Modeling Sorting, Intercalation, and Involution Tissue Behaviors due to Regulated Cell Adhesion
Jason Ko and Daniel Lobo
UMBC, US
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.
Multi-Compartment Model (MCM) enables mechanistic interpretations in regulation of transcriptomic dynamics
Xiaotong Liu, Chad Myers, and Fumiaki Katagiri
University of Minnesota, US
Plants’ innate immune response to pathogens is supported by a resilient signaling network that facilitates a robust and specific response when presented with a pathogen. The plant immune signaling network is large (hundreds of molecular components), and its dynamics are complex (e.g. transient response with multiple peaks). We sought a modeling framework based on ordinary differential equations (ODE) that was sufficiently flexible to be applied to a reduced network with complex dynamics. We found that the Multi-Compartment Modeling (MCM) framework, an ODE-based approach used frequently in other domains such as pharmacokinetics and epidemiology, is suitable for this purpose. Each compartment in the model operates as an ODE with first-order decay. A series of compartments, which can be considered a signaling pathway, were used to model single-peak dynamics, with a delayed peak time at each compartment. Output signals from two compartments in series were linearly combined as input signals to a third compartment, and this combination produced double-peak dynamics, consistent with our observations of some genes’ transcription following Arabidopsis exposure to a pathogen. We applied this MCM platform to fit genome-wide Arabidopsis transcriptional profiles in response to pathogen exposure and characterize the distinct dynamic mechanisms of different modes of plant immunity.
Understanding the evolutionary dynamics of microbial communities through in silico studies
Gayathri Sambamoorthy and Karthik Raman
Indian Institute of Technology Madras, IN
Microbes naturally co-exist in communities, where they predominantly interact through the exchange of metabolites. Beyond natural communities, synthetic communities have been employed in the overproduction of biomolecules. Previous studies have shown that microbes in a community co-evolve to fulfill a desired functionality. Thus, it is interesting to understand the stability of interactions in a microbial community, as they co-evolve. In this work, we employ a novel computational approach to study the co-evolution of two-member microbial communities in silico, when subject to different community-level selection pressures. Specifically, we study pairwise communities of organisms, which exhibit different interaction types such as mutualism, amensalism and parasitism, and analyse the stability of the interactions upon co-evolution in silico. We predict the nature of interactions between organisms in a community metabolic network, using flux balance analysis. We identify the fitness benefits conferred upon evolution and investigate the metabolites exchanged among the organisms in the communities. Interestingly, when the selection pressure is altered to higher growth of organisms in the community, we observe a much higher tendency of the communities to evolve towards co-operation, i.e. mutualism. Overall, our study throws light on important aspects of microbial co-evolution and the stability of microbial interactions.
Mathematical modelling of cell cycle and circadian rhythm after DNA damage
Zsófia Bujtár, Judit Zámborszky, Toru Matsu-Ura, Christian I. Hong, and Attila Csikász-Nagy
Pázmány Péter Catholic University, HU
Most cancer treatment induces DNA damage to eliminate highly proliferative cells. This process also affects healthy cells creating side effects. Cells respond differently to DNA damage depending on where they are in their cell cycle at the time of the treatment. Intriguingly, circadian rhythms control the timing of cell divisions. Personalized chronotherapy could be designed based on this observation: fitting the timing of the treatment to the patient’s natural daily rhythm. We built a mathematical model integrating the circadian rhythm, cell cycle, DNA damage response in Neurospora crassa and performed in-silico experiments. We simulated the perturbation of the circadian clock and the cell cycle upon DNA damage and analysed the consequences using phase response curves. We observed that DNA damage induces significant phase shifts during the subjective day in contrast to minimal phase shift during the subjective night. We analysed the behaviour of the cell cycle, where the cell cycle time and the strength of coupling between the circadian rhythm and cell cycle was noisy. We observed, depending on the timing of the induction of DNA damage, the healthy cells react differently: they can continue their normal division or they can be perturbed as the side effect of the treatment.
Structural and dynamical analysis of an integrated human/virus metabolic model provides insight to new treatment strategies against Covid-19
Bridget Bannerman, Alexandru Oarga, and Jorgez Julvez
University of Cambridge, UK
The coronavirus disease 2019 (COVID-19) pandemic outbreak caused by the new coronavirus (SARS-CoV-2) is currently responsible for over 351 thousand deaths in 217 countries across the world (https://www.who.int/emergencies/diseases/novel-coronavirus-2019). The absence of FDA approved drugs against the new SARS-CoV-2 virus has prompted the urgent need to design new drugs and treatment management strategies against COVID-19.
We provide a combination of structural and dynamic modelling approaches to predict new drug targets against the SARS-CoV-2 virus and to determine drug optimisation strategies. This methodology involves the analysis of a stoichiometric metabolic model that integrates cell metabolism in humans with the SARS-CoV-2 virus. We also compared the interactions that occur between the Angiotensin-converting enzyme 2 (ACE2), the cellular receptor for the 2002 SARS-CoV virus, the new coronavirus (SARS-CoV-2) and associated cofactors. The model has provided an in-silico comparison of the biochemical demands of the viruses versus the host cells and predicted 18 essential reactions as drug targets from an integrated human cell and SARS-CoV-2 virus system. We are expanding the model to predict the effect of various treatment regimens to ensure maximum drug optimisation strategies against the virus.
Computational model reveals a stochastic mechanism behind germinal center clonal bursts
Aurelien Pelissier, Maria Rodriguez Martinez, Youcef Akrout, Ulf Klein, Jack Kuipers, Katharina Jahn, and Niko Beerenwinkel
IBM Research, CH
Germinal centers (GCs) are specialized microanatomical structures that emerge within the secondary lymphoid organs upon infection or immunization and that play a central role in mounting an effective immune response. In this work, we present a stochastic multiscale model of the GC that includes an abstract representation of individual B cell receptors(BCRs) as strings of nucleotides. In addition, our model also accounts for the transcriptional program associated with B cell differentiation and the random cellular interactions that shape the GC reaction. The explicit sequence-based BCR representation allows us to track the effect of mutation son the BCR affinity to antigen by means of phylogenetic trees, which we compare to sequenced BCR repertoires. We extensively investigate the emergence of clonal dominance, which is observed in a subset of GCs. We find that small advantages in affinity acquired through random mutations are amplified and result in clonal dominance within a week, suggesting hence that stochastic effects play a fundamental role in the emergence of clonal bursts and dominance. Our faithful modelling of the GC reaction has broad implications in our understanding of the mechanisms underlying autoimmune disease and lymphomas, and it also contributes towards in silico vaccine design.
Model reduction and optimal control for multicellular biological oscillator systems
Narasimhan Balakrishnan and Neda Bagheri
Northwestern University, US
The core circadian clock comprises a network of specialized neurons that regulate many essential rhythmic processes throughout an animal’s body. Its function, both endogenous and entrained, is paramount to ensuring health. Computational models of this clock often involve multiple ODEs per cell, and are expensive to simulate extensively. In this work, we introduce a method to reduce a high dimensional multicellular model of the circadian network to a lower dimensional phase-based model that preserves key features and enables efficient exploration of entrainment and control related problems. Our method preserves rhythmicity, cellular heterogeneity, and the ability to adjust phase via exogenous inputs. We use the reduced model in conjunction with the full model to explore two optimal control problems relevant: adjust the cell population phase in minimum time, and adjust the system’s phase using optimal effort. These problems inform the design of interventions for acute and chronic circadian misalignment (i.e., jetlag, seasonal affective disorders, or shiftwork). Preliminary results demonstrate that cellular heterogeneity enables greater ability to entrain and control the system, up to a limit. Insights gained from our findings can guide clinical interventions and enable better design of synthetic biological systems involving large populations of cells with cell-to-cell communication.
A Model of the Response to Hypoxia of Cell Lines and Human Hematopoietic Progenitors
Irina Piazza, Claire Léger, Inès Nahoui-Zarouri, Angélique Stéphanou, Jean-Marc Moulis, and Éric Fanchon
TIMC-IMAG, IT
The bone marrow microenvironment is characterized by the existence of compartments with low oxygen partial pressure (a condition called hypoxia). The primary response of cells to hypoxia is the induction of hypoxia-inducible factors (HIF1 and HIF2), a transcription factor with many targets. This in turn triggers several changes, in particular in iron homeostasis and at the metabolic level. In this project we focus on Acute Myeloid Leukemia (AML), a set of genetically heterogeneous cancers of generally poor prognosis. We built a dynamical model of the network involved in the response to hypoxia of progenitor (human) cells. It comprises: the iron homeostasis module; signaling through the REDD1-mTOR pathway; energy homeostasis and the energy sensor AMPK; glucose uptake and ATP production.
Experiments specifically designed to build this model are underway. The model is expressed in terms of parameterized ordinary differential equations. We will present a simplified version of the model, with parameter values estimated from data obtained within the project. The goal is to provide an alternative approach to classify patients at diagnosis based on functional data with the aim of
eventually providing improved personalized therapeutic options.
Identifying characteristic features of metabolic states using Genome-Scale Metabolic Models
Chaitra Sarathy, Marian Breuer, Martina Kutmon, Michiel Adriaens, Chris Evelo, and Ilja Arts
Maastricht University, NL
Increased demand for systems-level understanding of cellular metabolism has led to the widespread use of genome-scale metabolic models. However, analysis of large-scale systems, such as human metabolic networks, still presents considerable challenges for determining the systemic effects of individual metabolic perturbations. In this study, we have developed a model-based method for distinguishing between multiple metabolic conditions by highlighting the most significant differences in their flux spaces. Our approach first calculates the principal components describing each flux space. Subsequent network-based comparison of components between conditions yields the components which explain the differences between the flux spaces. The principal components also decompose the achievable steady-state flux states into low-dimensional reaction sets. The reaction sets thus obtained from all the conditions shed light on the metabolic features characterising each state. The biggest advantage of our approach is its scalability to even the largest models currently available (humans). As an application, we investigated the differences in the human adipocyte network arising due to presence/absence of metabolite uptake and identified the network-wide effects caused by the metabolic perturbation. We strongly believe that our method can be used to provide mechanistic insights into condition-specific metabolic changes in large genome-scale metabolic models.
Variability of Biomass Composition in Chinese Hamster Ovary Cells
Diana Széliová, David Ruckerbauer, Sarah Galleguillos, Michael Hanscho, Christina Troyer, Harald Schöny, Gunda Köllensperger, Hanne B. Christensen, Stephan Hann, Nathan E. Lewis, Lars K. Nielsen, Jürgen Zanghellini, and Nicole Borth
Austrian Centre of Industrial Biotechnology, Vienna, AT
Chinese Hamster Ovary (CHO) cells belong to the most important organisms for the production of biopharmaceuticals. Metabolic modelling methods, such as flux balance analysis (FBA) have a potential to predict engineering strategies to increase protein production. To make these modelling tools available for CHO, a genome-scale metabolic model of CHO cells was built in an international collaboration. The composition of biomass is a key factor in the accuracy of FBA predictions. However the current model’s biomass is, due to the lack of available CHO-specific data, based on data from a mix of non-CHO sources. Here we close this gap by providing a comprehensive overview of the biomass composition in exponential phase of 13 different cell lines / conditions, including hosts and producers cell lines. We have observed big variability in cell dry mass, DNA, RNA and carbohydrate content and differences in lipid composition. Although the total protein content was significantly different, the amino acid composition was fairly stable for all cell lines and conditions. Uptake and secretion rates of amino acids, glucose, lactate and ammonium were determined. Finally, feeding these data into the metabolic model improved the growth rate predictions made by FBA and the reliability of the model.
Discrete model of macrophage simulates six clinically relevant activation states
Adam A. Butchy, Cheryl Telmer, and Natasa Miskov-Zivanov
University of Pittsburgh, US
Macrophages have contextual phenotypes, with a variety of activation states, contingent on the cytokines and chemokines in their extracellular environment. This plasticity makes them a challenge for network analysis and modeling. Recently, a transcriptomic network analysis suggested the spectrum of macrophage activations states beyond the canonical M1/M2-polarization model. We have previously developed a discrete model of intracellular signaling in macrophages, and we used the transcriptomic data to validate this model. We identified six induction signals from the data, with distinct and clinically relevant activation states. For each induction signal, we found key response pathways and elements. Stochastic model simulations, conducted for each induction scenario, provided the information about the changes in the behavior of key model elements in time. We then assessed model performance by comparing element behavior against the transcriptome data. Of the 28 model elements that are found in the transcription database, 23 matched the data, that is, the macrophage model is able to reproduce the response to three induction signals, while for the other three signals it slightly diverges from the expected behavior. These results suggest that our model is comprehensive, and therefore, it can be beneficial in studying macrophage response to changes in their microenvironment.
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in Montréal, Canada. The meeting will feature several keynote talks and contributed presentations.
