Few crucial links assure checkpoint efficiency in the yeast cell-cycle network

Motivation: The ability of cells to complete mitosis with high fidelity relies on elaborate checkpoint mechanisms. We study S- and M-phase checkpoint responses in silico in the budding yeast with a stochastic dynamical model for the cell-cycle. We aim to provide an unbiased functional classification of network interactions that reflect the contribution of each link to checkpoint efficiency in the presence of cellular fluctuations. Results: We developed an algorithm BNetDyn to compute stochastic dynamical trajectories for an input gene network and its structural perturbations. User specified output measures like the mutual information between trigger and output nodes are then evaluated on the stationary state of the Markov process. Systematic perturbations of the yeast cell-cycle model by Li et al. classify each link according to its effect on checkpoint efficiencies and stabilities of the main cell-cycle phases. This points to the crosstalk in the cascades downstream of the SBF/MBF transcription activator complexes as determinant for checkpoint optimality; a finding that consistently reflects recent experiments. Finally our stochastic analysis emphasizes how dynamical stability in the yeast cell-cycle network crucially relies on backward inhibitory circuits next to forward induction. Availability: C++ source code and network models can be downloaded at Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

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

Prediction of Mutations to Control Pathways Enabling Tumor Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial)

Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modeling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualization of results using the Python library matplotlib. To illustrate our approach, we use a recent Boolean model of the signaling network controlling tumor cell invasion and migration. Our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype

Few crucial links assure checkpoint efficiency in the yeast cell-cycle network

MOTIVATION: The ability of cells to complete mitosis with high fidelity relies on elaborate checkpoint mechanisms. We study S- and M-phase checkpoint responses in silico in the budding yeast with a stochastic dynamical model for the cell-cycle. We aim to provide an unbiased functional classification of network interactions that reflect the contribution of each link to checkpoint efficiency in the presence of cellular fluctuations. RESULTS: We developed an algorithm BNetDyn to compute stochastic dynamical trajectories for an input gene network and its structural perturbations. User specified output measures like the mutual information between trigger and output nodes are then evaluated on the stationary state of the Markov process. Systematic perturbations of the yeast cell-cycle model by Li et al. classify each link according to its effect on checkpoint efficiencies and stabilities of the main cell-cycle phases. This points to the crosstalk in the cascades downstream of the SBF/MBF transcription activator complexes as determinant for checkpoint optimality; a finding that consistently reflects recent experiments. Finally our stochastic analysis emphasizes how dynamical stability in the yeast cell-cycle network crucially relies on backward inhibitory circuits next to forward induction. AVAILABILITY: C++ source code and network models can be downloaded at http://www.vital-it.ch/Software

Setting the basis of best practices and standards for curation and annotation of logical models in biology

International audienceThe fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements

A multiple proxy and model study of Cretaceous upper ocean temperatures and atmospheric CO2 concentrations

Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Paleoceanography 21 (2206): PA2002, doi:10.1029/2005PA001203.We estimate tropical Atlantic upper ocean temperatures using oxygen isotope and Mg/Ca ratios in well-preserved planktonic foraminifera extracted from Albian through Santonian black shales recovered during Ocean Drilling Program Leg 207 (North Atlantic Demerara Rise). On the basis of a range of plausible assumptions regarding seawater composition at the time the data support temperatures between 33° and 42°C. In our low-resolution data set spanning ~84–100 Ma a local temperature maximum occurs in the late Turonian, and a possible minimum occurs in the mid to early late Cenomanian. The relation between single species foraminiferal δ18O and Mg/Ca suggests that the ratio of magnesium to calcium in the Turonian-Coniacian ocean may have been lower than in the Albian-Cenomanian ocean, perhaps coincident with an ocean 87Sr/86Sr minimum. The carbon isotopic compositions of distinct marine algal biomarkers were measured in the same sediment samples. The δ13C values of phytane, combined with foraminiferal δ13C and inferred temperatures, were used to estimate atmospheric carbon dioxide concentrations through this interval. Estimates of atmospheric CO2 concentrations range between 600 and 2400 ppmv. Within the uncertainty in the various proxies, there is only a weak overall correspondence between higher (lower) tropical temperatures and more (less) atmospheric CO2. The GENESIS climate model underpredicts tropical Atlantic temperatures inferred from ODP Leg 207 foraminiferal δ18O and Mg/Ca when we specify approximate CO2 concentrations estimated from the biomarker isotopes in the same samples. Possible errors in the temperature and CO2 estimates and possible deficiencies in the model are discussed. The potential for and effects of substantially higher atmospheric methane during Cretaceous anoxic events, perhaps derived from high fluxes from the oxygen minimum zone, are considered in light of recent work that shows a quadratic relation between increased methane flux and atmospheric CH4 concentrations. With 50 ppm CH4, GENESIS sea surface temperatures approximate the minimum upper ocean temperatures inferred from proxy data when CO2 concentrations specified to the model are near those inferred using the phytane δ13C proxy. However, atmospheric CO2 concentrations of 3500 ppm or more are still required in the model in order to reproduce inferred maximum temperatures.Funding for this research was provided by the U.S. Science Support Program of the JOI, the Andrew W. Mellon Foundation Endowed Fund for Innovative Research, and Deutsche Forschungsgemeinschaft through the DFG-Research Center Ocean Margins

Neuroinflammation after intracerebral hemorrhage

Spontaneous intracerebral hemorrhage (ICH) is a particularly severe type of stroke for which no specific treatment has been established yet. Although preclinical models of ICH have substantial methodological limitations, important insight into the pathophysiology has been gained. Mounting evidence suggests an important contribution of inflammatory mechanisms to brain damage and potential repair. Neuroinflammation evoked by intracerebral blood involves the activation of resident microglia, the infiltration of systemic immune cells and the production of cytokines, chemokines, extracellular proteases and reactive oxygen species (ROS). Previous studies focused on innate immunity including microglia, monocytes and granulocytes. More recently, the role of adaptive immune cells has received increasing attention. Little is currently known about the interactions among different immune cell populations in the setting of ICH. Nevertheless, immunomodulatory strategies are already being explored in ICH. To improve the chances of translation from preclinical models to patients, a better characterization of the neuroinflammation in patients is desirable