Generic predictions of output probability based on complexities of inputs and outputs
Scientific reports Nature Research 10:1 (2020) 4415
Abstract:
For a broad class of input-output maps, arguments based on the coding theorem from algorithmic information theory (AIT) predict that simple (low Kolmogorov complexity) outputs are exponentially more likely to occur upon uniform random sampling of inputs than complex outputs are. Here, we derive probability bounds that are based on the complexities of the inputs as well as the outputs, rather than just on the complexities of the outputs. The more that outputs deviate from the coding theorem bound, the lower the complexity of their inputs. Since the number of low complexity inputs is limited, this behaviour leads to an effective lower bound on the probability. Our new bounds are tested for an RNA sequence to structure map, a finite state transducer and a perceptron. The success of these new methods opens avenues for AIT to be more widely used.Boolean Threshold Networks as Models of Genotype-Phenotype Maps
Springer Proceedings in Complexity (2020) 143-155
Abstract:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020. Boolean threshold networks (BTNs) are a class of mathematical models used to describe complex dynamics on networks. They have been used to study gene regulation, but also to model the brain, and are similar to artificial neural networks used in machine learning applications. In this paper we study BTNs from the perspective of genotype-phenotype maps, by treating the network’s set of nodes and connections as its genotype, and dynamic behaviour of the model as its phenotype. We show that these systems exhibit (1) Redundancy, that is many genotypes map to the same phenotypes; (2) Bias, the number of genotypes per phenotypes varies over many orders of magnitude; (3) Simplicity bias, simpler phenotypes are exponentially more likely to occur than complex ones; (4) Large robustness, many phenotypes are surprisingly robust to random perturbations in the parameters, and (5) this robustness correlates positively with the evolvability, the ability of the system to find other phenotypes by point mutations of the parameters. These properties should be relevant for the wide range of systems that can be modelled by BTNs.Complexity and modularity in a simple model of self-assembling polycubes
17th Annual Conference on Foundations of Nanoscience, FNANO 2020: Self-Assembled Architectures and Devices (2020) 138-139
TacoxDNA: A user-friendly web server for simulations of complex DNA structures, from single strands to origami
Journal of Computational Chemistry Wiley 40:29 (2019) 2586-2595
Abstract:
Simulations of nucleic acids at different levels of structural details are increasingly used to complement and interpret experiments in different fields, from biophysics to medicine and materials science. However, the various structural models currently available for DNA and RNA and their accompanying suites of computational tools can be very rarely used in a synergistic fashion. The tacoxDNA webserver and standalone software package presented here are a step toward a long-sought interoperability of nucleic acids models. The webserver offers a simple interface for converting various common input formats of DNA structures and setting up molecular dynamics (MD) simulations. Users can, for instance, design DNA rings with different topologies, such as knots, with and without supercoiling, by simply providing an XYZ coordinate file of the DNA centre-line. More complex DNA geometries, as designable in the cadnano, CanDo and Tiamat tools, can also be converted to all-atom or oxDNA representations, which can then be used to run MD simulations. Though the latter are currently geared toward the native and LAMMPS oxDNA representations, the open-source package is designed to be further expandable. TacoxDNA is available at http://tacoxdna.sissa.it. © 2019 Wiley Periodicals, Inc.Identifying physical causes of apparent enhanced cyclization of short DNA molecules with a coarse-grained model
Journal of Chemical Theory and Computation American Chemical Society 15:8 (2019) 4660-4672