A Characterization of Scale Invariant Responses in Enzymatic Networks

An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions

Deep Archetypal Analysis

"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes. The proposed method is an extension of linear "Archetypal Analysis" (AA), an unsupervised method to represent multivariate data points as sparse convex combinations of extremal elements of the dataset. Unlike the original formulation of AA, "Deep AA" can also handle side information and provides the ability for data-driven representation learning which reduces the dependence on expert knowledge. Our method is motivated by studies of evolutionary trade-offs in biology where archetypes are species highly adapted to a single task. Along these lines, we demonstrate that "Deep AA" also lends itself to the supervised exploration of chemical space, marking a distinct starting point for de novo molecular design. In the unsupervised setting we show how "Deep AA" is used on CelebA to identify archetypal faces. These can then be superimposed in order to generate new faces which inherit dominant traits of the archetypes they are based on.Comment: Published at the German Conference on Pattern Recognition 2019 (GCPR

Oscillatory stimuli differentiate adapting circuit topologies

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this record.Biology emerges from interactions between molecules, which are challenging to elucidate with current techniques. An orthogonal approach is to probe for 'response signatures' that identify specific circuit motifs. For example, bistability, hysteresis, or irreversibility are used to detect positive feedback loops. For adapting systems, such signatures are not known. Only two circuit motifs generate adaptation: negative feedback loops (NFLs) and incoherent feed-forward loops (IFFLs). On the basis of computational testing and mathematical proofs, we propose differential signatures: in response to oscillatory stimulation, NFLs but not IFFLs show refractory-period stabilization (robustness to changes in stimulus duration) or period skipping. Applying this approach to yeast, we identified the circuit dominating cell cycle timing. In Caenorhabditis elegans AWA neurons, which are crucial for chemotaxis, we uncovered a Ca2+ NFL leading to adaptation that would be difficult to find by other means. These response signatures allow direct access to the outlines of the wiring diagrams of adapting systems.The work was supported by US National Institutes of Health grant 5RO1-GM078153-07 (F.R.C.), NRSA Training Grant CA009673-36A1 (S.J.R.), a Merck Postdoctoral Fellowship at The Rockefeller University (S.J.R.), and the Simons Foundation (S.J.R.). J.L. was supported by a fellowship from the Boehringer Ingelheim Fonds. E.D.S. was partially supported by the US Office of Naval Research (ONR N00014-13-1-0074) and the US Air Force Office of Scientific Research (AFOSR FA9550-14-1-0060)

Phospholipase A2-activating protein is associated with a novel form of leukoencephalopathy

Leukoencephalopathies are a group of white matter disorders related to abnormal formation, maintenance, and turnover of myelin in the central nervous system. These disorders of the brain are categorized according to neuroradiological and pathophysiological criteria. Herein, we have identified a unique form of leukoencephalopathy in seven patients presenting at ages 2 to 4 months with progressive microcephaly, spastic quadriparesis, and global developmental delay. Clinical, metabolic, and imaging characterization of seven patients followed by homozygosity mapping and linkage analysis were performed. Next generation sequencing, bioinformatics, and segregation analyses followed, to determine a loss of function sequence variation in the phospholipase A2-activating protein encoding gene (PLAA). Expression and functional studies of the encoded protein were performed and included measurement of prostaglandin E2 and cytosolic phospholipase A2 activity in membrane fractions of fibroblasts derived from patients and healthy controls. Plaa-null mice were generated and prostaglandin E2 levels were measured in different tissues. The novel phenotype of our patients segregated with a homozygous loss-of-function sequence variant, causing the substitution of leucine at position 752 to phenylalanine, in PLAA, which causes disruption of the protein's ability to induce prostaglandin E2 and cytosolic phospholipase A2 synthesis in patients' fibroblasts. Plaa-null mice were perinatal lethal with reduced brain levels of prostaglandin E2 The non-functional phospholipase A2-activating protein and the associated neurological phenotype, reported herein for the first time, join other complex phospholipid defects that cause leukoencephalopathies in humans, emphasizing the importance of this axis in white matter development and maintenance

Serum amyloid A (SAA): a novel biomarker for uterine serous papillary cancer

BACKGROUND: Uterine serous papillary carcinoma (USPC) is a biologically aggressive variant of endometrial cancer. We investigated the expression of Serum Amyloid A (SAA) and evaluated its potential as a serum biomarker in USPC patients. METHODS: SAA gene and protein expression levels were evaluated in USPC and normal endometrial tissues (NEC) by real-time PCR, immunohistochemistry (IHC), flow cytometry and by a sensitive bead-based immunoassay. SAA concentration in 123 serum samples from 51 healthy women, 42 women with benign diseases, and 30 USPC patients were also studied. RESULTS: SAA gene expression levels were significantly higher in USPC when compared with NEC (mean copy number by RT\u2013PCR\ubc162 vs 2.21; P\ubc0.0002). IHC revealed diffuse cytoplasmic SAA protein staining in USPC tissues. High intracellular levels of SAA were identified in primary USPC cell lines evaluated by flow cytometry and SAA was found to be actively secreted in vitro. SAA concentrations (mgml 1) had a median (95% CIs) of 6.0 (4.0\u20138.9) in normal healthy females and 6.0 (4.2\u20138.1) in patients with benign disease (P\ubc0.92). In contrast, SAA values in the serum of USPC patients had a median (95% CI) of 15.6 (9.2\u201356.2), significantly higher than those in the healthy group (P\ubc0.0005) and benign group (P\ubc0.0006). Receiver operating characteristics (ROC) analysis of serum SAA to classify advanced- and early-stage USPC yielded an area under the ROC curve of 0.837 (P\ubc0.0024). CONCLUSION: SAA is not only a liver-secreted protein but is also a USPC cell product. SAA may represent a novel biomarker for USPC to assist in staging patients preoperatively, and to monitor early-disease recurrence and response to therapy

Integration of DFDs into a UML - based model-driven engineering approach

The main aim of this article is to discuss how the functional and the object-oriented views can be inter-played to represent the various modeling perspectives of embedded systems.We discuss whether the object-oriented modeling paradigm, the predominant one to develop software at the present time, is also adequate for modeling embedded software and how it can be used with the functional paradigm.More specifically, we present how the main modeling tool of the traditional structured methods, data flow diagrams, can be integrated in an object-oriented development strategy based on the unified modeling language. The rationale behind the approach is that both views are important for modeling purposes in embedded systems environments, and thus a combined and integrated model is not only useful, but also fundamental for developing complex systems. The approach was integrated in amodel-driven engineering process, where tool support for the models used was provided. In addition, model transformations have been specified and implemented to automate the process.We exemplify the approach with an IPv6 router case study.FEDER -Fundação para a Ciência e a Tecnologia(HH-02-383

Dose-Response Aligned Circuits in Signaling Systems

Cells use biological signal transduction pathways to respond to environmental stimuli and the behavior of many cell types depends on precise sensing and transmission of external information. A notable property of signal transduction that was characterized in the Saccharomyces cerevisiae yeast cell and many mammalian cells is the alignment of dose-response curves. It was found that the dose response of the receptor matches closely the dose responses of the downstream. This dose-response alignment (DoRA) renders equal sensitivities and concordant responses in different parts of signaling system and guarantees a faithful information transmission. The experimental observations raise interesting questions about the nature of the information transmission through DoRA signaling networks and design principles of signaling systems with this function. Here, we performed an exhaustive computational analysis on network architectures that underlie the DoRA function in simple regulatory networks composed of two and three enzymes. The minimal circuits capable of DoRA were examined with Michaelis-Menten kinetics. Several motifs that are essential for the dynamical function of DoRA were identified. Systematic analysis of the topology space of robust DoRA circuits revealed that, rather than fine-tuning the network's parameters, the function is primarily realized by enzymatic regulations on the controlled node that are constrained in limiting regions of saturation or linearity

Smart Tourism Destinations: Can the Destination Management Organizations Exploit Benefits of the ICTs? Evidences from a Multiple Case Study

Recent developments of ICTs enable new ways to experience tourism and conducted to the concept of smart tourism. The adoption of cutting-edge technologies and its combination with innovative organizational models fosters cooperation, knowledge sharing, and open innovation among service providers in tourism destination. Moreover, it offers innovative services to visitors. In few words, they become smart tourism destinations. In this paper, we report first results of the SMARTCAL project aimed at conceiving a digital platform assisting Destination Management Organizations (DMOs) in providing smart tourism services. A DMO is the organization charged with managing the tourism offer of a collaborative network, made up of service providers acting in a destination. In this paper, we adopted a multiple case studies approach to analyze five Italian DMOs. Our aims were to investigate (1) if, and how, successful DMOs were able to offer smart tourism services to visitors; (2) if the ICTs adoption level was related to the collaboration level among DMO partners. First results highlighted that use of smart technologies was still in an embryonic stage of development, and it did not depend from collaboration levels

Differential Gene Expression Regulated by Oscillatory Transcription Factors

Cells respond to changes in the internal and external environment by a complex regulatory system whose end-point is the activation of transcription factors controlling the expression of a pool of ad-hoc genes. Recent experiments have shown that certain stimuli may trigger oscillations in the concentration of transcription factors such as NF-B and p53 influencing the final outcome of the genetic response. In this study we investigate the role of oscillations in the case of three different well known gene regulatory mechanisms using mathematical models based on ordinary differential equations and numerical simulations. We considered the cases of direct regulation, two-step regulation and feed-forward loops, and characterized their response to oscillatory input signals both analytically and numerically. We show that in the case of indirect two-step regulation the expression of genes can be turned on or off in a frequency dependent manner, and that feed-forward loops are also able to selectively respond to the temporal profile of oscillating transcription factors