pwc-archive/papers-with-abstracts huggingface dataset:
The basal level of gene expression associated with chromatin loosening shapes Waddington landscapes and controls cell differentiation
Authors: Gilles Flouriot, Charly Jehanno, Yann Le Page, Pascale Le Goff, Benjamin Boutin, Denis Michel
Date: 2020-12-08 00:00:00

The baseline level of transcription, which is variable and difficult to quantify, seriously complicates the normalization of comparative transcriptomic data, but its biological importance remains unappreciated. We show that this currently neglected ingredient is essential for controlling gene network multistability and therefore cellular differentiation. Basal expression is correlated to the degree of chromatin loosening measured by DNA accessibility, and systematically leads to cellular dedifferentiation as assessed by transcriptomic signatures, irrespective of the molecular and cellular tools used. Modeling gene network motifs formally involved in developmental bifurcations, reveals that the epigenetic landscapes of Waddington are restructured by the level of non specific expression, such that the attractors of progenitor and differentiated cells can be mutually exclusive. This mechanism is universal and holds beyond the particular nature of the genes involved, provided the multistable circuits are correctly described with autonomous basal expression. These results explain the relationships long established between gene expression noise, chromatin decondensation and cellular dedifferentiation, and highlight how heterochromatin maintenance is essential for preventing pathological cellular reprogramming, age-related diseases and cancer.
The Gossip Paradox: why do bacteria share genes?
Authors: Alastair Jamieson-Lane, Bernd Blasius
Date: 2020-12-08 00:00:00

Bacteria, in contrast to eukaryotic cells contain two types of genes: chromosomal genes that are fixed to the cell, and plasmids that are mobile genes, easily shared to other cells. The sharing of plasmid genes between individual bacteria and between bacterial lineages has contributed vastly to bacterial evolution, allowing specialized traits to `jump ship' between one lineage or species and the next. The benefits of this generosity from the point of view of both recipient and plasmid are generally understood, but come at the expense of chromosomal genes in the donor cell, which share potentially advantageous genes with their competition while receiving no benefit. Using both continuous models and agent based simulations, we demonstrate that `secretive' genes which restrict horizontal gene transfer are favored over wide range of models and parameter values. Our findings lead to a peculiar paradox: given the obvious benefits of keeping secrets, why do bacteria share information so freely?
Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes
Authors: Masayo Inoue, Kunihiko Kaneko
Date: 2020-12-08 00:00:00
Tasks: Dynamic Time Warping

Living organisms must respond to environmental changes. Generally, accurate and rapid responses are provided by simple, unidirectional networks that connect inputs with outputs. Besides accuracy and speed, biological responses should also be robust to environmental or intracellular noise and mutations. Furthermore, cells must also respond to unforeseen environmental changes that have not previously been experienced, to avoid extinction prior to the evolutionary rewiring of their networks, which takes numerous generations. We have investigated gene regulatory networks that mutually activate or inhibit, and have demonstrated that complex entangled networks can make appropriate input-output relationships that satisfy the robust and adaptive responses required for unforeseen challenges. Such entangled networks function for sloppy and unreliable responses with low Hill coefficient reactions for the expression of each gene. To compensate for such sloppiness, several detours in the regulatory network exist. By taking advantage of the averaging over such detours, the network shows a higher robustness to environmental and intracellular noise as well as to mutations in the network, when compared to simple unidirectional circuits. Furthermore, the appropriate response to unforeseen challenges, allowing for functional outputs, is achieved as many genes exhibit similar dynamic expression responses, irrespective of inputs, as confirmed by applying dynamic time warping and dynamic mode decomposition. As complex entangled networks are common in gene regulatory networks and global gene expression responses are observed in microbial experiments, the present results provide a novel design principle for cellular networks.
Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
Authors: Tadamune Kaneko, Macoto Kikuchi
Date: 2020-12-05 00:00:00

The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To understand the particularities of this process theoretically, evolutionary simulation (ES) alone is insufficient because the outcomes of ES depend on evolutionary pathways. We need a reference system for comparison. An appropriate reference system for this purpose is an ensemble of the randomly sampled genotypes. However, generating high-fitness genotypes by simple random sampling is difficult because such genotypes are rare. In this study, we used the multicanonical Monte Carlo method developed in statistical physics to construct a reference ensemble of GRNs and compared it with the outcomes of ES. We obtained the following results. First, mutational robustness was significantly higher in ES than in the reference ensemble at the same fitness level. Second, the emergence of a new phenotype, bistability, was delayed in evolution. Third, the bistable group of GRNs contains many mutationally fragile GRNs compared with those in the non-bistable group. This suggests that the delayed emergence of bistability is a consequence of the mutation-selection mechanism.
Walking droplets, swimming microbes: on memory in physics and life
Authors: Albert Libchaber, Tsvi Tlusty
Date: 2020-11-22 00:00:00

Whirling and swerving, a bacterium is swimming in a test tube, foraging for food. On the surface of a vibrating bath, a droplet starts walking. A certain similarity, but mostly dissimilarity, between the physical memory that emerges in Couder's droplet experiments and the biological memory of the bacterium is noted. It serves as a starting point for a short perspective and speculation on the multilevel, loopy memory of living matter.
Computation capacities of a broad class of signaling networks are higher than their communication capacities
Authors: Iman Habibi, Effat S Emamian, Osvaldo Simeone, Ali Abdi
Date: 2020-11-20 00:00:00

Due to structural and functional abnormalities or genetic variations and mutations, there may be dysfunctional molecules within an intracellular signaling network that do not allow the network to correctly regulate its output molecules, such as transcription factors. This disruption in signaling interrupts normal cellular functions and may eventually develop some pathological conditions. In this paper, computation capacity of signaling networks is introduced as a fundamental limit on signaling capability and performance of such networks. The computation capacity measures the maximum number of computable inputs, that is, the maximum number of input values for which the correct functional output values can be recovered from the erroneous network outputs, when the network contains some dysfunctional molecules. This contrasts with the conventional communication capacity that measures instead the maximum number of input values that can be correctly distinguished based on the erroneous network outputs. The computation capacity is higher than the communication capacity, if the network response function is not a one-to-one function of the input signals. By explicitly incorporating the effect of signaling errors that result in the network dysfunction, the computation capacity provides more information about the network and its malfunction. Two examples of signaling networks are studied here, one regulating caspase3 and another regulating NFkB, for which computation and communication capacities are analyzed. Higher computation capacities are observed for both networks. One biological implication of this finding is that signaling networks may have more capacity than that specified by the conventional communication capacity metric. The effect of feedback is also studied. In summary, this paper reports findings on a new fundamental feature of the signaling capability of cell signaling networks.
Homeostasis in Networks with Multiple Input Nodes and Robustness in Bacterial Chemotaxis
Authors: João Luiz de Oliveira Madeira, Fernando Antoneli
Date: 2020-11-05 00:00:00

A biological system achieve homeostasis when there is a regulated quantity that is maintained within a narrow range of values. Here we consider homeostasis as a phenomenon of network dynamics. In this context, we improve a general theory for the analysis of homeostasis in network dynamical systems with distinguished input and output nodes, called `input-output networks'. The theory allows one to define `homeostasis types' of a given network in a `model independent' fashion, in the sense that the classification depends on the network topology rather than on the specific model equations. Each `homeostasis type' represents a possible mechanism for generating homeostasis and is associated with a suitable `subnetwork motif' of the original network. Our contribution is an extension of the theory to the case of networks with multiple input nodes. To showcase our theory, we apply it to bacterial chemotaxis, a paradigm for homeostasis in biochemical systems. By considering a representative model of Escherichia coli chemotaxis, we verify that the corresponding abstract network has multiple input nodes. Thus showing that our extension of the theory allows for the inclusion of an important class of models that were previously out of reach. Moreover, from our abstract point of view, the occurrence of homeostasis in the studied model is caused by a new mechanism, called input counterweight homeostasis. This new homeostasis mechanism was discovered in the course of our investigation and is generated by a balancing between the several input nodes of the network -- therefore, it requires the existence of at least two input nodes to occur. Finally, the framework developed here allows one to formalize a notion of `robustness' of homeostasis based on the concept of `genericity' from the theory dynamical systems. We discuss how this kind of robustness of homeostasis appears in the chemotaxis model.
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
Authors: Paul F. Lang, Sungho Shin, Victor M. Zavala
Date: 2020-11-05 00:00:00

Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia programming language was recently developed as a high-level and high-performance language for scientific computing, systems biologists have only started to realise its potential. For instance, we have recently used Julia to cut down the optimization time of a microbial community model by a factor of 140. To facilitate access of the systems biology community to the efficient nonlinear solvers used for this optimisation, we developed SBML2Julia. SBML2Julia translates optimisation problems specified in SBML and TSV files (PEtab format) into Julia for Mathematical Programming (JuMP), executes the optimization and returns the results in tabular format. Availability and implementation: SBML2Julia is freely available under the MIT license. It comes with a command line interface and Python API. Internally, SBML2Julia calls the Julia LTS release v1.0.5 for optimisation. All necessary dependencies can be pulled from Docker Hub (https://hub.docker.com/repository/docker/paulflang/sbml2julia). Source code and documentation are available at https://github.com/paulflang/SBML2Julia.
COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery
Authors: Brandt Bessell, Josh Loecker, Zhongyuan Zhao, Sara Sadat Aghamiri, Sabyasachi Mohanty, Rada Amin, Tomáš Helikar, Bhanwar Lal Puniya
Date: 2020-11-04 00:00:00
Tasks: Data Integration, Drug Discovery

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogenous biological datasets, which can be challenging without sophisticated tools. We developed COMO, a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases, and disease data to aid drug discovery. COMO can be installed as a Docker image and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for multi-omics integration of bulk and single-cell RNA-seq, microarrays, and proteomics to develop context-specific metabolic models. Using public databases, open-source solutions for model construction, and a streamlined approach for predicting repurposable drugs, COMO empowers researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict 25 and 23 metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies.
Exact $p$-values for global network alignments via combinatorial analysis of shared GO terms (Subtitle: REFANGO: Rigorous Evaluation of Functional Alignments of Networks using Gene Ontology)
Authors: Wayne B. Hayes
Date: 2020-10-09 00:00:00

Network alignment aims to uncover topologically similar regions in the protein-protein interaction (PPI) networks of two or more species under the assumption that topologically similar regions tend to perform similar functions. Although there exist a plethora of both network alignment algorithms and measures of topological similarity, currently no gold standard exists for evaluating how well either is able to uncover functionally similar regions. Here we propose a formal, mathematically and statistically rigorous method for evaluating the statistical significance of shared GO terms in a global, 1-to-1 alignment between two PPI networks. We use combinatorics to precisely count the number of possible network alignments in which $k$ proteins share a particular GO term. When divided by the number of all possible network alignments, this provides an explicit, exact $p$-value for a network alignment with respect to a particular GO term. Just as with BLAST's p-values and bit-scores, this method is designed not to guide the formation of any particular alignment, but instead to provide an after-the-fact evaluation of a fixed, given alignment.
Evaluation of the Topological Agreement of Network Alignments
Authors: Concettina Guerra, Pietro Hiram Guzzi
Date: 2020-10-09 00:00:00

Aligning protein interaction networks (PPI) of two or more organisms consists of finding a mapping of the nodes (proteins) of the networks that captures important structural and functional associations (similarity). It is a well studied but difficult problem. It is provably NP-hard in some instances thus computationally very demanding. The problem comes in several versions: global versus local alignment; pairwise versus multiple alignment; one-to-one versus many-to-many alignment. Heuristics to address the various instances of the problem abound and they achieve some degree of success when their performance is measured in terms of node and/or edges conservation. However, as the evolutionary distance between the organisms being considered increases the results tend to degrade. Moreover, poor performance is achieved when the considered networks have remarkably different sizes in the number of nodes and/or edges. Here we address the challenge of analyzing and comparing different approaches to global network alignment, when a one-to-one mapping is sought. We consider and propose various measures to evaluate the agreement between alignments obtained by existing approaches. We show that some such measures indicate an agreement that is often about the same than what would be obtained by chance. That tends to occur even when the mappings exhibit a good performance based on standard measures.
Stability of Imbalanced Triangles in Gene Regulatory Networks of Cancerous and Normal Cells
Authors: Abbas Karimi Rizi, Mina Zamani, Amirhossein Shirazi, G. Reza Jafari, János Kertész
Date: 2020-10-07 00:00:00

Genes communicate with each other through different regulatory effects, which lead to the emergence of complex structures in cells, and such structures are expected to be different for normal and cancerous cells. To study breast cancer differences, we have investigated the Gene Regulatory Network (GRN) of cells as inferred from RNA-sequencing data. The GRN is a signed weighted network corresponding to the inductive or inhibitory interactions. Here we focus on a particular of motifs in the GRN, the triangles, which are imbalanced if the number of negative interactions are odd. By studying the stability of imbalanced triangles in the GRN, we show that the network of cancerous cells has fewer imbalanced triangles compared to normal. Moreover, in the normal cells, imbalanced triangles are isolated from the main part of the network, while such motifs are part of the network's giant component in cancerous cells. Our result demonstrates that due to genes' collective behavior the complex structures are different in cancerous cells from those in normal ones.
A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer's Disease
Authors: Jingwen Zhang, Defu Yang, wei he, Guorong Wu, Minghan Chen
Date: 2020-09-10 00:00:00

Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.
Robustness of networked systems to unintended interactions with application to engineered genetic circuits
Authors: Yili Qian, Domitilla Del Vecchio
Date: 2020-09-09 00:00:00

A networked dynamical system is composed of subsystems interconnected through prescribed interactions. In many engineering applications, however, one subsystem can also affect others through "unintended" interactions that can significantly hamper the intended network's behavior. Although unintended interactions can be modeled as disturbance inputs to the subsystems, these disturbances depend on the network's states. As a consequence, a disturbance attenuation property of each isolated subsystem is, alone, insufficient to ensure that the network behavior is robust to unintended interactions. In this paper, we provide sufficient conditions on subsystem dynamics and interaction maps, such that the network's behavior is robust to unintended interactions. These conditions require that each subsystem attenuates constant external disturbances, is monotone or "near-monotone", the unintended interaction map is monotone, and the prescribed interaction map does not contain feedback loops. We employ this result to guide the design of resource-limited genetic circuits. More generally, our result provide conditions under which robustness of constituent subsystems is sufficient to guarantee robustness of the network to unintended interactions.
Mediating Ribosomal Competition by Splitting Pools
Authors: Jared Miller, M. Ali Al-Radhawi, Eduardo D. Sontag
Date: 2020-09-01 00:00:00
Tasks: Translation

Synthetic biology constructs often rely upon the introduction of "circuit" genes into host cells, in order to express novel proteins and thus endow the host with a desired behavior. The expression of these new genes "consumes" existing resources in the cell, such as ATP, RNA polymerase, amino acids, and ribosomes. Ribosomal competition among strands of mRNA may be described by a system of nonlinear ODEs called the Ribosomal Flow Model (RFM). The competition for resources between host and circuit genes can be ameliorated by splitting the ribosome pool by use of orthogonal ribosomes, where the circuit genes are exclusively translated by mutated ribosomes. In this work, the RFM system is extended to include orthogonal ribosome competition. This Orthogonal Ribosomal Flow Model (ORFM) is proven to be stable through the use of Robust Lyapunov Functions. The optimization problem of maximizing the weighted protein translation rate by adjusting allocation of ribosomal species is formulated and implemented.
SimpleSBML: A Python package for creating, editing, and interrogating SBML models: Version 2.0
Authors: Herbert M Sauro
Date: 2020-09-04 00:00:00

In this technical report, we describe a new version of SimpleSBML which provides an easier to use interface to python-libSBML allowing users of Python to more easily construct, edit, and inspect SBML based models. The most commonly used package for constructing SBML models in Python is python-libSBML based on the C/C++ library libSBML. python-libSBML is a comprehensive library with a large range of options but can be difficult for new users to learn and requires long scripts to create even the simplest models. Inspecting existing SBML models can also be difficult due to the complexity of the underlying object model. Instead, we present SimpleSBML, a package that allows users to add and inspect species, parameters, reactions, events, and rules to a libSBML model with only one command for each. Models can be exported to SBML format, and SBML files can be imported and converted to SimpleSBML commands making it very easy to edit the original SBML model. In the new version, a range of `get' methods is provided that allows users to inspect existing SBML models without having to understand the underlying object model used by libSBML.