on Knowledge Discovery in Systems Biology (FJ'09)

Abstracts

- 22nd September (Tuesday)

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"Inferring Rules and Facts by Meta-level Abduction on SOLAR"

Katsumi Inoue and Hidetomo Nabeshima

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ABSTRACT:

This work addresses discovery of unknown relations from

incomplete network data using abduction.

Given a network information such as causal relations and

metabolic pathways, we want to infer missing links and nodes

in the network to account for observations.

To this end, we introduce a framework of meta-level abduction,

which is implemented in SOLAR using a first-order representation

for algebraic properties of causality and full-clausal form of

network information and constraints. Meta-level abduction by

SOLAR is powerful enough to infer missing rules and facts.

Inferring unknown causes is also realized by predicate invention

in the form of existentially quantified hypotheses, in which

new variables represent unknown nodes to be added.

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"Efficient Equational Consequence Finding Calculus on SOL with Ordering
Constraints"

Koji Iwanuma and Katsumi Inoue

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In this talk, we study some issues of equational consequence finding
problems. Equational inference is an essentially important not only
in traditional theorem proving but also for consequence finding.
Efficient inference of equality, however, is a substantially difficult
problem, so huge amounts of researches have been conducted so far.
Among of them, some ordering constraint methods clearly succeeded in
improving the inference performance for equality. SOL is a well-known
efficient calculus for consequence finding over non-equational
problems . In this talk, we introduce into SOL a novel mechanism
for efficient equational consequence finding. The proposed mechanism
is based both on Bachmair et. al's modification method and Paskevich's
connection tableaux for equality using ordering constraints.

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"Applying a Declarative Constraint-based Method to the Re-examination of a Discrete Genetic Regulatory Network"

L.Trilling , joint work with F. Corblin, S. Tripodi, D. Ropers and E. Fanchon.

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ABSTRACT:

Dynamical modeling has proven useful for understanding how complex biological
processes emerge from the many components and interactions composing genetic
regulatory networks (GRNs). However, the development of models is hampered by
large uncertainties in both the network structure and parameter values.
To remedy this problem, the models are usually developed through an iterative
process based on numerous simulations, confronting model predictions with experimental
data and refining the model structure and/or parameter values to repair the inconsistencies. We will present an alternative to this generate-and-test approach through a four-step method for the systematic construction and analysis of discrete models of GRNs by means of a declarative approach. Instead of instantiating the models as in classical modeling approaches, the biological knowledge on the network structure and its dynamics is formulated in the form of constraints. The compatibility of the network structure with the constraints is queried and in case of inconsistencies, some constraints are relaxed. Common properties of the consistent models are then analyzed by means of logical languages that we will introduce. Removing questionable constraints or adding interesting ones allows to further analyze the models. We will illustrate the feasibility of our approach by applying it to the re-examination of a model describing the nutritional stress response in the bacterium Escherichia coli.
(see Corblin, F., et al., A declarative constraint-based method for analyzing
discrete genetic regulatory network. BioSystems (2009),
doi:10.1016/j.biosystems.2009.07.007)

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"Toward an Efficient Consequence Synthesis in SOL Tableaux"

Hidetomo Nabeshima, Koji Iwanuma and Katsumi Inoue

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TBA

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"Non-monotonic Reasoning and Abduction"

Pierre Siegel

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ABSTRACT:

Classical logic has the monotonic property: any information deductible from a
knowledge C, are deductible from C if C is increased. On other hand, an essential
component of the intelligence is to take into account incomplete information
(uncertain information, revisable information). For nonmonotonic logics a fact
deductible from C (from knowledge, from belief) is not true but only probable
in the sense that it can be invalidated or revised when adding new information.
The Abductive reasonning is close from nonmonotonic logics. For example, most
of proof procedures for these logics are using abductive algorihms.
In this presentation, we present some links between monmonotonic logics
(default logic, preferential logic, hypothesis theory, X-logic) and abduction.

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"Protocols for Multi-agent Diagnosis using SOLAR"

Gauvain Bourgne and Katsumi Inoue

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ABSTRACT:

TBA

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"An Incremental Way for Finding Characteristic Hypotheses
in CF-induction"

Yoshitaka Yamamoto, Katsumi Inoue and Koji Iwanuma

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ABSTRACT:

CF-induction is one of the hypothesis-finding procedures
in Inductive Logic Programming (ILP).

Compared with the other ILP systems, CF-induction has several
theoretical features like completeness for finding hypotheses.
However, It is still far from our goal that the hypotheses
which we wish to obtain can be automatically computed.

One problem is that CF-induction consists of several
non-deterministic procedures, each of which needs some
selections of users by hand.

In this talk, we first present an incremental way for finding
hypotheses which enables us to dramatically reduce the
non-determinisms in CF-induction. We implemented CF-induction
with this way by JAVA. We then show the performance of
the current version of this implementation.

- 23rd September (Wednesday)

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"An Implementation of a Model-based Abduction and its Application to Systems Biology"

Takehide Soh and Katsumi Inoue

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ABSTRACT:

Several studies have applied abduction to systems biology such as
predicting gene expression regulation, predicting the effects of a toxin
and completing a model of metabolic pathways.
In those approaches, abduction is achieved with resolution-based proof procedure
and its computation time tends to be large because intended organisms often contain
a lot of entities to be considered. To overcome this problem, we introduce
a model-based abduction by SAT solver.
Recent advance of satisfiability (SAT) technologies has been tremendous and
several problems such as planning, scheduling and packing problems are
successfully solved with SAT solvers.
In our approach, we compute the set of all Herbrand models of the given
background knowledge and observation. Then we choose correct hypotheses
from the models. In this talk, we show this procedure through simple example
and introduce its application to systems biology.

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"Robustness of Threshold Boolean Automata Networks to Iteration modes.
Application to Genetic Regulation Networks Modelling"

Adrien Elena

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ABSTRACT:

We study the influence of a change of iteration mode on the attractors of a
threshold boolean automata network. These networks are discrete mathematical object
classicaly used to model biological regulation systems. The aim is to underline the
importance of the choice of iteration mode for the dynamics of these networks,
especially for the observed limit cycles. First we run simulations of the dynamics
of unbiased samples of networks with size varying from one node to seven nodes.
The result of these simulations show that as the size of these networks increases
their dynamics becomes more and more sensitive to the choice of iteration mode.
Next we show a theoretical result that links the observed limit cycles for a network
iterated with parallel mode and other iteration modes with limit cycles.

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"An EM Algorithm Spetialized for Shared BDDs"

Ishihata Masakazu, Kameya Yoshitaka and Sato Taisuke

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Abstract:

The BDD-EM algorithm, which is an expectations-maximization (EM)
algorithm working on binary decision diagrams (BDDs), is used to learn
probabilities of statistical models described by Boolean formulas. We
extend the BDD-EM algorithm to be able to deal with shared BDDs
(SBDDs). A SBDD represents multiple BDDs as a single graph, and it
uses less memory than the sum of memories required for each of the
BDDs when they have common sub-graphs. We apply the extended
algorithm to a task to predict intermittent faults which
probabilistically occur in logic circuits. We show execution both time
and space are less consumed than ones of the BDD-EM algorithm, and
also show the reduction rate increases with the number of
observations.

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"Role of the Rho (A,B,C) GTPases in Cancer Progression"

G. Favre and J-C. Faye

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ABSTRACT:

The Rho GTPases proteins are members of a large superfamily of regulatory proteins
whose activities are controlled by regulated GDP/GTP cycling. To date, a total of
20 Rho family members have been suggested by the data available from the human
genome sequence project. Rho GTPases control a wide variety of signal transduction
pathways regulating many fundamental processes of cell biology, such as organization
of the actin cytoskeleton, gene expression, cell proliferation and survival.
Rho proteins are also implicated in participating in several steps of tumor
progression and development of metastasis.. They function in cell cycle regulation
by the modulation of cyclin D1 and by their involvement in endocytic traffic,
such as in regulation of epidermal growth factor receptor. Furthermore, Lacal et al
have shown that Rho GTPases are directly involved in signalling pathways that
trigger either proliferation or cell death. These studies linking Rho proteins to
many aspects of cellular proliferation are further extended by the study by Gomez del
Pulgar, which revealed that several human tumors contained aberrant expression and
activation of Rho GTPases. Elevated expression of RhoA and RhoC was found in breast,
lung, ovarian, gastric, and bladder cancers. The involvement of RhoA in testicular
human tumors was demonstrated by increased RhoA mRNA levels in relation to tumour grade.
Overexpression of the rhoC gene in adenocarcinoma of pancreas correlated with poorer
prognosis of patients, whereas we have shown that RhoB expression is lost in several
tumors. To identify proteins involved in the signal transduction pathways of these three
Rho GTPases, is an important challenge in the knowledge of their various biological
properties, and would open out onto new cancer treatments.

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"A Comparison Among Unsupervised Discretization Methods for
Time Series Data"

Yoshitaka Kameya

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Abstract:

Discretization is a key preprocessing step in symbolic data
mining, e.g. Apriori-style frequent pattern mining or inductive
logic programming, from real-valued data. This talk focuses on
unsupervised discretization for time series data. Many of the
existing methods (SAX, Persist, hidden Markov models and so on)
work under a common strategy, in which we perform smoothing at
the time axis, and binning/clustering at the measurement axis.
We review these existing methods and report some results of
comparative experiments on artificial and real data used in
previous work.

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"Comparative Analysis of the Model Based Diagnosis Approach From the Articial
Intelligence and Automatic Control Perspectives"

Louise Trave-Massuyes

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ABSTRACT:

Two distinct and parallel research communities have been working along the lines
of the model-based diagnosis approach: the fault detection and isolation (FDI)
community and the diagnostic (DX) community that have evolved in the elds of
automatic control and articial intelligence, respectively. This presentation claries
and links the concepts and assumptions that underlie the FDI analytical redundancy
approach and the DX con- sistency-based logical approach. A formal framework is proposed
in order to compare the two approaches and the theoretical proof of their equivalence
together with the necessary and sufcient conditions is provided.

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"Interacting Answer Sets"

Chiaki Sakama and Tran Cao Son

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ABSTRACT:

We consider agent societies represented by logic programs.
Four different types of social interactions among agents,
cooperation, competition, norms, and subjection,
are formulated as interactions between answer sets of different programs.
Answer sets satisfying conditions of interactions represent solutions coordinated in a multiagent society.
A unique feature of our framework is that answer set interactions are specified outside of individual
programs.

This enables us to freely change the social specifications among agents without the need of
modifying individual programs and
to separate beliefs of agents from social requirements over them.
Social interactions among agents are encoded in a single logic program using constraints.
Coordinated solutions are then computed using answer set programming.

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"Applications of a Constraint-based Approach to Build and Re-examine
Biological Regulatory and Formal Neural Networks"

Fabien Corblin, Hedi Ben Amor, Eric Fanchon, Laurent Trilling, Jacques Demongeot and Nicolas Glade

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ABSTRACT:

The biological knowledge obtained from numerous experiments is usually incomplete.
Two kinds of knowledge can be distinguished concerning the biological regulatory
network of interest : knowledge about its structure (usually formalized as an
interaction graph) and about its behaviour. Building a meaningful model of biological
regulatory network is usually done by specifying the components (molecular species,
genes, ...) and their interactions, by comparing the observed behaviours to the
predicted ones, and by modifying (architecture or parameters) it in order to reach
the optimal _tness.
The idea in this work is to avoid such a trial-error process (or learning process)
by building automatically the set of all models coherent with all the available knowledge
(structural and behavioural) and by characterizing this set. We propose to use the
formalism of Hop_eld-like networks (a usual model for neural networks and often for
regulatory networks) where the structure and the dynamics are formalized with formal
constraints, so as to construct and analyse the biological models by reverse engineering
methods. These formal constraints are compiled into boolean formula in conjunctive normal
form (CNF) and then submitted to a satis_ability solver (SAT). Using this method,
we construct queries in order to build or re-examine a biological regulatory networks
and formal neural networks.

- 24th September (Thursday)

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"Pronostic of Breast Cancer Based on a Fuzzy Classification"

L. Hedjazi, T. Kempowsky-Hamon, M.-V. Le Lann and J. Aguilar-Martin1

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ABSTRACT :

The techniques of learning and classification can help with data analysis
from ana-cyto-pathological cancerous tissue to develop a tool for the diagnosis
or prognosis of cancer. This paper presents the application of the classification
LAMDA with recent developments that allow the processing of information represented
by intervals of numerical values. This study includes the selection of the attributes
based on several preliminary analysis. This method was applied to the prognosis of
breast cancer from two databases and compared with results published previously.

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"Generative Modeling by PRISM"

Taisuke Sato

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ABSTRACT:

PRISM is a probabilistic extension of Prolog. It is a high
level language for probabilistic modeling capable of
learning statistical parameters from observed data. After
reviewing it from various viewpoints, we examine some
technical details related to logic programming, including
semantics, search and program synthesis.

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"Knowledge Representation in Systems Biology"

Andre Doncescu

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ABSTRACT:

The development of biological databases provides an opportunity to recognize
patterns not considered in the past and to use these patterns for diagnosis and
for knowledge science discovery. ** The different approaches are related to the type
of reasoning. The physical models basically represented by differential equations
"mime" physical structure and give a synoptic view. The engineering aspect is
defined by functional models which describe the chain of functions realized by
the system. The modelling of some systems demands an informational representation,
which is supposed to gather signals and find out the relations causality/effects.
The informational model is based on the fusion of information, which means the ability
to combine sources of information describing the same phenomena. But more of that, the
sources of information deliver data, decisions and models. To accomplish these tasks
the central concept is *knowledge representation which becomes fondamental for knowledge
discovery in systems biology.

Takehide Soh: soh at nii.ac.jp

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