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10 Publications visible to you, out of a total of 10
Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Abstract (Expand)

Abstract ICAM-1 is critical for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1. SARS-CoV-2 shares several features with … several features with these viruses in interactions between cells, and SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. However, ICAM-1 and its associated pathways are not identified as essential factors in interactions between cells in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM-1 pathways will be indispensable for clarifying the mechanism of COVID-19. This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, data analyses extracted coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways. These pathways indicate that (1) upstream pathways include proteins in noncanonical NF-kappaB pathway and that (2) downstream pathways contain integrins and cytoskeleton or motor proteins for cell transformation. In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanisms of COVID-19.

Authors: Mitsuhiro Odaka, Morgan Magnin, Katsumi Inoue

Date Published: 11th Feb 2022

Publication Type: Journal

Advice from a systems-biology model of the corona epidemics

Abstract (Expand)

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity … such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.

Authors: Hans V. Westerhoff, Alexey N. Kolodkin

Date Published: 1st Dec 2020

Publication Type: Journal

ROS networks: designs, aging, Parkinson's disease and precision therapies.

Abstract (Expand)

How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. … Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased alpha-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

Authors: A. N Kolodkin, R. P. Sharma, A. M. Colangelo, A. Ignatenko, F. Martorana, D. Jennen, J. J. Briede, N. Brady, M. Barberis, T. D. G. A. Mondeel, M. Papa, V. Kumar, B. Peters, A. Skupin, L. Alberghina, R. Balling, H. V. Westerhoff

Date Published: 26th Oct 2020

Publication Type: Journal

Iterative community-driven development of a SARS-CoV-2 tissue simulator

Abstract

Not specified

Authors: Michael Getz, Yafei Wang, Gary An, Maansi Asthana, Andrew Becker, Chase Cockrell, Nicholson Collier, Morgan Craig, Courtney L. Davis, James R. Faeder, Ashlee N. Ford Versypt, Tarunendu Mapder, Juliano F. Gianlupi, James A. Glazier, Sara Hamis, Randy Heiland, Thomas Hillen, Dennis Hou, Mohammad Aminul Islam, Adrianne L. Jenner, Furkan Kurtoglu, Caroline I. Larkin, Bing Liu, Fiona Macfarlane, Pablo Maygrundter, Penelope A Morel, Aarthi Narayanan, Jonathan Ozik, Elsje Pienaar, Padmini Rangamani, Ali Sinan Saglam, Jason Edward Shoemaker, Amber M. Smith, Jordan J.A. Weaver, Paul Macklin

Date Published: 5th Apr 2020

Publication Type: Journal

Conceptual and computational framework for logical modelling of biological networks deregulated in diseases.

Abstract (Expand)

Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the … effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline.

Authors: A. Montagud, P. Traynard, L. Martignetti, E. Bonnet, E. Barillot, A. Zinovyev, L. Calzone

Date Published: 19th Jul 2019

Publication Type: Journal

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

Abstract

Not specified

Authors: Gaelle Letort, Arnau Montagud, Gautier Stoll, Randy Heiland, Emmanuel Barillot, Paul Macklin, Andrei Zinovyev, Laurence Calzone

Date Published: 1st Apr 2019

Publication Type: Journal

MaBoSS 2.0: an environment for stochastic Boolean modeling.

Abstract (Expand)

Motivation: Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows … to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model. Results: We present a new version of MaBoSS (2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions. Availability and Implementation: MaBoSS software can be found at https://maboss.curie.fr , including tutorials on existing models and examples of models. Contact: gautier.stoll@upmc.fr or laurence.calzone@curie.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

Authors: G. Stoll, B. Caron, E. Viara, A. Dugourd, A. Zinovyev, A. Naldi, G. Kroemer, E. Barillot, L. Calzone

Date Published: 15th Jul 2017

Publication Type: Journal

Identification of HNRNPK as regulator of hepatitis C virus particle production

Abstract (Expand)

Hepatitis C virus (HCV) is a major cause of chronic liver disease affecting around 130 million people worldwide. While great progress has been made to define the principle steps of the viral life cycle, … detailed knowledge how HCV interacts with its host cells is still limited. To overcome this limitation we conducted a comprehensive whole-virus RNA interference-based screen and identified 40 host dependency and 16 host restriction factors involved in HCV entry/replication or assembly/release. Of these factors, heterogeneous nuclear ribonucleoprotein K (HNRNPK) was found to suppress HCV particle production without affecting viral RNA replication. This suppression of virus production was specific to HCV, independent from assembly competence and genotype, and not found with the related Dengue virus. By using a knock-down rescue approach we identified the domains within HNRNPK required for suppression of HCV particle production. Importantly, HNRNPK was found to interact specifically with HCV RNA and this interaction was impaired by mutations that also reduced the ability to suppress HCV particle production. Finally, we found that in HCV-infected cells, subcellular distribution of HNRNPK was altered; the protein was recruited to sites in close proximity of lipid droplets and colocalized with core protein as well as HCV plus-strand RNA, which was not the case with HNRNPK variants unable to suppress HCV virion formation. These results suggest that HNRNPK might determine efficiency of HCV particle production by limiting the availability of viral RNA for incorporation into virions. This study adds a new function to HNRNPK that acts as central hub in the replication cycle of multiple other viruses.

Authors: M. Poenisch, P. Metz, H. Blankenburg, A. Ruggieri, J. Y. Lee, D. Rupp, I. Rebhan, K. Diederich, L. Kaderali, F. S. Domingues, M. Albrecht, V. Lohmann, H. Erfle, R. Bartenschlager

Date Published: 8th Jan 2015

Publication Type: Not specified

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

Abstract (Expand)

UNLABELLED: 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.

Authors: G. Stoll, E. Viara, E. Barillot, L. Calzone

Date Published: 29th Aug 2012

Publication Type: Journal

Design principles of ROS dynamic networks relevant to precision therapies for age-related diseases

Abstract (Expand)

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinson’s disease (PD). How this molecular networking achieves … its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic. By computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fits in vitro data sets from two different laboratories. The model shows effective ROS-management for a century, followed by a sudden system’s collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the system’s collapse. Various in-silico interventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the system in silico, suggesting the model may help discover new medicinal and nutritional therapies.

Authors: Alexey Kolodkin, Raju Prasad Sharma, Anna Maria Colangelo, Andrew Ignatenko, Francesca Martorana, Danyel Jennen, Jacco J. Briede, Nathan Brady, Matteo Barberis, Thierry D.G.A. Mondeel, Michele Papa, Vikas Kumar, Bernhard Peters, Alexander Skupin, Lilia Alberghina, Rudi Balling, Hans V. Westerhoff

Date Published: No date defined

Publication Type: Not specified

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