arXiv subjects

Ian Leifer

Publications and source records attributed to Ian Leifer.

9 recordsLinked to original sources

arXiv2023

Complexity reduction by symmetry: uncovering the minimal regulatory network for logical computation in bacteria

Symmetry principles play an important role in geometry, and physics, allowing for the reduction of complicated systems to simpler, more comprehensible models that preserve the system's features of interest. Biological systems are often highly complex and may consist of a large number of interacting parts. Using symmetry fibrations, the relevant symmetries for biological "message-passing" networks, we introduce a scheme, called Complexity Reduction by Symmetry or ComSym, to reduce the gene regulatory networks of Escherichia coli and Bacillus subtilis bacteria to core networks in a way that preserves the dynamics and uncovers the computational capabilities of the network. Gene nodes in the original network that share isomorphic input trees are collapsed by the fibration into equivalence classes called fibers, whereby nodes that receive signals with the same "history" belong to one fiber and synchronize. Then we reduce the networks to its minimal computational core via k-core decomposition. This computational core consists of a few strongly connected components or "signal vortices", in which signals can cycle through. While between them, these "signal vortices" transmit signals in a feedforward manner. These connected components perform signal processing and decision making in the bacterial cell by employing a series of genetic toggle-switch circuits that store memory, plus oscillator circuits. These circuits act as the central computation device of the network, whose output signals then spread to the rest of the network. Our reduction method opens the door to narrow the vast complexity of biological systems to their minimal parts in a systematic way by using fundamental theoretical principles of symmetry.

q-bio.CB↗

arXiv2022

Symmetry-driven network reconstruction through pseudobalanced coloring optimization

Symmetries found through automorphisms or graph fibrations provide important insights in network analysis. Symmetries identify clusters of robust synchronization in the network which improves the understanding of the functionality of complex biological systems. Network symmetries can be determined by finding a {\it balanced coloring} of the graph, which is a node partition in which each cluster of nodes receives the same information (color) from the rest of the graph. In recent work we saw that biological networks such as gene regulatory networks, metabolic networks and neural networks in organisms ranging from bacteria to yeast and humans are rich in fibration symmetries related to the graph balanced coloring. Networks based on real systems, however, are built on experimental data which are inherently incomplete, due to missing links, collection errors, and natural variations within specimens of the same biological species. Therefore, it is fair to assume that some of the existing symmetries were not detected in our analysis. For that reason, a method to find pseudosymmetries and repair networks based on those symmetries is important when analyzing real world networks. In this paper we introduce the {\it pseudobalanced coloring} \eqref{eq:mainip} problem, and provide an integer programming formulation which (a) calculates a pseudobalanced coloring of the graph taking into account the missing data, and (b) optimally repairs the graph with the minimal number of added/removed edges to maximize the symmetry of the graph. We apply our formulation to the {\it C. elegans} connectome to find pseudocoloring and the optimal graph repair. Our solution compares well with a manually curated ground-truth {\it C. elegans} graph as well as solutions generated by other methods of missing link prediction.

q-bio.QM↗

arXiv2022

Matryoshka and Disjoint Cluster Synchronization of Networks

The main motivation for this paper is to present a definition of network synchronizability for the case of cluster synchronization (CS), in an analogous fashion to Barahona and Pecora for the case of complete synchronization. We find this problem to be substantially more complex than the original one. We distinguish between the two cases of networks with intertwined clusters and no intertwined clusters and between {the two cases that the master stability function is negative either in a bounded range or in an unbounded range of its argument. We first obtain a definition of synchronizability that applies to each individual cluster within a network and then attempt to generalize this definition to the entire network. For CS, the synchronous solution of each cluster may be stable independent of the stability of the other clusters, which results in possibly different ranges in which each cluster synchronizes (isolated CS.) For each pair of clusters, we distinguish between three different cases: Matryoshka Cluster Synchronization (when the range of the stability of the synchronous solution for one cluster is included in that of the other cluster), Partially Disjoint Cluster Synchronization (when the ranges of stability of the synchronous solutions partially overlap), and Complete Disjoint Cluster Synchronization (when the ranges of stability of the synchronous solutions do not overlap.)

eess.SY↗

arXiv2021

Quasifibrations of Graphs to Find Symmetries in Biological Networks

A fibration of graphs is an homomorphism that is a local isomorphism of in-neighbourhoods, much in the same way a covering projection is a local isomorphism of neighbourhoods. Recently, it has been shown that graph fibrations are useful tools to uncover symmetries and synchronization patterns in biological networks ranging from gene, protein,and metabolic networks to the brain. However, the inherent incompleteness and disordered nature of biological data precludes the application of the definition of fibration as it is; as a consequence, also the currently known algorithms to identify fibrations fail in these domains. In this paper, we introduce and develop systematically the theory of quasifibrations which attempts to capture more realistic patterns of almost-synchronization of units in biological networks. We provide an algorithmic solution to the problem of finding quasifibrations in networks where the existence of missing links and variability across samples preclude the identification of perfect symmetries in the connectivity structure. We test the algorithm against other strategies to repair missing links in incomplete networks using real connectome data and synthetic networks. Quasifibrations can be applied to reconstruct any incomplete network structure characterized by underlying symmetries and almost synchronized clusters.

q-bio.QM↗

arXiv2021

Fast algorithm to identify cluster synchrony through fibration symmetries in large information-processing networks

Recent studies revealed an important interplay between the detailed structure of fibration symmetric circuits and the functionality of biological and non-biological networks within which they have be identified. The presence of these circuits in complex networks are directed related to the phenomenon of cluster synchronization, which produces patterns of synchronized group of nodes. Here we present a fast, and memory efficient, algorithm to identify fibration symmetries over information-processing networks. This algorithm is specially suitable for large and sparse networks since it has runtime of complexity $O(M\log N)$ and requires $O(M+N)$ of memory resources, where $N$ and $M$ are the number of nodes and edges in the network, respectively. We propose a modification on the so-called refinement paradigm to identify circuits symmetrical to information flow (i.e., fibers) by finding the coarsest refinement partition over the network. Finally, we show that the presented algorithm provides an optimal procedure for identifying fibers, overcoming the current approaches used in the literature.

q-bio.MN↗

arXiv2021

Spectral denoising for unsupervised analysis of correlated ionic transport

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displacement covariance to learn a set of diffusion eigenmodes that encode the correlation structure and form a basis for analyzing the ionic trajectories. This allows to systematically reduce the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure. We provide mathematical and numerical proofs of the method's robustness, and demonstrate it on realistic electrolyte materials.

physics.comp-ph↗

arXiv2021

Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression may be predicted from symmetries in the gene regulatory networks (GRN) and described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models - with gene input functions differencing between genes - predict symmetry breaking and desynchronization. Results: To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions: Thus, gene fibrations provides a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

q-bio.MN↗

arXiv2020

Circuits with broken fibration symmetries perform core logic computations in biological networks

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits.

q-bio.GN↗

arXiv2020

Fibration symmetries uncover the building blocks of biological networks

A major ambition of systems science is to uncover the building blocks of any biological network to decipher how cellular function emerges from their interactions. Here, we introduce a graph representation of the information flow in these networks as a set of input trees, one for each node, which contains all pathways along which information can be transmitted in the network. In this representation, we find remarkable symmetries in the input trees that deconstruct the network into functional building blocks called fibers. Nodes in a fiber have isomorphic input trees and thus process equivalent dynamics and synchronize their activity. Each fiber can then be collapsed into a single representative base node through an information-preserving transformation called 'symmetry fibration', introduced by Grothendieck in the context of algebraic geometry. We exemplify the symmetry fibrations in gene regulatory networks and then show that they universally apply across species and domains from biology to social and infrastructure networks. The building blocks are classified into topological classes of input trees characterized by integer branching ratios and fractal golden ratios of Fibonacci sequences representing cycles of information. Thus, symmetry fibrations describe how complex networks are built from the bottom up to process information through the synchronization of their constitutive building blocks.

q-bio.MN↗