Background Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, since omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. Scope of the review We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. In particular, we review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. Major conclusions Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

Towards modelling metabolic state from single-cell transcriptomics

Phenotype-specific estimation of metabolic fluxes using gene expression data

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Se desarrollo un modelo de la camara anodica en una celda de combustible microbiana para mejorar la comprension de los fenomenos biologicos que suceden al interior de dicho dispositivo. La posibilidad de transferir electrones a un aceptor insoluble en el medio extracelular fue incluida en una reconstruccion metabolica a escala genomica de Geobacter sulfurreducens disponible en la literatura y luego se uso dicha reconstruccion para representar el comportamiento de este microorganismo en un ambiente modificado de la libreria BacArena en R. Fue posible reducir el tiempo de computo a una milesima del necesario con el codigo original de BacArena gracias a la aproximacion del estado estable presentada en este trabajo. Se logro recrear el comportamiento de la corriente reportado en la literatura, el desarrollo de biopeliculas de hasta 48 μm y la identificacion de la erosion, el gradiente de concentracion y el espacio como los limitantes de la generacion de corriente en una celda de combustible microbiana.

Simulación de una cámara anódica y el efecto de su composición sobre la e ciencia de una celda de combustible microbiana

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models. 

Toward modeling metabolic state from single-cell transcriptomics

Scale Metabolic Models (GEMs) emerged as a formal concept to describe metabolic pathways reconstructed from the information present in the genome of living systems as well as in the biochemical reactions described in the literature This formalization involves steps including determination of their stoichiometry and definition of model constraints Genome Scale Metabolic Models (GEMs) are mathematically structured knowledge that can be used to generate and test biological hypotheses when integrated with experimental data Recently the community started to recognize the potential of GEMS and their analysis with constraint-based modeling Therefore, when using GEMs for applications including in silico metabolic engineering, it´s important to understand the assumption behind its mathematical framework, but usually the theory behind is not accessible to a broad audience in one place This work aims to present a concise theoretical and practical introduction to GEMS that is accessible to a broad audience getting them closer to the use and applications of GEMS

Mathematical Framework Behind the Reconstruction and Analysis of Genome Scale Metabolic Models

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Mathematical Framework Behind the Reconstruction and Analysis of Genome Scale Metabolic Models