Applying Systems Thinking to Regenerative Medicine Proceedings of a Workshop (2021) / Chapter Skim
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5 Challenges and Opportunities Associated with Systems-Level Analysis and Modeling
5 Challenges and Opportunities Associated with Systems-Level Analysis and Modeling
Pages 65-84
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From page 65... ...
• High-throughput methods using high-quality data can precisely quantify and analyze cytokine response through a generative model of cytokine dynam ics. Effective two-dimensional dynamics controlled by immune velocity have been found to parsimoniously explain cytokine behavior.
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moderated the session, which featured presentations on the development of algorithms for single-cell genomics, modeling dynamic data to identify a latent space, and adapting metabolic modeling tools in biopharmaceutical drug development. This session's objectives were to discuss the current state of the art of systems thinking approaches and talk about how these approaches are being used to inform the identification of important variables to measure and to illuminate current gaps in knowledge and areas for further study.
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She discussed some of these new developments in single-cell RNA technologies related to cell types, axes of cellular identity, and cellular state transitions (Wagner et al., 2016)
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. She presented a threedimensional plot showing how some features that undergo the CoGAPS matrix factorization approach reflect individual retina cell types, such as the neurogenic cell type.
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These approaches, which use single-cell data, will help move the field through a systems approach that is data-driven but also integrate information about time from dynamic models, Fertig said. Molecular Heterogeneity with Distinct Cellular Subtypes Even within a single cell type, temporal changes can be observed, suggesting that there may be additional molecular heterogeneity that can occur within distinct cellular subtypes, Fertig said.
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In closing, Fertig said that singlecell algorithm development is a broad field with much promise. However, new approaches for matrix factorization and latent space analysis are still needed to answer the open questions in the field, such as studying cellular heterogeneity, conducting trajectory and velocity analyses, and identifying network analyses.
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General Modeling Approach Francois described the general approach used to model dynamic data by identifying a latent space. The first step is to take the data and reduce its dimensionality in a supervised way -- which accounts for biological knowledge -- using tailored algorithms, such as a reduction algorithm that reduces large networks (Proulx-Giraldeau et al., 2017)
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Francois and his colleagues developed a stepwise process with multiple readouts that uses a robot to study immune response in vitro at multiple time-points. The process begins with harvesting primary immune cells, after which mixtures of T cells and antigen-presenting cells (APCs)
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An entire ensemble of connections between cytokines was discovered using this dimensional reduction process by employing the simple parameter of immune velocity. Major Findings and Ways Forward in Applying the Model In conclusion, Francois reiterated that high-throughput methods using high-quality data can be used to precisely quantify and analyze cytokine response.
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ADOPTING METABOLIC MODELING TOOLS IN BIOPHARMACEUTICAL DRUG DEVELOPMENT Anne Richelle, a senior specialist on metabolic modeling at GlaxoSmith Kline, explored the adoption of metabolic modeling tools in the biopharmaceutical industry. She discussed how systems thinking can be applied to strengthen the global drug production pipeline and discussed challenges related to implementing systems thinking in the biopharmaceutical industry.
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In the case of using machine learning to optimize bioprocess development, the potential impact of exploiting data is relatively high, but the amount of data available is relatively low. Still, even though the volume of available data is relatively low, machine learning will likely have a substantial impact on the biopharmaceutical pipeline, Richelle predicted.
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For example, in designing new drugs, the tools can be used to inform target selection and to make it possible to engineer cells to rewire their metabolism toward the production of a product of interest. While the systems thinking approach has brought much value to the study and manipulation of biological networks, it also has potential to be applied to many other influence factors across the drug development pipeline, Richelle said.
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Therefore, these types of complex networks are not often used to develop feedback control and optimize processes. Various approaches have been proposed for tailoring metabolic networks based on a priori knowledge or available experimental data.
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Modeling with Hybrid Approaches Further challenges relate to modeling with hybrid approaches, Richelle said. Interest is increasing in the use of machine learning and artificial intelligence for bioprocessing and engineering, such as technologies that use digital imaging for automated counting of cell colonies grown on petri dishes (Ferrari et al., 2017)
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Furthermore, various computational methodologies will yield different dimensions or features, requiring different latent space methods based on the target dimension researchers are trying to uncover. Accounting for Zeros in Datasets A technical issue related to sampling can arise when a zero appears in a data matrix, and speakers were asked if that means "no expression" or "dropout." Relatively few efforts have explicitly addressed this question, Moos said, although Fabian Theis and colleagues have started to do so using an autoencoder.
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Using Immune Velocity to Phenotype Immune Health The possibility of using immune velocity to phenotype a patient's immune health is an area that is still being explored, Francois said. It has not yet reached the stage where it is possible to look at what happens to patients based on their age, gender, and pre-existing conditions, he said.
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Although a patient represents a sample of one, each individual hosts many complex, interacting biological activities. Given the aim of developing a broadly successful suite of therapies, it might be worthwhile to position iterative regenerative drug development processes so that they begin by distinguishing patients who respond from patients who do not.
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Moos asked how this limitation of existing clustering methods might be addressed. Fertig said that these concerns have motivated her work with latent space representations and matrix factorization, rather than clustering methods, because the former approaches allow for gene reuse.
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SYSTEMS-LEVEL ANALYSIS AND MODELING 83 important genes are being regulated in a bistable manner. He asked how this trend may be connected to the idea that multi-omics data -- if they too are regulated in a bistable manner -- may be amenable to simple scaling as 1s and 0s.
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