86 research outputs found
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Several diseases related to cell proliferation are characterized by the
accumulation of somatic DNA changes, with respect to wildtype conditions.
Cancer and HIV are two common examples of such diseases, where the mutational
load in the cancerous/viral population increases over time. In these cases,
selective pressures are often observed along with competition, cooperation and
parasitism among distinct cellular clones. Recently, we presented a
mathematical framework to model these phenomena, based on a combination of
Bayesian inference and Suppes' theory of probabilistic causation, depicted in
graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are
generative probabilistic graphical models that recapitulate the potential
ordering of accumulation of such DNA changes during the progression of the
disease. Such models can be inferred from data by exploiting likelihood-based
model-selection strategies with regularization. In this paper we discuss the
theoretical foundations of our approach and we investigate in depth the
influence on the model-selection task of: (i) the poset based on Suppes' theory
and (ii) different regularization strategies. Furthermore, we provide an
example of application of our framework to HIV genetic data highlighting the
valuable insights provided by the inferred
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
A model of protocell based on the introduction of a semi-permeable membrane in a stochastic model of catalytic reaction networks
In this work we introduce some preliminary analyses on the role of a
semi-permeable membrane in the dynamics of a stochastic model of catalytic
reaction sets (CRSs) of molecules. The results of the simulations performed on
ensembles of randomly generated reaction schemes highlight remarkable
differences between this very simple protocell description model and the
classical case of the continuous stirred-tank reactor (CSTR). In particular, in
the CSTR case, distinct simulations with the same reaction scheme reach the
same dynamical equilibrium, whereas, in the protocell case, simulations with
identical reaction schemes can reach very different dynamical states, despite
starting from the same initial conditions.Comment: In Proceedings Wivace 2013, arXiv:1309.712
A stochastic model of catalytic reaction networks in protocells
Protocells are supposed to have played a key role in the self-organizing
processes leading to the emergence of life. Existing models either (i) describe
protocell architecture and dynamics, given the existence of sets of
collectively self-replicating molecules for granted, or (ii) describe the
emergence of the aforementioned sets from an ensemble of random molecules in a
simple experimental setting (e.g. a closed system or a steady-state flow
reactor) that does not properly describe a protocell. In this paper we present
a model that goes beyond these limitations by describing the dynamics of sets
of replicating molecules within a lipid vesicle. We adopt the simplest possible
protocell architecture, by considering a semi-permeable membrane that selects
the molecular types that are allowed to enter or exit the protocell and by
assuming that the reactions take place in the aqueous phase in the internal
compartment. As a first approximation, we ignore the protocell growth and
division dynamics. The behavior of catalytic reaction networks is then
simulated by means of a stochastic model that accounts for the creation and the
extinction of species and reactions. While this is not yet an exhaustive
protocell model, it already provides clues regarding some processes that are
relevant for understanding the conditions that can enable a population of
protocells to undergo evolution and selection.Comment: 20 pages, 5 figure
Analysis of the spatial and dynamical properties of a multiscale model of intestinal crypts
The preliminary analyses on a multiscale model of intestinal crypt dynamics are here presented. The model combines a morphological model, based on the Cellular Potts Model (CPM), and a gene regulatory network model, based on Noisy Random Boolean Networks (NRBNs). Simulations suggest that the stochastic differentiation process is itself sufficient to ensure the general homeostasis in the asymptotic states, as proven by several measures
The role of backward reactions in a stochastic model of catalytic reaction networks
We investigate the role of backward reactions in a stochastic model of catalytic reaction network, with specific regard to the influence on the emergence of autocatalytic sets (ACSs), which are supposed to be one of the pre-requisites in the transition between non-living to living matter.
In particular, we analyse the impact that a variation in the kinetic rates of forward and backward reactions may have on the overall dynamics.
Significant effects are indeed observed, provided that the intensity of backward reactions is sufficiently high. In spite of an invariant activity of the system in terms of production of new species, as backward reactions are intensified, the emergence of ACSs becomes more likely and an increase in their number, as well as in the proportion of species belonging to them, is observed. Furthermore, ACSs appear to be more robust to fluctuations than in the usual settings with no backward reaction.
This outcome may rely not only on the higher average connectivity of the reaction graph, but also on the distinguishing property of backward reactions of recreating the substrates of the corresponding forward reactions
J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments
Background: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods.Result: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats.Conclusion: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl
Effects of delayed immune-response in tumor immune-system interplay
Tumors constitute a wide family of diseases kinetically characterized by the co-presence of multiple spatio-temporal scales. So, tumor cells ecologically interplay with other kind of cells, e.g. endothelial cells or immune system effectors, producing and exchanging various chemical signals. As such, tumor growth is an ideal object of hybrid modeling where discrete stochastic processes model agents at low concentrations, and mean-field equations model chemical signals. In previous works we proposed a hybrid version of the well-known Panetta-Kirschner mean-field model of tumor cells, effector cells and Interleukin-2. Our hybrid model suggested -at variance of the inferences from its original formulation- that immune surveillance, i.e. tumor elimination by the immune system, may occur through a sort of side-effect of large stochastic oscillations. However, that model did not account that, due to both chemical transportation and cellular differentiation/division, the tumor-induced recruitment of immune effectors is not instantaneous but, instead, it exhibits a lag period. To capture this, we here integrate a mean-field equation for Interleukins-2 with a bi-dimensional delayed stochastic process describing such delayed interplay. An algorithm to realize trajectories of the underlying stochastic process is obtained by coupling the Piecewise Deterministic Markov process (for the hybrid part) with a Generalized Semi-Markovian clock structure (to account for delays). We (i) relate tumor mass growth with delays via simulations and via parametric sensitivity analysis techniques, (ii) we quantitatively determine probabilistic eradication times, and (iii) we prove, in the oscillatory regime, the existence of a heuristic stochastic bifurcation resulting in delay-induced tumor eradication, which is neither predicted by the mean-field nor by the hybrid non-delayed models
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