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Found 11 papers, 4 papers with code
Hypergraph Animals
Together these two findings imply that we need to spend more effort on investigating and developing suitable conditional ensembles of random hypergraph that can capture real-world structures and their complex dependency structures.
Open Problems in Mathematical Biology
Biology is data-rich, and it is equally rich in concepts and hypotheses.
A group theoretic approach to model comparison with simplicial representations
First, we present a rigorous and automatable methodology for the core process of comparison by equivalence, namely determining the vertices in a simplicial representation, corresponding to model components, that are conceptually related and the identification of these vertices via simplicial operations.
Julia for Biologists
Increasing emphasis on data and quantitative methods in the biomedical sciences is making biological research more computational.
Bayesian and Algebraic Strategies to Design in Synthetic Biology
Innovation in synthetic biology often still depends on large-scale experimental trial-and-error, domain expertise, and ingenuity.
Model comparison via simplicial complexes and persistent homology
In many scientific and technological contexts we have only a poor understanding of the structure and details of appropriate mathematical models.
Model Selection
Algebraic Topology
Quantitative Methods
Non-equilibrium statistical physics, transitory epigenetic landscapes, and cell fate decision dynamics
Statistical physics provides a useful perspective for the analysis of many complex systems; it allows us to relate microscopic fluctuations to macroscopic observations.
SubNets of Scale-Free Networks Are Not Scale-Free: Sampling Properties of Networks
Most studies of networks have only looked at small subsets of the true network.
Construction of quasi-potentials for stochastic dynamical systems: an optimization approach
The construction of effective and informative landscapes for stochastic dynamical systems has proven a long-standing and complex problem.
Optimization and Control Dynamical Systems Quantitative Methods
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
Here we discuss how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a set of distributions that start out from a suitably defined prior and converge towards the unknown posterior.
Computation
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods.
Computation Methodology
