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Dr. Simon Mitchell

Reader in Cancer Systems Biology
Medical Research Building
University of Sussex

January 20, 2024 at 11:00 AM EST

Abstract

Systems biology has been widely used to study signalling in immune cells. Multiscale modelling approaches have provided insight into how cellular signalling leads to distinct cell fates, which control the immune response. Less attention has been paid to what happens to these networks, cell fates and patient outcomes when signalling is impacted by mutations. B cell lymphoma is a highly heterogeneous disease and treatment progress has been challenged by patient-to-patient variability. The Mitchell lab is asking whether systems biology models can enable us to overcome this patient-to-patient heterogeneity and get the right drugs to the right patients. Through combined computational modelling and experimental work, we found that mutations cause “crossed wires” within molecular signalling that result in tumour cells misinterpreting their microenvironment. We found that when mutations impact multiple signalling networks that control multiple cell fates, the resultant changes in cellular proliferation can be greater than expected. We find that by combining DNA sequencing data with ordinary differential equation models we can create heterogeneous populations of virtual patients. Within these patients, we computationally identify a new subgroup of patients who have co-occurring dysregulation of their cell cycle and apoptosis. We find the perturbed signalling within these patients results in dismal outcomes (progression-free survival). We need new treatment approaches for these patients. By simulating the impact of inhibitors within these molecular networks we find we can predict which inhibitors are most effective in each lymphoma cell population. Validating these predictions in the lab demonstrates how computational systems biology approaches are unlocking a personalized medicine approach to getting the right drugs to the right patients.

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Dr. Ashlee N Ford Versypt

Associate Professor
Department of Chemical and Biological Engineering
University at Buffalo (UB)
The State University of New York

August 29, 2023 at 11:00 AM EST

Abstract

Dr. Ford Versypt leads the Systems Biomedicine and Pharmaceutics research lab, which develops and uses multiscale systems engineering approaches including mathematical modeling and computational simulation to enhance understanding of the mechanisms governing tissue remodeling and damage as a result of diseases and infections and to simulate the treatment of those conditions to improve human health. The lab specializes in (a) modeling mass transport of biochemicals through heterogeneous porous materials—primarily extracellular matrices—that change morphology dynamically due to the influence of chemical reactions and (b) modeling dynamic, multi-species biological systems involving chemical, physical, and biological interactions of diverse, heterogeneous cell populations with these materials and the chemical species in tissue microenvironments.

In this seminar, vignettes of three lines of research will be highlighted including (1) glucose-stimulated damage to kidney cells during diabetes, (2) viral and immune-induced damage in SARS-CoV-2 infected lung tissue, and (3) bone restoration via dietary supplementation of short chain fatty acids. The work is currently supported by an NSF CAREER award and NIH R35 MIRA and R21 grants.

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Dr. Mohit Kumar Jolly

Assistant Professor
Centre for BioSystems Science and Engineering
Indian Institute of Science

January 17, 2023 at 11:00 AM EST

Abstract

Elucidating the design principles of regulatory networks driving cellular decision-making is of fundamental importance in mapping and controlling cell-fate. Despite their size and complexity, large regulatory networks often lead to a limited number of phenotypes. How this canalization is achieved remains largely elusive. Here, we investigated multiple different networks governing cellular plasticity during cancer metastasis, and identified a latent design principle in their topology that limits their phenotypic repertoire – the presence of two “teams” of nodes engaging in a mutually inhibitory feedback loop. These “teams” are specific to these networks and directly shape the phenotypic landscape and consequently the cell-fate trajectories. Our analysis reveals that network topology alone can contain information about phenotypic distributions it can lead to, thus obviating the need to simulate them. We present experimental evidence of such “teams” in transcriptomic datasets across many contexts (cancer cell plasticity in breast cancer, melanoma, lung cancer etc.). Overall, we propose these “teams” as a network design principle that can drive cell-fate canalization in diverse decision-making processes.

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Dr. Réka Albert

Distinguished Professor of Physics and Biology
152E Davey Laboratory
Pennsylvania State University

June 7, 2022 at 11:00 AM EST

Abstract

My group models cell types as attractors of a dynamic system of interacting (macro)molecules, and we aim to find the network patterns that determine these attractors. We collaborate with wet-bench biologists to develop and validate predictive dynamic models of specific systems. Over the years we found that network-based discrete dynamic modeling is very useful in synthesizing causal interaction information into a predictive, mechanistic model. We use the accumulated knowledge gained from specific models to draw general conclusions that connect a network’s structure and dynamics. An example of such a general connection is our identification of stable motifs, which are self-sustaining cyclic structures that determine points of no return in the dynamics of the system. We have shown that control of stable motifs can guide the system into a desired attractor. We have recently translated the concept of stable motif to a broad class of continuous models. Stable motif – based attractor control can form the foundation of therapeutic strategies on a wide application domain.

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Benjamin M. Gyori

Research Fellow, Director of Machine-assisted Modeling & Analysis
Laboratory of Systems Pharmacology
Harvard Medical School

January 18, 2022 at 11:00 AM EST

Abstract

The rate at which biomedical knowledge is produced (both at the level of new publications and data sets) is accelerating, and there is an increasing need to monitor, extract and assemble this knowledge in an actionable form. Classic mechanistic models take substantial human effort to construct and rarely scale to the level of omics datasets, while statistical approaches often do not make use of prior knowledge about mechanisms. To address these challenges, we present INDRA, an automated knowledge assembly system which integrates multiple text mining tools that process the scientific literature, and structured sources (pathway databases, drug-target databases, etc.). INDRA standardizes knowledge extracted from these sources and corrects errors, resolves redundancies, fills in missing information, and calculates confidence to create a coherent knowledge base. From this knowledge, various executable model types (ODEs, Boolean networks, etc.) and causal networks can be generated automatically for further analysis. We discuss technology built on top of INDRA, including human-machine dialogue systems, and EMMAA, a framework which makes available a set of self-updating and self-analyzing models of specific diseases and pathways. We present applications of these tools to automatically construct explanations for experimental observations in multiple disease areas.