Sponsors

• Claudine Chaouiya

The community of particular interest (COSI) in systems modeling (SysMod) organizes annual gatherings. In 2021 the meeting comprises six sessions covering various topics, beginning with two sessions on disease and multi-scale modeling, followed by a dedicated session on infectious diseases. It concludes with two sessions on integrative approaches and methodologies and one session on structure-based dynamic modeling. As a highlight, three keynote speakers will outline the trend-setting developments in these fields: Ines Thiele, Ruth E. Baker, and Boris N. Kholodenko. Juilee Thakar will close the event by bestowing this year’s poster awards. The event is hosted by Claudine Chaouiya, Anna Niarakis, Andreas Dräger, Laurence Calzone, and Matteo Barbaris on behalf of the team of all twelve COSI organizers. The first meeting day includes a virtual social event. This brief talk introduces all speakers, organizers, and the main topics of the 2021 meeting.

• Ines Thiele

Precision medicine relies on the availability of realistic, mechanistic models that capture the complexity of the human body. Comprehensive computational models of human metabolism have been assembled by the systems biology community, which summarise known metabolic processes occurring in at least one human cell or organ. However, these models have not yet been expanded to connect with whole-body level processes. To address this shortcoming, we have built whole-body metabolic models of a male (deemed Harvey) and a female (deemed Harvetta) starting from the existing human metabolic models, physiological and anatomic information, comprehensive proteomic and metabolomic data, as well as biochemical data obtained from an extensive manual literature review. We tested the predictive capabilities of the resulting whole-body metabolic models against the current knowledge of organ-specific and inter-organ metabolism. The final models contain 28 organs. Importantly, these whole-body models can be expanded to include the strain-resolved metabolic models of gut microbes. By parameterising the whole-body metabolic models with physiological and metabolomic data, we connected physiology with molecular-level processes through networks of genes, proteins, and biochemical reactions. As a sample application of the whole-body metabolic models, I will demonstrate how different microbial composition leads to differences in host metabolism, such as the capability to produce important neurotransmitters in the brain and flux through liver enzymes, with implications for the gut-brain axis as well as for microbiome-mediated liver toxicity. The predictions were consistent with our current understanding but also highlighted that different microbiota composition can lead to high inter-person variability. I envisage the microbiome-associated whole-body metabolic models will usher in a new era for research into causal host-microbiome relationships and greatly accelerate the development of targeted dietary and microbial intervention strategies.

• Andreas Dräger, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Reihaneh Mostolizadeh, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Manuel Glöckler, Department of Computer Science, University of Tübingen, Germany

The human nose harbors diverse microbes that play a fundamental role in the well-being of their host. Still, the contribution of many nasal microorganisms to human health remains undiscovered. Among all microbial species in the human nose, Staphylococcus aureus belongs to the most common human nasal pathogens. Multiple epidemiological studies identify Dolosigranulum pigrum as a candidate beneficial bacterium based on its positive association with health, including negative associations with S. aureus.

In this work, we propose a workflow for understanding the composition of the nasal microbiome community and the intricate interplay from a metabolic modeling perspective. To this end, we first create a basic community model that mimics the human nasal environment. Then we incorporate accurate genome-scale metabolic network models of D. pigrum and S. aureus.

Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method can open developing new ways to inhibit S. aureus and corresponding disease-causing infections through microbe-microbe interactions.

• Paul Lang, University of Oxford, Icahn School of Medicine at Mount Sinai, United Kingdom
• Yassmine Chebaro, Icahn School of Medicine at Mount Sinai, United States
• Xiaoyue Zheng, Icahn School of Medicine at Mount Sinai, United States
• John A. P. Sekar, Icahn School of Medicine at Mount Sinai, United States
• Bilal Shaikh, Georgetown University Medical Center, United States
• Darren A. Natale, Georgetown University Medical Center, United States
• Jonathan R. Karr, Icahn School of Medicine at Mount Sinai, United States

A central goal in systems biology is to understand how all of the molecules and processes in cells interact to generate behavior. While small molecules can be concretely described by molecular formats such as SMILES, macromolecules are frequently described as sequences of canonical residues. However, non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. One barrier towards models that explain how networks of such non-canonical macromolecules perform complex functions is our limited formats for concretely describing them. To overcome this barrier, we develop BpForms and BcForms, a toolkit for unambiguously representing the primary structure of macromolecules as combinations of residues, caps, crosslinks, and nicks. The toolkit can help omics researchers perform quality control and exchange information about macromolecules, help systems biologists assemble global models of cells that encompass processes such as post-translational modification, and help bioengineers design cells.

• Marco Ruscone, Curie Institute, France
• Arnau Montagud, Institute Barcelona Supercomputing Center (BSC), Spain
• Emmanuel Barillot, Curie Institute, France
• Andrei Zinovyev, Curie Institute, France
• Laurence Calzone, Curie Institute, France
• Vincent Noel, Institute Curie, France

Motivation: Mathematical models of biological processes are often represented as complex networks of signaling pathways, describing intracellular behaviors of specific cell types (epithelial, T cells, etc.). However, this representation prevents us from describing spatial information or cell-cell interactions, that plays an important role in the dynamics and gives a more complete view of a biological process. To test the effectiveness that comes from merging models of signalling pathways with spatial models of cell populations, we present a model of cell invasion made with PhysiBoSS, a multiscale framework which combines agent-based simulation and continuous time Markov processes applied on Boolean network.
Methods:The purpose of this model is to study the different modes of cell migration through an extracellular matrix by combining the spatial information obtained from the agent simulation and the intracellular information obtained from the possible stable states of the transcription factors signalling network. This includes different pathways involved in cell fates processes that can lead to death, proliferation, quiescence and invasion.
Results: The model allows to simulate different initial conditions and mutations, as well as monitoring each cell behaviors using 2D and 3D representations, successfully reproducing single, collective and trail migration processes.

• Eirini Tsirvouli, Norwegian University of Science and Technology, Norway
• Felicity Ashcroft, Norwegian University of Science and Technology, Norway
• Berit Johansen, Norwegian University of Science and Technology, Norway
• Martin Kuiper, Norwegian University of Science and Technology, Norway

Psoriasis arises from complex interactions between keratinocytes and infiltrating immune cells in the skin. Chronic psoriatic inflammation is perpetuated by a Th17-dependent intercellular signaling loop, and pro-inflammatory eicosanoids are hypothesized to play a role in this process. We aimed to emulate the regulatory network of psoriatic keratinocytes with a logical model representing current knowledge about disease mechanisms and use this to study modes of action of cPLA2 inhibitors, alone or in combination with other drugs, and explore the cytokine-mediated signaling that takes place in psoriatic lesions. Through the integration of in vitro and in silico experimentation, we describe the PLA2-dependent release of PGE2 in response to Th17 cytokines. Further analyses of the computational model revealed the immunomodulatory role of Th1 cytokines, the modulation of the physiological states of keratinocytes by Th17 cytokines, and how together they promote the development of psoriasis. The response to treatment with cPLA2 inhibitor and/or Calcipotriol revealed a distinct mode of action of the two drugs. Lastly, novel entities were identified as potential drug targets that could restore a normal phenotype. In addition to contributing to the knowledge about psoriasis, this work showcases how the study of complex diseases can benefit from integrated systems approaches.

• Danilo Dursoniah, Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France, France
• Maxime Folschette, Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France, France
• Cedric Lhoussaine, Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France, France
• Rebecca Goutchtat, Univ. Lille, Inserm, CHU Lille, U1190 - EGID, F-59000 Lille, France, France
• Francois Pattou, Univ. Lille, Inserm, CHU Lille, U1190 - EGID, F-59000 Lille, France, France
• Violeta Raverdy, Univ. Lille, Inserm, CHU Lille, U1190 - EGID, F-59000 Lille, France, France

Type 2 Diabetes (T2D) is a major epidemic characterized by an increased blood
glucose resulting from a defect of insulin secretion and insulin sensitivity.
Available drugs targeting these mechanisms do not cure T2D. The intestinal
absorption of ingested glucose is an underestimated but significant contributor
to T2D. Since the 1960, hundreds of mathematical models have been proposed with
the overall objective of predicting the dynamics of glucose homeostasis with
increasing accuracy. Despite decades of research and massive funding, the
underlying mechanisms of T2D remain poorly understood.

In this preliminary work, we consider one of the most cited model of
postprandial glucose response and explore its limits. First, we evaluate this
model to predict an original dataset of obese diabetic patients. Second, based
on partial parameter estimation, we investigate the capability of this model to
predict which physiological compartments (intestine, pancreas, liver, etc.) are
most likely able to restore a normal glycemia from a pathological one.
Considering the parameter values given by the original authors of the model, we
show that the model predictions are as expected. However, considering our own
experimental dataset, the model fails to predict how the glucose homeostasis is
altered after a physiological modification of intestinal absorption.

• Matteo Barberis, University of Surrey, United Kingdom
• Tomáš Helikar, University of Nebraska-Lincoln, United States
• Bhanwar Lal Puniya, University of Nebraska-Lincoln, United States
• Rada Amin, University of Nebraska-Lincoln, United States
• Bailee Lichter, University of Nebraska-Lincoln, United States
• Robert Moore, University of Nebraska-Lincoln, United States
• Alex Ciurej, University of Nebraska-Lincoln, United States
• Sydney Bennett, University of Nebraska-Lincoln, United States
• Ab Rauf Shah, University of Nebraska-Lincoln, United States
• Zhongyuan Zhao, University of Nebraska-Lincoln, United States
• Brandt Bessell, University of Nebraska-Lincoln, United States

CD4+ T cells provide cell-mediated protection against diseases. When dysregulated, CD4+ T cells are associated with autoimmune and other immune-mediated diseases. Metabolism of CD4+ T cells regulates their function, therefore offer an opportunity to explore as a drug target against autoimmune diseases. In this study, we developed constraint-based models of naive and T helper 1, 2, and 17 subtypes. We mapped existing drugs and compounds and simulated metabolic behaviors under drug-induced inhibitions of metabolic genes. We integrated these metabolic behaviors with gene expression data of three autoimmune diseases, rheumatoid arthritis (RA), multiple sclerosis (MS), and primary biliary cholangitis (PBC). We identified and prioritized drugs and their targets that reversed the directions of differentially expressed genes in diseases. We identified 68 metabolic drug targets for the three studied diseases. We performed in vitro experiments and mined experiments available in the literature to validate results. The experimental results showed that 50% of the drug targets suppressed CD4+ T cell proliferation. In the end, we developed an integrated pipeline to explore metabolic models to identify drug targets and repurposable drugs.

• Anna Niarakis

We are closing the session on “Disease and multi-scale modeling” with a brief intervention from experts of the field to launch a discussion on current limitations and future challenges for the effective modeling of diseases and multi-scale biological processes. Finally, the panel will conclude with a brief review of the first two SysMod sessions and prepare the audience for the upcoming infectious diseases sessions.

• Josua Aponte-Serrano, Indiana University Bloomington Department of Physics, United States
• Jordan J.A. Weaver, University of Pittsburgh Department of Chemical & Petroleum Engineering, United States
• T.J. Sego, Indiana University Bloomington Department of Physics, United States
• James Glazier, Indiana University Bloomington Department of Physics, United States
• Jason E. Shoemaker, University of Pittsburgh Department of Chemical & Petroleum Engineering, United States

Respiratory viruses present major health challenges, as evidenced by the 2009 influenza pandemic and the ongoing SARS-CoV-2 pandemic. Severe virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestation at the cell and tissue levels are vital to understanding the mechanisms of immunopathology and developing strain independent treatments. Here, we present a multicellular spatial model of two principal components: RNA virus replication and type-I interferon mediated antiviral response to infection within lung epithelial cells. The model exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. Modulating the phosphorylation of STAT or altering the ratio of the diffusion constants of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of arrest on diffusion constants highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro experiments to measure these diffusion constants. Findings suggest that plaque growth and cytokine assay measurements should be collected during arrested plaque growth, as the model parameters are significantly more sensitive and more likely to be identifiable.

• Lauren Benoodt, University of Rochester School of Medicine and Dentistry, United States
• Mukta G. Palshikar, University of Rochester School of Medicine and Dentistry, United States
• Meera V. Singh, University of Rochester School of Medicine and Dentistry, United States
• Giovanni Schifitto, University of Rochester School of Medicine and Dentistry, United States
• Sanjay B. Maggirwar, University of Rochester School of Medicine and Dentistry, United States
• Juilee Thakar, University of Rochester School of Medicine and Dentistry, United States

Typical analysis of large-scale data utilizes identification of differentially expressed genes and enrichment analysis for mechanistic information. Enrichment analysis uses predefined sets of genes such as gene-sets from literature. Gene-sets do not have network topology or regulatory information. Enrichment analysis is limited in single-cell transcriptomic data, fewer genes are measured compared to bulk sequencing. To enhance regulatory information from gene-sets we are developing an algorithm optimizing gene-set subnetworks, using differential network analysis across experimental groups. Specifically, we use a genetic algorithm(GA) to optimize subnetworks of co-expression networks informed by gene-sets. The optimization function utilizes prior information, such as transcriptional regulation. We investigate novel regulators of atherosclerosis(AS) in people living with HIV. In a set of cells expressing naive T-cell markers we identified associations among 360(AS-) and 304(AS+) genes using mutual information(MI)(MI>(MIµ±2σ)). These contain genes related to an inflammatory response in HIV infection. Gene-sets from MSigDB were selected based on overlap with co-expression networks. After optimization, gene-sets with differential degree centrality were identified which reveal cell type-specific dysregulation of gene-sets and can be used to determine novel targets for interventions and future experiments. STREGA-NONA integrates knowledge with data to identify gene-set specific topology and differential activity using single-cell RNAseq data.

• Dawson D. Payne, Department of Biomedical Engineering, University of Virginia, United States
• Alina Renz, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Laura J. Dunphy, Department of Biomedical Engineering, University of Virginia, United States
• Taylor Lewis, Department of Chemical Engineering, University of Massachusetts Amherst, United States
• Andreas Dräger, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Jason A. Papin, Department of Biomedical Engineering, University of Virginia, United States

Mucins are present in mucosal membranes throughout the body and play a key role in microbe clearance and infection prevention. Thus, understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through Memote, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and differential utilization of fumarate metabolism while also providing a novel insight into increased propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing novel biological insights.

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

A highly curated genome-scale metabolic model of cystic fibrosis (CF) pathogen, Pseudomonas aeruginosa PA14 was developed in this study. During the reconstruction process, a set of substrate utilization and gene-essentiality data was used to improve the predictive abilities of the model. Furthermore, strain-specific processes (e.g., phenazine transport and redox metabolism, cofactor metabolism, carnitine metabolism, oxalate metabolism, etc.) were added to the reconstruction after a thorough literature review. Through this extensive process, a three-compartment, BiGG model, iSD1511 was created which is a significant improvement over the previous modeling effort for this strain. iSD1511 was assessed using another set of gene essentiality and substrate utilization data, and the prediction accuracies as high as 92.7% and 93.5%, respectively, were computed. The model can simulate growth in both aerobic and anaerobic conditions. iSD1511 can also be used to simulate condition-dependent production of phenazines and their effect on the growth of PA14 strain. Finally, the model was used to provide mechanistic explanations for two case studies: a) effect of gene deletion on gluconate production, and b) metabolic influence on drug tolerance of P. aeruginosa. Overall, this work provides a computational framework to aid in the development of effective intervention strategies against P. aeruginosa.

• Laurence Calzone

This brief talk will welcome the audience and speakers to the second day of the annual SysMod meeting in 2021 and announce the schedule of today’s meeting. There will be two sessions on integrative approaches and methodologies, followed by the final keynote talk by Boris N. Kholodenko about structure-based dynamic modeling, as well as Juilee Thakar’s announcement of this year’s poster awards.

• Ruth E. Baker

Simple mathematical models have had remarkable successes in biology, framing how we understand a host of mechanisms and processes. However, with the advent of a host of new experimental technologies, the last ten years has seen an explosion in the amount and types of quantitative data now being generated. This sets a new challenge for the field – to develop, calibrate and analyse new, biologically realistic models to interpret these data. In this talk I will showcase how quantitative comparisons between models and data can help tease apart subtle details of biological mechanisms, as well as present some steps we have taken to tackle the mathematical challenges in developing models that are both identifiable and can be efficiently calibrated to quantitative data.

• Nantia Leonidou, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Alina Renz, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Reihaneh Mostolizadeh, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany
• Andreas Dräger, Computational Systems Biology, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Germany

COVID-19 has been characterized as one of the deadliest respiratory diseases. In this regard, scientists globally try to understand the host’s immunopathological response, how the novel coronavirus (SARS-CoV-2) adapts, and how it spreads. Currently, great efforts are made to detect effective therapies against the coronaviruses. Identifying potential antiviral targets is of great interest, and one way to detect them is by analyzing metabolic changes in infected cells. In 2012, Wang et al. published mCADRE, aiming to reconstruct tissue-specific models using gene expression data and network topology information. The algorithm is implemented in MATLAB, and its functionality is based solely on the first version of the human model resulting in its limited usability.

We present pymCADRE, a re-implementation of mCADRE in Python 3.8. Its functionality was tested using all three currently available versions of the human metabolic network. Internal optimizations done with fastFVA resulted in context-specific models closer to the ground truth. Additionally, host-virus models were created to help to identify potential antiviral targets against SARS-CoV-2. With those models, the recently identified potential antiviral target enzyme guanylate kinase was further investigated. Further improvements could be done to make it feasible with more complex models, like Recon2.2 and Recon3D.

• Melania Nowicka, Freie Universiteat Berlin, Germany
• Heike Siebert, Freie Universiteat Berlin, Germany

Cell classifiers are synthetic bio-devices performing type-specific, in vivo classification of the cell's molecular fingerprint. In particular, they can recognize cancerous cells and trigger their apoptosis, shaping novel therapies for cancer patients. Although a single circuit’s processing logic is usually described using a Boolean function, other architectures have also been considered, e.g. multi-circuit designs. Distributed classifiers consist of a group of single-circuit classifiers deciding collectively according to a predefined threshold function whether a cell is cancerous. Such architecture has shown the potential to predict the cell condition with high accuracy. However, the lack of far-reaching machinery to design and evaluate the classifiers, in particular, assessing their robustness to noise and novel information, makes their application limited. Here, we present a framework for designing miRNA-based distributed cell classifiers combining genetic algorithms and logic programming. We develop optimization criteria comprising the accuracy and robustness of the circuits that allow achieving high performance as shown in multiple simulated data studies. The evaluation performed on real cancer data demonstrates that distributed classifiers outperform single-circuit designs. Our classifiers include relevant miRNAs previously described in the literature, as well as more complex regulation patterns included in the data.

• Aurelien Pelissier, IBM Research, Switzerland
• Miroslav Phan, ETH Zurich, Switzerland
• Niko Beerenwinkel, ETH Zurich, Switzerland
• Maria Rodriguez Martinez, IBM, Zurich Research Laboratory, Switzerland

The Gillespie algorithm is commonly applied for simulating memoryless processes that follow an exponential waiting-time. However, stochastic processes governing biological interactions, such as cell apoptosis and epidemic spreading, are empirically known to exhibit properties of memory, an inherently non-Markovian feature. The presence of such non-Markovian processes can significantly influence the outcome of a simulation, While several extensions to the Gillespie algorithm have been proposed, most of them suffer from either a high computational cost or are only applicable to a narrow selection of probability distributions that do not match the experimentally observed biological data distributions. To tackle the aforementioned issues, we developed a Rejection Gillespie for non-Markovian Reactions (REGINR) that is capable of generating simulations with non-exponential waiting-times, while remaining an order of magnitude faster than alternative approaches. REGINR uses the Weibull distribution, which interpolates between the exponential, normal, and heavy-tailed distributions. We applied our algorithm to a mouse stem cell dataset with known non-Markovian dynamics and found it to faithfully recapitulate the underlying biological processes. We conclude that our algorithm is suitable for gaining insight into the role of molecular memory in stochastic models, as well as for accurately simulating real-world biological processes.

• Natasa Miskov-Zivanov, University of Pittsburgh, United States
• Emilee Holtzapple, University of Pittsburgh, United States
• Brent Cochran, Tufts University, United States

Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer that has a 5-year survival rate of about 5%. The low survival rate can be at least partially attributed to its heterogeneity. The presence of multiple genetically distinct clones within the solid tumor, as well as its stem cell nature, leads to treatment resistance. Predicting treatment resistance or susceptibility is difficult, even when the tumor has been classified according to genetic subtype. These classifications rely on few biomarkers and are informed by limited mechanistic details. Here we present a discrete computational model of GBM stem cell dynamics, which has been informed by expert knowledge, biomedical literature, and biological data. We show that the interactions within the model are well-supported by both literature and database sources. Our approach for parameterizing the model includes fitting to biological data such as gene expression to better predict individual cell line responses to treatment. We show that our GBM stem cell model is capable of predicting the success of a high percentage of kinase inhibitor treatments, and that these predictions are cell line specific. Our computational model offers a rapid, individualized approach for predicting drug treatment efficacy across GBM stem cell lines.

• Maurício Moreira-Soares, OCBE and Centre for Bioinformatics, University of Oslo, Norway
• Susana Pinto-Cunha, CQC, Department of Chemistry, University of Coimbra, Coimbra, Portugal
• José R. Bordin, Instituto de Física e Matemática, Departamento de Física, Universidade Federal de Pelotas, Pelotas, Brazil
• Rui D. M. Travasso, CFisUC, Department of Physics, University of Coimbra, Coimbra, Portugal

Understanding the mechanical strategies by which cancer cells migrate within confined spaces is essential to raise novel insights regarding metastasis prevention. We explored a phase-field model where cells are described as droplets with surface tension that interact with the extracellular matrix by adhesion and excluded volume. With the purpose of verifying that our results are model independent, we produced an equivalent system using a Dissipative Particle Dynamics (DPD) approach. We observed that adhesion regulates cell deformability and enhances migration under extreme confinement conditions for both models. Furthermore, we were able to reproduce different experimental conditions, both with and without matrix metalloproteinases (MMPs) inhibition, solely by changing the adhesion coefficient in our models. This might indicate that MMPs act not only on degradation but also play a key role in cell migration and adhesion.

• Marzia Di Filippo, University of Milano-Bicocca, Italy
• Dario Pescini, University of Milano-Bicocca, Italy
• Bruno Giovanni Galuzzi, University of Milano-Bicocca, Italy
• Lilia Alberghina, University of Milano-Bicocca, Italy
• Marco Vanoni, University of Milano-Bicocca, Italy
• Giancarlo Mauri, University of Milano-Bicocca, Italy
• Chiara Damiani, University of Milano-Bicocca, Italy

The study of metabolism and its regulation is finding increasing application in various fields, including health, wellness, and biotransformations. Complete characterization of regulatory mechanisms controlling metabolism requires knowledge of metabolic fluxes, whose direct determination lags behind other omic technologies, such as metabolomics and transcriptomics. In isolation, these methodologies do not allow accurate characterization of metabolic regulation. Hence, there is a need for integrated methodologies to disassemble the interdependence between different regulatory layers controlling metabolism.
To this aim, we propose a computational pipeline to characterize the landscape of metabolic regulation in different biological samples. The method integrates intracellular and extracellular metabolomics, and transcriptomics, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomic data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, using metabolomic data, we predict how differences in substrate availability translate into differences in metabolic fluxes. By intersecting these two output datasets, we discriminate fluxes regulated at the metabolic and/or transcriptional level. This information is valuable to better inform targeted action planning in different fields, including personalized prescriptions in multifactorial diseases, such as cancer, and metabolic engineering.

• Thierry Mondeel, University of Surrey, United Kingdom
• Christian Linke, University of Amsterdam, Netherlands
• Silvia Tognetti, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science, Barcelona, Spain, Spain
• Mart Loog, Institute of Technology, University of Tartu, Tartu, Estonia
• Francesc Posas, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science, Barcelona, Spain, Spain
• Matteo Barberis, University of Surrey, United Kingdom

The eukaryotic cell cycle is driven by waves of cyclin-dependent kinase (cyclin/Cdk) activities that rise and fall with a timely pattern called “waves of cyclins”. This pattern guarantees coordination and alternation of DNA synthesis with cell division, and its failure results in altered cyclin/Cdk dynamics and abnormal cell proliferation. Although details about transcription of cyclins are available, the network motifs responsible for this timely pattern are currently unknown. Here we reveal a novel principle of design that ensures cell cycle time keeping through interlocking transcription with cyclin/Cdk dynamics in budding yeast. Deterministic and stochastic analyses of 1024 kinetic models of the cyclin/Cdk network are verified against quantitative data of Clb dynamics. A novel regulatory design is unravelled, which involves the evolutionarily conserved Forkhead (Fkh) transcription factors. The Fkh-mediated cascade among Clb cyclins and Clb/Cdk1-mediated positive feedback loops are pivotal for synchronizing Clb/Cdk1 waves. Furthermore, our model predicts a definite Fkh activation pattern underlying this design, with a progressive Clb/Cdk1-mediated Fkh phosphorylation. Experimental validation confirms the computational prediction, highlighting the Clb/Cdk–Fkh axis being pivotal for timely cell cycle dynamics. This work rationalizes the quantitative model of Cdk control for budding yeast, identifying the regulatory motifs underlying cell proliferation dynamics.

• Matteo Barberis

This is a brief discussion on the two sessions on Integrative approaches and methodologies and an outlook to the final session about structure-based dynamic modeling and SysMod poster award 2021.

• Boris N. Kholodenko

Major problem encountered using small molecule cancer therapeutics in clinic is that even in susceptible cancers, these drugs rarely give durable responses, almost inevitably being hampered by signaling reactivation and development of resistance. Studying the causes of this resistance has revealed severe limitations in our understanding of the network properties and molecular mechanisms that control drug responses. We show that contrary to a common opinion, feedback loops by themselves cannot restore or overshoot steady state signaling. De novo synthesized negative feedback regulators can lead to a transient overshoot but still cannot fully restore output signaling. These findings can rationalize recent scientific and clinical disappointments that were based on the hypothesis that negative feedback loops can fully explain drug resistance. We demonstrate that there are two major means of complete, steady state revival of signaling, enabled by (1) the network topology or (2) molecular mechanisms rendering the primary drug target active again. Network topology analysis shows that at least two, activating and inhibitory, connection routes from a primary drug target to the output, must exist for complete reactivation or overshoot of steady-state output activity that existed before the inhibition.
Irrespective of the network topology, drug-induced overexpression of the primary drug target or drug-induced increase in its dimerization or oligomerization can restore the pathway output activity. The formation of kinase homo- or heterodimers is a major course of resistance. In this constellation one protomer is drug-bound and allosterically activates the other, drug-free protomer thereby conferring resistance. The emergence of different drug affinities between protomers in a dimer has been enigmatic, but can be explained by thermodynamics (https://www.ncbi.nlm.nih.gov/pubmed/26344764). A striking example is so-called paradoxical activation of the extracellular regulated kinase (ERK) pathway by RAF inhibitors, which is caused by RAF homo- or heterodimerization. This dimerization is promoted by RAF inhibitors and amplified by mutant RAS and negative feedback regulations, but if an inhibitor does not facilitate dimerization, negative feedback can only result in a transient overshoot of the pathway activity. Exciting and counterintuitive discoveries of ways to overcome resistance were made using next generation modelling, which combines aspects of protein structure, posttranslational modifications, thermodynamics, network architecture, mutation data and dynamic reaction mechanisms (https://www.ncbi.nlm.nih.gov/pubmed/30007540). As a specific example, we show that a treatment with Type I½ and Type II RAF inhibitors can counterbalance ERK pathway reactivation and concomitant drug resistance.

• Juilee Thakar

This talk briefly reviews the two days of SysMod, including all the speakers, chairpersons, and organizers. The first day comprised three sessions on disease and multi-scale modeling, with one session on infectious disease modeling in particular. In addition, two keynote talks were given by Ines Thile and Ruth E. Baker. The second day included two sessions on integrative approaches and methodologies and one session on Structure-based dynamic modeling, with the final keynote just given by Boris N. Kholodenko. Next, it is time to thank all contributions to scientific posters and bestow the best ones with the annual SysMod poster awards in 2021. Finally, we will conclude the meeting and open the discussion with the audience for feedback to the next SysMod in 2022.