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Schedule subject to change
Monday, July 13th
10:35 AM-10:40 AM
Introduction to SysMod 2020
Format: Live-stream

  • Laurence Calzone , France

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This talk introduces the community of special interest for systems modeling and the first virtual meeting in 2020.

10:40 AM-11:00 AM
Maintenance energy is essential for accurate predictions of intracellular fluxes in CHO
Format: Pre-recorded with live Q&A

  • Diana Széliová, University of Natural Resources and Life Sciences, Vienna, Austria
  • David E. Ruckerbauer, acib - Austrian Centre of Industrial Biotechnology, Vienna; University of Natural Resources and Life Sciences, Vienna, Austria
  • Jerneja Štor, acib - Austrian Centre of Industrial Biotechnology, Vienna; University of Natural Resources and Life Sciences, Vienna, Austria
  • Isabella Thiel, University of Natural Resources and Life Sciences, Vienna, Austria
  • Isabel Rocha, Centro de Engenharia Biológica, Universidade do Minho, Braga; ITQB‐NOVA, Oeiras, Portugal
  • Nicole Borth, acib - Austrian Centre of Industrial Biotechnology, Vienna; University of Natural Resources and Life Sciences, Vienna, Austria
  • Jürgen Zanghellini, Department of Analytical Chemistry, University of Vienna, Austria

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Chinese Hamster Ovary (CHO) cells are the most valuable mammalian cell hosts for the production of complex protein biopharmaceuticals. Currently, cell line and bioprocess development must be done individually for each new product and rely mostly on trial and error approaches and screening of thousands of clones. Systems biology approaches, including metabolic modeling, could elucidate bottlenecks in protein production and enable more targeted process and cell line engineering. The reconstruction of CHO genome-scale metabolic model (GSMM) was an important step towards applying these methods to CHO. The model can reliably predict growth rates when using flux balance analysis (FBA). However, the quality of the predicted internal fluxes has not yet been validated. In this work we compared the fluxes predicted by parsimonious FBA, using the CHO GSMM, with published 13C flux data. Our analysis revealed that most fluxes of the central carbon metabolism are grossly underestimated if cellular maintenance energy (mATP) is not accounted for (R^2=0.44). Because of the lack of experimental data for CHO, we estimated mATP computationally. Adding the estimated mATP as a constraint significantly improved the predictions of internal fluxes by pFBA for all data sets (R^2=0.96). Furthermore, we validated the mATP experimentally for CHO-K1 cell line.

11:00 AM-11:20 AM
Using genome-scale model of metabolism and macromolecular expression (ME-model) to study biofilm development in Pseudomonas aeruginosa PAO1
Format: Pre-recorded with live Q&A

  • Sanjeev Dahal, Queen's University, Canada
  • Laurence Yang, Queen's University, Canada

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In this work, we seek to use genome-scale models (GEMs) to gain mechanistic insights into the biofilm formation and development in Pseudomonas aeruginosa PAO1. P. aeruginosa is an opportunistic human pathogen which is the main cause of mortality in cystic fibrosis (CF) patients and one of the leading nosocomial pathogens affecting hospitalized patients. P. aeruginosa possesses a wide array of mechanisms for antibiotic resistance including biofilms which primarily consist of extracellular polymeric substances (EPS) such as DNA, proteins and polysaccharides. Within EPS, multiple interactions can occur that make biofilms a robust protective barrier against multiple antibiotics. We will apply GEMs to study biofilm development in P. aeruginosa. GEMs rely on mathematical optimization principles using the flux balance analysis (FBA) and constraint-based modelling approaches. Within the GEM framework, ME (metabolism and macromolecular expression) models can predict the interplay between the expression of macromolecules and the metabolic state of an organism under a given genetic and environmental condition. Using a ME-model of P. aeruginosa, we intend to determine the genotype-phenotype relationship involved in the expression of EPS and biofilm development. Such knowledge can then be used to predict the possible mechanisms to disrupt biofilms and treat infections caused by P. aeruginosa.

11:20 AM-11:40 AM
Genome-scale metabolic modelling reveals key features of a minimal gene set
Format: Pre-recorded with live Q&A

  • Jean-Christophe Lachance, Université de Sherbrooke, Canada
  • Dominick Matteau, Université de Sherbrooke, Canada
  • Joëlle Brodeur, Université de Sherbrooke, Canada
  • Colton Lloyd, UC San Diego, United States
  • Nathan Mih, UC San Diego, United States
  • Zachary King, UC San Diego, United States
  • Adam Feist, UC San Diego, United States
  • Berhard Palsson, UC San Diego, United States
  • Jonathan Monk, UC San Diego, United States
  • Pierre-Étienne Jacques, Université de Sherbrooke, Canada
  • Sébastien Rodrigue, Université de Sherbrooke, Canada

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Minimal organisms represent a stepping stone for the rational design of entire genomes. Mesoplasma florum, a fast-growing near-minimal organism for which genetic engineering techniques have been developed, is an interesting model for this task. Using sequence and structural homology, the set of metabolic functions encoded in its genome was identified, allowing the reconstruction of a metabolic network covering ~30% of its proteome. Experimental biomass composition, defined media compositions, substrate uptake and secretion rates were integrated as species-specific constraints to produce a functional model. Sensitivity analysis revealed oxygen dependency for the secretion of acetate, consistent with M. florum’s known facultative anaerobe phenotype. The model was validated and refined using genome-wide expression and essentiality datasets as well as growth data on varying carbohydrates. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. The validated model, along with essentiality data and the complete transcription units architecture were used for the design of a reduced genome, thereby targeting 167 genes for removal.

12:00 PM-12:40 PM
Robotic mapping and generative modelling of cytokine response
Format: Live-stream

  • Paul Francois, McGill University, Canada

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We have developed a robotic platform allowing us to monitor cytokines dynamics (including IL-2, IFNgamma, TNF alpha, IL-6) of immune cells in vitro, with unprecedented resolution. To understand the complex emerging dynamics, we use interpretable machine learning techniques to build a generative model of cytokine response. We discover that, surprisingly, immune activity is encoded into one global parameter, encoding ligand antigenic properties and to a less extent ligand quantity. Based on this we build a simple interpretable model which can fully explain the broad variability of cytokines dynamics. We validate our approach using different lines of cells and different ligands. Two processes are identified, connected to timing and intensity of cytokine response, which we successfully modulate using drugs or by changing conditions such as initial T cell numbers. Our work reveals a simple "cytokine code", which can be used to better understand immune response in different contexts including immunotherapy. More generally, it reveals how robotic platforms and machine learning can be leveraged to build and validate systems biology models.
Work in collaboration with Grégoire Altan-Bonnet, National Cancer Institute

2:00 PM-2:40 PM
Cross-Species Translation of Biological Information via Computational Systems Modeling Frameworks
Format: Live-stream

  • Douglas A. Lauffenburger , United States

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A vital challenge that the vast majority of biological research must address is how to translate observations from one physiological context to another—most commonly from experimental animals (e.g., rodents, primates) or technological constructs (e.g., organ-on-chip platforms) to human subjects. This is typically required for understanding human biology because of the strong constraints on measurements and perturbations in human in vivo contexts. Direct translation of
observations from experimental animals to human subjects is generally unsatisfactory because of significant differences among organisms at all levels of molecular properties from genome to transcriptome to proteome and so forth. Accordingly, addressing inter-species translation requires an integrated experimental/computational approach for mapping comparable but not identical molecule-to-phenotype relationships. This presentation will describe methods we have developed for a variety of cross-species translation examples, demonstrated on applications in inflammatory pathologies and cancer.

2:40 PM-3:00 PM
Towards a Human Whole-Cell Model: A Prototype Model of Human Embryonic Stem Cells
Format: Pre-recorded with live Q&A

  • Jonathan Karr, Icahn School of Medicine at Mount Sinai, United States
  • Yin Hoon Chew, Icahn School of Medicine at Mount Sinai, United States

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Whole-cell (WC) models that integrate diverse data into a mechanistic understanding of every gene function could transform medicine and bioengineering. Towards this goal, we are prototyping a WC model of human embryonic stem cells, which have great potential for regenerative medicine. First, we combined multi-omics, biochemical, and physiological data into a structured knowledge base. Second, we used the knowledge base to generate submodels of multiple pathways, including metabolism, transcription, translation, macromolecular complexation, and RNA and protein degradation. Next, we integrated the submodels into a single model, and used the knowledge base to estimate the parameters and initial conditions of the model. We are using a multi-algorithmic approach to simulate the wide range of concentrations and fluxes involved in the model. We use stochastic simulation to simulate slow pathways such as transcription, we use flux balance analysis to simulate fast pathways such as metabolism, and we synchronize the shared variables of these sub-simulations throughout each simulation. To enable this work, we are developing several new databases, methods, and tools for building and simulating large models. Going forward, we aim to incorporate additional submodels of signal transduction and cell cycle regulation, and use the model to gain insights into stem cell self-renewal.

3:20 PM-3:40 PM
Whole-body regeneration and size-dependent fission controlled by a self-regulated Turing system in planaria
Format: Pre-recorded with live Q&A

  • Daniel Lobo, UMBC, United States

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Planarian worms have the extraordinary ability to regenerate any body part after an amputation. This ability allows them to reproduce asexually by fission, cutting themselves to produce two separated pieces each repatterning and regenerating a complete animal. The induction of this process is known to be dependent on the size of the worm as well as on environmental factors such as population density, temperature, and light intensity. Models based on Turing systems can explain the self-regulation of many biological mechanisms, from skin patterns to digit formation. Here, we combine experimental evidence with a modeling approach to show how a cross-inhibited Turing system can explain at once both the signaling mechanism of regeneration and fission in planaria. The model explains in a growing domain the precise signals that control the regeneration of the different body parts after amputations as well as when and where planaria fission, and its dependence on the worm length. We provide molecular implementations of the proposed model, which also explains the effects of environmental factors in the signaling of fission. In summary, the proposed controlled cross-inhibited Turing system represents a completely self-regulated model of the whole-body regeneration and fission signaling in planaria.

3:40 PM-4:00 PM
Clb3-centered regulations are pivotal for autonomous cell cycle oscillator designs in yeast
Format: Live-stream

  • Thierry Mondeel, University of Amsterdam, Netherlands
  • Christian Linke, University of Amsterdam, Netherlands
  • Silvia Tognetti, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
  • Hans Westerhoff, University of Amsterdam, Netherlands
  • Francesc Posas, UPF, Spain
  • Matteo Barberis, University of Surrey, United Kingdom

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Some biological networks exhibit oscillations in their components to convert stimuli to time-dependent responses. The eukaryotic cell cycle is such a case, being governed by waves of cyclin-dependent kinase (cyclin/Cdk) activities that rise and fall with specific timing and guarantee its timely occurrence. Disruption of cyclin/Cdk oscillations could result in dysfunction through reduced cell division. Therefore, it is of interest to capture properties of network designs that exhibit robust oscillations. Here we show that a minimal cell cycle network in budding yeast is able to oscillate autonomously, and that cyclin/Cdk-mediated positive feedback loops (PFLs) and Clb3-centered regulations sustain cyclin/Cdk oscillations, in known and hypothetical network designs. An integrative, computational and experimental approach pinpoints how robustness of cell cycle control is realized by revealing a novel and conserved principle of design that ensures a timely interlock of transcriptional and cyclin/Cdk oscillations. Given the evolutionary conservation of the cell cycle network across eukaryotes, the cyclin/Cdk network can be used as a core building block of multi-scale models that integrate regulatory modules to address cellular physiology.

4:00 PM-4:20 PM
Robust Inference of Kinase Activity Using Functional Networks
Format: Pre-recorded with live Q&A

  • Serhan Yılmaz, Case Western Reserve University, United States
  • Marzieh Ayati, University of Texas Rio Grande Valley, United States
  • Daniela Schlatzer, Case Western Reserve University, United States
  • A. Ercument Cicek, Bilkent University, United States
  • Mark Chance, Case Western Reserve University, United States
  • Mehmet Koyutürk, Case Western Reserve University, United States

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Recent developments in mass spectrometry (MS) enable high-throughput screening of phospho-proteins across a broad range of biological contexts. Phospho-proteomic data complemented by computational algorithms enable the inference of kinase activity facilitating the identification of dysregulated kinases in various diseases, including cancer, Alzheimer’s disease and Parkinson’s disease, among others. However, the inadequacy of known kinase-substrate associations and the incompleteness of MS-based phosphorylation data pose important limitations on the inference of kinase activity. With a view to enhancing the reliability of kinase activity inference, we present a network-based framework named RoKAI that integrates various sources of functional information. These functional information include structure distance, co-evolution evidence, shared kinase associations, and protein-protein interaction networks. By propagating phosphorylation data across these networks, RoKAI obtains representative phosphorylation profiles capturing coordinated changes in signaling. The resulting phosphorylation profiles can be used in conjunction with any existing or future inference methods to predict kinase activity. The results of our computational experiments show that RoKAI consistently improves the accuracy of commonly used kinase activity inference methods and makes them more robust to missing kinase-substrate annotations. To provide an easy to use interface to users, RoKAI is available as a web-based tool at http://rokai.ngrok.io.

4:20 PM-4:40 PM
Probabilistic Factor Graph Modeling and Analysis of Biological Networks
Format: Pre-recorded with live Q&A

  • Stephen Kotiang, Wichita State University, United States
  • Ali Eslami, Wichita State University, United States

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Reverse engineering of molecular networks from biological data is one of the most challenging tasks in systems biology. Numerous inference and computational methodologies have been formalized to enhance the deduction of reliable and testable predictions in today’s biology. However, there is little research aimed to quantify how well the existing state-of-the-art molecular networks correspond to the measured gene expressions. We present a computational framework that combines formulation of probabilistic graphical model, standard statistical estimation, and integration of high-throughput gene expression data. The model is represented as a probabilistic bipartite graph, which accommodates partial information of diverse biological entities to study and analyze the global behavior of a biological system. This result provides a building block for performing simulations on the consistency between inferred gene regulatory networks and corresponding biological data. We test the applicability of our model to explore the allowable stable-states in two experimentally verified regulatory pathways in Escherechia Coli using real microarray expression data from the M3D database. Furthermore, the model is employed to quantify how well the pathways are explained by the extant microarray data. Results show a surprisingly high correlation between the observed states of the experimental data under various conditions and the inferred system’s behavior.

5:00 PM-5:20 PM
Executable models of pathways built using single-cell RNAseq data reveal immune cell heterogeneity in people living with HIV and atherosclerosis
Format: Pre-recorded with live Q&A

  • Mukta G. Palshikar, University of Rochester, United States
  • Rohith Palli, University of Rochester, United States
  • Giovanni Schiffito, University of Rochester, United States
  • Sanjay Maggirwar, George Washington University School of Medicine and Health Sciences, United States
  • Juilee Thakar, University of Rochester, United States

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Single-cell RNA-sequencing (scRNA-seq) enables the profiling of mixed tissues at unprecedented resolution. Clustering of these sequenced cells into cell types is a major challenge, compounded by cell heterogeneity in terms of signaling pathways that cause observed phenotypes. Knowledge-driven methods utilize cell type specific marker genes for clustering. However, these methods do not account for the pathway-level transcriptional differences that define cell types.

We propose a knowledge-driven clustering method using Boolean modeling of transcriptional networks. We have previously developed an algorithm called BONITA that infers Boolean rules characterizing pre-defined topologies from bulk transcriptomic data [1]. We demonstrate the utility of our approach on an scRNA-seq dataset of peripheral blood mononuclear cells from HIV+ individuals with and without atherosclerosis. Specifically, we (i) simulated networks and identified reachable attractors (ii) identified subpopulation-specific pathways dysregulated in HIV-associated atherosclerosis and (iii) clustered cells in multi-attractor space. We propose that cell clusters such as CD14+ CD16+ monocytes, that are implicated in HIV-associated atherosclerosis, can be characterized based on pathway-level similarities obtained using Boolean pathway models.

1. Palli R, Palshikar MG, Thakar J (2019) Executable pathway analysis using ensemble discrete-state modeling for large-scale data. PLoS Comput Biol 15(9): e1007317.

5:20 PM-5:40 PM
Modeling Sorting, Intercalation, and Involution Tissue Behaviors due to Regulated Cell Adhesion
Format: Pre-recorded with live Q&A

  • Daniel Lobo, UMBC, United States
  • Jason Ko, UMBC, United States

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Cell-cell adhesion can dictate tissue growth and multicellular pattern formation and it is crucial for the cellular dynamics during embryogenesis and cancer progression. While it is known that these adhesive forces are generated by cell adhesion molecules (CAMs), the regulation of CAMs is not well understood due to complex nonlinear interactions that span multiple levels of biological organization–from genetic regulation to whole-organism shape formation. We present a novel continuous model that can explain the dynamic relationships between genetic regulation, CAM expression, and differential adhesion. This approach can demonstrate the mechanisms responsible for cell-sorting behaviors, cell intercalation in proliferating populations, and the involution of zebrafish germ layer cells during gastrulation. The model can predict the physical parameters controlling the amplitude and wavelength of a cellular intercalation interface as shown in vitro. We demonstrate the crucial role of N-cadherin regulation for the involution and migration of cells beyond the gradient of the morphogen Nodal during zebrafish gastrulation. Integrating the emergent spatial tissue behaviors with the regulation of genes responsible for essential cellular properties such as adhesion will pave the way toward understanding the genetic regulation of large-scale complex patterns and shapes formation in developmental, regenerative, and cancer biology.

5:40 PM-6:00 PM
Closing remarks of the first SysMod day
Format: Live-stream

  • Juilee Thakar, University of Rochester, United States

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A brief recap of the first day of the meeting.

Tuesday, July 14th
10:40 AM-11:00 AM
A stochastic hybrid model for DNA replication incorporating protein mobility dynamics
Format: Pre-recorded with live Q&A

  • Maria Rodriguez Martinez, IBM Research Zurich, Switzerland
  • Jonas Windhager, IBM Research Zurich, Switzerland
  • Amelia Paine, IBM Research Zurich, United States
  • Patroula Nathanailidou, School of Medicine, University of Patras, Greece
  • Eve Tasiudi, Automatic Control Lab, ETH Zurich, Switzerland
  • Zoi Lygerou, School of Medicine, University of Patras, Greece
  • John Lygeros, Automatic Control Lab, ETH Zurich, Switzerland
  • Maria Anna Rapsomaniki, IBM Research Zurich, Switzerland

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DNA replication is a complex process that ensures genetic information maintenance. As recently observed, DNA replication timing is highly correlated with chromatin folding and global nuclear architecture. Here, we present a stochastic hybrid model of DNA replication that incorporates protein mobility and three-dimensional chromatin structure. Our model provides a framework to realistically simulate DNA replication for a complete eukaryotic genome and investigate the relationship between three-dimensional chromatin conformation and replication timing. Performing simulations for three model variants and a broad range of parameter values, we collected about 300,000 in silico replication profiles for fission yeast. We find that the number of firing factors initiating replication is rate-limiting and dominates the DNA replication completion time. We also find that explicitly modeling the recruitment of firing factors by the spindle pole body best reproduces experimental data and provide an independent validation of these findings in vivo. Further investigation of replication kinetics confirmed earlier observations of a rate-limiting number of firing factors in conjunction with their recycling upon replication fork collision. While the model faithfully reproduces global patterns of replication initiation, additional analysis of firing concurrence suggests that a uniform binding probability is too simplistic to capture local neighborhood effects in origin firing.

11:00 AM-11:40 AM
Quantitative and Systems Pharmacology (QSP) and Model-Informed Drug Development (MIDD) of a “Smart” Insulin
Format: Live-stream

  • Carolyn Cho , United States

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One of the many challenges of drug development programs is the strictly limited volume of pharmacological data for investigatory drug candidates. To help manage the risk of exposing volunteers to drug candidates of unknown safety and efficacy while evaluating hypothesized benefits, programs seek to integrate all relevant data including molecular and cellular profiling data and clinical trial electronic data capture. The integration of relevant data has been used successfully to help guide decisions at each step of the development process. The success of such efforts across the industry is evidenced by the FDA’s launch of a MIDD pilot program to facilitate the use of quantitative methods to ultimately help improve clinical trial efficiency and optimize therapeutic individualization in the absence of dedicated trials.

The integration of quantitative methods was used to guide validation of putative glucose dysregulation targets and to guide the design of a complex clinical trial of glucose control. Although every program has individual challenges, these case studies will be discussed to highlight open questions in the application of quantitative systems biology in drug development.

12:00 PM-12:20 PM
Cellular robustness is not a byproduct of environmental flexibility
Format: Pre-recorded with live Q&A

  • Jürgen Zanghellini, Department of Analytical Chemistry, University of Vienna, Austria
  • Julian Julian Libiseller-Egger, epartment of Biotechnology, University of Natural Resources and Life Sciences, Austria
  • Benjamin L. Coltman, epartment of Biotechnology, University of Natural Resources and Life Sciences, Austria
  • Matthias P Gerstl, Austrian Centre of Industrial Biotechnology, Austria

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Cells show remarkable resilience against perturbations. Its evolutionary origin remains obscure. In order to leverage methods of systems biology to examine cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in (genome-scale) metabolic models based on concepts prevalent in reliability engineering.

The probability of failure (PoF) is defined as the (weighted) portion of all combinations of loss-of-function mutations that disable network functionality. It can be exactly determined if all essential reactions, all synthetic lethal pairs of reactions, all synthetic lethal triplets of reactions etc., are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. We demonstrate that even at the genome-scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF.

We analyzed the robustness of 459 E. coli, Shigella, and Salmonella strains. In contrast to the congruence theory, which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that the core-reactome, i.e. the set of reactions shared across strains, dominates robustness.

12:20 PM-12:40 PM
Closing remarks of the SysMod meeting 2020
Format: Live-stream

  • Andreas Dräger, University of Tübingen, Institute for Bioinformatics and Medical Informatics (IBMI), Germany

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A brief recap of the two meeting days and the SysMod community