Fanconi anemia cells with unrepaired DNA damage activate components of the checkpoint recovery process

International audienceBACKGROUND:The FA/BRCA pathway repairs DNA interstrand crosslinks. Mutations in this pathway cause Fanconi anemia (FA), a chromosome instability syndrome with bone marrow failure and cancer predisposition. Upon DNA damage, normal and FA cells inhibit the cell cycle progression, until the G2/M checkpoint is turned off by the checkpoint recovery, which becomes activated when the DNA damage has been repaired. Interestingly, highly damaged FA cells seem to override the G2/M checkpoint. In this study we explored with a Boolean network model and key experiments whether checkpoint recovery activation occurs in FA cells with extensive unrepaired DNA damage.METHODS:We performed synchronous/asynchronous simulations of the FA/BRCA pathway Boolean network model. FA-A and normal lymphoblastoid cell lines were used to study checkpoint and checkpoint recovery activation after DNA damage induction. The experimental approach included flow cytometry cell cycle analysis, cell division tracking, chromosome aberration analysis and gene expression analysis through qRT-PCR and western blot.RESULTS:Computational simulations suggested that in FA mutants checkpoint recovery activity inhibits the checkpoint components despite unrepaired DNA damage, a behavior that we did not observed in wild-type simulations. This result implies that FA cells would eventually reenter the cell cycle after a DNA damage induced G2/M checkpoint arrest, but before the damage has been fixed. We observed that FA-A cells activate the G2/M checkpoint and arrest in G2 phase, but eventually reach mitosis and divide with unrepaired DNA damage, thus resolving the initial checkpoint arrest. Based on our model result we look for ectopic activity of checkpoint recovery components. We found that checkpoint recovery components, such as PLK1, are expressed to a similar extent as normal undamaged cells do, even though FA-A cells harbor highly damaged DNA.CONCLUSIONS:Our results show that FA cells, despite extensive DNA damage, do not loss the capacity to express the transcriptional and protein components of checkpoint recovery that might eventually allow their division with unrepaired DNA damage. This might allow cell survival but increases the genomic instability inherent to FA individuals and promotes cancer

Modeling the Calvin-Benson cycle

<p>Abstract</p> <p>Background</p> <p>Modeling the Calvin-Benson cycle has a history in the field of theoretical biology. Anyone who intends to model this system will look at existing models to adapt, refine and improve them. With the goal to study the regulation of carbon metabolism, we investigated a broad range of relevant models for their suitability to provide the basis for further modeling efforts. Beyond a critical analysis of existing models, we furthermore investigated the question how adjacent metabolic pathways, for instance photorespiration, can be integrated in such models.</p> <p>Results</p> <p>Our analysis reveals serious problems with a range of models that are publicly available and widely used. The problems include the irreproducibility of the published results or significant differences between the equations in the published description of the model and model itself in the supplementary material. In addition to and based on the discussion of existing models, we furthermore analyzed approaches in PGA sink implementation and confirmed a weak relationship between the level of its regulation and efficiency of PGA export, in contrast to significant changes in the content of metabolic pool within the Calvin-Benson cycle.</p> <p>Conclusions</p> <p>In our study we show that the existing models that have been investigated are not suitable for reuse without substantial modifications. We furthermore show that the minor adjacent pathways of the carbon metabolism, neglected in all kinetic models of Calvin-Benson cycle, cannot be substituted without consequences in the mass production dynamics. We further show that photorespiration or at least its first step (O₂fixation) has to be implemented in the model if this model is aimed for analyses out of the steady state.</p

A REVIEW: CONCEPTUAL DATA MODELS FOR BIOLOGICAL DOMAIN

ABSTRACT This paper demonstrates the survey and review of conceptual data models and the novel data modeling techniques of biological data. The term conceptual data modeling is used in broad categories in this sense. The biological data, its concepts and frameworks have diversity of expressiveness under the umbrella of bioinformatics. If we consider the biological data a single field of research, it is not possible to handle all these things efficiently and completely. For provision of highly maintainable and efficient solutions, which will have less cost and complexity, we must reduce its scope by making its sub domains in bioinformatics. Keep in mind the aforementioned reasons, we considered only the concept of central dogma of molecular biology; produces sequence biological data (DNA, RNA and protein structures); to describe this reviewed study of conceptual modeling. Our objectives are to provide a current state of art study of conceptual data models for a sequence biological data. Based on this research, we will propose a uniform data model for biological data for unification purposes. In this review paper, we provide the analysis and post-mortems of existing conceptual biological data models, and present their comparison, provided on the basis of conceptually proposed methodologies, Meta data, modeling methods and other critical aspects, necessary for sequence data. This study provides us the cutting edge for the integration of biological data

MI-NODES multiscale models of metabolic reactions, brain connectome, ecological, epidemic, world trade, and legal-social networks

[Abstract] Complex systems and networks appear in almost all areas of reality. We find then from proteins residue networks to Protein Interaction Networks (PINs). Chemical reactions form Metabolic Reactions Networks (MRNs) in living beings or Atmospheric reaction networks in planets and moons. Network of neurons appear in the worm C. elegans, in Human brain connectome, or in Artificial Neural Networks (ANNs). Infection spreading networks exist for contagious outbreaks networks in humans and in malware epidemiology for infection with viral software in internet or wireless networks. Social-legal networks with different rules evolved from swarm intelligence, to hunter-gathered societies, or citation networks of U.S. Supreme Court. In all these cases, we can see the same question. Can we predict the links based on structural information? We propose to solve the problem using Quantitative Structure-Property Relationship (QSPR) techniques commonly used in chemo-informatics. In so doing, we need software able to transform all types of networks/graphs like drug structure, drug-target interactions, protein structure, protein interactions, metabolic reactions, brain connectome, or social networks into numerical parameters. Consequently, we need to process in alignment-free mode multitarget, multiscale, and multiplexing, information. Later, we have to seek the QSPR model with Machine Learning techniques. MI-NODES is this type of software. Here we review the evolution of the software from chemoinformatics to bioinformatics and systems biology. This is an effort to develop a universal tool to study structure-property relationships in complex systems

Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems

In natural environments, cells live in complex communities and experience a high degree of heterogeneity internally and in the environment. Even in &lsquo;ideal&rsquo; laboratory environments, cells can experience a high degree of heterogeneity in their environments. Unfortunately, most of the metabolic modeling approaches that are currently used assume ideal conditions and that each cell is identical, limiting their application to pure cultures in well-mixed vessels. Here we describe our development of Multiscale Multiobjective Systems Analysis (MiMoSA), a metabolic modeling approach that can track individual cells in both space and time, track the diffusion of nutrients and light and the interaction of cells with each other and the environment. As a proof-of concept study, we used MiMoSA to model the growth of Trichodesmium erythraeum, a filamentous diazotrophic cyanobacterium which has cells with two distinct metabolic modes. The use of MiMoSA significantly improves our ability to predictively model metabolic changes and phenotype in more complex cell cultures. </div

Modelling the molecular mechanisms of ageing

This document is the Accepted Manuscript version of a published work that appeared in final form in Bioscience reports. To access the final edited and published work see http://www.bioscirep.org/content/37/1/BSR20160177.The ageing process is driven at the cellular level by random molecular damage which slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the ageing process. The complexity of the ageing process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards, and discusses many specific examples of models which have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field

An integrative workflow to study large-scale biochemical networks

I propose an integrative workflow to study large-scale biochemical networks by combining omics data, network structure and dynamical analysis to unravel disease mechanisms. Using the workflow, I identified core regulatory networks from the E2F1 network underlying EMT in bladder and breast cancer and detected disease signatures and drug targets, which were experimentally validated. Further, I developed a hybrid modeling framework that combines ODE- with logical-models to analyze the dynamics of large-scale non-linear systems. This thesis is a contribution to interdisciplinary cancer research