ALEXANDRE FERREIRA RAMOS
Projetos de Pesquisa
Unidades Organizacionais
SIN-86, EACH - Docente
LIM/24 - Laboratório de Oncologia Experimental, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/26 - Laboratório de Pesquisa em Cirurgia Experimental, Hospital das Clínicas, Faculdade de Medicina
LIM/24 - Laboratório de Oncologia Experimental, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/26 - Laboratório de Pesquisa em Cirurgia Experimental, Hospital das Clínicas, Faculdade de Medicina
13 resultados
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- A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene(2020) GIOVANINI, Guilherme; SABINO, Alan U.; BARROS, Luciana R. C.; RAMOS, Alexandre F.This manuscript presents a comparison of noise properties exhibited by two stochastic binary models for: (i) a self-repressing gene; (ii) a repressed or activated externally regulating one. The stochastic models describe the dynamics of probability distributions governing two random variables, namely, protein numbers and the gene state as ON or OFF. In a previous work, we quantify noise in protein numbers by means of its Fano factor and write this quantity as a function of the covariance between the two random variables. Then we show that distributions governing the number of gene products can be super-Fano, Fano or sub-Fano if the covariance is, respectively, positive, null or negative. The latter condition is exclusive for the self-repressing gene and our analysis shows the conditions for which the Fano factor is a sufficient classifier of fluctuations in gene expression. In this work, we present the conditions for which the noise on the number of gene products generated from a self-repressing gene or an externally regulating one are quantitatively similar. That is important for inference of gene regulation from noise in gene expression quantitative data. Our results contribute to a classification of noise function in biological systems by theoretically demonstrating the mechanisms underpinning the higher precision in expression of a self-repressing gene in comparison with an externally regulated one.
- Stochastic model for gene transcription on Drosophila melanogaster embryos(2016) PRATA, Guilherme N.; HORNOS, Jose Eduardo M.; RAMOS, Alexandre F.We examine immunostaining experimental data for the formation of stripe 2 of even-skipped (eve) transcripts on D. melanogaster embryos. An estimate of the factor converting immunofluorescence intensity units into molecular numbers is given. The analysis of the eve dynamics at the region of stripe 2 suggests that the promoter site of the gene has two distinct regimes: an earlier phase when it is predominantly activated until a critical time when it becomes mainly repressed. That suggests proposing a stochastic binary model for gene transcription on D. melanogaster embryos. Our model has two random variables: the transcripts number and the state of the source of mRNAs given as active or repressed. We are able to reproduce available experimental data for the average number of transcripts. An analysis of the random fluctuations on the number of eves and their consequences on the spatial precision of stripe 2 is presented. We show that the position of the anterior or posterior borders fluctuate around their average position by similar to 1% of the embryo length, which is similar to what is found experimentally. The fitting of data by such a simple model suggests that it can be useful to understand the functions of randomness during developmental processes.
- Binary Expression Enhances Reliability of Messaging in Gene Networks(2020) GAMA, Leonardo R.; GIOVANINI, Guilherme; BALAZSI, Gabor; RAMOS, Alexandre F.The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by Shannon's entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter with long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a binary gene expression regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon's theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.
- Symmetry-guided design of topologies for supercomputer networks(2018) SABINO, Alan U.; VASCONCELOS, Miguel F. S.; DENG, Yuefan; RAMOS, Alexandre F.A family of graphs optimized as the topologies for interconnection networks is proposed. The needs of such topologies with minimal diameters and minimal mean path lengths are met by special constructions of the weight vectors in a representation of the symplectic algebra. Such design of topologies can conveniently reconstruct the mesh and hypercube, widely used as network topologies, as well as many other classes of graphs potentially suitable for network topologies.
- Physical implications of so(2,1) symmetry in exact solutions for a self-repressing gene(2019) RAMOS, Alexandre F.; REINITZ, JohnWe chemically characterize the symmetries underlying the exact solutions of a stochastic negatively self-regulating gene. The breaking of symmetry at a low molecular number causes three effects. Two branches of the solution exist, having high and low switching rates, such that the low switching rate branch approaches deterministic behavior and the high switching rate branch exhibits sub-Fano behavior. The average protein number differs from the deterministically expected value. Bimodal probability distributions appear as the protein number becomes a readout of the ON/OFF state of the gene.
- Optimal low-latency network topologies for cluster performance enhancement(2020) DENG, Yuefan; GUO, Meng; RAMOS, Alexandre F.; HUANG, Xiaolong; XU, Zhipeng; LIU, WeifengWe propose that clusters interconnected with network topologies having minimal mean path length will increase their processing speeds. We approach our heuristic by constructing clusters of up to 32 nodes having torus, ring, Chvatal, Wagner, Bidiakis and optimal topology for minimal mean path length and by simulating the performance of 256 nodes clusters with the same network topologies. The optimal (or near-optimal) low-latency network topologies are found by minimizing the mean path length of regular graphs. The selected topologies are benchmarked using ping-pong messaging, the MPI collective communications and the standard parallel applications including effective bandwidth, FFTE, Graph 500 and NAS parallel benchmarks. We established strong correlations between the clusters' performances and the network topologies, especially the mean path lengths, for a wide range of applications. In communication-intensive benchmarks, optimal graphs enabled network topologies with multifold performance enhancement in comparison with mainstream graphs. It is striking that mere adjustment of the network topology suffices to reclaim performance from the same computing hardware.
- A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy(2022) GIOVANINI, Guilherme; BARROS, Luciana R. C.; GAMA, Leonardo R.; TORTELLI, Tharcisio C.; RAMOS, Alexandre F.Simple Summary Gene editing technologies reached a turning point toward epigenetic modulation for cancer treatment. Gene networks are complex systems composed of multiple non-trivially coupled elements capable of reliably processing dynamical information from the environment despite unavoidable randomness. However, this functionality is lost when the cells are in a diseased state. Hence, gene-editing-based therapeutic design can be viewed as a gene network dynamics modulation toward a healthy state. Enhancement of this control relies on mathematical models capable of effectively describing the regulation of stochastic gene expression. We use a two-state stochastic model for gene expression to investigate treatment response with a switching target gene. We show the necessity of modulating multiple gene-expression-related processes to reach a heterogeneity-reduced specific response using epigenetic-targeting cancer treatment designs. Our approach can be used as an additional tool for developing epigenetic-targeting treatments. In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter's ON state duration; (2) to increase the mRNAs' synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.
- Stochastic model of contact inhibition and the proliferation of melanoma in situ(2017) MORAIS, Mauro Cesar Cafundo; STUHL, Izabella; SABINO, Alan U.; LAUTENSCHLAGER, Willian W.; QUEIROGA, Alexandre S.; TORTELLI JR., Tharcisio Citrangulo; CHAMMAS, Roger; SUHOV, Yuri; RAMOS, Alexandre F.Contact inhibition is a central feature orchestrating cell proliferation in culture experiments; its loss is associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SKMEL-147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) which considers cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in the conducted experiments.
- Myokines in treatment-na & iuml;ve patients with cancer-associated cachexia(2021) CASTRO, Gabriela S. de; CORREIA-LIMA, Joanna; SIMOES, Estefania; ORSSO, Camila E.; XIAO, Jingjie; GAMA, Leonardo R.; GOMES, Silvio P.; GONCALVES, Daniela Caetano; COSTA, Raquel G. F.; RADLOFF, Katrin; LENZ, Ulrike; TARANKO, Anna E.; BIN, Fang Chia; FORMIGA, Fernanda B.; GODOY, Louisie G. L. de; SOUZA, Rafael P. de; NUCCI, Luis H. A.; FEITOZA, Mario; CASTRO, Claudio C. de; TOKESHI, Flavio; ALCANTARA, Paulo S. M.; OTOCH, Jose P.; RAMOS, Alexandre F.; LAVIANO, Alessandro; COLETTI, Dario; MAZURAK, Vera C.; PRADO, Carla M.; SEELAENDER, MariliaCancer-associated cachexia is a complex metabolic syndrome characterized by weight loss and systemic inflammation. Muscle loss and fatty infiltration into muscle are associated with poor prognosis in cancer patients. Skeletal muscle secretes myokines, factors with autocrine, paracrine and/or endocrine action, which may be modified by or play a role in cachexia. This study examined myokine content in the plasma, skeletal muscle and tumor homogenates from treatment-na & iuml;ve patients with gastric or colorectal stages I-IV cancer with cachexia (CC, N = 62), or not (weight stable cancer, WSC, N = 32). Myostatin, interleukin (IL) 15, follistatin-like protein 1 (FSTL-1), fatty acid binding protein 3 (FABP3), irisin and brain-derived neurotrophic factor (BDNF) protein content in samples was measured with Multiplex technology; body composition and muscle lipid infiltration were evaluated in computed tomography, and quantification of triacylglycerol (TAG) in the skeletal muscle. Cachectic patients presented lower muscle FSTL-1 expression (p = 0.047), higher FABP3 plasma content (p = 0.0301) and higher tumor tissue expression of FABP3 (p = 0.0182), IL-15 (p = 0.007) and irisin (p = 0.0110), compared to WSC. Neither muscle TAG content, nor muscle attenuation were different between weight stable and cachectic patients. Lumbar adipose tissue (AT) index, visceral AT index and subcutaneous AT index were lower in CC (p = 0.0149, p = 0.0455 and p = 0.0087, respectively), who also presented lower muscularity in the cohort (69.2% of patients; p = 0.0301), compared to WSC. The results indicate the myokine profile in skeletal muscle, plasma and tumor is impacted by cachexia. These findings show that myokines eventually affecting muscle wasting may not solely derive from the muscle itself (as the tumor also may contribute to the systemic scenario), and put forward new perspectives on cachexia treatment targeting myokines and associated receptors and pathways. (c) 2020 The Author(s).
- Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation(2017) BARR, Kenneth A.; MARTINEZ, Carlos; MORAN, Jennifer R.; KIM, Ah-Ram; RAMOS, Alexandre F.; REINITZ, JohnBackground: Models that incorporate specific chemical mechanisms have been successful in describing the activity of Drosophila developmental enhancers as a function of underlying transcription factor binding motifs. Despite this, the minimum set of mechanisms required to reconstruct an enhancer from its constituent parts is not known. Synthetic biology offers the potential to test the sufficiency of known mechanisms to describe the activity of enhancers, as well as to uncover constraints on the number, order, and spacing of motifs. Results: Using a functional model and in silico compensatory evolution, we generated putative synthetic even-skipped stripe 2 enhancers with varying degrees of similarity to the natural enhancer. These elements represent the evolutionary trajectories of the natural stripe 2 enhancer towards two synthetic enhancers designed ab initio. In the first trajectory, spatially regulated expression was maintained, even after more than a third of binding sites were lost. In the second, sequences with high similarity to the natural element did not drive expression, but a highly diverged sequence about half the length of the minimal stripe 2 enhancer drove ten times greater expression. Additionally, homotypic clusters of Zelda or Stat92E motifs, but not Bicoid, drove expression in developing embryos. Conclusions: Here, we present a functional model of gene regulation to test the degree to which the known transcription factors and their interactions explain the activity of the Drosophila even-skipped stripe 2 enhancer. Initial success in the first trajectory showed that the gene regulation model explains much of the function of the stripe 2 enhancer. Cases where expression deviated from prediction indicates that undescribed factors likely act to modulate expression. We also showed that activation driven Bicoid and Hunchback is highly sensitive to spatial organization of binding motifs. In contrast, Zelda and Stat92E drive expression from simple homotypic clusters, suggesting that activation driven by these factors is less constrained. Collectively, the 40 sequences generated in this work provides a powerful training set for building future models of gene regulation.