322 research outputs found
Regulatory network reconstruction using an integral additive model with flexible kernel functions
<p>Abstract</p> <p>Background</p> <p>Reconstruction of regulatory networks is one of the most challenging tasks of systems biology. A limited amount of experimental data and little prior knowledge make the problem difficult to solve. Although models that are currently used for inferring regulatory networks are sometimes able to make useful predictions about the structures and mechanisms of molecular interactions, there is still a strong demand to develop increasingly universal and accurate approaches for network reconstruction.</p> <p>Results</p> <p>The additive regulation model is represented by a set of differential equations and is frequently used for network inference from time series data. Here we generalize this model by converting differential equations into integral equations with adjustable kernel functions. These kernel functions can be selected based on prior knowledge or defined through iterative improvement in data analysis. This makes the integral model very flexible and thus capable of covering a broad range of biological systems more adequately and specifically than previous models.</p> <p>Conclusion</p> <p>We reconstructed network structures from artificial and real experimental data using differential and integral inference models. The artificial data were simulated using mathematical models implemented in JDesigner. The real data were publicly available yeast cell cycle microarray time series. The integral model outperformed the differential one for all cases. In the integral model, we tested the zero-degree polynomial and single exponential kernels. Further improvements could be expected if the kernel were selected more specifically depending on the system.</p
Cell death and life in cancer: mathematical modeling of cell fate decisions
Tumor development is characterized by a compromised balance between cell life
and death decision mechanisms, which are tighly regulated in normal cells.
Understanding this process provides insights for developing new treatments for
fighting with cancer. We present a study of a mathematical model describing
cellular choice between survival and two alternative cell death modalities:
apoptosis and necrosis. The model is implemented in discrete modeling formalism
and allows to predict probabilities of having a particular cellular phenotype
in response to engagement of cell death receptors. Using an original parameter
sensitivity analysis developed for discrete dynamic systems, we determine the
critical parameters affecting cellular fate decision variables that appear to
be critical in the cellular fate decision and discuss how they are exploited by
existing cancer therapies
Spectral analysis of gene expression profiles using gene networks
Microarrays have become extremely useful for analysing genetic phenomena, but
establishing a relation between microarray analysis results (typically a list
of genes) and their biological significance is often difficult. Currently, the
standard approach is to map a posteriori the results onto gene networks to
elucidate the functions perturbed at the level of pathways. However,
integrating a priori knowledge of the gene networks could help in the
statistical analysis of gene expression data and in their biological
interpretation. Here we propose a method to integrate a priori the knowledge of
a gene network in the analysis of gene expression data. The approach is based
on the spectral decomposition of gene expression profiles with respect to the
eigenfunctions of the graph, resulting in an attenuation of the high-frequency
components of the expression profiles with respect to the topology of the
graph. We show how to derive unsupervised and supervised classification
algorithms of expression profiles, resulting in classifiers with biological
relevance. We applied the method to the analysis of a set of expression
profiles from irradiated and non-irradiated yeast strains. It performed at
least as well as the usual classification but provides much more biologically
relevant results and allows a direct biological interpretation
NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps
Molecular biology knowledge can be systematically represented in a
computer-readable form as a comprehensive map of molecular interactions. There
exist a number of maps of molecular interactions containing detailed
description of various cell mechanisms. It is difficult to explore these large
maps, to comment their content and to maintain them. Though there exist several
tools addressing these problems individually, the scientific community still
lacks an environment that combines these three capabilities together. NaviCell
is a web-based environment for exploiting large maps of molecular interactions,
created in CellDesigner, allowing their easy exploration, curation and
maintenance. NaviCell combines three features: (1) efficient map browsing based
on Google Maps engine; (2) semantic zooming for viewing different levels of
details or of abstraction of the map and (3) integrated web-based blog for
collecting the community feedback. NaviCell can be easily used by experts in
the field of molecular biology for studying molecular entities of their
interest in the context of signaling pathways and cross-talks between pathways
within a global signaling network. NaviCell allows both exploration of detailed
molecular mechanisms represented on the map and a more abstract view of the map
up to a top-level modular representation. NaviCell facilitates curation,
maintenance and updating the comprehensive maps of molecular interactions in an
interactive fashion due to an imbedded blogging system. NaviCell provides an
easy way to explore large-scale maps of molecular interactions, thanks to the
Google Maps and WordPress interfaces, already familiar to many users. Semantic
zooming used for navigating geographical maps is adopted for molecular maps in
NaviCell, making any level of visualization meaningful to the user. In
addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte
Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.
International audienceABSTRACT: Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. BACKGROUND: There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. RESULTS: Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions. CONCLUSIONS: Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations
- …