SysMod
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The SysMod COSI organizes annual gatherings at ISMB. This short talk will introduce all speakers, organizers, and the main topics of the 2023 meeting. This year’s meeting incorporates four sessions covering, beginning with “Modeling Biological Systems from Micro- to Macro-Scale,” followed by “Integrative Modeling Approaches for Biological Systems” and “Computational Modeling of Diseases.” It will conclude with a session on “Modeling Metabolic and Dynamic Tumor Evolution.” Two outstanding keynote speakers will present their visions on developments in these fields: Ina Koch from the Goethe University Frankfurt am Main, Germany, and Thomas Höfer from the German Cancer Research Center (DKFZ), Heidelberg, Germany. Chiara Damiani will close the event by awarding this year’s poster prizes. The event is hosted by Matteo Barberis and Chiara Damiani.
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The increasing amount of available experimental data enables us to consider biological systems at multi-scale levels. Different computational methods, ranging from discrete techniques to differential equation-based methods, have been developed to build models at different scales depending on the experiment. The talk addresses the possibilities and challenges of the application of Petri nets to analyze biological systems. To illustrate the different applications, we present in-silico knockout studies of xenophagic capturing of Salmonella and analysis of cellular processes in the lymph node. Finally, the combination of Petri nets with agent-based modeling will be discussed as a future direction.
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The organization of biological systems as distinct but connected layers poses a grand challenge for biomathematical modeling, because processes occurring at the various layers have different time scales and almost always focus on different types of variables. The higher layers usually correspond to a “big picture” of physiological events, whereas the lower levels account for increasing granularity and detail. When investigating a system at a high level, it is infeasible to carry along all details from lower levels, partly for technical reasons, but more so because they would overwhelm insights at the higher level due to their sheer numbers and the fact that they typically run on much faster time scales. We address this situation with the first application of “Template-and-Anchor modeling” in the proposed implementation. A template is a high-level model that focuses exclusively on the main physiological components. Anchor models provide specific biological details characterizing the mechanisms that govern the system and are represented in the template model as variables. We use this approach to investigate the effect of dioxin exposure on human health. By adjusting parameter values within the anchor models, the overall template model can be personalized, thereby offering the option of personal health risk assessments.
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This study addresses the need to understand better human embryonic development to improve assisted reproductive technologies such as in vitro fertilization. Novel technologies such as transcriptomics can provide single-cell level data to understand embryo development from a genetic and metabolic point of view. The study aims to develop a computational model to discriminate different developmental stages during trophectoderm (TE) maturation using scRNAseq data. The proposed method involves selecting pseudo-perturbations specific to each developmental stage, allowing for learning Boolean network models. These models are inferred from the pseudo-perturbations and prior-regulatory networks and optimally fit scRNAseq data for each developmental stage. The main result is the proposal of a general framework for inferring Boolean networks from scRNAseq data. Another result is identifying a family of Boolean networks specific to medium and late TE developmental stages, revealing opposite regulation pathways and supporting biological hypotheses in this domain.
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The cell cycle is a highly regulated process that ensures the accurate replication and transmission of genetic information from one generation of cells to the next. We devised a quantitative description of gene expression dynamics during the cell cycle in mouse embryonic stem cells. We combined high-depth single-cell multiomics sequencing, biophysical modeling, and advanced deep learning techniques to develop a novel method that allows us to infer cell cycle dependent gene expression dynamics. We performed multiome sequencing, namely scRNA-seq and scATAC-seq in mouse embryonic stem cells. We used a generative deep learning tool that assigns a latent cell-cycle phase to the cells based on the spliced and unspliced mRNA reads. Using this latent cell-cycle phase, we developed a biophysical model that describes the dynamics of gene-specific mRNA synthesis and degradation rates during the cell cycle. Our model helped to identify key regulators that drive the transcriptional dynamics during the cell cycle. By extending this approach to scATAC-seq, we were able to investigate the chromatin accessibility during cell cycle progression.
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The animal testing ban for safety evaluation of cosmetic ingredients urges the need for new approach methodologies’ development, mainly to better understand xenobiotic metabolic Mechanisms of Action (mMoA) for systemic toxicity assessment. To this end, we developed a workflow to combine endogenous metabolic knowledge from a Genome Scale Metabolic Network (GSMN) and in vitro transcriptomics data by building condition-specific metabolic networks, which involves 3 main steps. The first step consists in building condition-specific models representing the metabolic impact of each exposure condition, while taking into account the diversity of possible optimal solutions with a partial enumeration step. Then, 2 conditions, represented by 2 sets of several optimal condition-specific networks, are compared by extracting differentially activated reactions (DARs) between these 2 sets. Finally, using distance-based clustering and a subnetwork extraction method, DARs are grouped into clusters of functionally interconnected metabolic reactions. The workflow was applied to two well-known hepatotoxic molecules, amiodarone and valproic acid. Despite large disparities in evidenced transcriptomic effects for these two chemicals i.e., 2 DEGs for Amiodarone and 5709 DEGs for Valproic Acid, we were able to model and visualize different mMoA, fitting several evidence in the literature.
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Single-cell time series data frequently display considerable variability across a cell population. The current gold standard for inferring parameter distributions across cell populations is the Global Two Stage (GTS) approach for nonlinear mixed-effects models. Although the GTS method is reliable, its current implementation requires repeated use of non-convex optimization, which is not guaranteed to converge, while each optimization run requires multiple simulations of the system. We propose an alternative, computationally efficient implementation of the GTS method for mixed-effects dynamical systems which are nonlinear in the states but linear in the parameters (a class that encompasses a wide range of models such as those based on mass-action kinetics). For such systems, point parameter estimates can be obtained using least squares regression on time derivatives of smoothed measurement data, an approach called gradient matching. Here, we extend the application of gradient matching to the inference problem for mixed-effects dynamical systems and integrate it into the GTS method by properly accounting for uncertainties in individual cell parameters in the first stage. We also present an Expectation Maximization (EM) algorithm and associated parameter uncertainty estimates which are applicable when not all system states are observed, as is typically the case for biological systems.
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Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. However, these models typically have unknown parameters that need to be estimated from experimental data. While there are various methods and software tools available for quantitative data, the options for semi-quantitative and qualitative data are limited and computationally demanding.
To address this challenge, we propose a novel approach that integrates censored, ordinal, and nonlinear-monotone data into the parameter estimation process using a combination of optimal scaling and spline modeling approaches. To integrate ordinal and censored data, we use the optimal scaling approach, which involves representing qualitative data as quantitative surrogate data that accounts for constraints on their relation. For nonlinear-monotone data, we optimize splines to model the unknown data dependencies. These approaches enable us to treat the data as if it were quantitative, such that we can use pre-existing software parameter estimation pipelines.
To improve the method's efficiency, we formulate the inverse problem as a bi-level optimization problem and compute gradients using an efficient semi-analytical algorithm. We apply it to a model with all four data types and compare the results. The approach is implemented in the open-source Python Parameter Estimation TOolbox (pyPESTO).
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Several studies attempted to link genotype-phenotype relationships yet it remains unclear how genetic interactions between quantitative trait nucleotides (QTNs) can drive phenotypic variation. If QTNs modulate phenotypic variation in a metabolically driven process, it is obvious to ask: how do these QTNs individually and in combinations change the connectivity of metabolic regulators? Furthermore, how does metabolic flux distribution change as QTN interacts? To test our hypothesis, we harness the gene expression data obtained from an allele replacement panel of Saccharomyces cerevisiae and study how QTNs in the three genes: two coding polymorphisms in IME1 and RSF1 and two non-coding polymorphisms in RME1 and IME1, can modulate sporulation efficiency variation. Using differential gene expression analysis and network analysis we show several metabolic regulators change connectivity as QTNs interacts. We integrated the gene expression data of each QTN combination into genome-scale metabolic models to reconstruct QTN-specific metabolic models. Using genome-scale differential flux analysis we observed flux variation in the amino acid biosynthesis pathway, pentose phosphate pathway, and glycerophospholipid metabolism as a consequence of QTN-QTN interactions. The underlying principles gained from this study can be anticipated for complex human diseases where multiple SNPs can interact and contribute to a disease phenotype.
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Infections caused by multiple pathogens, known as poly-microbial infections, can worsen patient prognosis. Acinetobacter baumannii and Staphylococcus aureus are two bacterial pathogens frequently co-isolated in infections. Both belong to the ESKAPE group, which is associated with high rates of antimicrobial resistance, and are responsible for the majority of nosocomial infections. However, the interaction between these two pathogens during co-infection remains poorly studied.
Therefore, we implemented an extended logistic growth model based on ordinary differential equations to quantitatively compare the growth parameters of the two species in different experimental settings. Experiments were performed using a variety of different laboratory strains as well as clinical isolates for both species in order to identify the key mechanisms of their interaction, while taking into account the biological variation observed in the clinics. In addition, wild-type strains and specific knock-out mutants were co-cultured and grown separately in the supernatant of the other strain to elucidate contact-dependent and contact-independent processes. Calibration of the model using this big volume dataset revealed a complex network of interactions between the species, involving both cooperative and competitive elements. This systems biology approach advances our understanding of co-infection processes and paves the way for developing improved treatment strategies.
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Our systems biology approach integrates mathematical modeling, multi-omics data, and molecular signaling networks to gain a comprehensive understanding of biological systems. This framework was applied to Alzheimer's disease (AD) to develop a model of insulin signaling in the blood-brain barrier (BBB) and its impairment in metabolic syndrome and AD. The model was based on ordinary differential equations (ODEs) and encompassed two interrelated subsystems: insulin signaling transduction in BBB endothelial cells and turnover of vascular-cell adhesion molecule 1 (VCAM1), a marker for cerebrovascular inflammation. The model was validated using western blot and proteomics data and applied to an AD patient and control RNA-Seq data. The in-vitro findings showed that insulin stimulation triggered the phosphorylation of various targets in a time- and dose-dependent manner and VCAM1 expression was reduced by insulin treatment. The mechanistic model successfully described the experimental results and predicted potential signaling perturbations due to amyloid beta (Aβ) exposure. The model was also used to examine transcriptomic changes in individuals with AD, identifying molecular mediators contributing to BBB dysfunction in AD. This study shows a systems biology model accurately representing the insulin signaling cascade and downstream expression of VCAM1 in BBB. The model can identify potential therapeutic targets for AD.
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BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAFV600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this study, we investigate mechanisms of adaptive rewiring in BRAFV600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAFV600E-driven channel is fully inhibited.
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We introduce SMITH – Stochastic Model of Intra-Tumor Heterogeneity – a novel approach to computational modelling of cancer cell populations and their evolution. SMITH introduces the concept of “confinement”, a mathematical representation of growth constraints within a foundational branching model of cancer development. Using this confinement mechanism, SMITH can emulate the heterogeneity observed in various cancer types with distinct spatial structures, such as breast cancer or lymphoma. In doing so, we achieve comparable outcomes to results produced by more intricate cellular-automata-based models. However, in contrast to cellular automata, the simplicity of our form of branching process model permits the simulation of realistically-sized tumours of up to one billion cells. To showcase the efficacy of SMITH, we performed over 10,000 simulations with a billion cells each. We then used a point cloud distance minimization over our simulation results to obtain parameters matching the different tumour types in both their mutation load and clonal diversity. Our analyses show that the confinement mechanism is sufficient to reproduce commonly observed evolutionary patterns and clonal dynamics.
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In recent times, to investigate the interplay dynamics between immune and tumor cells in human cancer, several computational modeling methods like agent-based models have been employed. However, since each tumor has its unique tumor microenvironment (TME), a personalized and specialized study of each cancer case is necessary.
In this perspective, we introduce MAST, which is a hybrid Multi-Agent Spatio-Temporal model that reproduces specific TME scenarios starting from high-throughput sequencing data. The integration of an agent-based model with a continuous partial differential equations (PDE) model, enables the inclusion of crucial aspects of the tumor microenvironment. This encompasses the spatio-temporal nature of cancer progression, its reliance on the availability of nutrients, the immune response, as well as the development of mutation-based mechanisms that lead to evasion. The proposed approach was tested by simulating four human colorectal cancer subtypes starting from genomics and transcriptomics data, coming from both bulk and single-cell sequencing technologies, of human colorectal cancer tissue. Both the emergent properties of the four simulated TMEs and the spatial and temporal evolution of the four TME specific in-silico cancer progression largely agree with the current biological knowledge and patient outcomes, thus supporting the validity of the approach.
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Epithelial ovarian cancer involves forming spheroids responsible for disease metastasis, recurrence, and lower chances of recovery. Although cancer progression has already been linked with metabolic differences in tumor cells, possible associations between metabolic landscape and metastatic morphological transitions remain unexplored. The present study aimed to identify metabolic perturbations during the phenotypic shifts of three distinct morphologies (2D monolayers and two geometrically individual three-dimensional spheroidal states) of the high-grade serous ovarian cancer line OVCAR-3. We performed quantitative proteomics and integrated protein states onto genome-scale metabolic models to construct context-specific metabolic models for each morphological stage of the OVCAR-3 cell line. Using these models, we obtained metabolic reaction modules responsible for disease progression and determined gene knockout strategies to reduce metabolic alterations associated with disease progression. The DrugBank database was explored to mine drugs and evaluated their effect in impairing metastatic morphological transitions. Finally, we experimentally validated our predictions by confirming the ability of one of our predicted drugs: the neuraminidase inhibitor oseltamivir, to disrupt the metastatic spheroidal morphologies without any cytotoxic effect on untransformed stromal mesothelial monolayers. The current work expands our horizon on ovarian cancer progression and provides a methodological framework to identify novel targets against cancer progression.
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Somatic evolution is a complex process shaped by the interplay of stem and progenitor cell dynamics, mutation and selection. None of the associated parameters can be directly measured in humans. In the first part of my talk, I will discuss inference approaches to reconstruct the evolution of tumors from genomic sequencing data. Focusing on a tumor of early childhood, neuroblastoma, I will show how insight into tumor evolution might help improve treatment decisions. A key insight of this work is the interplay between stem/progenitor cell dynamics on the hand, and the occurrence and fixation of driver mutations on the other hand. In the second part, I will discuss how mathematical analysis of this interplay has supported engineering tumorigenesis in mice without introducing oncogenes.
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This remark will provide a brief overview of the SysMod meeting, covering the speakers, chairpersons, and organizers. The event consisted of four sessions that delved into diverse methodologies and applications of computational systems modeling in the field of biology. We were privileged to have two distinguished keynote speakers, Ina Koch and Thomas Höfer, who delivered insightful talks. We express our gratitude for all the valuable contributions made to the scientific posters and we recognize and honor the best ones through the annual SysMod poster awards in 2023.