title: Human and computer learning: An experimental study

abstract:
  Simple memorizing tasks have been chosen such as a binary code on a matrix.
After the establishment of an appropriate protocol, the codified matrices were
individually presented to 150 university students who had to memorize them. A
computer simulation for a similar task is available which uses a perceptron on
which an algorithm was implemented allowing for some degree of globality
(technically referred to as entropic nonextensivity within a current
generalization of the usual, Boltzmann-Gibbs, statistical mechanics). Our main
observation is that, for the very specific learning task on which we focus
here, humans perform similarly to slightly nonextensive perceptrons.


title: Inverted Repeats in Viral Genomes

abstract:
  We investigate 738 complete genomes of viruses to detect the presence of
short inverted repeats. The number of inverted repeats found is compared with
the prediction obtained for a Bernoullian and for a Markovian control model. We
find as a statistical regularity that the number of observed inverted repeats
is often greater than the one expected in terms of a Bernoullian or Markovian
model in several of the viruses and in almost all those with a genome longer
than 30,000 bp.


title: Self-Organized Criticality, Optimization and Biodiversity

abstract:
  By driven to extinction species less or poorly adapted, the Darwinian
evolutionary theory is intrinsically an optimization theory. We investigate two
optimization algorithms with such evolutionary characteristics: the Bak-Sneppen
and the Extremal Optimization. By comparing their mean fitness in the steady
state regime, we conclude that the Bak-Sneppen dynamics is more efficient than
the Extremal Optimization if the parameter $\tau$ is in the interval
$[0,0.86]$. The determination of the spatial correlation and the probability
distribution of the avalanches show that the Extremal Optimization dynamics
does not lead the system into a critical self-organized state. Trough a
discrete form of the Bak-Sneppen model we argument that biodiversity is an
essential requisite to preserve the self-organized criticality.


title:
Needed for completion of the human genome: hypothesis driven experiments
  and biologically realistic mathematical models

abstract:
  With the sponsorship of ``Fundacio La Caixa'' we met in Barcelona, November
21st and 22nd, to analyze the reasons why, after the completion of the human
genome sequence, the identification all protein coding genes and their variants
remains a distant goal. Here we report on our discussions and summarize some of
the major challenges that need to be overcome in order to complete the human
gene catalog.


title: Scale-rich metabolic networks: background and introduction

abstract:
  Recent progress has clarified many features of the global architecture of
biological metabolic networks, which have highly organized and optimized
tolerances and tradeoffs (HOT) for functional requirements of flexibility,
efficiency, robustness, and evolvability, with constraints on conservation of
energy, redox, and many small moieties. One consequence of this architecture is
a highly structured modularity that is self-dissimilar and scale-rich, with
extremes in low and high variability, including power laws, in both metabolite
and reaction degree distributions. This paper illustrates these features using
the well-understood stoichiometry of metabolic networks in bacteria, and a
simple model of an abstract metabolism.


title: Nonstationary time series analysis of heart rate variability

abstract:
  An analysis of the RR-interval time series, $t_i$, is presented for the case
in which the average time, $\bar{t}$, changes slowly. In particular, $\bar{t}$
and a short-time scale variability parameter, $V$, are simultaneously measured
while $\bar{t}$ decreases for subjects in the reclined position. The initial
decrease in $\bar{t}$ is usually linear with $V$ yielding parameters that can
be related to physiological quantities.


title:
Co-expression of statistically over-represented peptides in proteomes: a
  key to phylogeny ?

abstract:
  It is proposed that the co-expression of statistically significant motifs
among the sequences of a proteome is a phylogenetic trait. From the
co-expression matrix of such motifs in a group of prokaryotic proteomes a
suitable definition of a phylogenetic distance is introduced and the
corresponding distance matrix between proteomes is constructed. From the
distance matrix a phylogenetic tree is inferred, following a standard
procedure. It compares well with a reference tree deduced from a distance
matrix obtained from the alignment of ribosomal RNA sequences. Our results are
consistent with the hypothesis that biological evolution manifests itself with
a modulation of basic correlations between shared peptides of short length,
present in protein sequences. Moreover, the simple procedure we propose
reconfirms that it is possible, sampling entire proteomes, to average the
effects of lateral gene transfer and infer reasonable phylogenies.


title: Exact Asymptotic Results for a Model of Sequence Alignment

abstract:
  Finding analytically the statistics of the longest common subsequence (LCS)
of a pair of random sequences drawn from c alphabets is a challenging problem
in computational evolutionary biology. We present exact asymptotic results for
the distribution of the LCS in a simpler, yet nontrivial, variant of the
original model called the Bernoulli matching (BM) model which reduces to the
original model in the large c limit. We show that in the BM model, for all c,
the distribution of the asymptotic length of the LCS, suitably scaled, is
identical to the Tracy-Widom distribution of the largest eigenvalue of a random
matrix whose entries are drawn from a Gaussian unitary ensemble. In particular,
in the large c limit, this provides an exact expression for the asymptotic
length distribution in the original LCS problem.

categories: q-bio.GN cond-mat.stat-mech math.ST stat.TH


title: Synonymous codon usage and selection on proteins

abstract:
  Selection pressures on proteins are usually measured by comparing homologous
nucleotide sequences (Zuckerkandl and Pauling 1965). Recently we introduced a
novel method, termed `volatility', to estimate selection pressures on protein
sequences from their synonymous codon usage (Plotkin and Dushoff 2003, Plotkin
et al 2004a). Here we provide a theoretical foundation for this approach. We
derive the expected frequencies of synonymous codons as a function of the
strength of selection, the mutation rate, and the effective population size. We
analyze the conditions under which we can expect to draw inferences from biased
codon usage, and we estimate the time scales required to establish and maintain
such a signal. Our results indicate that, over a broad range of parameters,
synonymous codon usage can reliably distinguish between negative selection,
positive selection, and neutrality. While the power of volatility to detect
negative selection depends on the population size, there is no such dependence
for the detection of positive selection. Furthermore, we show that phenomena
such as transient hyper-mutators in microbes can improve the power of
volatility to detect negative selection, even when the typical observed neutral
site heterozygosity is low.


title: Theory of localized synfire chain

abstract:
  Neuron is a noisy information processing unit and conventional view is that
information in the cortex is carried on the rate of neurons spike emission.
More recent studies on the activity propagation through the homogeneous network
have demonstrated that signals can be transmitted with millisecond fidelity;
this model is called the Synfire chain and suggests the possibility of the
spatio-temporal coding. However, the more biologically realistic, structured
feedforward network generates spatially distributed inputs. It results in the
difference of spike timing. This poses a question on how the spatial structure
of a network effect the stability of spatio-temporal spike patterns, and the
speed of a spike packet propagation. By formulating the Fokker-Planck equation
for the feedforwardly coupled network with Mexican-Hat type connectivity, we
show the stability of localized spike packet and existence of Multi-stable
phase where both uniform and localized spike packets are stable depending on
the initial input structure. The Multi-stable phase enables us to show that a
spike pattern, or the information of its own, determines the propagation speed.


title: Recombination dramatically speeds up evolution of finite populations

abstract:
  We study the role of recombination, as practiced by genetically-competent
bacteria, in speeding up Darwinian evolution. This is done by adding a new
process to a previously-studied Markov model of evolution on a smooth fitness
landscape; this new process allows alleles to be exchanged with those in the
surrounding medium. Our results, both numerical and analytic, indicate that for
a wide range of intermediate population sizes, recombination dramatically
speeds up the evolutionary advance.


title: Molecular Dynamics Simulations of a Nucleosome and Free DNA

abstract:
  Nucleosomes organize the folding of DNA into chromatin and significantly
influence transcription, replication, regulation and repair. All atom molecular
dynamics simulations of a nucleosome and of its 146 basepairs of DNA free in
solution have been conducted. DNA helical parameters are extracted from each
trajectory to compare the conformation, effective force constants, persistence
length measures, and fluctuations of nucleosomal DNA to free DNA. A method for
disassembling and reconstructing the conformation and dynamics of the
nucleosome using Fourier analysis is presented. Results indicate that the
superhelical path of DNA in the nucleosome is irregular. Long length variations
in the conformation of nucleosomal DNA are identified other than those
associated with helix repeat. These variations are required to create a
proposed tetrasome conformation or to qualitatively reconstruct the 1.75 turns
of the nuclesomal superhelix. Free DNA achieves enough bend and shear in
solution to create an ideal nucleosome superhelix, but these deformations are
not organized so the conformation is essentially linear. Reconstruction of free
DNA using selected long wavelength variations in conformation can produce
either a left-handed or a right-handed superhelix. DNA is less flexible in the
nucleosome than when free in solution, however such measures are length scale
dependent.


title: High-dimensional switches and the modeling of cellular differentiation

abstract:
  Many genes have been identified as driving cellular differentiation, but
because of their complex interactions, the understanding of their collective
behaviour requires mathematical modelling. Intriguingly, it has been observed
in numerous developmental contexts, and particularly hematopoiesis, that genes
regulating differentiation are initially co-expressed in progenitors despite
their antagonism, before one is upregulated and others downregulated. We
characterise conditions under which 3 classes of generic "master regulatory
networks", modelled at the molecular level after experimentally-observed
interactions (including bHLH protein dimerisation), and including an arbitrary
number of antagonistic components, can behave as a "multi-switch", directing
differentiation in an all-or-none fashion to a specific cell-type chosen among
more than 2 possible outcomes. bHLH dimerisation networks can readily display
coexistence of many antagonistic factors when competition is low (a simple
characterisation is derived). Decision-making can be forced by a transient
increase in competition, which could correspond to some unexplained
experimental observations related to Id proteins; the speed of response varies
with the initial conditions the network is subjected to, which could explain
some aspects of cell behaviour upon reprogramming. The coexistence of
antagonistic factors at low levels, early in the differentiation process or in
pluripotent stem cells, could be an intrinsic property of the interaction
between those factors, not requiring a specific regulatory system.


title: Rydberg-London Potential for Diatomic Molecules and Unbonded Atom Pairs

abstract:
  We propose and test a pair potential that is accurate at all relevant
distances and simple enough for use in large-scale computer simulations. A
combination of the Rydberg potential from spectroscopy and the London
inverse-sixth-power energy, the proposed form fits spectroscopically determined
potentials better than the Morse, Varnshi, and Hulburt-Hirschfelder potentials
and much better than the Lennard-Jones and harmonic potentials. At long
distances, it goes smoothly to the correct London force appropriate for gases
and preserves van der Waals's "continuity of the gas and liquid states," which
is routinely violated by coefficients assigned to the Lennard-Jones 6-12 form.


title: Stimulus competition by inhibitory interference

abstract:
  When two stimuli are present in the receptive field of a V4 neuron, the
firing rate response is between the weakest and strongest response elicited by
each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience
19:1736-1753). When attention is directed towards the stimulus eliciting the
strongest response (the preferred stimulus), the response to the pair is
increased, whereas the response decreases when attention is directed to the
other stimulus (the poor stimulus). These experimental results were reproduced
in a model of a V4 neuron under the assumption that attention modulates the
activity of local interneuron networks. The V4 model neuron received
stimulus-specific asynchronous excitation from V2 and synchronous inhibitory
inputs from two local interneuron networks in V4. Each interneuron network was
driven by stimulus-specific excitatory inputs from V2 and was modulated by a
projection from the frontal eye fields. Stimulus competition was present
because of a delay in arrival time of synchronous volleys from each interneuron
network. For small delays, the firing rate was close to the rate elicited by
the preferred stimulus alone, whereas for larger delays it approached the
firing rate of the poor stimulus. When either stimulus was presented alone the
neuron's response was not altered by the change in delay. The model suggests
that top-down attention biases the competition between V2 columns for control
of V4 neurons by changing the relative timing of inhibition rather than by
changes in the degree of synchrony of interneuron networks. The mechanism
proposed here for attentional modulation of firing rate - gain modulation by
inhibitory interference - is likely to have more general applicability to
cortical information processing.


title: Neural Graphs and Category of Memory States

abstract:
  The brain as an astonishingly remarkable device has been studied from various
angles. It is now well known that neurons are the seat of all activities of the
brain function. The dynamical properties pertaining to a single neuron and a
collection of neurons may be widely different owing to the clustering
properties of a group of neurons. As it can be clearly understood theory of
complex physical systems has been more and more employed to study the behaviour
of neurons and neuronal circuits. We here mainly discuss neural correlates of
memory and cognitive functions utilizing graph theory and ideas from geometry.
It has been suggested that stochastic processes being at the helm of affairs in
the neuronal level there may exist surfaces to some extent like a hologram for
the existence of memory functions.It is also instructive to mention that
Amari's developments \cite{amari} as regards information geometry has acted as
an important inspiration. Unlike some previous analysis categorization of
memory from neural perspectives have been reconsidered at the neuronal level.
In essence the main point of discussion here has been to give an alternative
model of memory where stochastic geometry and algebraic surfaces is an
important ingredient.


title:
Prediction of site-specific amino acid distributions and limits of
  divergent evolutionary changes in protein sequences

abstract:
  We derive an analytic expression for site-specific stationary distributions
of amino acids from the Structurally Constrained Neutral (SCN) model of protein
evolution with conservation of folding stability. The stationary distributions
that we obtain have a Boltzmann-like shape, and their effective temperature
parameter, measuring the limit of divergent evolutionary changes at a given
site, can be predicted from a site-specific topological property, the principal
eigenvector of the contact matrix of the native conformation of the protein.
These analytic results, obtained without free parameters, are compared with
simulations of the SCN model and with the site-specific amino acid
distributions obtained from the Protein Data Bank. These results also provide
new insights into how the topology of a protein fold influences its
designability, i.e. the number of sequences compatible with that fold. The
dependence of the effective temperature on the principal eigenvector decreases
for longer proteins, a possible consequence of the fact that selection for
thermodynamic stability becomes weaker in this case.


title: A Unified Model of Codon Reassignment in Alternative Genetic Codes

abstract:
  Many modified genetic codes are found in specific genomes in which one or
more codons have been reassigned to a different amino acid from that in the
canonical code. We present a model that unifies four possible mechanisms for
reassignment, based on the observation that reassignment involves a gain and a
loss. The loss could be the deletion or loss of function of a tRNA or release
factor. The gain could be the gain of a new type of tRNA for the reassigned
codon, or the gain of function of an existing tRNA due to a mutation or a base
modification. In the codon disappearance mechanism, the codon disappears from
the genome during the period of reassignment. In the other mechanisms, the
codon does not disappear. In the ambiguous intermediate mechanism, the gain
precedes the loss; in the unassigned codon mechanism, the loss precedes the
gain; and in the compensatory change mechanism, the loss and gain spread
through the population simultaneously. We present simulations of the gain-loss
model and demonstrate that all four mechanisms are possible. The frequencies of
the different mechanisms are influenced by selection strengths, number of
codons undergoing reassignment, directional mutation pressure and the
possibility of selection for reduced genome size.


title: Network of tRNA Gene Sequences

abstract:
  We showed in this paper that similarity network can be used as an powerful
tools to study the relationship of tRNA genes. We constructed a network of 3719
tRNA gene sequences using simplest alignment and studied its topology, degree
distribution and clustering coefficient. It is found that the behavior of the
network shift from fluctuated distribution to scale-free distribution when the
similarity degree of the tRNA gene sequences increase. tRNA gene sequences with
the same anticodon identity are more self-organized than the tRNA gene
sequences with different anticodon identities and form local clusters in the
network. An interesting finding in our studied is some vertices of the local
cluster have a high connection with other local clusters, the probable reason
is given. Moreover, a network constructed by the same number of random tRNA
sequences is used to make comparisons. The relationships between properties of
the tRNA similarity network and the characters of tRNA evolutionary history are
discussed.


title:
Offdiagonal Complexity: A computationally quick complexity measure for
  graphs and networks

abstract:
  A vast variety of biological, social, and economical networks shows
topologies drastically differing from random graphs; yet the quantitative
characterization remains unsatisfactory from a conceptual point of view.
Motivated from the discussion of small scale-free networks, a biased link
distribution entropy is defined, which takes an extremum for a power law
distribution. This approach is extended to the node-node link
cross-distribution, whose nondiagonal elements characterize the graph structure
beyond link distribution, cluster coefficient and average path length. From
here a simple (and computationally cheap) complexity measure can be defined.
This Offdiagonal Complexity (OdC) is proposed as a novel measure to
characterize the complexity of an undirected graph, or network. While both for
regular lattices and fully connected networks OdC is zero, it takes a
moderately low value for a random graph and shows high values for apparently
complex structures as scale-free networks and hierarchical trees. The
Offdiagonal Complexity apporach is applied to the Helicobacter pylori protein
interaction network and randomly rewired surrogates.


title: Two Major Paths of Gene-Duplicates Evolution

abstract:
  Evolving genomes increase a number of their genes by gene duplications. To
escape degradation in a functionless pseudogene, any gene duplicate needs to be
guarded by negative (purifying) selection from otherwise inevitable fixation of
degenerative mutations. In the present study we focus on the evolutionary stage
at which new duplicates come under such surveillance.
  Our analyses of several genomes indicate that in about 10% gene pairs,
selection begins to guard a new gene copy very soon after a duplication event
whereas the vast majority (90%) of extra genes remain redundant and
unrecognised by selection. Such duplicates accumulate all mutations (including
degenerative) in neutral fashion and are actually destined to become
pseudogenes. We revealed this "two-stream" evolutionary pattern by the analysis
of mutations in 2nd versus 3rd codon positions but not by the routinely used
ratio of amino acid replacements (R) versus silent substitutions (S), i.e. the
'2nd vs. 3rd' metric proved to be more resolving than the traditional 'R vs. S'
one for distinguishing neutrally evolving future pseudogenes from their
functional counterparts controlled by negative selection.
  In gene databases for large genomes, hundreds of future pseudogenes are
annotated as functional genes because they do look like intact and valuable by
standard criteria, including even active transcription and translation.
Apparently, these "pseudogenes-to-be" over-cloud and mimic those very
infrequent gene duplicates with increased sequence evolution rates driven by
positive selection.


title:
Multiple, weak hits confuse complex systems: A transcriptional
  regulatory network as an example

abstract:
  Robust systems, like the molecular networks of living cells are often
resistant to single hits such as those caused by high-specificity drugs. Here
we show that partial weakening of the Escherichia coli and Saccharomyces
cerevisiae transcriptional regulatory networks at a small number (3-5) selected
nodes can have a greater impact than the complete elimination of a single
selected node. In both cases, the targeted nodes have the greatest possible
impact; still the results suggest that in some cases broad specificity
compounds or multitarget drug therapies may be more effective than individual
high-affinity, high-specificity ones. Multiple but partial attacks mimic well a
number of in vivo scenarios and may be useful in the efficient modification of
other complex systems.


title: Inter-species regression analysis

abstract:
  When conducting inter-species regression analyses, the phylogenetic
relationships between the individual species need to be taken into account. In
this paper, a procedure for conducting such analyses is proposed, which only
requires the use of a measure of relationship between pairs of species, rather
than a complete phylogeny, and which at the same time assesses the importance
to be attached to the relationships with regard to the conclusions reached. The
procedure is applied to previous data, relating testis size to mean hind tibia
length, duct length and spermathecal area in 15 species of Scathophagidae.


title:
Specific and non specific hybridization of oligonucleotide probes on
  microarrays

abstract:
  Gene expression analysis by means of microarrays is based on the sequence
specific binding of mRNA to DNA oligonucleotide probes and its measurement
using fluorescent labels. The binding of RNA fragments involving other
sequences than the intended target is problematic because it adds a "chemical
background" to the signal, which is not related to the expression degree of the
target gene. The paper presents a molecular signature of specific and non
specific hybridization with potential consequences for gene expression
analysis. We analyzed the signal intensities of perfect match (PM) and mismatch
(MM) probes of GeneChip microarrays to specify the effect of specific and non
specific hybridization. We found that these events give rise to different
relations between the PM and MM intensities as function of the middle base of
the PMs, namely a triplet- (C>G=T>A>0) and a duplet-like (C=T>0>G=A) pattern of
the PM-MM log-intensity difference upon binding of specific and non specific
RNA fragments, respectively. The systematic behaviour of the intensity
difference can be rationalized on the level of base pairings of DNA/RNA
oligonucleotide duplexes in the middle of the probe sequence. Non-specific
binding is characterized by the reversal of the central Watson Crick (WC)
pairing for each PM/MM probe pair, whereas specific binding refers to the
combination of a WC and a self complementary (SC) pairing in PM and MM probes,
respectively. The intensity of complementary MM introduces a systematic source
of variation which decreases the precision of expression measures based on the
MM intensities.


title:
Effect of Synchronous Incoming Spikes on Activity Pattern in A Network
  of Spiking Neurons

abstract:
  Although recent neurophysiological experiments suggest that synchronous
neural activity is involved in some perceptual and cognitive processes, the
functional role of such coherent neuronal behavior is not well understood. As a
first step in clarifying this role, we investigate how the temporal coherence
of certain neuronal activity affects the activity pattern in a neural network.
Using a simple network of leaky integrate-and-fire neurons, we study the
effects of synchronized incoming spikes on the functioning of two mechanisms
typically used in model neural systems, winner-take-all competition and
associative memory. We demonstrate that a pair of switches undergone by the
incoming spikes, from asynchronous to synchronous and then back to
asynchronous, triggers a transition of the network from one state to another
state. In the case of associative memory, for example, this switching controls
the timing of the next recalling, whereas the firing rate pattern in the
asynchronous state prepares the network for the next retrieval pattern.


title: An explanatory model for food-web structure and evolution

abstract:
  Food webs are networks describing who is eating whom in an ecological
community. By now it is clear that many aspects of food-web structure are
reproducible across diverse habitats, yet little is known about the driving
force behind this structure. Evolutionary and population dynamical mechanisms
have been considered. We propose a model for the evolutionary dynamics of
food-web topology and show that it accurately reproduces observed food-web
characteristic in the steady state. It is based on the observation that most
consumers are larger than their resource species and the hypothesis that
speciation and extinction rates decrease with increasing body mass. Results
give strong support to the evolutionary hypothesis.


title: Lattice tube model of proteins

abstract:
  We present a new lattice model for proteins that incorporates a tube-like
anisotropy by introducing a preference for mutually parallel alignments in the
conformations. The model is demonstrated to capture many aspects of real
proteins.


title: Electrostatics and the Assembly of an RNA Virus

abstract:
  Electrostatic interactions play a central role in the assembly of
single-stranded RNA viruses. Under physiological conditions of salinity and
acidity, virus capsid assembly requires the presence of genomic material that
is oppositely charged to the core proteins. In this paper we apply basic
polymer physics and statistical mechanics methods to the self-assembly of a
synthetic virus encapsidating generic polyelectrolyte molecules. We find that
(i) the mean concentration of the encapsidated polyelectrolyte material depends
on the surface charge density, the radius of the capsid, and the linear charge
density of the polymer but neither on the salt concentration or the Kuhn
length, (ii) the total charge of the capsid interior is equal but opposite to
that of the empty capsid, a form of charge reversal. Unlike natural viruses,
synthetic viruses are predicted not to be under an osmotic swelling pressure.
The design condition that self-assembly only produces filled capsids is shown
to coincide with the condition that the capsid surface charge exceeds the
desorption threshold of polymer surface adsorption. We compare our results with
studies on the self-assembly of both synthetic and natural viruses.


title:
Four basic symmetry types in the universal 7-cluster structure of 143
  complete bacterial genomic sequences

abstract:
  Coding information is the main source of heterogeneity (non-randomness) in
the sequences of bacterial genomes. This information can be naturally modeled
by analysing cluster structures in the "in-phase" triplet distributions of
relatively short genomic fragments (200-400bp). We found a universal 7-cluster
structure in bacterial genomic sequences and explained its properties. We show
that codon usage of bacterial genomes is a multi-linear function of their
genomic G+C-content with high accuracy. Based on the analysis of 143 completely
sequenced bacterial genomes available in Genbank in August 2004, we show that
there are four "pure" types of the 7-cluster structure observed. All 143
cluster animated 3D-scatters are collected in a database and is made available
on our web-site: http://www.ihes.fr/~zinovyev/7clusters The finding can be
readily introduced into any software for gene prediction, sequence alignment or
bacterial genomes classification.


title: In vivo Cancer Marker in Mice

abstract:
  This work introduces an in vivo marker in mice from the altitude of the well,
determined by this cancer.The duration of development and the altitude of the
cancer well are obtained from the synchronization of the mechanics and the
kinetics of the processes of normal and anomalous transport under tumor mass
growth.The mechanics and the kinetics of these two transports are found from
the cancer fugacity.The cancer fugacity is defined as a photograph of the rate
of the scaling exponent under tumor mass growth.


title: Advantages of Hopping on a Zig-zag Course

abstract:
  We investigate self-moving particles which prefer to hop with a certain
turning angle equally distributed to the right or left. We assume this turning
angle distribution to be given by a double Gaussian distribution. Based on the
model of Active Brownian particles and we calculate the diffusion coefficient
in dependence on the mean and the dispersion of the turning angles. It is shown
that bounded distribution of food in patches will be optimally consumed by the
objects if they hop preferably with a given angle and not straight forwardly.


title:
On The Reconstruction of Interaction Networks with Applications to
  Transcriptional Regulation

abstract:
  A novel information-theoretic method for reconstruction of interaction
networks is introduced. We prove that the method is exact for some class of
networks. Performance tests on large synthetic transcriptional regulatory
networks produce very encouraging results.


title:
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks
  in a Mammalian Cellular Context

abstract:
  Background: Elucidating gene regulatory networks is crucial for understanding
normal cell physiology and complex pathologic phenotypes. Existing
computational methods for the genome-wide ``reverse engineering'' of such
networks have been successful only for lower eukaryotes with simple genomes.
Here we present ARACNE, a novel algorithm, using microarray expression
profiles, specifically designed to scale up to the complexity of regulatory
networks in mammalian cells, yet general enough to address a wider range of
network deconvolution problems. This method uses an information theoretic
approach to eliminate the majority of indirect interactions inferred by
co-expression methods.
  Results: We prove that ARACNE reconstructs the network exactly
(asymptotically) if the effect of loops in the network topology is negligible,
and we show that the algorithm works well in practice, even in the presence of
numerous loops and complex topologies. We assess ARACNE's ability to
reconstruct transcriptional regulatory networks using both a realistic
synthetic dataset and a microarray dataset from human B cells. On synthetic
datasets ARACNE achieves very low error rates and outperforms established
methods, such as Relevance Networks and Bayesian Networks. Application to the
deconvolution of genetic networks in human B cells demonstrates ARACNE's
ability to infer validated transcriptional targets of the c MYC proto-oncogene.
We also study the effects of mis estimation of mutual information on network
reconstruction, and show that algorithms based on mutual information ranking
are more resilient to estimation errors.


title:
Folding is Not Required for Bilayer Insertion: Replica Exchange
  Simulations of an a-Helical Peptide with an Explicit Lipid Bilayer

abstract:
  We implement the replica exchange molecular dynamics algorithm to study the
interactions of a model peptide (WALP-16) with an explicitly represented DPPC
membrane bilayer. We observe the spontaneous, unbiased insertion of WALP-16
into the DPPC bilayer and its folding into an a-helix with a trans-bilayer
orientation. We observe that the insertion of the peptide into the DPPC bilayer
precedes secondary structure formation. Although the peptide has some
propensity to form a partially helical structure in the interfacial region of
the DPPC/water system, this state is not a productive intermediate but rather
an off-pathway trap for WALP-16 insertion. Equilibrium simulations show that
the observed insertion/folding pathway mirrors the potential of mean force
(PMF). Calculation of the enthalpic and entropic contributions to this PMF show
that the surface bound conformation of WALP-16 is significantly lower in energy
than other conformations, and that the insertion of WALP-16 into the bilayer
without regular secondary structure is enthalpically unfavorable by 5-10
kcal/mol/residue. The observed insertion/folding pathway disagrees with the
dominant conceptual model, which is that a surface bound helix is an obligatory
intermediate for the insertion of a-helical peptides into lipid bilayers. In
our simulations, the observed insertion/folding pathway is favored because of a
large (> 100 kcal/mol) increase in system entropy that occurs when the
unstructured WALP-16 peptide enters the lipid bilayer interior. The
insertion/folding pathway that is lowest in free energy depends sensitively on
the near cancellation of large enthalpic and entropic terms. This suggests that
intrinsic membrane peptides may have a diversity of insertion/folding behaviors
depending on the exact system of peptide and lipid under consideration.


title: Solvable senescence model with positive mutations

abstract:
  We build upon our previous analytical results for the Penna model of
senescence to include positive mutations. We investigate whether a small but
non-zero positive mutation rate gives qualitatively different results to the
traditional Penna model in which no positive mutations are considered. We find
that the high-lifespan tail of the distribution is radically changed in
structure, but that there is not much effect on the bulk of the population. Th
e mortality plateau that we found previously for a stochastic generalization of
the Penna model is stable to a small positive mutation rate.


title:
Conditional Network Analysis Identifies Candidate Regulator Genes in
  Human B Cells

abstract:
  Cellular phenotypes are determined by the dynamical activity of networks of
co-regulated genes. Elucidating such networks is crucial for the understanding
of normal cell physiology as well as for the dissection of complex pathologic
phenotypes. Existing methods for such "reverse engineering" of genetic networks
from microarray expression data have been successful only in prokaryotes (E.
coli) and lower eukaryotes (S. cerevisiae) with relatively simple genomes.
Additionally, they have mostly attempted to reconstruct average properties
about the network connectivity without capturing the highly conditional nature
of the interactions. In this paper we extend the ARACNE algorithm, which we
recently introduced and successfully applied to the reconstruction of
whole-genome transcriptional networks from mammalian cells, precisely to link
the existence of specific network structures to the expression or lack thereof
of specific regulator genes. This is accomplished by analyzing thousands of
alternative network topologies generated by constraining the data set on the
presence or absence of putative regulator genes. By considering interactions
that are consistently supported across several such constraints, we identify
many transcriptional interactions that would not have been detectable by the
original method. By selecting genes that produce statistically significant
changes in network topology, we identify novel candidate regulator genes.
Further analysis shows that transcription factors, kinases, phosphatases, and
other gene families known to effect biochemical interactions, are significantly
overrepresented among the set of candidate regulator genes identified in
silico, indirectly supporting the validity of the approach.


title: Enumeration of RNA structures by Matrix Models

abstract:
  We enumerate the number of RNA contact structures according to their genus,
i.e. the topological character of their pseudoknots. By using a recently
proposed matrix model formulation for the RNA folding problem, we obtain exact
results for the simple case of an RNA molecule with an infinitely flexible
backbone, in which any arbitrary pair of bases is allowed. We analyze the
distribution of the genus of pseudoknots as a function of the total number of
nucleotides along the phosphate-sugar backbone.


title:
Adsorption models of hybridization and post-hybridisation behaviour on
  oligonucleotide microarrays

abstract:
  Analysis of data from an Affymetrix Latin Square spike-in experiment
indicates that measured fluorescence intensities of features on an
oligonucleotide microarray are related to spike-in RNA target concentrations
via a hyperbolic response function, generally identified as a Langmuir
adsorption isotherm. Furthermore the asymptotic signal at high spike-in
concentrations is almost invariably lower for a mismatch feature than for its
partner perfect match feature. We survey a number of theoretical adsorption
models of hybridization at the microarray surface and find that in general they
are unable to explain the differing saturation responses of perfect and
mismatch features. On the other hand, we find that a simple and consistent
explanation can be found in a model in which equilibrium hybridization followed
by partial dissociation of duplexes during the post-hybridization washing
phase.


title:
Lethality and synthetic lethality in the genome-wide metabolic network
  of Escherichia coli

abstract:
  Recent genomic analyses on the cellular metabolic network show that reaction
flux across enzymes are diverse and exhibit power-law behavior in its
distribution. While one may guess that the reactions with larger fluxes are
more likely to be lethal under the blockade of its catalyzing gene products or
gene knockouts, we find, by in silico flux analysis, that the lethality rarely
has correlations with the flux level owing to the widespread backup pathways
innate in the genome-wide metabolism of \textit{Escherichia coli}. Lethal
reactions, of which the deletion generates cascading failure of following
reactions up to the biomass reaction, are identified in terms of the Boolean
network scheme as well as the flux balance analysis. The avalanche size of a
reaction, defined as the number of subsequently blocked reactions after its
removal, turns out to be a useful measure of lethality. As a means to elucidate
phenotypic robustness to a single deletion, we investigate synthetic lethality
in reaction level, where simultaneous deletion of a pair of nonlethal reactions
leads to the failure of the biomass reaction. Synthetic lethals identified via
flux balance and Boolean scheme are consistently shown to act in parallel
pathways, working in such a way that the backup machinery is compromised.


title: Analytical solution of a generalized Penna model

abstract:
  In 1995 T.J.Penna introduced a simple model of biological aging. A modified
Penna model has been demonstrated to exhibit behaviour of real-life systems
including catastrophic senescence in salmon and a mortality plateau at advanced
ages. We present a general steady-state, analytic solution to the Penna model,
able to deal with arbitrary birth and survivability functions. This solution is
employed to solve standard variant Penna models studied by simulation.
Different Verhulst factors regulating both the birth rate and external death
rate are considered.


title: Population Dynamics in the Penna Model

abstract:
  We build upon the recent steady-state Penna model solution, Phys.Rev.Lett.
89, 288103 (2002), to study the population dynamics within the Penna model. We
show, that any perturbation to the population can be broken into a collection
of modes each of which decay exponentially with its respective time constant.
The long time behaviour of population is therefore likely to be dominated by
the modes with the largest time constants. We confirm our analytical approach
with simulation data.


title:
Growth-Driven Percolations: The Dynamics of Community Formation in
  Neuronal Systems

abstract:
  The quintessential property of neuronal systems is their intensive patterns
of selective synaptic connections. The current work describes a physics-based
approach to neuronal shape modeling and synthesis and its consideration for the
simulation of neuronal development and the formation of neuronal communities.
Starting from images of real neurons, geometrical measurements are obtained and
used to construct probabilistic models which can be subsequently sampled in
order to produce morphologically realistic neuronal cells. Such cells are
progressively grown while monitoring their connections along time, which are
analysed in terms of percolation concepts. However, unlike traditional
percolation, the critical point is verified along the growth stages, not the
density of cells, which remains constant throughout the neuronal growth
dynamics. It is shown, through simulations, that growing beta cells tend to
reach percolation sooner than the alpha counterparts with the same diameter.
Also, the percolation becomes more abrupt for higher densities of cells, being
markedly sharper for the beta cells.


title:
Stretching single RNAs: exact numerical and stochastic simulation
  methods

abstract:
  Exact numerical methods and stochastic simulation methods are developed to
study the force stretching single RNA issue on the secondary structure level in
equilibrium. By computing the force-extension curves on the constant force and
the constant extension ensembles, we find the two independent methods agree
with each other quite well. To show the precision of our methods in predicting
unfolding experiments, the unfolding forces of different RNA molecules under
different experimental conditions are calculated. We find that the ionic
corrections on the RNA free energies alone might not account for the apparent
differences between the theoretical calculations and the experimental data; an
ionic correction to the persistent length of single-stranded RNA should be
necessary.


title:
Thermodynamics of RNA/DNA hybridization in high density oligonucleotide
  microarrays

abstract:
  We analyze a series of publicly available controlled experiments (Latin
square) on Affymetrix high density oligonucleotide microarrays using a simple
physical model of the hybridization process. We plot for each gene the signal
intensity versus the hybridization free energy of RNA/DNA duplexes in solution,
for perfect matching and mismatching probes. Both values tend to align on a
single master curve in good agreement with Langmuir adsorption theory, provided
one takes into account the decrease of the effective target concentration due
to target-target hybridization in solution. We give an example of a deviation
from the expected thermodynamical behavior for the probe set 1091\_at due to
annotation problems, i.e. the surface-bound probe is not the exact complement
of the target RNA sequence, because of errors present in public databases at
the time when the array was designed. We show that the parametrization of the
experimental data with RNA/DNA free energy improves the quality of the fits and
enhances the stability of the fitting parameters compared to previous studies.


title: Graded and Binary Responses in Stochastic Gene Expression

abstract:
  Recently, several theoretical and experimental studies have been undertaken
to probe the effect of stochasticity on gene expression (GE). In experiments,
the GE response to an inducing signal in a cell, measured by the amount of
mRNAs/proteins synthesized, is found to be either graded or binary. The latter
type of response gives rise to a bimodal distribution in protein levels in an
ensemble of cells. One possible origin of binary response is cellular
bistability achieved through positive feedback or autoregulation. In this
paper, we study a simple, stochastic model of GE and show that the origin of
binary response lies exclusively in stochasticity. The transitions between the
active and inactive states of the gene are random in nature. Graded and binary
responses occur in the model depending on the relative stability of the
activated and deactivated gene states with respect to that of
mRNAs/proteins.The theoretical results on binary response provide a good
description of the ``all-or-none'' phenomenon observed in an eukaryotic system.


title: Noise Characteristics of Feed Forward Loops

abstract:
  A prominent feature of gene transcription regulatory networks is the presence
in large numbers of motifs, i.e, patterns of interconnection, in the networks.
One such motif is the feed forward loop (FFL) consisting of three genes X, Y
and Z. The protein product of x of X controls the synthesis of protein product
y of Y. Proteins x and y jointly regulate the synthesis of z proteins from the
gene Z. The FFLs, depending on the nature of the regulating interactions, can
be of eight different types which can again be classified into two categories:
coherent and incoherent. In this paper, we study the noise characteristics of
FFLs using the Langevin formalism and the Monte Carlo simulation technique
based on the Gillespie algorithm. We calculate the variances around the mean
protein levels in the steady states of the FFLs and find that, in the case of
coherent FFLs, the most abundant FFL, namely, the Type-1 coherent FFL, is the
least noisy. This is however not so in the case of incoherent FFLs. The results
suggest possible relationships between noise, functionality and abundance.

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


title:
Hybridization Isotherms of DNA Microarrays and the Quantification of
  Mutation Studies

abstract:
  Background: Diagnostic DNA arrays for detection of point mutations as markers
for cancer usually function in the presence of a large excess of wild type DNA.
This excess can give rise to false positives due to competitive hybridization
of the wild type target at the mutation spot. The analysis of the DNA array
data is typically qualitative aiming to establish the presence or absence of a
particular point mutation. Our theoretical approach yields methods for
quantifying the analysis so as to obtain the ratio of concentrations of mutated
and wild type DNA. Method: The theory is formulated in terms of the
hybridization isotherms relating the hybridization fraction at the spot to the
composition of the sample solutions at thermodynamic equilibrium. It focuses on
samples containing an excess of single stranded DNA and on DNA arrays with low
surface density of probes. The hybridization equilibrium constants can be
obtained by the nearest neighbor method. Results: Two approaches allow us to
obtain quantitative results from the DNA array data. In one the signal of the
mutation spot is compared with that of the wild type spot. The implementation
requires knowledge of the saturation intensity of the two spots. The second
approach requires comparison of the intensity of the mutation spot at two
different temperatures. In this case knowledge of the saturation signal is not
always necessary. Conclusions: DNA arrays can be used to obtain quantitative
results on the concentration ratio of mutated DNA to wild type DNA in studies
of somatic point mutations.


title:
An Unusual 500,000 Bases Long Oscillation of Guanine and Cytosine
  Content in Human Chromosome 21

abstract:
  An oscillation with a period of around 500 kb in guanine and cytosine content
(GC%) is observed in the DNA sequence of human chromosome 21. This oscillation
is localized in the rightmost one-eighth region of the chromosome, from 43.5 Mb
to 46.5 Mb. Five cycles of oscillation are observed in this region with six
GC-rich peaks and five GC-poor valleys. The GC-poor valleys comprise regions
with low density of CpG islands and, alternating between the two DNA strands,
low gene density regions. Consequently, the long-range oscillation of GC%
result in spacing patterns of both CpG island density, and to a lesser extent,
gene densities.


title:
Universal 1/f noise, cross-overs of scaling exponents, and chromosome
  specific patterns of GC content in DNA sequences of the human genome

abstract:
  Spatial fluctuations of guanine and cytosine base content (GC%) are studied
by spectral analysis for the complete set of human genomic DNA sequences. We
find that (i) the 1/f^alpha decay is universally observed in the power spectra
of all twenty-four chromosomes, and that (ii) the exponent alpha \approx 1
extends to about 10^7 bases, one order of magnitude longer than what has
previously been observed. We further find that (iii) almost all human
chromosomes exhibit a cross-over from alpha_1 \approx 1 (1/f^alpha_1) at lower
frequency to alpha_2 < 1 (1/f^alpha_2) at higher frequency, typically occurring
at around 30,000--100,000 bases, while (iv) the cross-over in this frequency
range is virtually absent in human chromosome 22. In addition to the universal
1/f^alpha noise in power spectra, we find (v) several lines of evidence for
chromosome-specific correlation structures, including a 500,000 bases long
oscillation in human chromosome 21. The universal 1/f^alpha spectrum in human
genome is further substantiated by a resistance to variance reduction in
guanine and cytosine content when the window size is increased.


title:
Spectral Analysis of Guanine and Cytosine Fluctuations of Mouse Genomic
  DNA

abstract:
  We study global fluctuations of the guanine and cytosine base content (GC%)
in mouse genomic DNA using spectral analyses. Power spectra S(f) of GC%
fluctuations in all nineteen autosomal and two sex chromosomes are observed to
have the universal functional form S(f) \sim 1/f^alpha (alpha \approx 1) over
several orders of magnitude in the frequency range 10^-7< f < 10^-5 cycle/base,
corresponding to long-ranging GC% correlations at distances between 100 kb and
10 Mb. S(f) for higher frequencies (f > 10^-5 cycle/base) shows a flattened
power-law function with alpha < 1 across all twenty-one chromosomes. The
substitution of about 38% interspersed repeats does not affect the functional
form of S(f), indicating that these are not predominantly responsible for the
long-ranged multi-scale GC% fluctuations in mammalian genomes. Several
biological implications of the large-scale GC% fluctuation are discussed,
including neutral evolutionary history by DNA duplication, chromosomal bands,
spatial distribution of transcription units (genes), replication timing, and
recombination hot spots.


title:
Model for the robust establishment of precise proportions in the early
  Drosophila embryo

abstract:
  During embryonic development, a spatial pattern is formed in which
proportions are established precisely. As an early pattern formation step in
Drosophila embryos, an anterior-posterior gradient of Bicoid (Bcd) induces
hunchback (hb) expression (Driever et al. 1989; Tautz et al. 1988). In contrast
to the Bcd gradient, the Hb profile includes information about the scale of the
embryo. Furthermore, the resulting hb expression pattern shows a much lower
embryo-to-embryo variability than the Bcd gradient (Houchmandzadeh et al.
2002). An additional graded posterior repressing activity could theoretically
account for the observed scaling. However, we show that such a model cannot
produce the observed precision in the Hb boundary, such that a fundamentally
different mechanism must be at work. We describe and simulate a model that can
account for the observed precise generation of the scaled Hb profile in a
highly robust manner. The proposed mechanism includes Staufen (Stau), an RNA
binding protein that appears essential to precision scaling (Houchmandzadeh et
al. 2002). In the model, Stau is released from both ends of the embryo and
relocalises hb RNA by increasing its mobility. This leads to an effective
transport of hb away from the respective Stau sources. The balance between
these opposing effects then gives rise to scaling and precision. Considering
the biological importance of robust precision scaling and the simplicity of the
model, the same principle may be employed more often during development.


title: A simple derivation of the Gompertz law for human mortality

abstract:
  The Gompertz law of dependence of human mortality rate on age is derived from
a simple model of death as a result of the exponentially rare escape of
abnormal cells from immunological response.

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


title: A Field Approach to 3D Gene Expression Pattern Characterization

abstract:
  We present a vector field method for obtaining the spatial organization of 3D
patterns of gene expression based on gradients and lines of force obtained by
numerical integration. The convergence of these lines of force in local maxima
are centers of gene expression, providing a natural and powerful framework to
characterize the organization and dynamics of biological structures. We apply
this novel methodology to analyze the expression pattern of the Enhanced Green
Fluorescent Protein (EGFP) driven by the promoter of light chain myosin II
during zebrafish heart formation.

categories: q-bio.GN cond-mat.stat-mech physics.bio-ph q-bio.TO


title:
Combinatorial complexity and dynamical restriction of network flows in
  signal transduction

abstract:
  The activities and interactions of proteins that govern the cellular response
to a signal generate a multitude of protein phosphorylation states and
heterogeneous protein complexes. Here, using a computational model that
accounts for 307 molecular species implied by specified interactions of four
proteins involved in signalling by the immunoreceptor Fc$\epsilon$RI, we
determine the relative importance of molecular species that can be generated
during signalling, chemical transitions among these species, and reaction paths
that lead to activation of the protein tyrosine kinase (PTK) Syk. By all of
these measures and over 2- and 10-fold ranges of model parameters--rate
constants and initial concentrations--only a small portion of the biochemical
network is active. The spectrum of active complexes, however, can be shifted
dramatically, even by a change in the concentration of a single protein, which
suggests that the network can produce qualitatively different responses under
different cellular conditions and in response to different inputs. Reduced
models that reproduce predictions of the full model for a particular set of
parameters lose their predictive capacity when parameters are varied over
2-fold ranges.


title: Mechanical properties of the domains of titin in a Go-like model

abstract:
  Comparison of properties of three domains of titin, I1, I27 and I28, in a
simple geometry-based model shows that despite a high structural homology
between their native states different domains show similar but distinguishable
mechanical properties. Folding properties of the separate domains are predicted
to be diversified which reflects sensitivity of the kinetics to the details of
native structures. The Go-like model corresponding to the experimentally
resolved native structure of the I1 domain is found to provide the biggest
thermodynamic and mechanical stability compared to the other domains studied
here. We analyze elastic, thermodynamic and kinetic properties of several
structures corresponding to the I28 domain as obtained through homology-based
modeling. We discuss the ability of the models of the I28 domain to reproduce
experimental results qualitatively. A strengthening of contacts that involve
hydrophobic amino acids does not affect theoretical comparisons of the domains.
Tandem linkages of up to five identical or different domains unravel in a
serial fashion at low temperatures. We study the nature of the intermediate
state that arises in the early stages of the serial unraveling and find it to
qualitatively agree with the results of Marszalek et al.


title: Model for processive movement of dynein

abstract:
  A model for the processive movement of dynein is presented based on
experimental observations available. In the model, the change from strong
microtubule-binding to weak binding of dynein is determined naturally by the
variation of the relative orientation between the two interacting surfaces of
the stalk tip and the microtubule as the stalk rotates from the ADP.Vi-state
orientation to the apo-state orientation. This means that the puzzling
communication from the ATP binding site in the globular head to the MT-binding
site in the tip of the stalk, which is prerequisite in the conventional model,
is not required. Using the present model, the previous experimental results,
such as (i) the step size of a dynein being an integer times of the period of
the MT lattice, (ii) the dependence of the step size on load, i.e., the step
size decreasing with the increase of load, and (iii) the stall force being
proportional to [ATP] at low [ATP] and becoming saturated at high [ATP], are
well explained.


title: Gene splice sites correlate with nucleosome positions

abstract:
  Gene sequences in the vicinity of splice sites are found to possess
dinucleotide periodicities, especially RR and YY, with the period close to the
pitch of nucleosome DNA. This confirms previously reported finding about
preferential positioning of splice junctions within the nucleosomes. The RR and
YY dinucleotides oscillate counterphase, i.e., their respective preferred
positions are shifted about half-period one from another, as it was observed
earlier for AA and TT dinucleotides. Species specificity of nucleosome
positioning DNA pattern is indicated by predominant use of the periodical
GG(CC) dinucleotides in human and mouse genes, as opposed to predominant AA(TT)
dinucleotides in Arabidopsis and C.elegans.
  Keywords: chromatin; gene splicing; intron; exon; dinucleotide; periodical
pattern


title:
A Phenomenological model of Myosin II dynamics in the presence of
  external loads

abstract:
  We address the controversial hot question concerning the validity of the
loose coupling versus the lever-arm theories in the actomyosin dynamics by
re-interpreting and extending the phenomenological washboard potential model
proposed by some of us in a previous paper. In this new model a Brownian motion
harnessing thermal energy is assumed to co-exist with the deterministic swing
of the lever-arm, to yield an excellent fit of the set of data obtained by some
of us on the sliding of Myosin II heads on immobilized actin filaments under
various load conditions. Our theoretical arguments are complemented by accurate
numerical simulations, and the robustness of the model is tested via different
choices of parameters and potential profiles.


title: Geometry of Axoneme-like Filament Bundles

abstract:
  We develop a formalism that describes the bending and twisting of
axoneme-like filament bundles. We obtain general formulas to determine the
relative sliding between any arbitrary filaments in a bundle subjected to
unconstrained deformations. Particular examples for bending, twisting, helical
and toroidal shapes, and combinations of these are discussed. Resulting
equations for sliding and transversal shifting, expressed in terms of the
curvature and torsion of the bundle, are applied to flagellar bend data. We
prove that simultaneous combination of twisting and bending can produce a
drastically drop in the sliding, by decreasing bending rigidity.

categories: q-bio.CB math-ph math.MP nlin.PS physics.bio-ph q-bio.QM


title: DNA looping and physical constraints on transcription regulation

abstract:
  DNA looping participates in transcriptional regulation, for instance, by
allowing distal binding sites to act synergistically. Here we study this
process and compare different regulatory mechanisms based on repression with
and without looping. Within a simple mathematical model for the lac operon, we
show that regulation based on DNA looping, in addition to increasing the
repression level, can reduce the fluctuations of transcription and, at the same
time, decrease the sensitivity to changes in the number of regulatory proteins.
Looping is thus able to circumvent some of the constraints inherent to
mechanisms based solely on binding to a single operator site and provides a
mechanism to regulate not only the average properties of transcription but also
its fluctuations.

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


title: Predicting Genetic Regulatory Response Using Classification

abstract:
  We present a novel classification-based method for learning to predict gene
regulatory response. Our approach is motivated by the hypothesis that in simple
organisms such as Saccharomyces cerevisiae, we can learn a decision rule for
predicting whether a gene is up- or down-regulated in a particular experiment
based on (1) the presence of binding site subsequences (``motifs'') in the
gene's regulatory region and (2) the expression levels of regulators such as
transcription factors in the experiment (``parents''). Thus our learning task
integrates two qualitatively different data sources: genome-wide cDNA
microarray data across multiple perturbation and mutant experiments along with
motif profile data from regulatory sequences. We convert the regression task of
predicting real-valued gene expression measurement to a classification task of
predicting +1 and -1 labels, corresponding to up- and down-regulation beyond
the levels of biological and measurement noise in microarray measurements. The
learning algorithm employed is boosting with a margin-based generalization of
decision trees, alternating decision trees. This large-margin classifier is
sufficiently flexible to allow complex logical functions, yet sufficiently
simple to give insight into the combinatorial mechanisms of gene regulation. We
observe encouraging prediction accuracy on experiments based on the Gasch S.
cerevisiae dataset, and we show that we can accurately predict up- and
down-regulation on held-out experiments. Our method thus provides predictive
hypotheses, suggests biological experiments, and provides interpretable insight
into the structure of genetic regulatory networks.


title:
Neurokinematic Modeling of Complex Swimming Patterns of the Larval
  Zebrafish

abstract:
  Larval zebrafish exhibit a variety of complex undulatory swimming patterns.
This repertoire is controlled by the 300 neurons projecting from brain into
spinal cord. Understanding how descending control signals shape the output of
spinal circuits, however, is nontrivial. We have therefore developed a
segmental oscillator model (using NEURON) to investigate this system. We found
that adjusting the strength of NMDA and glycinergic synapses enabled the
generation of oscillation (tail-beat) frequencies over the range exhibited in
different larval swim patterns. In addition, we developed a kinematic model to
visualize the more complex axial bending patterns used during prey capture.


title: Statistical Mechanics Characterization of Neuronal Mosaics

abstract:
  The spatial distribution of neuronal cells is an important requirement for
achieving proper neuronal function in several parts of the nervous system of
most animals. For instance, specific distribution of photoreceptors and related
neuronal cells, particularly the ganglion cells, in mammal's retina is required
in order to properly sample the projected scene. This work presents how two
concepts from the areas of statistical mechanics and complex systems, namely
the \emph{lacunarity} and the \emph{multiscale entropy} (i.e. the entropy
calculated over progressively diffused representations of the cell mosaic),
have allowed effective characterization of the spatial distribution of retinal
cells.

categories: q-bio.NC cond-mat.dis-nn cs.CV physics.bio-ph q-bio.QM


title: Abstract DNA-type systems

abstract:
  An abstract DNA-type system is defined by a set of nonlinear kinetic
equations with polynomial nonlinearities that admit soliton solutions
associated with helical geometry. The set of equations allows for two different
Lax representations: A von Neumann form and a Darboux-covariant Lax pair. We
explain why non-Kolmogorovian probability models occurring in soliton kinetics
are naturally associated with chemical reactions. The most general known
characterization of soliton kinetic equations is given and a class of explicit
soliton solutions is discussed. Switching between open and closed states is a
generic behaviour of the helices. The effect does not crucially depend on the
order of nonlinearity (i.e. types of reactions), a fact that may explain why
simplified models possess properties occuring in realistic systems. We explain
also why fluctuations based on Darboux transformations will not destroy the
dynamics but only switch between a finite number of helical structures.


title:
Signal processing of acoustic signals in the time domain with an active
  nonlinear nonlocal cochlear model

abstract:
  A two space dimensional active nonlinear nonlocal cochlear model is
formulated in the time domain to capture nonlinear hearing effects such as
compression, multi-tone suppression and difference tones. The micromechanics of
the basilar membrane (BM) are incorporated to model active cochlear properties.
An active gain parameter is constructed in the form of a nonlinear nonlocal
functional of BM displacement. The model is discretized with a boundary
integral method and numerically solved using an iterative second order accurate
finite difference scheme. A block matrix structure of the discrete system is
exploited to simplify the numerics with no loss of accuracy. Model responses to
multiple frequency stimuli are shown in agreement with hearing experiments. A
nonlinear spectrum is computed from the model, and compared with FFT spectrum
for noisy tonal inputs. The discretized model is efficient and accurate, and
can serve as a useful auditory signal processing tool.


title: An Information-Theoretic Approach to Network Modularity

abstract:
  Exploiting recent developments in information theory, we propose, illustrate,
and validate a principled information-theoretic algorithm for module discovery
and resulting measure of network modularity. This measure is an order parameter
(a dimensionless number between 0 and 1). Comparison is made to other
approaches to module-discovery and to quantifying network modularity using
Monte Carlo generated Erdos-like modular networks. Finally, the Network
Information Bottleneck (NIB) algorithm is applied to a number of real world
networks, including the "social" network of coauthors at the APS March Meeting
2004.


title:
MultiNeuron - Neural Networks Simulator For Medical, Physiological, and
  Psychological Applications

abstract:
  This work describes neural software applied in medicine and physiology to:
investigate and diagnose immune deficiencies; diagnose and study allergic and
pseudoallergic reactions; forecast emergence or aggravation of stagnant cardiac
insufficiency in patients with cardiac rhythm disorders; forecast development
of cardiac arrhythmia after myocardial infarction; reveal relationships between
the accumulated radiation dose and a set of immunological, hormonal, and
bio-chemical parameters of human blood and find a method to be able to judge by
these parameters the dose value; propose a technique for early diagnosis of
chor-oid melanomas; Neural networks also help to predict human relations within
a group.


title: Length, Protein-Protein Interactions, and Complexity

abstract:
  The evolutionary reason for the increase in gene length from archaea to
prokaryotes to eukaryotes observed in large scale genome sequencing efforts has
been unclear. We propose here that the increasing complexity of protein-protein
interactions has driven the selection of longer proteins, as longer proteins
are more able to distinguish among a larger number of distinct interactions due
to their greater average surface area. Annotated protein sequences available
from the SWISS-PROT database were analyzed for thirteen eukaryotes, eight
bacteria, and two archaea species. The number of subcellular locations to which
each protein is associated is used as a measure of the number of interactions
to which a protein participates. Two databases of yeast protein-protein
interactions were used as another measure of the number of interactions to
which each \emph{S. cerevisiae} protein participates. Protein length is shown
to correlate with both number of subcellular locations to which a protein is
associated and number of interactions as measured by yeast two-hybrid
experiments. Protein length is also shown to correlate with the probability
that the protein is encoded by an essential gene. Interestingly, average
protein length and number of subcellular locations are not significantly
different between all human proteins and protein targets of known, marketed
drugs. Increased protein length appears to be a significant mechanism by which
the increasing complexity of protein-protein interaction networks is
accommodated within the natural evolution of species. Consideration of protein
length may be a valuable tool in drug design, one that predicts different
strategies for inhibiting interactions in aberrant and normal pathways.


title: Modeling network dynamics: the lac operon, a case study

abstract:
  We use the lac operon in Escherichia coli as a prototype system to illustrate
the current state, applicability, and limitations of modeling the dynamics of
cellular networks. We integrate three different levels of description
-molecular, cellular, and that of cell population- into a single model, which
seems to capture many experimental aspects of the system.

categories: q-bio.MN cond-mat.stat-mech physics.bio-ph q-bio.CB


title:
Remodeling of biological tissue: Mechanically induced reorientation of a
  transversely isotropic chain network

abstract:
  A new class of micromechanically motivated chain network models for soft
biological tissues is presented. On the microlevel, it is based on the
statistics of long chain molecules. A wormlike chain model is applied to
capture the behavior of the collagen microfibrils. On the macrolevel, the
network of collagen chains is represented by a transversely isotropic eight
chain unit cell introducing one characteristic material axis. Biomechanically
induced remodeling is captured by allowing for a continuous reorientation of
the predominant unit cell axis driven by a biomechanical stimulus. To this end,
we adopt the gradual alignment of the unit cell axis with the direction of
maximum principal strain. The evolution of the unit cell axis' orientation is
governed by a first-order rate equation. For the temporal discretization of the
remodeling rate equation, we suggest an exponential update scheme of
Euler-Rodrigues type. For the spatial discretization, a finite element strategy
is applied which introduces the current individual cell orientation as an
internal variable on the integration point level. Selected model problems are
analyzed to illustrate the basic features of the new model. Finally, the
presented approach is applied to the biomechanically relevant boundary value
problem of an in vitro engineered functional tendon construct.


title: Collective charge excitations along cell membranes

abstract:
  A significant part of the thin layers of counter-ions adjacent to the
exterior and interior surfaces of a cell membrane form quasi-two-dimensional
(2D) layers of mobile charge. Collective charge density oscillations, known as
plasmon modes, in these 2D charged systems of counter-ions are predicted in the
present paper. This is based on a calculation of the self-consistent response
of this system to a fast electric field fluctuation. The possibility that the
membrane channels might be using these excitations to carry out fast
communication is suggested and experiments are proposed to reveal the existence
of such excitations.


title: Emergence of Complex Dynamics in a Simple Model of Signaling Networks

abstract:
  A variety of physical, social and biological systems generate complex
fluctuations with correlations across multiple time scales. In physiologic
systems, these long-range correlations are altered with disease and aging. Such
correlated fluctuations in living systems have been attributed to the
interaction of multiple control systems; however, the mechanisms underlying
this behavior remain unknown. Here, we show that a number of distinct classes
of dynamical behaviors, including correlated fluctuations characterized by
$1/f$-scaling of their power spectra, can emerge in networks of simple
signaling units. We find that under general conditions, complex dynamics can be
generated by systems fulfilling two requirements: i) a ``small-world'' topology
and ii) the presence of noise. Our findings support two notable conclusions:
first, complex physiologic-like signals can be modeled with a minimal set of
components; and second, systems fulfilling conditions (i) and (ii) are robust
to some degree of degradation, i.e., they will still be able to generate
$1/f$-dynamics.


title:
Opportunistic infection as a cause of transient viremia in chronically
  infected HIV patients under treatment with HAART

abstract:
  When highly active antiretroviral therapy is administered for long periods of
time to HIV-1 infected patients, most patients achieve viral loads that are
``undetectable'' by standard assay (i.e., HIV-1 RNA $ < 50$ copies/ml). Yet
despite exhibiting sustained viral loads below the level of detection, a number
of these patients experience unexplained episodes of transient viremia or viral
"blips". We propose here that transient activation of the immune system by
opportunistic infection may explain these episodes of viremia. Indeed, immune
activation by opportunistic infection may spur HIV replication, replenish viral
reservoirs and contribute to accelerated disease progression. In order to
investigate the effects of concurrent infection on chronically infected HIV
patients under treatment with highly active antiretroviral therapy (HAART), we
extend a simple dynamic model of the effects of vaccination on HIV infection
[Jones and Perelson, JAIDS 31:369-377, 2002] to include growing pathogens. We
then propose a more realistic model for immune cell expansion in the presence
of pathogen, and include this in a set of competing models that allow low
baseline viral loads in the presence of drug treatment. Programmed expansion of
immune cells upon exposure to antigen is a feature not previously included in
HIV models, and one that is especially important to consider when simulating an
immune response to opportunistic infection. Using these models we show that
viral blips with realistic duration and amplitude can be generated by
concurrent infections in HAART treated patients.


title:
Why high-error-rate random mutagenesis libraries are enriched in
  functional and improved proteins

abstract:
  Recently, several groups have used error-prone polymerase chain reactions to
construct mutant libraries containing up to 27 nucleotide mutations per gene on
average, and reported a striking observation: although retention of protein
function initially declines exponentially with mutations as has previously been
observed, orders of magnitude more proteins remain viable at the highest
mutation rates than this trend would predict. Mutant proteins having improved
or novel activity were isolated disproportionately from these heavily mutated
libraries, leading to the suggestion that distant regions of sequence space are
enriched in useful cooperative mutations and that optimal mutagenesis should
target these regions. If true, these claims have profound implications for
laboratory evolution and for evolutionary theory. Here, we demonstrate that
properties of the polymerase chain reaction can explain these results and,
consequently, that average protein viability indeed decreases exponentially
with mutational distance at all error rates. We show that high-error-rate
mutagenesis may be useful in certain cases, though for very different reasons
than originally proposed, and that optimal mutation rates are inherently
protocol-dependent. Our results allow optimal mutation rates to be found given
mutagenesis conditions and a protein of known mutational robustness.


title:
Action Potential Onset Dynamics and the Response Speed of Neuronal
  Populations

abstract:
  The result of computational operations performed at the single cell level are
coded into sequences of action potentials (APs). In the cerebral cortex, due to
its columnar organization, large number of neurons are involved in any
individual processing task. It is therefore important to understand how the
properties of coding at the level of neuronal populations are determined by the
dynamics of single neuron AP generation. Here we analyze how the AP generating
mechanism determines the speed with which an ensemble of neurons can represent
transient stochastic input signals. We analyze a generalization of the
$\theta$-neuron, the normal form of the dynamics of Type-I excitable membranes.
Using a novel sparse matrix representation of the Fokker-Planck equation, which
describes the ensemble dynamics, we calculate the transmission functions for
small modulations of the mean current and noise noise amplitude. In the
high-frequency limit the transmission function decays as $\omega^{-\gamma}$,
where $\gamma$ surprisingly depends on the phase $\theta_{s}$ at which APs are
emitted. In a physiologically plausible regime up to 1kHz the typical response
speed is, however, independent of the high-frequency limit and is set by the
rapidness of the AP onset, as revealed by the full transmission function. In
this regime modulations of the noise amplitude can be transmitted faithfully up
to much higher frequencies than modulations in the mean input current. We
finally show that the linear response approach used is valid for a large regime
of stimulus amplitudes.


title: Activity-dependent brain model explaining EEG spectra

abstract:
  Most brain models focus on associative memory or calculation capability,
experimentally inaccessible using physiological methods. Here we present a
model explaining a basic feature of electroencephalograms (EEG). Our model is
based on an electrical network with threshold firing and plasticity of synapses
that reproduces very robustly the measured exponent 0.8 of the medical EEG
spectra, a solid evidence for self-organized criticality. Our result are also
valid on small-world lattices. We propose that an universal scaling behaviour
characterizes many physiological signal spectra for brain controlled
activities.


title: Native geometry and the dynamics of protein folding

abstract:
  In this paper we investigate the role of native geometry on the kinetics of
protein folding based on simple lattice models and Monte Carlo simulations.
Results obtained within the scope of the Miyazawa-Jernigan indicate the
existence of two dynamical folding regimes depending on the protein chain
length. For chains larger than 80 amino acids the folding performance is
sensitive to the native state's conformation. Smaller chains, with less than 80
amino acids, fold via two-state kinetics and exhibit a significant correlation
between the contact order parameter and the logarithmic folding times. In
particular, chains with N=48 amino acids were found to belong to two broad
classes of folding, characterized by different cooperativity, depending on the
contact order parameter. Preliminary results based on the G\={o} model show
that the effect of long range contact interaction strength in the folding
kinetics is largely dependent on the native state's geometry.


title:
Systematic identification of abundant A-to-I editing sites in the human
  transcriptome

abstract:
  RNA editing by members of the double-stranded RNA-specific ADAR family leads
to site-specific conversion of adenosine to inosine (A-to-I) in precursor
messenger RNAs. Editing by ADARs is believed to occur in all metazoa, and is
essential for mammalian development. Currently, only a limited number of human
ADAR substrates are known, while indirect evidence suggests a substantial
fraction of all pre-mRNAs being affected. Here we describe a computational
search for ADAR editing sites in the human transcriptome, using millions of
available expressed sequences. 12,723 A-to-I editing sites were mapped in 1,637
different genes, with an estimated accuracy of 95%, raising the number of known
editing sites by two orders of magnitude. We experimentally validated our
method by verifying the occurrence of editing in 26 novel substrates. A-to-I
editing in humans primarily occurs in non-coding regions of the RNA, typically
in Alu repeats. Analysis of the large set of editing sites indicates the role
of editing in controlling dsRNA stability.


title:
The Go model revisited: Native structure and the geometric coupling
  between local and long-range contacts

abstract:
  Monte Carlo simulations show that long-range interactions play a major role
in determining the folding rates of 48-mer three-dimensional lattice polymers
modelled by the Go potential. For three target structures with different native
geometries we found a sharp increase in the folding time when the relative
contribution of the long-range interactions to the native state's energy is
decreased from ~50% towards zero. However, the dispersion of the simulated
folding times depends strongly on the native geometry and Go polymers folding
to one of the target structures exhibit folding times spanning three orders of
magnitude. We have also found that, depending on the target geometry, a strong
geometric coupling may exist between local and long-range contacts meaning
that, when this coupling exists, the formation of long-range contacts is forced
by the previous formation of local contacts. The absence of a strong geometric
coupling leads to kinetics that are more sensitive to the interaction energy
parameters; in this case the formation of local contacts is not sufficient to
promote the establishment of long-range ones when these are strongly penalized
energetically, leading to longer folding times.


title:
Path integral formulation and Feynman rules for phylogenetic branching
  models

abstract:
  A dynamical picture of phylogenetic evolution is given in terms of Markov
models on a state space, comprising joint probability distributions for
character types of taxonomic classes. Phylogenetic branching is a process which
augments the number of taxa under consideration, and hence the rank of the
underlying joint probability state tensor. We point out the combinatorial
necessity for a second-quantised, or Fock space setting, incorporating discrete
counting labels for taxa and character types, to allow for a description in the
number basis. Rate operators describing both time evolution without branching,
and also phylogenetic branching events, are identified. A detailed development
of these ideas is given, using standard transcriptions from the microscopic
formulation of nonequilibrium reaction-diffusion or birth-death processes.
These give the relations between stochastic rate matrices, the matrix elements
of the corresponding evolution operators representing them, and the integral
kernels needed to implement these as path integrals. The `free' theory (without
branching) is solved, and the correct trilinear `interaction' terms
(representing branching events) are presented. The full model is developed in
perturbation theory via the derivation of explicit Feynman rules which
establish that the probabilities (pattern frequencies of leaf colourations)
arising as matrix elements of the time evolution operator are identical with
those computed via the standard analysis. Simple examples (phylogenetic trees
with 2 or 3 leaves), are discussed in detail. Further implications for the work
are briefly considered including the role of time reparametrisation covariance.


title:
Evolutionary dynamics of adult stem cells: Comparison of random and
  immortal strand segregation mechanisms

abstract:
  This paper develops a point-mutation model describing the evolutionary
dynamics of a population of adult stem cells. Such a model may prove useful for
quantitative studies of tissue aging and the emergence of cancer. We consider
two modes of chromosome segregation: (1) Random segregation, where the daughter
chromosomes of a given parent chromosome segregate randomly into the stem cell
and its differentiating sister cell. (2) ``Immortal DNA strand''
co-segregation, for which the stem cell retains the daughter chromosomes with
the oldest parent strands. Immortal strand co-segregation is a mechanism,
originally proposed by Cairns (J. Cairns, {\it Nature} {\bf 255}, 197 (1975)),
by which stem cells preserve the integrity of their genomes. For random
segregation, we develop an ordered strand pair formulation of the dynamics,
analogous to the ordered strand pair formalism developed for quasispecies
dynamics involving semiconservative replication with imperfect lesion repair
(in this context, lesion repair is taken to mean repair of postreplication
base-pair mismatches). Interestingly, a similar formulation is possible with
immortal strand co-segregation, despite the fact that this segregation
mechanism is age-dependent. From our model we are able to mathematically show
that, when lesion repair is imperfect, then immortal strand co-segregation
leads to better preservation of the stem cell lineage than random chromosome
segregation. Furthermore, our model allows us to estimate the optimal lesion
repair efficiency for preserving an adult stem cell population for a given
period of time. For human stem cells, we obtain that mispaired bases still
present after replication and cell division should be left untouched, to avoid
potentially fixing a mutation in both DNA strands.


title:
A Statistical Analysis of RNA Folding Algorithms Through Thermodynamic
  Parameter Perturbation

abstract:
  Computational RNA secondary structure prediction is rather well established.
However, such prediction algorithms always depend on a large number of
experimentally measured parameters. Here, we study how sensitive structure
prediction algorithms are to changes in these parameters. We find that already
for changes corresponding to the actual experimental error to which these
parameters have been determined 30% of the structure are falsly predicted and
the ground state structure is preserved under parameter perturbation in only 5%
of all cases. We establish that base pairing probabilities calculated in a
thermal ensemble are a viable though not perfect measure for the reliability of
the prediction of individual structure elements. A new measure of stability
using parameter perturbation is proposed, and its limitations discussed.


title: Finite size effects on thermal denaturation of globular proteins

abstract:
  Finite size effects on the cooperative thermal denaturation of proteins are
considered. A dimensionless measure of cooperativity, Omega, scales as N^zeta,
where N is the number of amino acids. Surprisingly, we find that zeta is
universal with zeta = 1 + gamma, where the exponent gamma characterizes the
divergence of the susceptibility for a self-avoiding walk. Our lattice model
simulations and experimental data are consistent with the theory. Our finding
rationalizes the marginal stability of proteins and substantiates the earlier
predictions that the efficient folding of two-state proteins requires the
folding transition temperature to be close to the collapse temperature.


title: Scaling effects in the Penna ageing model

abstract:
  We have analysed the possibility of scaling the sexual Penna ageing model.
Assuming that the number of genes expressed before the reproduction age grows
linearly with the genome size and that the mutation rate per genome and
generation is constant, we have found that the fraction of defective genes
expressed before the minimum reproduction age drops with the genome size, while
the number of defective genes eliminated by the genetic death grows with genome
size. Thus, the evolutionary costs decrease with enlarging the genome. After
rescaling the time scale according to the mutational clock, age distributions
of populations do not depend on the genome size. Nevertheless, enlarging the
genome increases the reproduction potential of populations.


title: Duplication-divergence model of protein interaction network

abstract:
  We show that the protein-protein interaction networks can be surprisingly
well described by a very simple evolution model of duplication and divergence.
The model exhibits a remarkably rich behavior depending on a single parameter,
the probability to retain a duplicated link during divergence. When this
parameter is large, the network growth is not self-averaging and an average
vertex degree increases algebraically. The lack of self-averaging results in a
great diversity of networks grown out of the same initial condition. For small
values of the link retention probability, the growth is self-averaging, the
average degree increases very slowly or tends to a constant, and a degree
distribution has a power-law tail.


title:
Coupled dynamics of DNA-breathing and single-stranded DNA binding
  proteins

abstract:
  We study the size fluctuations of a local denaturation zone in a DNA molecule
in the presence of proteins that selectively bind to single-stranded DNA, based
on a (2+1)-dimensional master equation. By tuning the physical parameters we
can drive the system from undisturbed bubble fluctuations to full, binding
protein-induced denaturation. We determine the effective free energy landscape
of the DNA-bubble and explore its relaxation modes.


title: Chirality and Protein Folding

abstract:
  There are several simple criteria of folding to a native state in model
proteins. One of them involves crossing of a threshold value of the RMSD
distance away from the native state. Another checks whether all native contacts
are established, i.e. whether the interacting amino acids come closer than some
characteristic distance. We use Go-like models of proteins and show that such
simple criteria may prompt one to declare folding even though fragments of the
resulting conformations have a wrong sense of chirality. We propose that a
better condition of folding should augment the simple criteria with the
requirement that most of the local values of the chirality should be nearly
native. The kinetic discrepancy between the simple and compound criteria can be
substantially reduced in the Go-like models by providing the Hamiltonian with a
term which favors native values of the local chirality. We study the effects of
this term as a function of its amplitude and compare it to other models such as
with the side groups and with the angle-dependent potentials.


title:
Cognitive styles sex the brain, compete neurally, and quantify deficits
  in autism

abstract:
  Introduction: Two key dimensions of the mind are understanding and responding
to another's mental state (empathizing), and analysing lawful behaviour
(systemizing).
  Methods: Two questionnaires, the Systemizing Quotient (SQ) and the Empathy
Quotient (EQ), were administered to a normal control group and a group of
individuals with Asperger Syndrome (AS) or High-Functioning Autism (HFA). The
multivariate correlations of the joint scores were analysed using principal
components analysis.
  Results: The principal components were well-approximated by the sums and
differences of the SQ and EQ scores. The differences in the scores corresponded
to sex differences within the control group and also separated out the AS/HFA
group, which showed stronger systemizing than the control group, but
below-average empathy. The sums of the scores did not show sex differences, but
did distinguish the AS/HFA group.
  Conclusions: These tests reliably sex the brain, and their correlations show
that empathizing and systemizing are not independent, but compete neurally.
Their combined score (EQ + SQ) quantifies the deficit in autism spectrum
conditions.


title:
A Structured-Population Model of Proteus mirabilis Swarm-Colony
  Development

abstract:
  In this paper we present continuous age- and space-structured models and
numerical computations of Proteus mirabilis swarm-colony development. We base
the mathematical representation of the cell-cycle dynamics of Proteus mirabilis
on those developed by Esipov and Shapiro, which are the best understood aspects
of the system, and we make minimum assumptions about less-understood
mechanisms, such as precise forms of the spatial diffusion. The models in this
paper have explicit age-structure and, when solved numerically, display both
the temporal and spatial regularity seen in experiments, whereas the Esipov and
Shapiro model, when solved accurately, shows only the temporal regularity.
  The composite hyperbolic-parabolic partial differential equations used to
model Proteus mirabilis swarm-colony development are relevant to other
biological systems where the spatial dynamics depend on local physiological
structure. We use computational methods designed for such systems, with known
convergence properties, to obtain the numerical results presented in this
paper.


title:
Looking at structure, stability, and evolution of proteins through the
  principal eigenvector of contact matrices and hydrophobicity profiles

abstract:
  We review and further develop an analytical model that describes how
thermodynamic constraints on the stability of the native state influence
protein evolution in a site-specific manner. To this end, we represent both
protein sequences and protein structures as vectors: Structures are represented
by the principal eigenvector (PE) of the protein contact matrix, a quantity
that resembles closely the effective connectivity of each site; Sequences are
represented through the ``interactivity'' of each amino acid type, using novel
parameters that are correlated with hydropathy scales. These interactivity
parameters are more strongly correlated than the other hydropathy scales that
we examine with: (1) The change upon mutations of the unfolding free energy of
proteins with two-states thermodynamics; (2) Genomic properties as the
genome-size and the genome-wide GC content; (3) The main eigenvectors of the
substitution matrices. The evolutionary average of the interactivity vector
correlates very strongly with the PE of a protein structure. Using this result,
we derive an analytic expression for site-specific distributions of amino acids
across protein families in the form of Boltzmann distributions whose ``inverse
temperature'' is a function of the PE component. We show that our predictions
are in agreement with site-specific amino acid distributions obtained from the
Protein Data Bank, and we determine the mutational model that best fits the
observed site-specific amino acid distributions. Interestingly, the optimal
model almost minimizes the rate at which deleterious mutations are eliminated
by natural selection.


title: Genetic networks with canalyzing Boolean rules are always stable

abstract:
  We determine stability and attractor properties of random Boolean genetic
network models with canalyzing rules for a variety of architectures. For all
power law, exponential, and flat in-degree distributions, we find that the
networks are dynamically stable. Furthermore, for architectures with few inputs
per node, the dynamics of the networks is close to critical. In addition, the
fraction of genes that are active decreases with the number of inputs per node.
These results are based upon investigating ensembles of networks using
analytical methods. Also, for different in-degree distributions, the numbers of
fixed points and cycles are calculated, with results intuitively consistent
with stability analysis; fewer inputs per node implies more cycles, and vice
versa. There are hints that genetic networks acquire broader degree
distributions with evolution, and hence our results indicate that for single
cells, the dynamics should become more stable with evolution. However, such an
effect is very likely compensated for by multicellular dynamics, because one
expects less stability when interactions among cells are included. We verify
this by simulations of a simple model for interactions among cells.