• Matteo Barberis

• Nathan Price, Chief Scientific Officer, Thorne HealthTech, United States

Healthcare must become increasingly focused on extending healthspan and not only on treating disease after symptoms arise. Indeed, this transition is essential to satisfactorily deal with the chronic diseases that account for the vast majority of healthcare costs today. To enable the precision health strategies of the future — what Lee Hood and I call The Age of Scientific Wellness (book, Harvard Press) — it is necessary to generate not only genomic data but also a large amount of longitudinal multi-omic data to quantify the health phenotype and to observe the earliest transitions to disease. Such an approach enables predictive and preventive medicine. I will discuss how we have used such longitudinal 'deep phenotyping’ data to: (1) map out the manifestation of genetic risk in the body, giving insight into intervention strategies to preemptively reduce disease risk on a personalized basis; (2) inform about how our gut microbiome and blood metabolites are related, and how the gut microbiome becomes more unique to each individual in healthy aging; (3) how the success of lifestyle/dietary-aimed interventions is quantitatively predicted by personal genetics, and (4) technological advancements to make gathering these data easier and cheaper for people, and (5) how digital twins provide new insights into deep personalization for brain health and new strategies for multi-modal clinical trials. Taken together, such approaches help enable the future of health optimization and personalized, preventive medicine.

• Yukai You, Northeastern University, United States
• Cristian Caranica, Northeastern University, United States
• Mingyang Lu, Northeastern University, United States

A major question in systems biology is to elucidate the gene regulatory mechanisms of cellular state transitions during developmental processes like cell differentiation and disease progression such as tumorigenesis. The advances in single-cell RNA-sequencing (scRNA-seq) technology has enabled an enhanced understanding of the dynamics of genome-wide gene expression. Yet, establishing gene regulatory networks driving cellular state transitions using scRNA-seq data remains challenging for a mechanistic understanding of cellular state transitions. Here, we introduce NetDes, a combined top-down bioinformatics and bottom-up systems biology approach, aimed at computationally generating ODE-based nonlinear dynamical models of core transcription factor regulatory circuits that recapitulate observed gene expression time trajectories. Our in-silico benchmarking demonstrates the advantage of NetDes in inferring the ground-truth regulators and their combinations. We applied NetDes to build the core regulatory circuit driving the differentiation of human iPSC to definitive endoderm using time series scRNA-seq data. The constructed gene circuit captures the regulatory interactions between stemness and Epithelial-Mesenchymal Transition (EMT) during this cell differentiation. Compared to existing network construction methods, NetDes has the advantage in capturing the gene expression dynamics during cellular state transitions using a single dynamical circuit model. Additionally, we performed systems biology simulations on the established ODE model to identify possible regulators and their combinations to drive the observed gene expression dynamics of the system. Our approach paves the way for a high-quality mechanistic modeling of the gene regulation of cellular state transitions.

• Humza Hemani, Washington University in St. Louis School of Medicine, United States
• Rintsen Sherpa, Washington University in St. Louis School of Medicine, University of Michigan, United States
• Lynn Robbins, Washington University in St. Louis School of Medicine, United States
• Jeanne Basta, Washington University in St. Louis School of Medicine, United States
• Michael Rauchman, Washington University in St. Louis School of Medicine, United States
• Shamim Mollah, Washington University in St. Louis School of Medicine, United States

Epigenetics changes within the cellular microenvironment play a significant role in both normal tissue development and the initiation and advancement of breast cancer. The extracellular matrix, a crucial element of the cellular microenvironment, engages with growth factors to modulate cellular behaviors that contribute to growth, progression, and metastasis. Interferon-gamma (IFNγ) is a cytokine based growth factor known for its immunomodulatory effects, primarily in the context of the immune response against pathogens and cancer has been implicated in influencing cancer cell behavior, including epithelial to mesenchymal transition (EMT), a process involved in cancer progression and metastasis. However, the epigenetic regulation of IFNγ (interferon gamma)-induced EMT particularly during breast cancer development, remain inadequately elucidated. Using a previously developed tensor-based HOCMO (Higher Order Correlation Model) on multi-omics data, we describe the modulatory mechanisms of EMT during breast cancer progression, focusing on epigenetic regulation and IFNγ induction that target these epigenetic modifiers. Using HOC scores on proteomics data (mass spectrometry, reverse phase array) as well as RNAseq, ATACseq, and CycIF data we identified a histone mark associated with IFNγ-induced EMT pathway of breast cells. We further validate our finding using CUT&RUN experiments. An expanding description of the epigenetic regulations that underlie the contribution of histone specific IFNγ-induced EMT to cancer progression will provide momentous insights for “immunoepidrug” to treat cancer progression and metastasis.

• Tatum Liparulo, University of Pittsburgh, United States
• Jason Shoemaker, University of Pittsburgh, United States

In humans, females of reproductive age are at greater risk than their male, age-matched counterparts for hospitalization and death from influenza infection. The innate immune response has been implicated as a factor of these sex differences in influenza pathogenesis. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in rates/speeds of select immune component responses. We modified an existing mathematical model and fit the model to data from male and female mice infected with influenza to identify sex-specific rates of male and female immunoregulation. We implemented a large computational screen to rapidly identify immune rates that may be sex-specific. We used Bayesian information criteria (BIC) to guide scenario selection because the BIC balances the goodness of fit of the competing models against model complexity. Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte activity, provides the simplest (lowest BIC) explanation for the difference observed in the male and female responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top model was performed to provide rigorous estimates of the sex-specific parameter distributions and provide insight into which parameters most effect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be targeted to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

• Jennifer Riccio, Università degli Studi di Milano-Bicocca, Italy
• Luca Presotto, Università degli Studi di Milano-Bicocca, Italy
• Liad Doniza, Tel Aviv University, Israel
• Donato Inverso, Vita-Salute San Raffaele University, Italy
• Uri Nevo, Tel Aviv University, Israel
• Giuseppe Chirico, Università degli Studi di Milano-Bicocca, Italy

Macrophages are immune cells which play a key role in the reaction to biomaterials. They exhibit a functional phenotype (or state) induced by the stimulus received and conditions of the microenvironment. This polarization process is governed by specific cytokines that are released by the macrophage itself, as well as produced by other cellular activation mechanisms. Cytokines act as phenotype markers within a heterogeneous range whose extremes are historically identified as pro-inflammatory or M1 and anti-inflammatory or M2.

In such a context, this work aims to propose a predictive modeling approach for the simulation of the response to a pro-inflammatory stimulus in macrophages. This will allow us to subsequently simulate the immune reaction induced by the presence of biomaterials at the cellular level, with the final goal to build a digital twin of the inflammatory response in a foreign body reaction. To do that, existing Ordinary Differential Equation (ODE) and Agent Based (AB) models have been considered and validated with in-vitro experimental data.

Preliminary results highlight a better agreement of the AB approach over the ODE models taken into account in this work.
This specific scheme is making simplified assumptions on spatial resolution and diffusion of inflammation (both cytokine and macrophages). However, the good agreement that we have observed in this simplified model encourages the use of a more advanced and comprehensive hybrid simulation platform based on AB modeling which implements a more thorough description of the intracellular pathways and the microenvironment.

• Weston Murdock, US Environmental Protection Agency, United States
• Imran Shah, US Environmental Protection Agency, United States

Adaptive stress response networks (SRNs) are invoked when chemical exposures induce DNA damage, oxidative stress, unfolded proteins, hypoxia, or heat shock and are essential for maintaining cell health. With a highly conserved architecture for sensing and countering cellular stress, SRNs are also pivotal in activating senescence, apoptosis, and autophagy pathways if stress cannot be resolved. Perturbing SRNs beyond some threshold tips cells over from adaptive to adverse phenotypes. We are investigating these critical ""tipping points"" using a combined approach of literature mining, computational modeling, and high-throughput data analysis. We aim to elucidate how SRN dynamics dictate cellular phenotypes and propose Boolean Networks (BNs) to identify these tipping points by: 1) Constructing a biological knowledge graph (KG) of chemical stress inducers; 2) Utilizing the KG to build BNs and simulate SRNs; and, 3) Validating predictions with transcriptomic data from HepaRG cells. Herein, we describe the KG of 500 chemical stress inducers for which we developed BNs to simulate dynamic cell trajectories in DNA damage response for drugs and chemicals. By elucidating the molecular determinants of tipping points, this research furthers our understanding of cellular resilience in health and toxicity.


This abstract does not reflect US EPA policy.

• Thierry Mondeel, University of Amsterdam, Netherlands
• Anastasiya Malishava, Imperial College London, United Kingdom
• Tom Ellis, Imperial College London, United Kingdom
• 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. Through computational modelling, we have recently identified a minimal network underlying cyclin/Cdk1 autonomous oscillations in budding yeast that we name the Yeast Minimal Cell Cycle. Here, we first explore by ‘learning from building’ whether these cell cycle oscillations may be achieved by synthesizing a functional genome consisting of a minimal set of cell cycle genes. We consider nine genes involved in the waves of cyclins: G1 cyclins (CLN1,2), mitotic cyclins (CLB1-6), and their positive and negative regulators (FKH1,2 and SIC1, respectively). Selected genes were pairwise deleted by CRISPR from their native loci in the yeast genome and simultaneously relocated with their native promoters into a synthetic gene cluster in the same cell. We then remove combinations of genes from this cluster. The frequency of gene loss and the growth rates of these strains were analysed and compared to kinetic models of the cyclin/Cdk network that are verified against quantitative data of Clb dynamics. We unravel a novel molecular design that synchronizes Clb/Cdk1 oscillations. Through integration of Synthetic and Systems Biology, this work shows that a minimal set of genes of the Yeast Minimal Cell Cycle can reproduce cell cycle oscillations, indicating that the genetic complexity of the yeast cell cycle can be reduced to identify a novel molecular network underlying cell proliferation dynamics.

• Vishvak Raghavan, McGill University, Canada
• Yue Li, McGill University, Canada
• Jun Ding, McGill University, Canada

Understanding cell-cell interactions (CCIs) is essential yet challenging due to the inherent intricacy and diversity of cellular dynamics. Existing approaches often analyze global patterns of CCIs using statistical frameworks, missing the nuances of individual cell behavior due to their focus on aggregate data. This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems as collections of autonomous agents governed by biologically inspired principles and rules. Validated against seven diverse single-cell datasets, CellAgentChat demonstrates its effectiveness in detecting intricate signaling events across different cell populations. Moreover, CellAgentChat offers the ability to generate animated visualizations of single-cell interactions and provides flexibility in modifying agent behavior rules, facilitating thorough exploration of both close and distant cellular communications. Furthermore, CellAgentChat leverages ABM features to enable intuitive in silico perturbations via agent rule modifications, pioneering new avenues for innovative intervention strategies. This ABM method empowers an in-depth understanding of cellular signaling interactions across various biological contexts, thereby enhancing in-silico studies for cellular communication-based therapies.

• Da-Wei Lin, Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI, 48109, USA., United States
• Ling Zhang, Center for Stem Cell and Regenerative Medicine, Zhejiang University, Hangzhou, China, China
• Jin Zhang, Center for Stem Cell and Regenerative Medicine, Zhejiang University, Hangzhou, China, China
• Sriram Chandrasekaran, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA., United States

Cell-type transitions, crucial for processes including cell quiescence, cell cycle, and embryogenesis, involve intricate metabolic rewiring to optimize competing biological objectives. The competition for cellular resources is often explored through the lens of Pareto optimality and metabolic trade-offs. Despite advancements in understanding these dynamics in unicellular organisms, the metabolic trade-offs in multicellular systems, especially during embryonic cell-state transitions, remain largely unexplored. Addressing this gap, we introduce the Single Cell Optimization Objective and Tradeoff Inference (SCOOTI) framework, a novel computational approach grounded in optimization theory, designed to infer cell-specific metabolic objectives from omics data. By integrating gene expression, protein abundance, and metabolite concentration data with genome-scale metabolic models, SCOOTI leveraged meta-learner regressors to quantitatively elucidate the metabolic objectives underlying cell quiescence, proliferation, and embryogenesis. Our analysis reveals distinct metabolic objectives across cell-cycle phases and embryonic development stages, highlighting the role of specific metabolites in driving these transitions. Notably, the framework uncovers a shift from a high entropy, multitasking metabolic system in early embryogenesis to a more deterministic metabolic focus on biomass production and cell growth in later stages. This shift is exemplified by the trade-offs between glutathione-mediated redox balance and biomass precursor synthesis, suggesting a Pareto optimality scenario where balancing redox status and growth-related objectives is crucial for embryonic development. Our findings challenge the traditional biomass maximization model, proposing instead that cellular metabolic objectives are highly context-dependent, varying significantly between quiescent and proliferative states, and across developmental stages.

• Dharshini Priya Selvaganesan, SASTRA Deemed to be University, India
• Sree Lakshmi Danthuluri, Sastra Deemed to be University, India
• Aruldoss Immanuel, Sastra Deemed to be University, India
• Ragothaman Yennamalli, Sastra Deemed to be University, India
• Venkatasubramanian Ulaganathan, Sastra Deemed to be University, India

Bacillus subtilis is a key organism in biotechnology, with its metabolic capabilities offering potential for various valuable products like levan. Levan, a fructose polymer, has diverse applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. But B. subtilis’ metabolic models till date are not that well-annotated as compared to other model organisms’ metabolic models and the exploitation of an organism’s metabolic capability requires an improved model with expanded gene coverage. So, this study aims to enhance the metabolic model of B. subtilis for improving levan biosynthesis, addressing the need for accurate prediction of metabolic outcomes in industrial applications. We used structural systems biology technique, which offers a promising approach to enhance predictive power, for the refinement of the levan biosynthesis pathway in B. subtilis. Using AlphaFold2, structural models were generated for critical genes lacking full-length PDB structures, which were then integrated into the model. Findings elucidate previously overlooked structural aspects of levan biosynthesis, while leveraging the STRING database to optimize product yield. We provide a detailed understanding of the levan biosynthesis in B. subtilis, shedding light on previously overlooked structural aspects of a pathway. This metabolic model acts as an input to further set of applications in advancing metabolic engineering efforts.

• Melissa Kemp, Georgia Institute of Technology & Emory University, United States

Engineering multicellular systems is enhanced by understanding how collective organization arises during developmental processes through mechanical, biochemical and electrical communication. Which aspects of these processes can be circumvented, accelerated or modified according to specification to yield robust, reproducible organoids? Computational models that simulate the growth, division, and differentiation of pluripotent cells into emergent structures could accelerate experimental design, yet currently lag in their ability to inform organoid culture protocols. I will discuss my lab's computational results from developing agent-based models that capture heterogeneity and stochasticity within colonies and aggregates to both i) formulate hypotheses of intercellular communication during stem cell differentiation and ii) design new organoid structures using synthetic biology components. To address the challenges of agent-based model optimization, we have pursued new methods for analyzing microscopy images and simulation results by topological data analysis. Through a tight iteration between computation and experimentation, we established a critical role of intercellular transport, adhesion, and cell cycle asynchrony in the propagation of dynamic patterning in engineered iPSC systems.

• Matteo Barberis