25,485 research outputs found
Geometric Path Integrals. A Language for Multiscale Biology and Systems Robustness
In this paper we suggest that, under suitable conditions, supervised learning
can provide the basis to formulate at the microscopic level quantitative
questions on the phenotype structure of multicellular organisms. The problem of
explaining the robustness of the phenotype structure is rephrased as a real
geometrical problem on a fixed domain. We further suggest a generalization of
path integrals that reduces the problem of deciding whether a given molecular
network can generate specific phenotypes to a numerical property of a
robustness function with complex output, for which we give heuristic
justification. Finally, we use our formalism to interpret a pointedly
quantitative developmental biology problem on the allowed number of pairs of
legs in centipedes
Multi-agent decision-making dynamics inspired by honeybees
When choosing between candidate nest sites, a honeybee swarm reliably chooses
the most valuable site and even when faced with the choice between near-equal
value sites, it makes highly efficient decisions. Value-sensitive
decision-making is enabled by a distributed social effort among the honeybees,
and it leads to decision-making dynamics of the swarm that are remarkably
robust to perturbation and adaptive to change. To explore and generalize these
features to other networks, we design distributed multi-agent network dynamics
that exhibit a pitchfork bifurcation, ubiquitous in biological models of
decision-making. Using tools of nonlinear dynamics we show how the designed
agent-based dynamics recover the high performing value-sensitive
decision-making of the honeybees and rigorously connect investigation of
mechanisms of animal group decision-making to systematic, bio-inspired control
of multi-agent network systems. We further present a distributed adaptive
bifurcation control law and prove how it enhances the network decision-making
performance beyond that observed in swarms
Biological control networks suggest the use of biomimetic sets for combinatorial therapies
Cells are regulated by networks of controllers having many targets, and
targets affected by many controllers, but these "many-to-many" combinatorial
control systems are poorly understood. Here we analyze distinct cellular
networks (transcription factors, microRNAs, and protein kinases) and a
drug-target network. Certain network properties seem universal across systems
and species, suggesting the existence of common control strategies in biology.
The number of controllers is ~8% of targets and the density of links is 2.5%
\pm 1.2%. Links per node are predominantly exponentially distributed, implying
conservation of the average, which we explain using a mathematical model of
robustness in control networks. These findings suggest that optimal
pharmacological strategies may benefit from a similar, many-to-many
combinatorial structure, and molecular tools are available to test this
approach.Comment: 33 page
Characterizing Distances of Networks on the Tensor Manifold
At the core of understanding dynamical systems is the ability to maintain and
control the systems behavior that includes notions of robustness,
heterogeneity, or regime-shift detection. Recently, to explore such functional
properties, a convenient representation has been to model such dynamical
systems as a weighted graph consisting of a finite, but very large number of
interacting agents. This said, there exists very limited relevant statistical
theory that is able cope with real-life data, i.e., how does perform analysis
and/or statistics over a family of networks as opposed to a specific network or
network-to-network variation. Here, we are interested in the analysis of
network families whereby each network represents a point on an underlying
statistical manifold. To do so, we explore the Riemannian structure of the
tensor manifold developed by Pennec previously applied to Diffusion Tensor
Imaging (DTI) towards the problem of network analysis. In particular, while
this note focuses on Pennec definition of geodesics amongst a family of
networks, we show how it lays the foundation for future work for developing
measures of network robustness for regime-shift detection. We conclude with
experiments highlighting the proposed distance on synthetic networks and an
application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex
Networks 201
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination
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