Leading Professional Society for Computational Biology and Bioinformatics
Connecting, Training, Empowering, Worldwide


SysMod: Computational Modeling of Biological Systems

COSI Track Presentations

Attention Presenters - please review the Speaker Information Page available here
Schedule subject to change
Monday, July 22nd
10:15 AM-10:20 AM
Introduction to SysMod 2019
Room: Montreal (2nd Floor)
  • Andreas Dräger, Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, University of Tübingen, Tübingen, Germany

Presentation Overview: Show

The community of special interest (COSI) in systems modeling (SysMod) organizes annual one-day gatherings. In 2019 the meeting comprises three sessions that cover a broad variety of topics, beginning with human cells and disease modeling, followed by the afternoon session on the systems biology of microorganisms and concludes with current trends in the field. Each of the three sessions features one of the keynote speakers, Douglas Lauffenburger, Edda Klipp, and Jörg Stelling. The event is hosted by Claudine Chaouiya, María Rodriguez Martinez, and Andreas Dräger on behalf of the ten COSI organizers. This brief talk introduces all speakers, organizers, and main topics of the 2019 meeting.

10:20 AM-11:00 AM
SysMod Keynote: Interpreting the cancer genome through physical and functional models of the cancer cell
Room: Montreal (2nd Floor)
  • Trey Ideker, Department of Medicine, University of California, San Diego, United States

Presentation Overview: Show

Recently we and other laboratories have launched the Cancer Cell Map Initiative (ccmi.org) and have been building momentum. The goal of the CCMI is to produce a complete map of the gene and protein wiring diagram of a cancer cell. We and others believe this map, currently missing, will be a critical component of any future system to decode a patient's cancer genome. I will describe efforts along several lines: 1. Coalition building. We have made notable progress in building a coalition of institutions to generate the data, as well as to develop the computational methodology required to build and use the maps. 2. Development of technology for mapping gene-gene interactions rapidly using the CRISPR system. 3. Causal network maps connecting DNA mutations (somatic and germline, coding and noncoding) to the cancer events they induce downstream. 4. Development of software and database technology to visualize and store cancer cell maps. 5. A machine learning system for integrating the above data to create multi-scale models of cancer cells. In a recent paper by Ma et al., we have shown how a hierarchical map of cell structure can be embedded with a deep neural network, so that the model is able to accurately simulate the effect of mutations in genotype on the cellular phenotype.

11:00 AM-11:20 AM
Personalization of logical models using multi-omics data and its use in the study of clinical stratification and drug response
Room: Montreal (2nd Floor)
  • Jonas Béal, Institut Curie, France
  • Arnau Montagud, Barcelona Supercomputing Center, Spain
  • Laurence Calzone, Institut Curie, France
  • Emmanuel Barillot, Institut Curie, 26 rue d'Ulm, F-75248 Paris France, France

Presentation Overview: Show

Mathematical models of cancer pathways are built by mining the literature for relevant experimental observations or extracting information from pathway databases. As a consequence, these models generally do not capture the heterogeneity of tumors and their therapeutic responses. We present here a novel framework, PROFILE, to tailor logical models to particular biological samples such as patient tumors, compare the model simulations to individual clinical data and investigate therapeutic strategies.

Our approach makes use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models resulting in model state probabilities. This semi-quantitative framework allows to integrate mutation data, copy number alterations (CNA), and transcriptomics/proteomics into logical models. These personalized models are validated by comparing simulation outputs with patients’ clinical data (subtypes, survival) and then used for cell line-specific investigations regarding the effects of drug perturbations, allowing both verification of the theoretical behavior of the model and comparison with experimental drug sensitivities.

Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient or cell line-relevant models that can serve as tools for analyzing therapeutic responses.

11:20 AM-11:40 AM
Making Sense of Large Kinetic Models
Room: Montreal (2nd Floor)
  • Fabian Fröhlich, Harvard University, United States
  • Luca Gerosa, Harvard University, United States
  • Peter Sorger, Harvard University, United States

Presentation Overview: Show

In recent years, simulation and training of large kinetic multi-pathway models with hundreds to thousands of species and parameters has become increasingly tractable. These large kinetics models are often constructed with the aim to deepen our mechanistic understanding of signaling pathways. Yet, the high complexity of large kinetic models impedes our ability to understand signal transduction in the model and thus limits possibilities for mechanistic insight and hypothesis generation.

Here we propose the use of model structure to provide high- and low-level descriptions of signaling dynamics. We exploit the abstraction of rule-based models to provide protein-level summaries of signaling dynamics. To study emergent properties of the model, we apply a combination of causal compression and hierarchical modularization to provide pathway-level summaries of signal transduction.

We apply these methods to an ordinary differential equation model of adaptive resistance in melanoma (EGFR and ERK pathways, >1k state variables, >10k reactions). We trained the model on absolute proteomic and phospho-proteomic as well as time-resolved immunofluorescence data, both in dose-response to small molecule inhibitors. We illustrate how low- and high-level descriptions can be used to probe signaling dynamics in the trained model and provide simple explanations for the observe nonlinear dose-response data.

11:40 AM-12:00 PM
Constraint-based modeling of human single cells to investigate metabolic heterogeneity in cancer subpopulations
Room: Montreal (2nd Floor)
  • Davide Maspero, Biotechnology and Biosciences, University Milano-Bicocca, Italy
  • Giancarlo Mauri, University of Milano-Bicocca, Italy
  • Marco Vanoni, University of Milan - Bicocca, Italy
  • Lilia Alberghina, University of Milan - Bicocca, Italy
  • Hans V. Westerhoff, Manchester Centre for Integrative Systems Biology, University of Manchester, United Kingdom
  • Alex Graudenzi, University of Milan - Bicocca, Italy
  • Dario Pescini, Department of Statistics and Quantitative Methods - University of Milano Bicocca, Italy
  • Riccardo Colombo, BioRep S.r.l., Milan, Italy, Italy
  • Marzia Di Filippo, University Milan-Bicocca, Italy
  • Chiara Damiani, Department of Informatics, Systems and Communication University of Milan Bicocca, Italy

Presentation Overview: Show

Intratumour heterogeneity characterizing cancer populations represent a key factor in fostering the disease progression. In particular, metabolic intratumour heterogeneity increases the repertoire of possible cellular responses to drugs and boosts the adaptive nature of cellular behaviors, hindering the identification of effective treatments. Unfortunately, current metabolomics technologies depict the average cell population behavior, but disregard both internal interactions and differences. To explore such metabolic heterogeneity, characterization of metabolic programs at the single-cell level must be used. In this regard, single-cell metabolomics is still at its infancy thus is less advanced than single-cell sequencing. To bridge this gap, we present a computational framework to characterize metabolism at the single cell level and possible metabolic interactions among cells, by integrating bulk metabolomics and single-cell transcriptomics data. Than, we exploit constraint-based modeling to simulate a set of replicates of a human metabolic network corresponding to interacting distinct cells of a given population.
The integration of transcriptomics profiles of individual tumour cells isolated from lung adenocarcinoma and breast cancer patients allowed to compute single-cell fluxomes, to identify clusters of cells with different growth rates, and to point out the possible metabolic interactions among cells via exchange of metabolites by showing adherence to experimental evidences.

12:00 PM-12:20 PM
Modeling the propagation of the innate immune response to control influenza virus infection
Room: Montreal (2nd Floor)
  • Gregory Smith, Icahn School of Medicine at Mount Sinai Hospital, United States
  • Aleya Dhanji, The Ohio State University, United States
  • Ciriyam Jayaprakash, The Ohio State University, United States
  • Stuart Sealfon, Icahn School of Medicine at Mount Sinai Hospital, United States
  • Irene Ramos, Department of Microbiology and Global Health & Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, United States

Presentation Overview: Show

Influenza remains a major threat to global health resulting in millions of severe infections and hundreds of thousands of deaths each year. The rapidly mutating nature and diverse strains of the influenza virus limit vaccine efficacy and highlight the necessity of novel thinking to produce more effective treatment options. Understanding the early innate immune response to infection is an essential component to this process. Despite considerable study into the dynamics of influenza infection, much is still unknown about the interplay between viral antagonism and the propagation of innate immune response across a cell population. Computational modeling provides an ability to measure this interplay with a real-time resolution that would be infeasible experimentally. We have devised a spatial, stochastic agent-based model of influenza virus infection of lung epithelium that tracks the spread of a viral infection and corresponding host cytokine response across a layer of epithelial cells. In order to fit our model, we apply in vitro infection time course data and single cell RNA sequencing data, including novel findings of paracrine signaling-induced IFNλ production. Our findings suggest this feed forward paracrine signaling loop can have a significant impact on the effectiveness of host immune response.

12:20 PM-12:40 PM
Modeling recovery of Crohn's disease, by simulating microbial community dynamics under perturbations
Room: Montreal (2nd Floor)
  • Jorge Carrasco Muriel, Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain, Spain
  • Beatriz García-Jiménez, Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain
  • Mark D. Wilkinson, Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain, Spain

Presentation Overview: Show

There are few large longitudinal microbiome studies, and fewer that include planned, annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time.

Our novel computational system simulates the dynamics of microbial communities under perturbations, using genome-scale metabolic models (GEM). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; or b) the microorganisms present in the community, to model probiotics or pathogen infection. These simulations generate the quantity and types of information used as input to the MDPbiome system, which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state.

We demonstrate that this novel combination, called MDPbiomeGEM, is able to model the influence of prebiotic fiber and probiotic in the case of a Crohn’s disease microbiome. The output's recommended perturbation to recover from dysbiosis is to consume inulin, which promotes butyrate production to reach homeostasis, consistent with prior biomedical knowledge. Our system could also contribute to design (perturbed) microbial community dynamics experiments, potentially saving resources both in natural microbiome scenarios by optimizing sequencing sampling, or to optimize in-vitro culture formulations for generating performant synthetic microbial communities.

2:00 PM-2:40 PM
Systematic integration of models and data for yeast growth, division and stress response
Room: Montreal (2nd Floor)
  • Edda Klipp, Humboldt-Universität zu Berlin, Germany

Presentation Overview: Show

With the progress of genome-wide experimental approaches we witness the establishment of more and more libraries of genome-wide data for proteins or RNA or metabolites, especially for well-studied model organisms such as bakers’ yeast. However, the separated consideration of metabolic networks or gene regulation networks does not tell us how these networks are integrated to allow a cell to grow, divide and respond to changing environments.

We use the yeast Saccharomyces cerevisiae as the model organism for eukaryotic cells allowing to comprehensively analyzing regulatory networks and their integration with cellular physiology. Here, we focus on processes during the cell division cycle and study the changes of signaling, metabolism, or ion transport during the growth of a single cell.

We use a modular and iterative approach that allows for a systematic integration of cellular functions into a comprehensive model allowing to connect processes that are strongly interlinked in cellular life, but measured separately. The modular concept also to zoom in and out if different aspects of regulation or dynamics become important.

2:40 PM-3:00 PM
Stochastic system identification without an a priori chosen kinetic model — exploring feasible cell regulation with piecewise linear functions
Room: Montreal (2nd Floor)
  • Martin Hoffmann, Fraunhofer ITEM, Division of Personalized Tumor Therapy, Regensburg, Germany
  • Jörg Galle, Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany, Germany

Presentation Overview: Show

Background: Kinetic models are at the heart of system identification. A priori chosen rate functions may, however, be unfitting or too restrictive for complex or previously unanticipated regulation.
Methods: We applied general purpose piecewise linear functions for stochastic system identification in one dimension using published flow cytometry data on E.coli and report on identification results for equilibrium state and dynamic time series.
Results: In metabolic labelling experiments during yeast osmotic stress response, we find mRNA production and degradation to be strongly co-regulated. In addition, mRNA degradation appears overall uncorrelated with mRNA level. Comparison of different system identification approaches using semi-empirical synthetic data revealed the superiority of single-cell tracking for parameter identification. Generally, we find that even within restrictive error bounds for deviation from experimental data, the number of viable regulation types may be large. Indeed, distinct regulation can lead to similar expression behaviour over time.
Conclusion: Our results demonstrate that molecule production and degradation rates may often differ from classical constant, linear or Michaelis–Menten type kinetics.

(1) NPJ Syst Biol Appl. 2018 Apr 11; 4:15. doi: 10.1038/s41540-018-0049-0, PMID 29675268

3:00 PM-3:20 PM
Pleiades Toolkit: Automatic rule-based modeling of bacterial gene regulation enables simulation, prediction, and perturbation of gene responses
Room: Montreal (2nd Floor)
  • Rodrigo Santibáñez, Network Biology Lab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Chile
  • Daniel Garrido, Laboratorio de Microbiología de Sistemas, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Chile
  • Alberto J.M. Martin, Universidad Mayor, Chile

Presentation Overview: Show

Regulation of gene expression is essential for cell homeostasis and adaptation. This regulation relies on transcription factors and other proteins that trigger specific genetic programs. However, the complexity of this regulatory network precludes efforts to model gene regulation at genome-scale. In this work, we developed the Pleiades toolkit that is currently composed by Atlas, Pleione, and Sterope. Atlas reconstruct a Rule-Based Model (RBMs) from biological networks. These Rules are similar to chemical equations and Atlas interpret nodes as model components and edges as a set of reactions, depending on the encoded nature of the networks. After model reconstruction, Pleione parameterizes RBMs employing one of four stochastic simulation software and distribute calculations with subprocesses or SLURM, taking advantage of high-performance computers and computational clusters. Finally, Sterope performs a global sensitivity analysis of selected parameters, calculating the interference or contribution of one Rule to itself and the remaining Rules. We validate the Pleiades employing the Escherichia coli regulatory and metabolic networks retrieved from Ecocyc and expression data from the literature. The developed Toolkit allows assessing of the impact of modifications like gene copy number, operon architecture, and other common genetic modifications to understand bacterial physiology, disease, and eventually, engineering of those systems.

3:20 PM-3:40 PM
Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis
Room: Montreal (2nd Floor)
  • Van Du T. Tran, Vital-IT group, SIB Swiss Institute of Bioinformatics, Switzerland
  • Marco Pagni, Vital-IT group, SIB Swiss Institute of Bioinformatics, Switzerland

Presentation Overview: Show

Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging. We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data and the produced sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to reveal the difference between experimental conditions. The method, named metaboGSE, was validated on public data for Yarrowia lipolytica. It was shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Furthermore, metaboGSE permits identifying genes that are not necessarily differentially expressed, but nevertheless responsible for functional differences between conditions. We are currently investigating this aspect as part of a study about the early modifications leading to metaflammation in white adipose tissue of mice under high-fat diet. The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.

3:40 PM-4:00 PM
µbialSim: Simulating complex microbial communities at their natural diversity
Room: Montreal (2nd Floor)
  • Florian Centler, Helmholtz Centre for Environmental Research - UFZ, Germany
  • Denny Popp, Helmholtz Centre for Environmental Research - UFZ, Germany

Presentation Overview: Show

Microbial communities are ubiquitous in nature and impact human well-being in many ways. They close global elemental cycles, are harnessed in biotechnological applications such as biogas production, and play an important role in human health. To uncover the complex web of metabolic interactions in these systems, we introduce µbialSim (pronounced ‘microbialSim’), a novel numerical simulator that implements the dynamic Flux-Balance-Analysis approach. By employing a novel numerical integration scheme, our simulator can consider communities at their natural diversity, going beyond current simulator codes which are restricted to few species only. As an example, we apply µbialSim to the entirety of a model collection of 773 species of the human gut microbiome. We demonstrate how the predicted pattern of compound exchange and its dynamics can be analyzed as the community feeds on a western-diet substrate pulse. While quantitative predictions have to be interpreted in the light of the simulator’s current limitations – being restricted to metabolic interactions only – we envision µbialSim as a starting point for an extensive in silico characterization of community dynamics at an unprecedented level of detail and helping in elucidating general principles in microbial ecology, and as a tool for experimental design and the design of communities.

4:40 PM-5:00 PM
Learning dynamical information from static protein and sequencing data
Room: Montreal (2nd Floor)
  • Philip Pearce, Massachusetts Institute of Technology, United States
  • Francis Woodhouse, University of Oxford, United Kingdom
  • Aden Forrow, University of Oxford, United Kingdom
  • Halim Kusumaatmaja, Durham University, United Kingdom
  • Jorn Dunkel, Massachusetts Institute of Technology, United States

Presentation Overview: Show

Many complex processes, from protein folding and virus evolution to brain activity and neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape, but little is understood about the reliable inference of dynamics from static data in such settings. Here, we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. Our approach combines Gaussian mixture approximations and self-consistent dimensionality reduction with minimal-energy path estimation and multi-dimensional transition-state theory. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein folding transitions, gene regulatory network motifs and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent data in each case. The underlying numerical protocol thus allows the recovery of relevant dynamical information from instantaneous ensemble measurements, effectively alleviating the need for time-dependent data in many situations. Owing to its generic structure, the framework introduced here will be applicable to modern experimental technologies including cryo-electron-microscopy and high-throughput single-cell RNA sequencing data.

5:00 PM-5:05 PM
Towards Homogeneous Modeling and Simulation of Whole-Cells
Room: Montreal (2nd Floor)
  • Paulo Eduardo Pinto Burke, University of São Paulo, Brazil
  • Luciano Costa, University of São Paulo, Brazil

Presentation Overview: Show

Computational models of biological systems are growing in complexity, approaching the whole-cell scale. Both modeling and simulation of such systems are far from trivial, yet, significant advances in this direction have already been performed. Current whole-cell models yield heterogeneous representations of cellular processes, each one being approached using established methods. Their integration is achieved by exchanging information between them from time to time. Although this approach proved to be useful, its organism-specificity makes it hard to scale and adapt to other organisms. Here, we present a homogeneous approach to model and simulate whole-cells where all cellular process are represented through their underlying biochemical reactions. Such a representation results in a map of all possible biochemical interactions between molecular entities of a cell in the form of a single biochemical network, naturally integrating cellular processes. We discuss the implications of such an approach on automated model generation, user-friendliness, parameter estimation, scalable simulation methods, and computational costs. We also present an example of the entire pipeline extending from model construction up to simulation and analysis using toy models. In addition, we present a biochemical network model of a whole real organism.

5:05 PM-5:10 PM
The regulation of aquaporin 2 vesicle transport by localized cyclic AMP pools.
Room: Montreal (2nd Floor)
  • Christoph Leberecht, Hochschule Mittweida, Germany
  • Dirk Labudde, Hochschule Mittweida, Germany

Presentation Overview: Show

The average basal cAMP concentration in eukaryotic cells is 1 micro mole per liter. The reported cAMP concentration to half-maximally activate protein kinase A (PKA) in vitro is about 200 nano mole per liter. This relationship suggests that PKA should be constantly active. However, in vivo studies determined the sensitivity of PKA to be significantly lower. A promising hypothesis for the apparent low sensitivity is, that cAMP abundance is highly regulated by concentration gradients. As a model system we choose collecting duct principal cells that require PKA signaling for the transport of vesicles storing the water channel aquaporin 2.

We modeled the interplay of localized cAMP, PKA, and phosphodiesterase and their effect on vesicle transport in a spatial model. To model the movement and behavior of vesicles as well as reaction kinetics and diffusion a hybrid simulation technique was devised. We have found that cAMP concentration forms localized sinks around vesicles that act as a threshold to prevent unjustified transport initiation. Further, cAMP concentration is further decreased along the path of traveling vesicles. The paths might temporarily prevent other vesicles from following the initial vesicles and therefore regulate the throughput of vesicles to the membrane.

5:10 PM-5:15 PM
Optimal information acquisition of the molecular systems in living organisms require a non-minimal level of noise
Room: Montreal (2nd Floor)
  • Eugenio Azpeitia, University of Zurich, Switzerland
  • Andreas Wagner, University of Zurich, Switzerland

Presentation Overview: Show

Organisms are constantly acquiring information from the environmental. Information improves the decisions made by organisms, directly affecting their survival and reproductive success. Signaling pathways are the basic mechanisms used by cells to obtain information. They rely on reversible reactions for the binding of signals to receptors, the activation of molecules via allosteric regulation, and the binding of transcription factors to the DNA. However, reversible reactions are noisy, because of random fluctuations in the concentration and activity of molecules. Noise causes uncertainty about the information conveyed by transforming an input into a distribution of possible outputs. For this reason, it is commonly stated that noise reduces the capacity to acquire information. Interestingly, our results show that, under realistic biological conditions, reversible chemical reactions unavoidably produce non-minimal levels of noise for information acquisition. We study how this phenomenon affects the capacity of signaling pathways to acquire and transmit information. We show that the non-minimal levels of noise are transmitted from reversible reactions to the production of mRNA and protein. However, the strength of the binding of a reversible reaction modulates information acquisition and noise levels. Finally, we test our results using the nuclear receptor signaling pathway as an example.

5:15 PM-5:20 PM
An in silico mechanistic representation of an in vitro neutropenia assay to explore dose and schedules
Room: Montreal (2nd Floor)
  • Cristina Santini, Celgene, Spain
  • Carla Guarinos, Celgene, Spain
  • Alicia Benitez, Celgene, Spain
  • Estela Torano, Celgene, Spain
  • Mark McConnell, Celgene, Spain
  • Matthew Trotter, Celgene, Spain
  • James Carmichael, Celgene, Spain
  • Soraya Carrancio, Celgene, Spain
  • Alex Ratushny, Celgene, Spain

Presentation Overview: Show

Objectives: Lenalidomide, an immunomodulatory agent, is approved for the treatment of multiple myeloma, del 5q myelodysplastic syndrome and mantle cell lymphoma. Lenalidomide causes a reversible block in neutrophil maturation. To investigate the dose and schedule that allows for neutrophil recovery, we developed an in silico model based on an in vitro assay. This model is applied to explore dosing regimens.
Methods: A compartmental model was developed to represent the in vitro maturation assay [1]. Donor related parameters were fitted to DMSO treatment data and compound related parameters were fitted to the effect upon treatment with a concentration range of lenalidomide.
Results: The proposed model quantitatively represents the in vitro neutropenia maturation system and the block in neutrophil maturation caused by lenalidomide. In silico predictions for neutrophil recovery after off-drug period were validated experimentally (predicted vs experimental data R2 = 0.985).
Conclusions: An in silico model that represents an in vitro neutropenia assay was developed. Good parameter fit and validated predictions support the applicability of the model to explore dose and schedule of lenalidomide in silico and propose regimens that could minimize a key clinical toxicity of this compound.
[1] Chiu et al., Br J Haematol 2019 Feb 14

5:20 PM-6:00 PM
Systems Analysis of Cell-to-Cell Variability
Room: Montreal (2nd Floor)
  • Jörg Stelling, ETH Zürich, and Swiss Institute of Bioinformatics, Basel, Switzerland

Presentation Overview: Show

A key step for understanding heterogeneity in cell populations is to disentangle sources of cell-to-cell and intra-cellular variability. Single-cell time-lapse data provides potential means for this, but single-cell analysis with dynamic models is a challenging open problem. Most of the existing inference methods address only single-gene expression or neglect correlations between processes that underlie heterogeneous cell behaviors. The focus of the talk will be a simple, flexible, and scalable method for estimating cell-specific and population-average parameters to characterize sources and effects of cell-to-cell variability. The framework relies on non-linear mixed effects models of cellular networks. Its accuracy and performance compared to state-of-the-art methods from pharmacokinetics is demonstrated with a published model and data set. An application to endocytosis in yeast demonstrates that one can develop dynamic models of realistic size for the analysis of single-cell data. Combined with sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability, this application shows that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation.

6:00 PM-6:10 PM
Closing remarks and poster award of SysMod 2019
Room: Montreal (2nd Floor)
  • Claudine Chaouiya, Instituto Gulbenkian de Ciência, Portugal - Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France

Presentation Overview: Show

This concluding talk aims to briefly discuss the diversity of topics presented at the “Computational Modeling of Biological Systems“ (SysMod) COSI track. This diversity illustrates the importance of the field and the broad range of applications in systems biology and disease. Then, forthcoming meetings of interest will be announced, and the three poster awards will be delivered as a closing event.