The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring ap riorisystems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model—suggesting nonlinear terms and structural modifications—or even constructing a new model that agrees with the system’s time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time.

/pdf/automated-refinement-and-inference-of-analytical-models-for-5y4nwvuulf.pdf

Automated refinement and inference of analytical models for metabolic networks.

Background
Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction.

/pdf/elementary-signaling-modes-predict-the-essentiality-of-1ebsmqd75a.pdf

Elementary signaling modes predict the essentiality of signal transduction network components

Background
Stomata are tiny pores in plant leaves that regulate gas and water exchange between the plant and its environment. Abscisic acid and ethylene are two well-known elicitors of stomatal closure when acting independently. However, when stomata are presented with a combination of both signals, they fail to close.

/pdf/compound-stress-response-in-stomatal-closure-a-mathematical-4zkaibm8dp.pdf

Compound stress response in stomatal closure: a mathematical model of ABA and ethylene interaction in guard cells

During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein-protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis. 

https://scholarshare.temple.edu/bitstream/20.500.12613/8155/1/LangstonEtAl-JournalArticle-2022.pdf

Omics of endothelial cell dysfunction in sepsis

The invention provides a method of co-culturing mammalian muscle cells and mammalian motoneurons. The method comprises preparing one or more carriers coated with a covalently bonded monolayer of trimethoxysilylpropyl diethylenetriamine (DETA); suspending isolated fetal mammalian skeletal muscle cells in serum-free medium according to medium composition 1; suspending isolated fetal mammalian spinal motoneurons in serum-free medium according to medium composition 1; plating the suspended muscle cells onto the one or more carriers at a predetermined density and allowing the muscle cells to attach; plating the suspended motoneurons at a predetermined density onto the one or more carriers and allowing the motoneurons to attach; covering the one or more carriers with a mixture of medium composition 1 and medium composition 2; and incubating the carriers covered in the media mixture.

/pdf/method-of-co-culturing-mammalian-muscle-cells-and-2vplv9onem.pdf

Method of co-culturing mammalian muscle cells and motoneurons

Network theory has established that highly connected nodes in regulatory networks (hubs) show a strong correlation with criticality in network function. Although topological analysis is fully capable of identifying network hubs, it does not provide an objective method for ranking the importance of a particular node by relating its contribution to the overall network response. Towards this end, the authors have developed an augmented Boolean pseudo-dynamics approach to a priori determine the critical network interactions in biological interaction networks. The approach utilises network topology and dynamic state information to determine the set of active pathways. The active pathways are used in conjunction with the key cellular properties of efficiency and robustness, to rank the network interactions based on their importance in the sustenance of network function. To demonstrate the utility of the approach, the authors consider the well characterised guard cell signalling network in plant cells. An integrated analysis of the network revealed the critical mechanisms resulting in stomata closure in the presence and absence of abscisic acid, in excellent agreement with published results.

S.S. Sundaram

Papers

Determination of critical network interactions: an augmented Boolean pseudo-dynamics approach.