PrPC and PrPSc. Conventional studies suggest the
S1-H1-S2 region in PrPC to be the template of S1-S2 $\beta$-sheet in PrPSc, and
the conformational conversion of PrPC into PrPSc may involve an unfolding of H1
in PrPC and its refolding into the $\beta$-sheet in PrPSc. Here we conduct a
series of simulation experiments to test the idea of transient interaction of
the template-assistance model. We find that the integrity of H1 in PrPC is
vulnerable to a transient interaction that alters the native dihedral angles at
residue Asn$^{143}$, which connects the S1 flank to H1, but not to interactions
that alter the internal structure of the S1 flank, nor to those that alter the
relative orientation between H1 and the S2 flank.


title: Epistasis and Shapes of Fitness Landscapes

abstract:
  The relationship between the shape of a fitness landscape and the underlying
gene interactions, or epistasis, has been extensively studied in the two-locus
case. Gene interactions among multiple loci are usually reduced to two-way
interactions. We present a geometric theory of shapes of fitness landscapes for
multiple loci. A central concept is the genotope, which is the convex hull of
all possible allele frequencies in populations. Triangulations of the genotope
correspond to different shapes of fitness landscapes and reveal all the gene
interactions. The theory is applied to fitness data from HIV and Drosophila
melanogaster. In both cases, our findings refine earlier analyses and reveal
previously undetected gene interactions.


title: Synchronization in Electrically Coupled Neural Networks

abstract:
  In this report, we investigate the synchronization of temporal activity in an
electrically coupled neural network model. The electrical coupling is
established by homotypic static gap-junctions (Connexin 43). Two distinct
network topologies, namely: {\em sparse random network, (SRN)} and {\em fully
connected network, (FCN)} are used to establish the connectivity. The strength
of connectivity in the FCN is governed by the {\em mean gap junctional
conductance} ($\mu$). In the case of the SRN, the overall strength of
connectivity is governed by the {\em density of connections} ($\delta$) and the
connection strength between two neurons ($S_0$). The synchronization of the
network with increasing gap junctional strength and varying population sizes is
investigated. It was observed that the network {\em abruptly} makes a
transition from a weakly synchronized to a well synchronized regime when
($\delta$) or ($\mu$) exceeds a critical value. It was also observed that the
($\delta$, $\mu$) values used to achieve synchronization decreases with
increasing network size.


title: Dynamic stability of spindles controlled by molecular motor kinetics

abstract:
  We analyze the role of the force-dependent kinetics of motor proteins in the
stability of antiparallel arrays of polar filaments, such as those in the
mitotic spindle. We determine the possible stable structures and show that
there exists an instability associated to the collective behavior of motors
that leads to the collapse of the spindle. Our analysis provides a general
framework to understand several experimental observations in eukaryotic cell
division.


title: Biochemical analysis of human breast tissues using FT-Raman spectroscopy

abstract:
  In this work we employ the Fourier Transform Raman Spectroscopy to study
normal and tumoral human breast tissues, including several subtypes of cancers.
We analyzed 194 Raman spectra from breast tissues that were separated into 9
groups according to their corresponding histopathological diagnosis. The
assignment of the relevant Raman bands enabled us to connect the several kinds
of breast tissues (normal and pathological) to their corresponding biochemical
moieties alterations and distinguish among 7 groups: normal breast, fibrocystic
condition, duct carcinoma-in-situ, duct carcinoma-in-situ with necrosis,
infiltrating duct carcinoma not otherwise specified, colloid infiltrating duct
carcinoma and invasive lobular carcinomas. We were able to establish the
biochemical basis for each spectrum, relating the observed peaks to specific
biomolecules that play special role in the carcinogenesis process. This work is
very useful for the premature optical diagnosis of a broad range of breast
pathologies. We noticed that we were not able to differentiate inflammatory and
medullary duct carcinomas from infiltrating duct carcinoma not otherwise
specified.


title:
Currency and commodity metabolites: Their identification and relation to
  the modularity of metabolic networks

abstract:
  The large-scale shape and function of metabolic networks are intriguing
topics of systems biology. Such networks are on one hand commonly regarded as
modular (i.e. built by a number of relatively independent subsystems), but on
the other hand they are robust in a way not expected of a purely modular
system. To address this question we carefully discuss the partition of
metabolic networks into subnetworks. The practice of preprocessing such
networks by removing the most abundant substrates, "currency metabolites," is
formalized into a network-based algorithm. We study partitions for metabolic
networks of many organisms and find cores of currency metabolites and modular
peripheries of what we call "commodity metabolites." The networks are found to
be more modular than random networks but far from perfectly divisible into
modules. We argue that cross-modular edges are the key for the robustness of
metabolism.


title: Amplified biochemical oscillations in cellular systems

abstract:
  We describe a mechanism for pronounced biochemical oscillations, relevant to
microscopic systems, such as the intracellular environment. This mechanism
operates for reaction schemes which, when modeled using deterministic rate
equations, fail to exhibit oscillations for any values of rate constants. The
mechanism relies on amplification of the underlying stochasticity of reaction
kinetics within a narrow window of frequencies. This amplification allows
fluctuations to beat the central limit theorem, having a dominant effect even
though the number of molecules in the system is relatively large. The mechanism
is quantitatively studied within simple models of self-regulatory gene
expression, and glycolytic oscillations.


title:
Conductance versus current noise in a neuronal model for noisy
  subthreshold oscillations and related spike generation

abstract:
  Biological systems are notoriously noisy. Noise, therefore, also plays an
important role in many models of neural impulse generation. Noise is not only
introduced for more realistic simulations but also to account for cooperative
effects between noisy and nonlinear dynamics. Often, this is achieved by a
simple noise term in the membrane equation (current noise). However, there are
ongoing discussions whether such current noise is justified or whether rather
conductance noise should be introduced because it is closer to the natural
origin of noise. Therefore, we have compared the effects of current and
conductance noise in a neuronal model for subthreshold oscillations and action
potential generation. We did not see any significant differences in the model
behavior with respect to voltage traces, tuning curves of interspike-intervals,
interval distributions or frequency responses when the noise strength is
adjusted. These findings indicate that simple current noise can give reasonable
results in neuronal simulations with regard to physiological relevant noise
effects.


title:
Solitons in the Yakushevich model of DNA beyond the contact
  approximation

abstract:
  The Yakushevich model of DNA torsion dynamics supports soliton solutions,
which are supposed to be of special interest for DNA transcription. In the
discussion of the model, one usually adopts the approximation $\ell_0 \to 0$,
where $\ell_0$ is a parameter related to the equilibrium distance between bases
in a Watson-Crick pair. Here we analyze the Yakushevich model without $\ell_0
\to 0$. The model still supports soliton solutions indexed by two winding
numbers $(n,m)$; we discuss in detail the fundamental solitons, corresponding
to winding numbers (1,0) and (0,1) respectively.


title:
Solitons in Yakushevich-like models of DNA dynamics with improved
  intrapair potential

abstract:
  The Yakushevich (Y) model provides a very simple pictures of DNA torsion
dynamics, yet yields remarkably correct predictions on certain physical
characteristics of the dynamics. In the standard Y model, the interaction
between bases of a pair is modelled by a harmonic potential, which becomes
anharmonic when described in terms of the rotation angles; here we substitute
to this different types of improved potentials, providing a more physical
description of the H-bond mediated interactions between the bases. We focus in
particular on soliton solutions; the Y model predicts the correct size of the
nonlinear excitations supposed to model the ``transcription bubbles'', and this
is essentially unchanged with the improved potential. Other features of soliton
dynamics, in particular curvature of soliton field configurations and the
Peierls-Nabarro barrier, are instead significantly changed.


title:
Diffusion of transcription factors can drastically enhance the noise in
  gene expression

abstract:
  We study by simulation the effect of the diffusive motion of repressor
molecules on the noise in mRNA and protein levels in the case of a repressed
gene. We find that spatial fluctuations due to diffusion can drastically
enhance the noise in gene expression. For a fixed repressor strength, the noise
due to diffusion can be minimized by increasing the number of repressors or by
decreasing the rate of the open complex formation. We also show that the effect
of spatial fluctuations can be well described by a two-step kinetic scheme,
where formation of an encounter complex by diffusion and the subsequent
association reaction are treated separately. Our results also emphasize that
power spectra are a highly useful tool for studying the propagation of noise
through the different stages of gene expression.


title: Graph Theory and Networks in Biology

abstract:
  In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.


title:
Optimization and evaluation of a coarse-grained model of protein motion
  using X-ray crystal data

abstract:
  Simple coarse-grained models, such as the Gaussian Network Model, have been
shown to capture some of the features of equilibrium protein dynamics. We
extend this model by using atomic contacts to define residue interactions and
introducing more than one interaction parameter between residues. We use
B-factors from 98 ultra-high resolution X-ray crystal structures to optimize
the interaction parameters. The average correlation between GNM fluctuation
predictions and the B-factors is 0.64 for the data set, consistent with a
previous large-scale study. By separating residue interactions into covalent
and noncovalent, we achieve an average correlation of 0.74, and addition of
ligands and cofactors further improves the correlation to 0.75. However,
further separating the noncovalent interactions into nonpolar, polar, and mixed
yields no significant improvement. The addition of simple chemical information
results in better prediction quality without increasing the size of the
coarse-grained model.


title: Tumors under periodic therapy -- Role of the immune response time delay

abstract:
  We model the interaction between the immune system and tumor cells including
a time delay to simulate the time needed by the latter to develop a chemical
and cell mediated response to the presence of the tumor. The results are
compared with those of a previous paper, concluding that the delay introduces
new instabilities in the system leading to an uncontrolable growth of the
tumour. Then a cytokine based periodic immunotherapy treatment is included in
the model and the effects of its dossage are studied for the case of a weak
immune system and a growing tumour. We find the existence of metastable states
(that may last for tens of years) induced by the treatment, and also of
potentially adverse effects of the dossage frequency on the stabilization of
the tumour. These two effects depend on the delay, the cytokine dose burden and
other parameters considered in the model.


title: Temporal and dimensional effects in evolutionary graph theory

abstract:
  The spread in time of a mutation through a population is studied analytically
and computationally in fully-connected networks and on spatial lattices. The
time, t_*, for a favourable mutation to dominate scales with population size N
as N^{(D+1)/D} in D-dimensional hypercubic lattices and as N ln N in
fully-connected graphs. It is shown that the surface of the interface between
mutants and non-mutants is crucial in predicting the dynamics of the system.
Network topology has a significant effect on the equilibrium fitness of a
simple population model incorporating multiple mutations and sexual
reproduction. Includes supplementary information.


title: Non-equilibrium theory of the allele frequency spectrum

abstract:
  A forward diffusion equation describing the evolution of the allele frequency
spectrum is presented. The influx of mutations is accounted for by imposing a
suitable boundary condition. For a Wright-Fisher diffusion with or without
selection and varying population size, the boundary condition is $\lim_{x
\downarrow 0} x f(x,t)=\theta \rho(t)$, where $f(\cdot,t)$ is the frequency
spectrum of derived alleles at independent loci at time $t$ and $\rho(t)$ is
the relative population size at time $t$. When population size and selection
intensity are independent of time, the forward equation is equivalent to the
backwards diffusion usually used to derive the frequency spectrum, but the
forward equation allows computation of the time dependence of the spectrum both
before an equilibrium is attained and when population size and selection
intensity vary with time. From the diffusion equation, we derive a set of
ordinary differential equations for the moments of $f(\cdot,t)$ and express the
expected spectrum of a finite sample in terms of those moments. We illustrate
the use of the forward equation by considering neutral and selected alleles in
a highly simplified model of human history. For example, we show that
approximately 30% of the expected heterozygosity of neutral loci is
attributable to mutations that arose since the onset of population growth in
roughly the last $150,000$ years.


title:
A Surprising Clarification of the Mechanism of Ion-channel
  Voltage-Gating

abstract:
  An intense controversy has surrounded the mechanism of voltage-gating in ion
channels. We interpreted the two leading models of voltage-gating with respect
to the thermodynamic energetics of membrane insertion of the voltage-sensing
'module' from a comprehensive set of potassium channels. KvAP is an archaeal
voltage-gated potassium channel whose x-ray structure was the basis for
determining the general mechanism of voltage-gating. The free energy of
membrane insertion of the KvAP voltage sensor was revealed to be a single
outlier. This was due to its unusual sequence that facilitated large gating
movements in its native lipid membrane. This degree of free energy was the
least typical of the other voltage sensors, including the Shaker potassium
channel. We inferred that the two leading models of voltage-gating referred to
alternative mechanisms of voltage-gating: each is applicable to an independent
set of ion channels. The large motion of the voltage-sensor during gating
proposed by the KvAP-paddle model of gating is unlikely to be mirrored by the
majority of ion channels whose voltage sensors are not located at the
membrane-cytoplasm interface in the channel closed state.


title: The adaptive filter of the yeast galactose pathway

abstract:
  In the yeast Saccharomyces cerevisiae, the interplay between galactose,
Gal3p, Gal80p and Gal4p determines the transcriptional status of the genes
required for galactose utilization. After an increase in galactose
concentration, galactose molecules bind onto Gal3p. This event leads via Gal80p
to the activation of Gal4p, which then induces GAL3 and GAL80 gene
transcription. Here we propose a qualitative dynamic model, whereby these
molecular interaction events represent the first two stages of a functional
feedback loop that closes with the capture of activated Gal4p by newly
synthesized Gal3p and Gal80p, decreasing transcriptional activation and
creating again the protein complex that can bind incoming galactose molecules.
Based on the differential time scales of faster protein interactions versus
slower biosynthetic steps, this feedback loop functions as a derivative filter
where galactose is the input step signal, and released Gal4p is the output
derivative signal. One advantage of such a derivative filter is that GAL genes
are expressed in proportion to the cellular requirement. Furthermore, this
filter adaptively protects the cellular receptors from saturation by galactose,
allowing cells to remain sensitive to variations in galactose concentrations
rather than to absolute concentrations. Finally, this feedback loop, by
allowing phosphorylation of some active Gal4p, may be essential to initiate the
subsequent long-term response.


title:
CRNPRED: Highly Accurate Prediction of One-dimensional Protein
  Structures by Large-scale Critical Random Networks

abstract:
  Background: One-dimensional protein structures such as secondary structures
or contact numbers are useful for three-dimensional structure prediction and
helpful for intuitive understanding of the sequence-structure relationship.
Accurate prediction methods will serve as a basis for these and other purposes.
Results: We implemented a program CRNPRED which predicts secondary structures,
contact numbers and residue-wise contact orders. This program is based on a
novel machine learning scheme called critical random networks. Unlike most
conventional one-dimensional structure prediction methods which are based on
local windows of an amino acid sequence, CRNPRED takes into account the whole
sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary
structure prediction, and correlation coefficients of 0.75 and 0.61 for contact
number and residue-wise contact order predictions, respectively. Conclusion:
CRNPRED will be a useful tool for computational as well as experimental
biologists who need accurate one-dimensional protein structure predictions.


title: A composite model for DNA torsion dynamics

abstract:
  DNA torsion dynamics is essential in the transcription process; a simple
model for it, in reasonable agreement with experimental observations, has been
proposed by Yakushevich (Y) and developed by several authors; in this, the DNA
subunits made of a nucleoside and the attached nitrogen bases are described by
a single degree of freedom. In this paper we propose and investigate, both
analytically and numerically, a ``composite'' version of the Y model, in which
the nucleoside and the base are described by separate degrees of freedom. The
model proposed here contains as a particular case the Y model and shares with
it many features and results, but represents an improvement from both the
conceptual and the phenomenological point of view. It provides a more realistic
description of DNA and possibly a justification for the use of models which
consider the DNA chain as uniform. It shows that the existence of solitons is a
generic feature of the underlying nonlinear dynamics and is to a large extent
independent of the detailed modelling of DNA. The model we consider supports
solitonic solutions, qualitatively and quantitatively very similar to the Y
solitons, in a fully realistic range of all the physical parameters
characterizing the DNA.


title: Qualitative Assessment of Gene Expression in Affymetrix Genechip Arrays

abstract:
  Affymetrix Genechip microarrays are used widely to determine the simultaneous
expression of genes in a given biological paradigm. Probes on the Genechip
array are atomic entities which by definition are randomly distributed across
the array and in turn govern the gene expression. In the present study, we make
several interesting observations. We show that there is considerable
correlation between the probe intensities across the array which defy the
independence assumption. While the mechanism behind such correlations is
unclear, we show that scaling behavior and the profiles of perfect match (PM)
as well as mismatch (MM) probes are similar and immune to background
subtraction. We believe that the observed correlations are possibly an outcome
of inherent non-stationarities or patchiness in the array devoid of biological
significance. This is demonstrated by inspecting their scaling behavior and
profiles of the PM and MM probe intensities obtained from publicly available
Genechip arrays from three eukaryotic genomes, namely: Drosophila Melanogaster,
Homo Sapiens and Mus musculus across distinct biological paradigms and across
laboratories, with and without background subtraction. The fluctuation
functions were estimated using detrended fluctuation analysis (DFA) with fourth
order polynomial detrending. The results presented in this study provide new
insights into correlation signatures of PM and MM probe intensities and
suggests the choice of DFA as a tool for qualitative assessment of Affymetrix
Genechip microarrays prior to their analysis. A more detailed investigation is
necessary in order to understand the source of these correlations.


title: A mean-field analysis of community structure in social and kin networks

abstract:
  We provide a mean-field analysis of community structure of social and
biological networks assuming that actors are able to evaluate some tree-derived
distance to the other actors and tend to aggregate with the less distant. We
show that such networks have small components, and give exact descriptions for
the probability distribution of a typical community size and the number of
communities. In particular, we show that the probability distribution of the
community size is well-approximated by a power-law distribution with exponent
two. We illustrate the robustness of the mean-field analysis by comparing its
predictions on previously studied social networks and biological data.


title: Complex Qualitative Models in Biology: a new approach

abstract:
  We advocate the use of qualitative models in the analysis of large biological
systems. We show how qualitative models are linked to theoretical differential
models and practical graphical models of biological networks. A new technique
for analyzing qualitative models is introduced, which is based on an efficient
representation of qualitative systems. As shown through several applications,
this representation is a relevant tool for the understanding and testing of
large and complex biological networks.


title: Algorithms for identification and categorization

abstract:
  The main features of a family of efficient algorithms for recognition and
classification of complex patterns are briefly reviewed. They are inspired in
the observation that fast synaptic noise is essential for some of the
processing of information in the brain.


title:
Competition between synaptic depression and facilitation in attractor
  neural networks

abstract:
  We study the effect of competition between short-term synaptic depression and
facilitation on the dynamical properties of attractor neural networks, using
Monte Carlo simulation and a mean field analysis. Depending on the balance
between depression, facilitation and the noise, the network displays different
behaviours, including associative memory and switching of the activity between
different attractors. We conclude that synaptic facilitation enhances the
attractor instability in a way that (i) intensifies the system adaptability to
external stimuli, which is in agreement with experiments, and (ii) favours the
retrieval of information with less error during short--time intervals.


title: Chaotic hopping between attractors in neural networks

abstract:
  We present a neurobiologically--inspired stochastic cellular automaton whose
state jumps with time between the attractors corresponding to a series of
stored patterns. The jumping varies from regular to chaotic as the model
parameters are modified. The resulting irregular behavior, which mimics the
state of attention in which a systems shows a great adaptability to changing
stimulus, is a consequence in the model of short--time presynaptic noise which
induces synaptic depression. We discuss results from both a mean--field
analysis and Monte Carlo simulations.


title:
Instability of attractors in autoassociative networks with bioinspired
  fast synaptic noise

abstract:
  We studied autoassociative networks in which synapses are noisy on a time
scale much shorter that the one for the neuron dynamics. In our model a
presynaptic noise causes postsynaptic depression as recently observed in
neurobiological systems. This results in a nonequilibrium condition in which
the network sensitivity to an external stimulus is enhanced. In particular, the
fixed points are qualitatively modified, and the system may easily scape from
the attractors. As a result, in addition to pattern recognition, the model is
useful for class identification and categorization.


title: Information and Protein Interfaces

abstract:
  To confer high specificity and affinity in binding, contacts at interfaces
between two interacting macromolecules are expected to exhibit pair preferences
for types of atoms or residues. Here we quantify these preferences by measuring
the mutual information of contacts for 895 protein-protein interfaces. The
information content is significant and is highest at the atomic resolution. A
simple phenomenological theory reveals a connection between information at
interfaces and the free energy spectrum of association. The connection is
presented in the form of a relation between mutual information and the energy
gap of the native bound state to off-target bound states. Measurement of
information content in designed lattice interfaces show the predicted scaling
behavior to the energy gap. Our theory also suggests that mutual information in
contacts emerges by a selection mechanism, and that strong selection, or high
conservation, of residues should lead to correspondingly high mutual
information. Amino acids which contribute more heavily to information content
are then expected to be more conserved. We verify this by showing a
statistically significant correlation between the conservation of each of the
twenty amino acids and their individual contribution to the information content
at protein-protein interfaces


title:
Analysis of a microscopic stochastic model of microtubule dynamic
  instability

abstract:
  A novel theoretical model of dynamic instability of a system of linear (1D)
microtubules (MTs) in a bounded domain is introduced for studying the role of a
cell edge in vivo and analyzing the effect of competition for a limited amount
of tubulin. The model differs from earlier models in that the evolution of MTs
is based on the rates of single unit (e.g., a heterodimer per protofilament)
transformations, in contrast to postulating effective rates/frequencies of
larger-scale changes, extracted, e.g., from the length history plots of MTs.
Spontaneous GTP hydrolysis with finite rate after polymerization is assumed,
and theoretical estimates of an effective catastrophe frequency as well as
other parameters characterizing MT length distributions and cap size are
derived. We implement a simple cap model which does not include vectorial
hydrolysis. We demonstrate that our theoretical predictions, such as steady
state concentration of free tubulin, and parameters of MT length distributions,
are in agreement with the numerical simulations. The present model establishes
a quantitative link between microscopic parameters governing the dynamics of
MTs and macroscopic characteristics of MTs in a closed system. Lastly, we use a
computational Monte Carlo model to provide an explanation for non-exponential
MT length distributions observed in experiments. In particular, we show that
appearance of such non-exponential distributions in the experiments can occur
because the true steady state has not been reached, and/or due to the presence
of a cell edge.


title: The transposition distance for phylogenetic trees

abstract:
  The search for similarity and dissimilarity measures on phylogenetic trees
has been motivated by the computation of consensus trees, the search by
similarity in phylogenetic databases, and the assessment of clustering results
in bioinformatics. The transposition distance for fully resolved phylogenetic
trees is a recent addition to the extensive collection of available metrics for
comparing phylogenetic trees. In this paper, we generalize the transposition
distance from fully resolved to arbitrary phylogenetic trees, through a
construction that involves an embedding of the set of phylogenetic trees with a
fixed number of labeled leaves into a symmetric group and a generalization of
Reidys-Stadler's involution metric for RNA contact structures. We also present
simple linear-time algorithms for computing it.


title: Combinatorial on/off Model for Olfactory Coding

abstract:
  We present a model for olfactory coding based on spatial representation of
glomerular responses. In this model distinct odorants activate specific subsets
of glomeruli, dependent upon the odorant's chemical identity and concentration.
The glomerular response specificities are understood statistically, based on
experimentally measured distributions of detection thresholds. A simple version
of the model, in which glomerular responses are binary (the on/off model),
allows us to account quantitatively for the following results of human/rodent
olfactory psychophysics: 1) just noticeable differences in the perceived
concentration of a single odor (Weber ratios) are dC/C ~ 0.04; 2) the number of
simultaneously perceived odors can be as high as 12; 3) extensive lesions of
the olfactory bulb do not lead to significant changes in detection or
discrimination thresholds. We conclude that a combinatorial code based on a
binary glomerular response is sufficient to account for the discrimination
capacity of the mammalian olfactory system.


title: Cross-species analysis of biological networks by Bayesian alignment

abstract:
  Complex interactions between genes or proteins contribute a substantial part
to phenotypic evolution. Here we develop an evolutionarily grounded method for
the cross-species analysis of interaction networks by {\em alignment}, which
maps bona fide functional relationships between genes in different organisms.
Network alignment is based on a scoring function measuring mutual similarities
between networks taking into account their interaction patterns as well as
sequence similarities between their nodes. High-scoring alignments and optimal
alignment parameters are inferred by a systematic Bayesian analysis. We apply
this method to analyze the evolution of co-expression networks between human
and mouse. We find evidence for significant conservation of gene expression
clusters and give network-based predictions of gene function. We discuss
examples where cross-species functional relationships between genes do not
concur with sequence similarity.


title:
Solitons in a double pendulums chain model, and DNA roto-torsional
  dynamics

abstract:
  It was first suggested by Englander et al to model the nonlinear dynamics of
DNA relevant to the transcription process in terms of a chain of coupled
pendulums. In a related paper [q-bio.BM/0604014] we argued for the advantages
of an extension of this approach based on considering a chain of double
pendulums with certain characteristics. Here we study a simplified model of
this kind, focusing on its general features and nonlinear travelling wave
excitations; in particular, we show that some of the degrees of freedom are
actually slaved to others, allowing for an effective reduction of the relevant
equations.


title:
Synaptic plasticity of Inhibitory synapse promote synchrony in
  inhibitory network in presence of heterogeneity and noise

abstract:
  Recently spike timing dependent plasticity was observed in inhibitory synapse
in the layer II of entorhinal cortex. The rule provides an interesting zero in
the region of $\Delta t=t_{post}-t_{pre}=0$ and in addition the dynamic range
of the rule lie in gamma frequency band. We propose a robust mechanism based on
this observed synaptic plasticity rule for inhibitory synapses for two mutually
coupled interneurons to phase lock in synchrony in the presence of intrisic
heterogeneity in firing. We study the stability of the phase locked solution by
defining a map for spike times dependent on the phase response curve for the
coupled neurons. Finally we present results on robustness of synchronization in
the presence of noise.


title:
Structure and function of negative feedback loops at the interface of
  genetic and metabolic networks

abstract:
  The molecular network in an organism consists of transcription/translation
regulation, protein-protein interactions/modifications and a metabolic network,
together forming a system that allows the cell to respond sensibly to the
multiple signal molecules that exist in its environment. A key part of this
overall system of molecular regulation is therefore the interface between the
genetic and the metabolic network. A motif that occurs very often at this
interface is a negative feedback loop used to regulate the level of the signal
molecules. In this work we use mathematical models to investigate the steady
state and dynamical behaviour of different negative feedback loops. We show, in
particular, that feedback loops where the signal molecule does not cause the
dissociation of the transcription factor from the DNA respond faster than loops
where the molecule acts by sequestering transcription factors off the DNA. We
use three examples, the bet, mer and lac systems in E. coli, to illustrate the
behaviour of such feedback loops.


title:
Self-organized patterns of coexistence out of a predator-prey cellular
  automaton

abstract:
  We present a stochastic approach to modeling the dynamics of coexistence of
prey and predator populations. It is assumed that the space of coexistence is
explicitly subdivided in a grid of cells. Each cell can be occupied by only one
individual of each species or can be empty. The system evolves in time
according to a probabilistic cellular automaton composed by a set of local
rules which describe interactions between species individuals and mimic the
process of birth, death and predation. By performing computational simulations,
we found that, depending on the values of the parameters of the model, the
following states can be reached: a prey absorbing state and active states of
two types. In one of them both species coexist in a stationary regime with
population densities constant in time. The other kind of active state is
characterized by local coupled time oscillations of prey and predator
populations. We focus on the self-organized structures arising from
spatio-temporal dynamics of the coexistence. We identify distinct spatial
patterns of prey and predators and verify that they are intimally connected to
the time coexistence behavior of the species. The occurrence of a prey
percolating cluster on the spatial patterns of the active states is also
examined.


title:
Effects of Growth on Dinitrogen on the Transcriptome and Predicted
  Proteome of Nostoc PCC 7120

abstract:
  Upon growth on dinitrogen, the filamentous cyanobacterium Nostoc PCC 7120
initiates metabolic and morphological changes. We analyzed the expression of
1249 genes from major metabolic categories under nitrogen fixing and
non-nitrogen fixing growth. The expression data were correlated with potential
target secondary structures, probe GC-content, predicted operon structures, and
nitrogen content of gene products. Of the selected genes, 494 show a more than
2-fold difference in the two conditions analyzed. Under nitrogen-fixing
conditions 465 genes, mainly involved in energy metabolism, photosynthesis,
respiration and nitrogen-fixation, were found to be stronger expressed, whereas
29 genes showed a stronger expression under non-nitrogen fixing conditions.
Analysis of the nitrogen content of regulated genes shows that Nostoc PCC 7120
growing on dinitrogen is freed from any constraints to save nitrogen. For the
first time the expression of high light-induced stress proteins (HLIP-family)
is shown to be linked to the nitrogen availability.


title: Dynamics of Semiflexible Polymers in a Flow Field

abstract:
  We present a novel method to investigate the dynamics of a single
semiflexible polymer, subject to anisotropic friction in a viscous fluid. In
contrast to previous approaches, we do not rely on a discrete bead-rod model,
but introduce a suitable normal mode decomposition of a continuous space curve.
By means of a perturbation expansion for stiff filaments we derive a closed set
of coupled Langevin equations in mode space for the nonlinear dynamics in two
dimensions, taking into account exactly the local constraint of
inextensibility. The stochastic differential equations obtained this way are
solved numerically, with parameters adjusted to describe the motion of actin
filaments. As an example, we show results for the tumbling motion in shear
flow.


title:
Online detection and sorting of extracellularly recorded action
  potentials in human medial temporal lobe recordings, in vivo

abstract:
  Understanding the function of complex cortical circuits requires the
simultaneous recording of action potentials from many neurons in awake and
behaving animals. Practically, this can be achieved by extracellularly
recording from multiple brain sites using single wire electrodes. However, in
densely packed neural structures such as the human hippocampus, a single
electrode can record the activity of multiple neurons. Thus, analytic
techniques that differentiate action potentials of different neurons are
required. Offline spike sorting approaches are currently used to detect and
sort action potentials after finishing the experiment. Because the
opportunities to record from the human brain are relatively rare, it is
desirable to analyze large numbers of simultaneous recordings quickly using
online sorting and detection algorithms. In this way, the experiment can be
optimized for the particular response properties of the recorded neurons. Here
we present and evaluate a method that is capable of detecting and sorting
extracellular single-wire recordings in realtime. We demonstrate the utility of
the method by applying it to an extensive data set we acquired from
chronically-implanted depth electrodes in the hippocampus of human epilepsy
patients. This dataset is particularly challenging because it was recorded in a
noisy clinical environment. This method will allow the development of
closed-loop experiments, which immediately adapt the experimental stimuli
and/or tasks to the neural response observed.