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Cells metabolize nutrients through a complex metabolic and signaling network that governs redox homeostasis. At the core of this, redox regulatory network is a mutually inhibitory relationship between reduced glutathione and reactive oxygen species (ROS)—two opposing metabolites that are linked to upstream nutrient metabolic pathways (glucose, cysteine, and glutamine) and downstream feedback loops of signaling pathways (calcium and NADPH oxidase). We developed a nutrient‐redox model of human cells to understand system‐level properties of this network. Combining in silico modeling and ROS measurements in individual cells, we show that ROS dynamics follow a switch‐like, all‐or‐none response upon glucose deprivation at a threshold that is approximately two orders of magnitude lower than its physiological concentration. We also confirm that this ROS switch can be irreversible and exhibits hysteresis, a hallmark of bistability. Our findings evidence that bistability modulates redox homeostasis in human cells and provide a general framework for quantitative investigations of redox regulation in humans., In‐silico modeling of a nutrient‐redox network and single‐cell measurements of ROS demonstrate that ROS dynamics follow a bistable response to glucose deprivation in human cells.

Functional buffering that ensures biological robustness is critical for maintaining tissue homeostasis, organismal survival, and evolution of novelty. However, the mechanism underlying functional buffering, particularly in multicellular organisms, remains largely elusive. Here, we developed an inference index (C-score) for Cell-specific Expression- BUffering (CEBU), whereby functional buffering is mediated via expression of buffering genes in specific cells and tissues in humans. By computing C-scores across 684 human cell lines using genome-wide CRISPR screens and transcriptomic RNA-seq, we report that C- score-identified putative buffering gene pairs are enriched for members of the same duplicated gene family, pathway, and protein complex. Furthermore, CEBU is especially prevalent in tissues of low regenerative capacity (e.g., bone and neuronal tissues) and is weakest in highly regenerative blood cells, linking functional buffering to tissue regeneration. Clinically, the buffering capacity enabled by CEBU can help predict patient survival for multiple cancers. Our results reveal CEBU as a buffering mechanism contributing to tissue homeostasis and cancer robustness in humans.Summary blurbWe unveil a genome-wide functional buffering mechanism, termed Cell-specific Expression Buffering (CEBU), whereby gene expression contributes to functional buffering in specific cell types and tissues. We link CEBU to genetic interactions, tissue homeostasis and cancer robustness.

While inflammation has generally been regarded as a negative factor in stroke recovery, this viewpoint has recently been challenged by demonstrating that inflammation is a necessary and sufficient factor for regeneration in the zebrafish brain injury model. This close relationship with inflammation suggests that a re-examination of the immune system’s role in strokes is necessary. We used a systems biology approach to investigate the role of immune-related functions via their interactions with other molecular functions in early cardioembolic stroke. Based on protein interaction models and on microarray data from the blood of stroke subjects and healthy controls, networks were constructed to delineate molecular interactions at four early stages (pre-stroke, 3 h, 5 h and 24 h after stroke onset) of cardioembolic stroke. A comparative analysis of functional networks identified interactions of immune-related functions with other molecular functions, including growth factors, neuro/hormone and housekeeping functions. These provide a potential pathomechanism for early stroke pathophysiology. In addition, several potential targets of miRNA and methylation regulations were derived based on basal level changes observed in the core networks and literature. The results provide a more comprehensive understanding of stroke progression mechanisms from an immune perspective and shed light on acute stroke treatments.

Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research.

Summary Background Animal behavior is governed by the activity of interconnected brain circuits. Comprehensive brain wiring maps are thus needed in order to formulate hypotheses about information flow and also to guide genetic manipulations aimed at understanding how genes and circuits orchestrate complex behaviors. Results To assemble this map, we deconstructed the adult Drosophila brain into approximately 16,000 single neurons and reconstructed them into a common standardized framework to produce a virtual fly brain. We have constructed a mesoscopic map and found that it consists of 41 local processing units (LPUs), six hubs, and 58 tracts covering the whole Drosophila brain. Despite individual local variation, the architecture of the Drosophila brain shows invariance for both the aggregation of local neurons (LNs) within specific LPUs and for the connectivity of projection neurons (PNs) between the same set of LPUs. An open-access image database, named FlyCircuit, has been constructed for online data archiving, mining, analysis, and three-dimensional visualization of all single neurons, brain-wide LPUs, their wiring diagrams, and neural tracts. Conclusion We found that the Drosophila brain is assembled from families of multiple LPUs and their interconnections. This provides an essential first step in the analysis of information processing within and between neurons in a complete brain.

Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.

The induction of stem cells toward a desired differentiation direction is required for the advancement of stem cell-based therapies. Despite successful demonstrations of the control of differentiation direction, the effective use of stem cell-based therapies suffers from a lack of systematic knowledge regarding the mechanisms underlying directed differentiation. Using dynamic modeling and the temporal microarray data of three differentiation stages, three dynamic protein-protein interaction networks were constructed. The interaction difference networks derived from the constructed networks systematically delineated the evolution of interaction variations and the underlying mechanisms. A proposed relevance score identified the essential components in the directed differentiation. Inspection of well-known proteins and functional modules in the directed differentiation showed the plausibility of the proposed relevance score, with the higher scores of several proteins and function modules indicating their essential roles in the directed differentiation. During the differentiation process, the proteins and functional modules with higher relevance scores also became more specific to the neuronal identity. Ultimately, the essential components revealed by the relevance scores may play a role in controlling the direction of differentiation. In addition, these components may serve as a starting point for understanding the systematic mechanisms of directed differentiation and for increasing the efficiency of stem cell-based therapies.

Systemic inflammation needs a precise control on the sequence and magnitude of occurring events. The high throughput data on the host-pathogen interactions gives us an opportunity to have a glimpse on the systemic inflammation. In this article, a dynamic Candida albicans-zebrafish interactive infectious network is built as an example to demonstrate how systems biology approach can be used to study systematic inflammation. In particular, based on microarray data of C. albicans and zebrafish during infection, the hyphal growth, zebrafish, and host-pathogen intercellular PPI networks were combined to form an integrated infectious PPI network that helps us understand the systematic mechanisms underlying the pathogenicity of C. albicans and the immune response of the host. The signaling pathways for morphogenesis and hyphal growth of C. albicans were 2 significant interactions found in the intercellular PPI network. Two cellular networks were also developed corresponding to the different infection stages (adhesion and invasion), and then compared with each other to identify proteins to gain more insight into the pathogenic role of hyphal development in the C. albicans infection process. Important defense-related proteins in zebrafish were predicted using the same approach. This integrated network consisting of intercellular invasion and cellular defense processes during infection can improve medical therapies and facilitate development of new antifungal drugs.

Systemic inflammation needs a precise control on the sequence and magnitude of occurring events. The high throughput data on the host-pathogen interactions gives us an opportunity to have a glimpse on the systemic inflammation. In this article, a dynamic Candida albicans-zebrafish interactive infectious network is built as an example to demonstrate how systems biology approach can be used to study systematic inflammation. In particular, based on microarray data of C. albicans and zebrafish during infection, the hyphal growth, zebrafish, and host-pathogen intercellular PPI networks were combined to form an integrated infectious PPI network that helps us understand the systematic mechanisms underlying the pathogenicity of C. albicans and the immune response of the host. The signaling pathways for morphogenesis and hyphal growth of C. albicans were 2 significant interactions found in the intercellular PPI network. Two cellular networks were also developed corresponding to the different infection stages (adhesion and invasion), and then compared with each other to identify proteins to gain more insight into the pathogenic role of hyphal development in the C. albicans infection process. Important defense-related proteins in zebrafish were predicted using the same approach. This integrated network consisting of intercellular invasion and cellular defense processes during infection can improve medical therapies and facilitate development of new antifungal drugs.

Information processing is an important characteristic of biological neurons. Here, we proposed a method to measure the information transmission ability of neurons. Due to the nonlinear dynamics of neuron, which is experimentally verified, we have to solve the Hamilton-Jacobi inequalities (HJIs) to get the information transmission ability of neuron. Instead of directly solving HJIs, fuzzy interpolation method was employed to help us to systematically measure information transmission ability by solving a set of linear matrix inequalities (LMIs). The information transmission ability measurement method will provide an insight into the mechanism of information processing in neurons.

Signal transduction plays an important role in biological organisms. Here, we proposed a method to access the information of the signal transduction ability of signal transduction pathway in the cells. Due to the nonlinear signal transduction pathway model, we have to solve the Hamilton-Jacobi inequalities (HJIs) to get the measurement. Instead of directly solving HJIs, fuzzy interpolation method was employed such that we can systematically measure signal transduction ability by solving the linear matrix inequalities (LMIs). The measurement will provide an insight to the characteristics of the signal transduction networks.

Procedures for generating the standard Drosophila brain model with coordinate information are presented. A deterministic reference template is pursued for data integration and comparison. We propose a shape-averaging algorithm for creating the standard template, including axial and non-axial structures. The standard Drosophila brain comprises the standard cortex and the standard neuropils. The standard neuropils are located within the standard cortex with the average position and orientation. A global coordinate system for the brain cortex and local coordinate systems for the standard neuropils are also generated for the standard Drosophila brain. By placing each circuit into the standard brain, we can acquire the information about the spatial and temporal relationship among neurons.

Background Molecular signaling of angiogenesis begins within hours after initiation of a stroke and the following regulation of endothelial integrity mediated by growth factor receptors and vascular growth factors. Recent studies further provided insights into the coordinated patterns of post-stroke gene expressions and the relationships between neurodegenerative diseases and neural function recovery processes after a stroke. Results Differential protein-protein interaction networks (PPINs) were constructed at 3 post-stroke time points, and proteins with a significant stroke relevance value (SRV) were discovered. Genes, including UBC, CUL3, APP, NEDD8, JUP, and SIRT7, showed high associations with time after a stroke, and Ingenuity Pathway Analysis results showed that these post-stroke time series-associated genes were related to molecular and cellular functions of cell death, cell survival, the cell cycle, cellular development, cellular movement, and cell-to-cell signaling and interactions. These biomarkers may be helpful for the early detection, diagnosis, and prognosis of ischemic stroke. Conclusions This is our first attempt to use our theory of a systems biology framework on strokes. We focused on 3 key post-stroke time points. We identified the network and corresponding network biomarkers for the 3 time points, further studies are needed to experimentally confirm the findings and compare them with the causes of ischemic stroke. Our findings showed that stroke-associated biomarker genes at different time points were significantly involved in cell cycle processing, including G2-M, G1-S and meiosis, which contributes to the current understanding of the etiology of stroke. We hope this work helps scientists reveal more hidden cellular mechanisms of stroke etiology and repair processes.