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The synaptic cleft has been vastly investigated in the last decades, leading to a novel and fascinating model of the functional and structural modifications linked to synaptic transmission and brain processing. The classic neurocentric model encompassing the neuronal pre- and post-synaptic terminals partly explains the fine-tuned plastic modifications under both pathological and physiological circumstances. Recent experimental evidence has incontrovertibly added oligodendrocytes, astrocytes, and microglia as pivotal elements for synapse formation and remodeling (tripartite synapse) in both the developing and adult brain. Moreover, synaptic plasticity and its pathological counterpart (maladaptive plasticity) have shown a deep connection with other molecular elements of the extracellular matrix (ECM), once considered as a mere extracellular structural scaffold altogether with the cellular glue (i.e., glia). The ECM adds another level of complexity to the modern model of the synapse, particularly, for the long-term plasticity and circuit maintenance. This model, called tetrapartite synapse, can be further implemented by including the neurovascular unit (NVU) and the immune system. Although they were considered so far as tightly separated from the central nervous system (CNS) plasticity, at least in physiological conditions, recent evidence endorsed these elements as structural and paramount actors in synaptic plasticity. This scenario is, as far as speculations and evidence have shown, a consistent model for both adaptive and maladaptive plasticity. However, a comprehensive understanding of brain processes and circuitry complexity is still lacking. Here we propose that a better interpretation of the CNS complexity can be granted by a systems biology approach through the construction of predictive molecular models that enable to enlighten the regulatory logic of the complex molecular networks underlying brain function in health and disease, thus opening the way to more effective treatments., Competing Interests: The authors declare no conflict of interests.

Background: Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences., Results: In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine)., Conclusions: This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.

Despite their different histological and molecular properties, different types of cancers share few essential functional alterations. Some of these cancer hallmarks may easily be studied in in vitro cultures, while others are related to the way in which tumors grow in vivo. According to the systems biology paradigm, complex cellular functions arise as system-level properties from the dynamic interaction of a large number of biomolecules. We previously newly defined four basic cancer cell properties derived from known cancer hallmarks amenable to system-level investigation in cell cultures: enhanced growth, altered response to apoptotic cues, genomic instability and inability to enter senescence following oncogenic signaling. Here we summarize the major properties of enhanced growth that is dependent on metabolism rewiring - in which glucose is mostly used by fermentation while glutamine provides nitrogen and carbon atoms for biosyntheses - and controlled by oncogene signaling. We then briefly review the major drugs used to target signaling pathways in preclinical and clinical studies, whose clinical efficacy is unfortunately severely limited by tumor resistance, substantially due to signaling cross-talk. We present a systems biology roadmap that integrates different types of mathematical models with conventional and post-genomic biomolecular analyses that will provide a deeper mechanistic understanding of the links between metabolism and uncontrolled cancer cell growth. This approach is taken to be instrumental both in unraveling cancer's first principles and in designing novel drugs able to target one or more control or execution steps of the cancer rewired metabolism, in order to achieve permanent arrest of tumor development.

Molecular systems biology aims to describe the functions of complex biological processes through recursive integration of molecular analysis, modeling, simulation and theory. It focuses on networks that originate from interconnection of genes, proteins and metabolites whose dynamic interactions generate, as an emergent property of the system, the corresponding function. Although evolutionary optimized, intracellular biochemical parameters, such as the expression level of gene products or the affinity between two or more proteins, must have a permissible range that gives robustness against perturbations to the system. Using the yeast G(1)-to-S transition network as an example we show that sophisticated relations exist among network structure, emergent property and robustness. Different emergent properties are generated from the same network by changing the strength of its interactions, not only by altering expression level, but also through mono and multi-site phosphorylation/dephosphorylation. Besides, multi-site protein phosphorylation modules, widespread in cell cycle, may ensure robust and coherent timing of cell cycle transitions as it happens for the onset of DNA replication. In conclusion, the modulation of biological function/emergent property by modifying interaction strength provides an efficient, highly tunable device to regulate biological processes. Furthermore, the principles outlined herein may provide new insight to network analysis in drug discovery.

Following a brief description of the operational procedures of systems biology (SB), the cell cycle of budding yeast is discussed as a successful example of a top-down SB analysis. After the reconstruction of the steps that have led to the identification of a sizer plus timer network in the G1 to S transition, it is shown that basic functions of the cell cycle (the setting of the critical cell size and the accuracy of DNA replication) are system-level properties, detected only by integrating molecular analysis with modelling and simulation of their underlying networks. A detailed network structure of a second relevant regulatory step of the cell cycle, the exit from mitosis, derived from extensive data mining, is constructed and discussed. To reach a quantitative understanding of how nutrients control, through signalling, metabolism and transcription, cell growth and cycle is a very relevant aim of SB. Since we know that about 900 gene products are required for cell cycle execution and control in budding yeast, it is quite clear that a purely systematic approach would require too much time. Therefore lines for a modular SB approach, which prioritises molecular and computational investigations for faster cell cycle understanding, are proposed. The relevance of the insight coming from the cell cycle SB studies in developing a new framework for tackling very complex biological processes, such as cancer and aging, is discussed.

Background: The cell cycle is a complex process that allows eukaryotic cells to replicate chromosomal DNA and partition it into two daughter cells. A relevant regulatory step is in the G0/G1 phase, a point called the restriction (R) point where intracellular and extracellular signals are monitored and integrated.Subcellular localization of cell cycle proteins is increasingly recognized as a major factor that regulates cell cycle transitions. Nevertheless, current mathematical models of the G1/S networks of mammalian cells do not consider this aspect. Hence, there is a need for a computational model that incorporates this regulatory aspect that has a relevant role in cancer, since altered localization of key cell cycle players, notably of inhibitors of cyclin-dependent kinases, has been reported to occur in neoplastic cells and to be linked to cancer aggressiveness., Results: The network of the model components involved in the G1 to S transition process was identified through a literature and web-based data mining and the corresponding wiring diagram of the G1 to S transition drawn with Cell Designer notation. The model has been implemented in Mathematica using Ordinary Differential Equations. Time-courses of level and of sub-cellular localization of key cell cycle players in mouse fibroblasts re-entering the cell cycle after serum starvation/re-feeding have been used to constrain network design and parameter determination. The model allows to recapitulate events from growth factor stimulation to the onset of S phase. The R point estimated by simulation is consistent with the R point experimentally determined., Conclusion: The major element of novelty of our model of the G1 to S transition is the explicit modeling of cytoplasmic/nuclear shuttling of cyclins, cyclin-dependent kinases, their inhibitor and complexes. Sensitivity analysis of the network performance newly reveals that the biological effect brought about by Cki overexpression is strictly dependent on whether the Cki is promoting nuclear translocation of cyclin/Cdk containing complexes.

Apoptosis is a programmed cell death that plays a critical role during the development of the nervous system and in many chronic neurodegenerative diseases, including Alzheimer's disease (AD). This pathology, characterized by a progressive degeneration of cholinergic function resulting in a remarkable cognitive decline, is the most common form of dementia with high social and economic impact. Current therapies of AD are only symptomatic, therefore the need to elucidate the mechanisms underlying the onset and progression of the disease is surely needed in order to develop effective pharmacological therapies. Because of its pivotal role in neuronal cell death, apoptosis has been considered one of the most appealing therapeutic targets, however, due to the complexity of the molecular mechanisms involving the various triggering events and the many signaling cascades leading to cell death, a comprehensive understanding of this process is still lacking. Modular systems biology is a very effective strategy in organizing information about complex biological processes and deriving modular and mathematical models that greatly simplify the identification of key steps of a given process. This review aims at describing the main steps underlying the strategy of modular systems biology and briefly summarizes how this approach has been successfully applied for cell cycle studies. Moreover, after giving an overview of the many molecular mechanisms underlying apoptosis in AD, we present both a modular and a molecular model of neuronal apoptosis that suggest new insights on neuroprotection for this disease.

Proliferative disorders are a major challenge for human health. The understanding of the organization of cell-cycle events is of the utmost importance to devise effective therapeutic strategies for cancer. The awareness that cells and organisms are complex, modular, hierarchical systems and the availability of genome-wide gene expression and protein analyses, should make it feasible to elucidate human diseases in terms of dysfunctions of molecular systems. Here we review evidence in support of a systems model of the cell cycle, in which two sequential growth-sensitive thresholds control entry into S-phase. The putative molecular determinants that set the threshold for entry into S-phase are consistently altered in cancer cells. Such a framework could be useful in guiding both experimental investigation and data analysis by allowing wiring to other relevant cell modules thereby highlighting the differential responses, or lack of response of cancer cells to intra- and extracellular factors. Pharmacological approaches that take advantage of transformation-induced fragility to glucose shortage are discussed. Extension of this hierarchical, modular approach to tumors as a whole holds promise for the development of effective drug discovery approaches and more efficient therapeutic protocols.

Coagulation and the immune system interact in several physiological and pathological conditions, including tissue repair, host defense, and homeostatic maintenance. This network plays a key role in diseases of the central nervous system (CNS) by involving several cells (CNS resident cells, platelets, endothelium, and leukocytes) and molecular pathways (protease activity, complement factors, platelet granule content). Endothelial damage prompts platelet activation and the coagulation cascade as the first physiological step to support the rescue of damaged tissues, a flawed rescuing system ultimately producing neuroinflammation. Leukocytes, platelets, and endothelial cells are sensitive to the damage and indeed can release or respond to chemokines and cytokines (platelet factor 4, CXCL4, TNF, interleukins), and growth factors (including platelet-derived growth factor, vascular endothelial growth factor, and brain-derived neurotrophic factor) with platelet activation, change in capillary permeability, migration or differentiation of leukocytes. Thrombin, plasmin, activated complement factors and matrix metalloproteinase-1 (MMP-1), furthermore, activate intracellular transduction through complement or protease-activated receptors. Impairment of the neuro-immune hemostasis network induces acute or chronic CNS pathologies related to the neurovascular unit, either directly or by the systemic activation of its main steps. Neurons, glial cells (astrocytes and microglia) and the extracellular matrix play a crucial function in a "tetrapartite" synaptic model. Taking into account the neurovascular unit, in this review we thoroughly analyzed the influence of neuro-immune hemostasis on these five elements acting as a functional unit ("pentapartite" synapse) in the adaptive and maladaptive plasticity and discuss the relevance of these events in inflammatory, cerebrovascular, Alzheimer, neoplastic and psychiatric diseases. Finally, based on the solid reviewed data, we hypothesize a model of neuro-immune hemostatic network based on protein–protein interactions. In addition, we propose that, to better understand and favor the maintenance of adaptive plasticity, it would be useful to construct predictive molecular models, able to enlighten the regulating logic of the complex molecular network, which belongs to different cellular domains. A modeling approach would help to define how nodes of the network interact with basic cellular functions, such as mitochondrial metabolism, autophagy or apoptosis. It is expected that dynamic systems biology models might help to elucidate the fine structure of molecular events generated by blood coagulation and neuro-immune responses in several CNS diseases, thereby opening the way to more effective treatments. [ABSTRACT FROM AUTHOR]

Highlights: [•] We briefly review the role of astrocytes in brain function. [•] We review the main pathways in astrocytic dysfunction. [•] We focus on astroglial-specific targets for clinical management of neurodegenerative disorders. [•] We describe the usefulness of a systems biology approach in drug discovery for neurodegenerative disorders. [Copyright &y& Elsevier]

Cell growth and proliferation require a complex series of tight-regulated and well-orchestrated events. Accordingly, proteins governing such events are evolutionary conserved, even among distant organisms. By contrast, it is more singular the case of "core functions" exerted by functional analogous proteins that are not homologous and do not share any kind of structural similarity. This is the case of proteins regulating the G1/S transition in higher eukaryotes-i.e., the retinoblastoma (Rb) tumor suppressor Rb-and budding yeast, i.e., Whi5. The interaction landscape of Rb and Whi5 is quite large, with more than one hundred proteins interacting either genetically or physically with each protein. The Whi5 interactome has been used to construct a concept map of Whi5 function and regulation. Comparison of physical and genetic interactors of Rb and Whi5 allows highlighting a significant core of conserved, common functionalities associated with the interactors indicating that structure and function of the network-rather than individual proteins-are conserved during evolution. A combined bioinformatics and biochemical approach has shown that the whole Whi5 protein is highly disordered, except for a small region containing the protein family signature. The comparison with Whi5 homologs from Saccharomycetales has prompted the hypothesis of a modular organization of structural disorder, with most evolutionary conserved regions alternating with highly variable ones. The finding of a consensus sequence points to the conservation of a specific phosphorylation rhythm along with two disordered sequence motifs, probably acting as phosphorylation-dependent seeds in Whi5 folding/unfolding. Thus, the widely disordered Whi5 appears to act as a hierarchical, "date hub" that has evolutionary assayed an original way of modular organization before being supplanted by the globular, multi-domain structured Rb, more suitable to cover the role of a "party hub". [ABSTRACT FROM AUTHOR]

Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by "omics" analyses. In this paper we describe four basic cancer cell properties that can easily be investigated in vitro: enhanced proliferation, evasion from apoptosis, genomic instability, and inability to undergo oncogene-induced senescence. Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells. [ABSTRACT FROM AUTHOR]

Abstract: In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer. [Copyright &y& Elsevier]

Abstract: In eukaryotes DNA replication takes place in the S phase of the cell cycle. It initiates from hundreds to thousands of replication origins in a coordinated manner, in order to efficiently duplicate the genome. The sequence of events leading to the onset of DNA replication is conventionally divided in two interdependent processes: licensing—a process during which replication origins acquire replication competence but are kept inactive— and firing—a process during which licensed origins are activated but not re-licensed. In this review we investigate the evolutionary conservation of the molecular machinery orchestrating DNA replication initiation both in yeast and in mammalian cells, highlighting a remarkable conservation of the general architecture of this central biological mechanism. Many steps are conserved down to molecular details and are performed by orthologous proteins with high sequence conservation, while differences in molecular structure of the performing proteins and their interactions are apparent in other steps. Tight regulation of initiation of DNA replication is achieved through protein phosphorylation, exerted mostly by Cyclin-dependent kinases in order to ensure that each chromosome is fully replicated once, and only once, during each cycle, and to avoid the formation of aberrant DNA structures and incorrect chromosomal duplication, that in mammalian cells are a prerequisite for genome instability and tumorigenesis. We then consider a molecular mathematical model of DNA replication, recently proposed by our group in a collaborative project, as a frame of reference to discuss similarities and differences observed in the regulatory program controlling DNA replication initiation in yeast and in mammalian cells and discuss whether they may be dependent upon different functional constraints. We conclude that a systems biology approach, integrating molecular analysis with modeling and computational investigations, is the best choice to investigate the control of DNA replication in mammalian cells. [Copyright &y& Elsevier]

The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa. [ABSTRACT FROM AUTHOR]

Abstract: Large-scale “omics” data are often represented as networks of interacting components, but such representation is inherently static and, as such, cannot provide a realistic picture of the temporal dynamics of complex cellular functions. These difficulties suggest moving to a modeling strategy that explicitly takes into account both the wiring of the components and the task they perform. From an engineering perspective, this problem resembles that of “circuit analysis”. In this paper, we focus on a limited but relevant biological circuit, the G1 to S transition in yeast cell cycle, and investigate both the network representation and the corresponding circuit described by a mathematical model, by means of a wide range of numerical simulation analysis. Reliable predictions of system-level properties are achieved and the parameters that mostly affect these properties are found out. [Copyright &y& Elsevier]