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Documents  92C40 | enregistrements trouvés : 35

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- xv; 346 p.
ISBN 978-3-642-37194-3

Lecture notes in bioinformatics , 7821

Localisation : Colloque 1er étage (BEIJ)

biologie moléculaire # méthode computationnelle # bioinformatique # génomique # algorithme génétique # cancer

92C40 ; 92-08 ; 92-06 ; 00B25

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- x; 264 p.
ISBN 978-3-540-87988-6

Lecture notes in bioinformatics , 5267

Localisation : Colloque 1er étage (PARI)

génomique # bioinformatique # algorithme génétique # programmation génétique

92C40 ; 92D10 ; 92-06 ; 00B25

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- x; 186 p.
ISBN 978-0-8218-4466-3

Proceedings of symposia in applied mathematics , 0066

Localisation : Collection 1er étage

variétés # théorie des noeuds # ADN # biologie

53A04 ; 57M25 ; 57M27 ; 57R56 ; 81T45 ; 82D60 ; 92C40 ; 92E10

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- 242 p.
ISBN 978-0-8218-3197-7

DIMACS series in discrete mathematics and theorerical computer science , 0061

Localisation : Collection 1er étage

biomathématique # mathématique appliquée à la biologie # arbre # bioconsensus # hypergraphe # ensemble ordonnée # classification # préférence de groupe # science comportementale # taxonomie # biologie moléculaire # évolution

00B25 ; 91F99 ; 92-06 ; 92B10 ; 05C05 ; 05C65 ; 06A99 ; 62H30 ; 92B05 ; 92C40 ; 92D15

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- 389 p.
ISBN 978-0-8218-0963-1

DIMACS series in discrete mathematics and theoretical computer science , 0049

Localisation : Collection 1er étage

analyse combinatoire # biomathématique # ensemble partiellement ordonné # informatique théorique # mathématiques discrètes # séquence # théorie des graphes # théorie des nombres # théorème de preuve

05-06 ; 05Cxx ; 05Dxx ; 06A07 ; 11Bxx ; 60C05 ; 68Q15 ; 68Rxx ; 92C40

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- 275 p.
ISBN 978-0-8218-0756-9

DIMACS series in discrete mathematics and theoretical computer science , 0044

Localisation : Collection 1er étage

biochimie # biologie cellulaire # biomathématique # calcul # dynamique de l'ADN # informatique théorique # modélisation # protéine

68Q05 ; 92-06 ; 92B05 ; 92C40

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Research talks;Numerical Analysis and Scientific Computing;Dynamical Systems and Ordinary Differential Equations

Baker’s yeast (Saccharomyces cerevisiae) has a diploid life-style, but under stress conditions, it forms spores, which then release haploid cells of mating types MATa and MATα. MATa cells are a frequently used model organism for many cell biological studies of cell cycle, metabolism or signaling. MATa and MATα cells can also mate to form diploid cells again. To this end, they secrete the pheromones α-factor and a-factor, sense the opposite pheromone and form protrusions in the direction of a potential mating partner. Importantly, they cannot move towards their mating partner, thus, the formation of the mating shape called shmoo is a significant growth investment.
Combining experimental studies of the cellular responses to mating factor and the resulting shape changes with spatial mathematical modeling, we investigated three major steps in the mating process. Specifically, we asked the following questions: (i) How do yeast cells communicate to form sharp gradients of pheromones allowing for precise decisions about whether to engage in mating or to continue dividing instead ? (ii) How do the individual cells sense the resulting gradients and how do they implement this information in order to decide about the spatial location of the polarization spot and later mating project? (iii) How do they translate the sensed information into shape changes, i.e. directed growth?
While we here use data and further information on yeast cells, the investigated processes occur in many cells under different circumstances. The developed theoretical concepts are therefore of general importance.
Baker’s yeast (Saccharomyces cerevisiae) has a diploid life-style, but under stress conditions, it forms spores, which then release haploid cells of mating types MATa and MATα. MATa cells are a frequently used model organism for many cell biological studies of cell cycle, metabolism or signaling. MATa and MATα cells can also mate to form diploid cells again. To this end, they secrete the pheromones α-factor and a-factor, sense the opposite ...

92C40 ; 92C42

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Research talks;Dynamical Systems and Ordinary Differential Equations;Partial Differential Equations

Computational modeling can be used to reveal insights into the mechanisms and potential side effects of a new drug. Here we will focus on two major diseases: diabetes, which affects 1 in 10 people in North America, and hypertension, which affects 1 in 3 adults. For diabetes, we are interested in a class of relatively novel drug treatment, the SGLT2 inhibitors (sodium-glucose co-transporter 2 inhibitors). E.g., Dapagliflozin, Canagliflozin, and Empagliflozin. We conduct simulations to better understand any side effect these drugs may have on our kidneys (which are the targets of SGLT2 inhibitors). Interestingly, these drugs may have both positive and negative side effects. For hypertension, we want to better understand the sex differences in the efficacy of some of the drug treatments. Women generally respond better to ARBs (angiotensin receptor blockers) than ACE inhibitors (angiontensin converting enzyme inhibitors), whereas the opposite is true for men. We have developed the first sex-specific computational model of blood pressure regulation, and applied that model to assess whether the ”one-size-fits-all” approach to blood pressure control is appropriate with regards to sex. Computational modeling can be used to reveal insights into the mechanisms and potential side effects of a new drug. Here we will focus on two major diseases: diabetes, which affects 1 in 10 people in North America, and hypertension, which affects 1 in 3 adults. For diabetes, we are interested in a class of relatively novel drug treatment, the SGLT2 inhibitors (sodium-glucose co-transporter 2 inhibitors). E.g., Dapagliflozin, Canagliflozin, and ...

92C42 ; 92C40 ; 92C20

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Research School;Mathematics in Science and Technology;Probability and Statistics

Dans une première partie, je présenterai différentes problématiques liées à des statistiques d'occurrences de mots dans des génomes et décortiquerai plus en détail la question de savoir comment détecter si un mot a une fréquence d'apparition significativement anormale dans une séquence. Dans une deuxième partie, je présenterai différentes extensions pour tenir compte du fait qu'un motif d'ADN fonctionnel n'est pas toujours un " mot ", mais qu'il peut avoir une structure plus complexe qui nécessite le développement de nouvelles méthodes statistiques. Dans une première partie, je présenterai différentes problématiques liées à des statistiques d'occurrences de mots dans des génomes et décortiquerai plus en détail la question de savoir comment détecter si un mot a une fréquence d'apparition significativement anormale dans une séquence. Dans une deuxième partie, je présenterai différentes extensions pour tenir compte du fait qu'un motif d'ADN fonctionnel n'est pas toujours un " mot ", mais qu'il ...

92C40 ; 62P10 ; 60J20 ; 92C42

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Research talks;Computer Science;Mathematics in Science and Technology

Start the video and click on the track button in the timeline to move to talk 1, 2 and to the discussion.

- Talk 1: Paul Apicella - Striatal dopamine and acetylcholine mechanisms involved in reward-related learning

The midbrain dopamine system has been identified as a major component of motivation and reward processing. One of its main targets is the striatum which plays an important role in motor control and learning functions. Other subcortical neurons work in parallel with dopamine neurons. In particular, striatal cholinergic interneurons participate in signaling the reward-related significance of stimuli and they may act in concert with dopamine to encode prediction error signals and control the learning of stimulus-response associations. Recent studies have revealed functional cooperativity between these two neuromodulatory systems of a complexity far greater than previously appreciated. In this talk I will review the difference and similarities between dopamine and acetylcholine reward-signaling systems, the possible nature of reward representation in each system, and discuss the involvement of striatal dopamine-acetylcholine interactions during leaning and behavior.

- Talk 2: Yonatan Loewenstein - Modeling operant learning: from synaptic plasticity to behavior

- Discussion with Paul Apicella and Yonatan Loewenstein
Start the video and click on the track button in the timeline to move to talk 1, 2 and to the discussion.

- Talk 1: Paul Apicella - Striatal dopamine and acetylcholine mechanisms involved in reward-related learning

The midbrain dopamine system has been identified as a major component of motivation and reward processing. One of its main targets is the striatum which plays an important role in motor control and learning functions. Other ...

68T05 ; 68Uxx ; 92B20 ; 92C20 ; 92C40

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Research talks;Mathematics in Science and Technology

Therapy resistance and tumour relapse after drug therapy are commonly explained by Darwinian selection of pre-existing drug-resistant, often stem-like cancer cells resulting from random mutations. However, the ubiquitous nongenetic heterogeneity and plasticity of tumour cell phenotype raises the question: are mutations really necessary and sufficient to promote cell phenotype changes during tumour progression? Tumorigenesis is a dynamic biological process that involves distinct cancer cell subpopulations proliferating at different rates and interconverting between them. Cancer therapy inevitably spares some cancer cells, even in the absence of resistant mutants. Accumulating observations suggest that the non-killed, residual tumour cells actively acquire a new phenotype simply by exploiting their developmental potential. These surviving cells are stressed by the cytotoxic treatment, and owing to phenotype plasticity, exhibit a variety of responses. By entering such stem-like, stress-response states, the surviving cells strengthen their capacity to cope with future noxious agents. Considering nongenetic cell state dynamics and the relative ease with which surviving but stressed cells can be tipped into latent attractors of the gene regulatory network provides a foundation for exploring new therapeutic approaches that seek not only to kill cancer cells but also to avoid promoting resistance and relapse that are inherently linked to the attempts to kill them.

Keywords: cancer attractor, epigenetic landscape, multi-drug resistance
Therapy resistance and tumour relapse after drug therapy are commonly explained by Darwinian selection of pre-existing drug-resistant, often stem-like cancer cells resulting from random mutations. However, the ubiquitous nongenetic heterogeneity and plasticity of tumour cell phenotype raises the question: are mutations really necessary and sufficient to promote cell phenotype changes during tumour progression? Tumorigenesis is a dynamic ...

92C50 ; 92C37 ; 92C40

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Research schools;Computer Science;Mathematics in Science and Technology

92C40 ; 92D10 ; 92D20 ; 92E10 ; 68T05 ; 68Q25

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Research schools;Computer Science;Mathematics in Science and Technology

92C40 ; 92D10 ; 92D20 ; 92E10 ; 68T05 ; 68Q25

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Research talks;Numerical Analysis and Scientific Computing;Partial Differential Equations;Mathematics in Science and Technology

The emergence of drug-resistance is a major challenge in chemotherapy. In this talk we will present our recent mathematical models for describing the dynamics of drug-resistance in solid tumors. Our models follow the dynamics of the tumor, assuming that the cancer cell population depends on a phenotype variable that corresponds to the resistance level to a cytotoxic drug. We incorporate the dynamics of nutrients and two different types of drugs: a cytotoxic drug, which directly impacts the death rate of the cancer cells, and a cytostatic drug that reduces the proliferation rate. Through analysis and simulations, we study the impact of spatial and phenotypic heterogeneity on the tumor growth under chemotherapy. We demonstrate that heterogeneous cancer cells may emerge due to the selection dynamics of the environment. Our models predict that under certain conditions, multiple resistant traits emerge at different locations within the tumor. We show that a higher dosage of the cytotoxic drug may delay a relapse, yet, when this happens, a more resistant trait emerges. Moreover, we estimate the expansion rate of the tumor boundary as well as the time of relapse, in terms of the resistance trait, the level of the nutrient, and the drug concentration. Finally, we propose an efficient drug schedule aiming at minimizing the growth rate of the most resistant trait. By combining the cytotoxic and cytostatic drugs, we demonstrate that the resistant cells can be eliminated. The emergence of drug-resistance is a major challenge in chemotherapy. In this talk we will present our recent mathematical models for describing the dynamics of drug-resistance in solid tumors. Our models follow the dynamics of the tumor, assuming that the cancer cell population depends on a phenotype variable that corresponds to the resistance level to a cytotoxic drug. We incorporate the dynamics of nutrients and two different types of drugs: ...

92C50 ; 92C37 ; 92C40

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Research schools;Computer Science;Mathematics in Science and Technology

92C40 ; 92D20 ; 92E10 ; 68Q10

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Research talks;Computer Science;Mathematics in Science and Technology

Cancer patients often respond differently to the same treatment. Precision oncology aims at predicting which treatments will be effective on a given patient. Such predictive biomarkers of drug response typically take the form of a particular somatic mutation. However, lessons from the past indicate that these single gene-drug response associations are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Our results show that combining multiple gene alterations of the tumours via ML often results in better discrimination than that provided by the corresponding single-gene marker. This approach also permits assessing which type of molecular profile is most predictive of tumour response depending on treatment and cancer type. Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive tumours (i.e. they have a higher recall) than the corresponding single-gene marker. The latter suggest that a higher proportion of patients could benefit from precision oncology by applying this ML methodology to existing clinical pharmacogenomics data sets. Cancer patients often respond differently to the same treatment. Precision oncology aims at predicting which treatments will be effective on a given patient. Such predictive biomarkers of drug response typically take the form of a particular somatic mutation. However, lessons from the past indicate that these single gene-drug response associations are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine ...

92C40 ; 68Uxx

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Research talks;Mathematics in Science and Technology

92C50 ; 92C40 ; 92D10 ; 92C37

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Research talks;Dynamical Systems and Ordinary Differential Equations;Mathematics in Science and Technology

92C50 ; 92C45 ; 92C40

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Research schools;Computer Science;Mathematics in Science and Technology

68Q25 ; 68Q42 ; 68Q87 ; 90C39 ; 92D20 ; 92C40

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- xxii; 443 p.
ISBN 978-3-319-16374-1

Interdisciplinary applied mathematics , 0039

Localisation : Ouvrage RdC (LEIM)

dynamique moléculaire # mécanique Hamiltonienne # équation différentielle stochastique # méthode numérique symplectique # gestion des contraintes # dynamique de Langevin # dynamique dissipative des particules

82-01 ; 82B80 ; 82-04 ; 92C40 ; 82-08

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