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Videothèque  | enregistrements trouvés : 21

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The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow time-scale corresponding to the jumps. I'll present different biological systems that can be modelled by PDMPs, explain how they can be simulated.
The second part will focus on random models for cell division when the whole branching population is taken into account. I'll present two data sets from biological experiments trying to determine whether cell division is symmetric or not. I'll explain how statistic tools can help answer this question.
The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow ...

60Jxx ; 92Bxx ; 90Cxx

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The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow time-scale corresponding to the jumps. I'll present different biological systems that can be modelled by PDMPs, explain how they can be simulated.
The second part will focus on random models for cell division when the whole branching population is taken into account. I'll present two data sets from biological experiments trying to determine whether cell division is symmetric or not. I'll explain how statistic tools can help answer this question.
The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow ...

60Jxx ; 92Bxx ; 90Cxx

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The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow time-scale corresponding to the jumps. I'll present different biological systems that can be modelled by PDMPs, explain how they can be simulated.
The second part will focus on random models for cell division when the whole branching population is taken into account. I'll present two data sets from biological experiments trying to determine whether cell division is symmetric or not. I'll explain how statistic tools can help answer this question.
The aim of this course is to present some examples of stochastic models suitable for population dynamics.
The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times. They are especially suitable to model phenomena with different time scales: a fast time-sacla corresponding to the deterministic behaviour and a slow ...

60Jxx ; 92Bxx ; 90Cxx

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Multi angle  Applying the infinitesimal model
Etheridge, Alison (Auteur de la Conférence) ; Barton, Nicholas H. (Auteur de la Conférence) | CIRM (Editeur )

The infinitesimal model is based on the assumption that, conditional on the pedigree, the joint distribution of trait values is multivariate normal, then, selecting parents does not alter the variance amongst offspring. We explain how the infinitesimal model extends to include dominance as well as epistasis. Then, the evolution of a population depends on just a few quantities, which define the components of genetic variance and the inbreeding depression. In practice, the main difficulty in applying the infinitesimal model in the presence of dominance is that one must calculate the probabilities of identity by descent amongst up to four genes, which means that very many identity coefficients must be traced. We show how these coefficients can be calculated and approximated, allowing the infinitesimal model to be applied to help understand the evolutionary consequences of inbreeding depression.
The infinitesimal model is based on the assumption that, conditional on the pedigree, the joint distribution of trait values is multivariate normal, then, selecting parents does not alter the variance amongst offspring. We explain how the infinitesimal model extends to include dominance as well as epistasis. Then, the evolution of a population depends on just a few quantities, which define the components of genetic variance and the inbreeding ...

60F05 ; 60K30 ; 92D10

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In the infinitesimal model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. The idea behind the normal distribution of the genetic component is that the genetic part of the trait of interest is the sum of the ‘infinitesimal' contributions of the allelic states at a very large number of loci. This model has been widely used in quantitative genetics, but less so in evolutionary biology and the precise conditionsunder which it holds has remained rather vague. In this talk, we shall provide a mathematical justification of the model as the limit as the number M of loci tends to infinity of a model with Mendelian inheritance, which includes different evolutionary processes (genetic drift, recombination, selection, mutation, population structure, ...). Generalisations of the simple version of the infinitesimal model presented here, as well as some applications, will be presented in the following talks by Nick Barton and Alison Etheridge.
In the infinitesimal model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. The idea behind the normal distribution of the genetic component is that the genetic part of the trait of interest is the sum of the ...

60F05 ; 60K30 ; 92D10

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Species live and interact in landscapes where enviornmental conditions vary both in time and space. In the face of this spatial-temporal heterogeneity, species may co-evolve their habitat choices which determine their spatial distributions. To understand this coevolution, I present an analysis of a general class of stochastic Lotka-Volterra models that account for space implicitly. For these equations, a (stochastic) coevolutionarily stable strategy (coESS) is a set of habitat choice strategies for each species that, with high probability, resists invasion attempts from mutant subpopulations utilizing other habitat choice strategies. We show that the coESS is characterized by a system of second-order equations. This characterization implies that the stochastic per-capita growth rates are negative in all occupied patches for all species despite all of the species coexisting. Applying this characterization to the coevolution of habitat-choice of competitors and predator-prey systems identifies under what environmental conditions, natural selection excorcises "the ghost of competition past'' and generates enemy-free and victimless habitats. Collectively, these results highlight the importance of temporal fluctuations, spatial heterogeneity and species interactions on the evolution of species spatial distributions.
Species live and interact in landscapes where enviornmental conditions vary both in time and space. In the face of this spatial-temporal heterogeneity, species may co-evolve their habitat choices which determine their spatial distributions. To understand this coevolution, I present an analysis of a general class of stochastic Lotka-Volterra models that account for space implicitly. For these equations, a (stochastic) coevolutionarily stable ...

92D25 ; 37H10

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Low-dimensional compartment models for biological systems can be fitted to time series data using Monte Carlo particle filter methods. As dimension increases, for example when analyzing a collection of spatially coupled populations, particle filter methods rapidly degenerate. We show that many independent Monte Carlo calculations, each of which does not attempt to solve the filtering problem, can be combined to give a global filtering solution with favorable theoretical scaling properties under a weak coupling condition. The independent Monte Carlo calculations are called islands, and the operation carried out on each island is called adapted simulation, so the complete algorithm is called an adapted simulation island filter. We demonstrate this methodology and some related algorithms on a model for measles transmission within and between cities.
Low-dimensional compartment models for biological systems can be fitted to time series data using Monte Carlo particle filter methods. As dimension increases, for example when analyzing a collection of spatially coupled populations, particle filter methods rapidly degenerate. We show that many independent Monte Carlo calculations, each of which does not attempt to solve the filtering problem, can be combined to give a global filtering solution ...

60G35 ; 60J20 ; 62M02 ; 62M05 ; 62M20 ; 62P10 ; 65C35

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Antibiotic resistance is a serious public health concern. Responding to this problem effectively requires characterising the factors (i.e. evolutionary and ecological processes) that determine resistance frequencies. At present, we do not have ecologically plausible models of resistance that are able to replicate observed trends - we are therefore unable to make credible predictions about resistance dynamics. In this talk, I will present work motivated by three tends observed in Streptococcus pneumoniae resistance data: the stable coexistence of antibiotic sensitivity and resistance, variation between resistance frequencies between pneumococcal lineages and correlation in resistance to different antibiotics. I will propose that variation in the fitness benefit gained from resistance arising from variation in the duration of carriage of pneumococcal lineages is a parsimonious explanation for all three trends. This eco-evolutionary framework could allow more accurate prediction of future resistance levels and play a role in informing strategies to prevent the spread of resistance.
Antibiotic resistance is a serious public health concern. Responding to this problem effectively requires characterising the factors (i.e. evolutionary and ecological processes) that determine resistance frequencies. At present, we do not have ecologically plausible models of resistance that are able to replicate observed trends - we are therefore unable to make credible predictions about resistance dynamics. In this talk, I will present work ...

92D30 ; 92D40

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In an epidemic model, the basic reproduction number $ R_{0}$ is a function of the parameters (such as infection rate) measuring disease infectivity. In a large population, if $ R_{0}> 1$, then the disease can spread and infect much of the population (supercritical epidemic); if $ R_{0}< 1$, then the disease will die out quickly (subcritical epidemic), with only few individuals infected.
For many epidemics, the dynamics are such that $ R_{0}$ can cross the threshold from supercritical to subcritical (for instance, due to control measures such as vaccination) or from subcritical to supercritical (for instance, due to a virus mutation making it easier for it to infect hosts). Therefore, near-criticality can be thought of as a paradigm for disease emergence and eradication, and understanding near-critical phenomena is a key epidemiological challenge.
In this talk, we explore near-criticality in the context of some simple models of SIS (susceptible-infective-susceptible) epidemics in large homogeneous populations.
In an epidemic model, the basic reproduction number $ R_{0}$ is a function of the parameters (such as infection rate) measuring disease infectivity. In a large population, if $ R_{0}> 1$, then the disease can spread and infect much of the population (supercritical epidemic); if $ R_{0}< 1$, then the disease will die out quickly (subcritical epidemic), with only few individuals infected.
For many epidemics, the dynamics are such that $ R_{0}$ can ...

92D30 ; 05C80 ; 92D25 ; 60J28

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Mathematical models of infectious disease transmission are increasingly used to guide public health and policy decisions. Hence, it is important that every effort is made to ensure that models are ‘correct’, made difficult by the frequent need to simulate a model numerically. The best we can do in most cases is to be able to replicate a model i.e. generate the same results from the same inputs (model plus parameters), or failing that, reproduce results that are similar. This can be achieved by sharing the computer code, and/or providing a sufficiently detailed description of the model. I will illustrate that it is often difficult to replicate or reproduce results of modeling publications, using case studies that highlight some of the many causes of this failure. I will argue that the FAIR principles proposed for data - that they should be Findable, Accessible, Interoperable and Reusable - are equally valid for modeling studies, and go a long way towards ensuring reproducibility. I will present Epirecipes (http://epirecip.es) a FAIR platform that both allows models to be replicated exactly, while fostering the idea that a wide variety of approaches are needed to ensure the robustness of model results. The added value from this platform includes resources for teaching, acting as a ‘Rosetta Stone’ - allowing models from one computer language to be ported to another, and as a repository of best practices, potential pitfalls, and technical tricks that are all too often tucked away in papers or textbooks. As quoted from ‘The Turing Way’ (https://the-turing-way.netlify.com), a handbook for reproducible science, reproducing models of infectious disease should be ‘too easy not to do’.
Mathematical models of infectious disease transmission are increasingly used to guide public health and policy decisions. Hence, it is important that every effort is made to ensure that models are ‘correct’, made difficult by the frequent need to simulate a model numerically. The best we can do in most cases is to be able to replicate a model i.e. generate the same results from the same inputs (model plus parameters), or failing that, reproduce ...

97B10 ; 97D40 ; 97M60

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Increased understanding of the molecular drivers of tumor initiation and progression has led to targeted manipulation of intracellular signaling pathways for patient-specific therapeutic benefit. In this talk, we outline a multiscale modeling strategy for linking intracellular signaling pathways critical to cell proliferation and apoptosis; receptor-ligand binding on the cell surface that triggers these intracellular signaling cascades; and population-level tumor growth dynamics and response to treatments targeting these pathways. Integration of these tiers of information is precisely the level of detail required to uncover possible hidden mechanisms that mediate both expected and potentially counter intuitive therapeutic effects of novel, targeted therapeutics on the multiple cell types responsible for tumor progression. We demonstrate the predictive therapeutic power of our multiscale computational approach with two specific examples. The first considers treatments targeting VEGF and its receptors. In this case, it is difficult to tease out the differential anti-angiogenic and anti-tumor effects of drug combinations experimentally due to the dynamic crosstalk between tumor cells and vascular endothelial cells, which impacts critical aspects of tumorigenesis, independent of angiogenesis. Our model predicts that certain therapeutic combinations result in antagonism in vivo, but not in vitro. In the second example, our computational approach is used to study the therapeutic impact of Tocilizumab, a competitive IL-6R inhibitor, on tumor growth and cancer stem cell fraction, alone and in combination with the traditional chemotherapeutic agent, Cisplatin. Targeting critical regulators of the cancer stem cell phenotype to overcome their acute influence on tumor growth is a promising new strategy for cancer treatment. Our results suggest that nonintuitive dose scheduling strategies will optimize the synergy of combination therapy. Both examples show that this predictive modeling framework can serve to evaluate strategies for signaling pathway modulation rapidly and can provide a basis for proposing optimized dose scheduling for combination treatments involving targeted therapeutics.
Increased understanding of the molecular drivers of tumor initiation and progression has led to targeted manipulation of intracellular signaling pathways for patient-specific therapeutic benefit. In this talk, we outline a multiscale modeling strategy for linking intracellular signaling pathways critical to cell proliferation and apoptosis; receptor-ligand binding on the cell surface that triggers these intracellular signaling cascades; and ...

92-XX

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The detailed mechanisms of many cell functions such as motility, traffic, division or filopodia formation is difficult to address due to cell complexity. In all these functions, a common observation is that cytoskeleton assembly correlates with membrane deformation based on active forces. The exact role, in particular, of the actin cytoskeleton in cell membrane deformation, with pushing or pulling forces is what we address here experimentally. We conceive stripped-down experimental systems that reproduce cellular behaviours in simplified conditions: cytoskeleton dynamics are reproduced on liposome membranes. Inspired by how actin forces can exert forces on membranes and organelles, we address now how the nucleus, which is the most rigid cell organelle, is deformed by the actin cytoskeleton during cell translocation.
Actin polymerization through the growth of a branched actin network is able to initiate membrane tubules and spikes by pushing or pulling, which mimics the formation of endocytic vesicles and filopodia. By changing experimentally membrane tension and cytoskeleton structure, we displace the system within a phase diagram where inward or outward deformations are favoured. Moreover, shells of branched actin networks grown around liposomes display buckling and wrinkling under an osmotic deflation, thereby confirming their elastic properties. The time during which we let the network grow around liposomes allows us to vary the shell thickness, and to unveil the transition at which buckling or wrinkling occurs. Our results are in excellent agreement with the general mechanisms of buckling and wrinkling found in various systems spanning from pollen grains to the development of the gut or the brain.
The role of actin on membrane trafficking is unveiled by using preformed membrane tubes and growing an actin network around them in a form of a sleeve. We show that actin is able to modulate the thickness of tubes maintained under force. In a cell, where membrane tubes are constantly pulled by motors walking on microtubules, we predict that actin provides a way of maintaining a variety of tube thicknesses.
We study the translocation of the nucleus when cells move through narrow spaces that are smaller than their nuclei. We find that proteins of the nuclear membrane, such as nesprins, accumulate at the nucleus front during nucleus deformation and pull the nucleus forward.
The detailed mechanisms of many cell functions such as motility, traffic, division or filopodia formation is difficult to address due to cell complexity. In all these functions, a common observation is that cytoskeleton assembly correlates with membrane deformation based on active forces. The exact role, in particular, of the actin cytoskeleton in cell membrane deformation, with pushing or pulling forces is what we address here experimentally. ...

37LXX ; 74BXX ; 76Axx ; 92Cxx

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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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Macrophages are a type of immune cells that can be present in high numbers in some solid tumours. The heterogeneity of macrophage populations (with the anti-tumour M1 cells and thepro-tumour M2 cells being the two extreme phenotypes) has led to difficulties in understanding the innate immune responses to tumours. Here we introduce a class of mathematical models for the interactions between a population of tumour-associated macrophages (structured by their phenotype) and a population of cancer cells (that could be structured by their mutation status). We then use this class of models to confirm that the M1 cells kill tumours, while the M2 cells can lead to tumour growth. In addition, we show that macrophages with mixed phenotypes can contribute to either tumour growth or tumour decay. We also show that tumour dormancy is associated not only with an increased heterogeneity of cancer population, but also with an increased heterogeneity of macrophage population.
Macrophages are a type of immune cells that can be present in high numbers in some solid tumours. The heterogeneity of macrophage populations (with the anti-tumour M1 cells and thepro-tumour M2 cells being the two extreme phenotypes) has led to difficulties in understanding the innate immune responses to tumours. Here we introduce a class of mathematical models for the interactions between a population of tumour-associated macrophages (...

92C15 ; 35L60 ; 92C42

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In the second talk, I will present some of our work on this area. Our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL), to tools to infer logic models from phosphoproteomic and phenotypic data (CellNOpt and PHONEMeS). We have recently adapted these tools to single-cell data. I will illustrate their utility in cases of biomedical relevance, in particular to improve our understanding of cancer and to develop novel therapeutic opportunities. As main application I will discuss our work analysing, as a model for personalized medicine, large pharmaco-genomic screenings in cell lines. These screenings provide rich information about alterations in tumours that confer drug sensitivity or resistance. Integration of this data with prior knowledge provides biomarkers and offer hypotheses for novel combination therapies. Our own analysis as well as the results of a crowdsourcing effort (as part of a DREAM
challenge) reveals that prediction of drug efficacy from basal omics data is that discussed above is far from accurate, implying important limitations for personalised medicine. An important aspect that deserves detailed attention is the dynamics of signaling networks and how they response to perturbations such as drug treatment.
I will present how cell-specific logic models, trained with measurements upon perturbations, can provides new biomarkers and treatment opportunities not noticeable by static molecular characterisation.
In the second talk, I will present some of our work on this area. Our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL), to tools to ...

92B05 ; 92-08 ; 92-10 ; 92C42

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Modern technologies allow us to profile in high detail biomedical samples at fast decreasing costs. New technologies are opening new data modalities, in particular to measure at the single cell level. Prior knowledge, and biological networks in particular, are useful to integrate this data and distill mechanistic insight. This can help to interpret the result of machine learning or statistical analysis, as well as generate input features for these methods. In addition, they can be converted in dynamic mechanistic models to gain more specific insight. I will give an overview of these approaches showcasing some examples and approaches used in the field.
Modern technologies allow us to profile in high detail biomedical samples at fast decreasing costs. New technologies are opening new data modalities, in particular to measure at the single cell level. Prior knowledge, and biological networks in particular, are useful to integrate this data and distill mechanistic insight. This can help to interpret the result of machine learning or statistical analysis, as well as generate input features for ...

92B05 ; 92-08 ; 92-10 ; 92C42

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Basics on the biology of molecular interactions and interaction data will be given all along the presentation of some of our research projects.

92C42 ; 92E10

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The reconstruction of graphical models (or networks) has become ubiquitous to analyze the rapidly expanding, information-rich data of biological or clinical interest. I will outline some network reconstruction methods and applications to large scale datasets. In particular, our group has developped information-theoretic methods and machine learning tools to infer and analyze interpretable graphical models from large scale genomics data (single cell transcriptomics, tumor expression and mutation data) as well as clinical data (analysis of medical records from breast cancer patients, Institut Curie, and from elderly patients with cognitive disorders, La Pitie-Salpetriere).
The reconstruction of graphical models (or networks) has become ubiquitous to analyze the rapidly expanding, information-rich data of biological or clinical interest. I will outline some network reconstruction methods and applications to large scale datasets. In particular, our group has developped information-theoretic methods and machine learning tools to infer and analyze interpretable graphical models from large scale genomics data (single ...

68T05 ; 92D10

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In this talk, I will present current activities of the Computational Systems Biology group at IBM Research, Zurich, focused on the inference and exploitation of networks of molecular interactions. Focusing first on the problem of network inference, a long-standing challenge for which many methods have been proposed, I will discuss how no single inference method performs optimally across all data sets.
However, a Wisdom of the Crowds approach based on the integration of multiple inference methods can increase the robustness and high performance of the inferred networks. To that aim, we have developed COSIFER, a web-based platform that enables the inference of molecular networks using different approaches and consensus strategies. Next, I will introduce INtERAcT, an approach to extract information about molecular interactions from a text corpus in a completely unsupervised manner. INtERAcT exploits word embeddings, a state-of-the-art technology for language modelling based on deep learning that does not require text labeling for training or domain-specific knowledge, and hence, can be easily applied to different scientific domains.
Moving into the applications, I will explain how prior information about the molecular interactions in a cell can be encoded in a network, which can be further used for gene prioritization. Such strategy is exploited by NetBiTE with the goal of identifying anti-cancer drug sensitivity biomarkers. Finally, I will discuss how a probabilistic application of network dynamics can enable the reconstruction of the cell-signaling dynamics using single-cell omics.
In this talk, I will present current activities of the Computational Systems Biology group at IBM Research, Zurich, focused on the inference and exploitation of networks of molecular interactions. Focusing first on the problem of network inference, a long-standing challenge for which many methods have been proposed, I will discuss how no single inference method performs optimally across all data sets.
However, a Wisdom of the Crowds approach ...

92-10 ; 68T09

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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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