pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of ERK regulation in the processive limit
Authors: Carsten Conradi, Nida Obatake, Anne Shiu, Xiaoxian Tang
Date: 2019-10-31 00:00:00

We consider a model of extracellular signal-regulated kinase (ERK) regulation by dual-site phosphorylation and dephosphorylation, which exhibits bistability and oscillations, but loses these properties in the limit in which the mechanisms underlying phosphorylation and dephosphorylation become processive. Our results suggest that anywhere along the way to becoming processive, the model remains bistable and oscillatory. More precisely, in simplified versions of the model, precursors to bistability and oscillations (specifically, multistationarity and Hopf bifurcations, respectively) exist at all "processivity levels". Finally, we investigate whether bistability and oscillations can exist together.
A meta-analysis of Boolean network models reveals design principles of gene regulatory networks
Authors: Claus Kadelka, Taras-Michael Butrie, Evan Hilton, Jack Kinseth, Addison Schmidt, Haris Serdarevic
Date: 2020-09-02 00:00:00
Tasks: Decision Making

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms.
Information Rates of Controlled Protein Interactions Using Terahertz Communication
Authors: Hadeel Elayan, Andrew W. Eckford, Raviraj Adve
Date: 2020-09-02 00:00:00

In this work, we present a paradigm bridging electromagnetic (EM) and molecular communication through a stimuli-responsive intra-body model. It has been established that protein molecules, which play a key role in governing cell behavior, can be selectively stimulated using Terahertz (THz) band frequencies. By triggering protein vibrational modes using THz waves, we induce changes in protein conformation, resulting in the activation of a controlled cascade of biochemical and biomechanical events. To analyze such an interaction, we formulate a communication system composed of a nanoantenna transmitter and a protein receiver. We adopt a Markov chain model to account for protein stochasticity with transition rates governed by the nanoantenna force. Both two-state and multi-state protein models are presented to depict different biological configurations. Closed form expressions for the mutual information of each scenario is derived and maximized to find the capacity between the input nanoantenna force and the protein state. The results we obtain indicate that controlled protein signaling provides a communication platform for information transmission between the nanoantenna and the protein with a clear physical significance. The analysis reported in this work should further research into the EM-based control of protein networks.
Parallel One-Step Control of Parametrised Boolean Networks
Authors: Lubos Brim, Samuel Pastva, David Safranek, Eva Smijakova
Date: 2020-09-01 00:00:00

Boolean network (BN) is a simple model widely used to study complex dynamic behaviour of biological systems. Nonetheless, it might be difficult to gather enough data to precisely capture the behavior of a biological system into a set of Boolean functions. These issues can be dealt with to some extent using parametrised Boolean networks (ParBNs), as it allows to leave some update functions unspecified. In this paper, we attack the control problem for ParBNs with asynchronous semantics. While there is an extensive work on controlling BNs without parameters, the problem of control for ParBNs has not been in fact addressed yet. The goal of control is to ensure the stabilisation of a system in a given state using as few interventions as possible. There are many ways to control BN dynamics. Here, we consider the one-step approach in which the system is instantaneously perturbed out of its actual state. A naive approach to handle control of ParBNs is using parameter scan and solve the control problem for each parameter valuation separately using known techniques for non-parametrised BNs. This approach is however highly inefficient as the parameter space of ParBNs grows doubly-exponentially in the worst case. In this paper, we propose a novel semi-symbolic algorithm for the one-step control problem of ParBNs, that builds on a symbolic data structures to avoid scanning individual parameters. We evaluate the performance of our approach on real biological models.
Dynamic metabolic resource allocation based on the maximum entropy principle
Authors: David S. Tourigny
Date: 2019-06-10 00:00:00

Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing `bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.
Advancing Drug Resistance Research Through Quantitative Modeling and Synthetic Biology
Authors: K. Farquhar, H. Flohr, D. A. Charlebois
Date: 2020-07-07 00:00:00

Antimicrobial resistance is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of antimicrobial resistance to discover effective therapies against drug-resistant infections.
Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs
Authors: Sumanta Ray, Snehalika Lall, Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Alexander Schönhuth
Date: 2020-07-05 00:00:00

Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context. We apply Variational Graph AutoEncoders (VGAEs), representing most advanced deep learning based methodology for the analysis of data that are subject to network constraints. Reliable simulations confirm that we operate at utmost accuracy in terms of predicting missing links. We then predict hitherto unknown links between drugs and human proteins against which virus proteins preferably bind. The corresponding therapeutic agents present splendid starting points for exploring novel host-directed therapy (HDT) options.
Improved estimations of stochastic chemical kinetics by finite state expansion
Authors: Tabea Waizmann, Luca Bortolussi, Andrea Vandin, Mirco Tribastone
Date: 2020-06-12 00:00:00

Stochastic reaction networks are a fundamental model to describe interactions between species where random fluctuations are relevant. The master equation provides the evolution of the probability distribution across the discrete state space consisting of vectors of population counts for each species. However, since its exact solution is often elusive, several analytical approximations have been proposed. The deterministic rate equation (DRE) gives a macroscopic approximation as a compact system of differential equations that estimate the average populations for each species, but it may be inaccurate in the case of nonlinear interaction dynamics. Here we propose finite state expansion (FSE), an analytical method mediating between the microscopic and the macroscopic interpretations of a stochastic reaction network by coupling the master equation dynamics of a chosen subset of the discrete state space with the mean population dynamics of the DRE. An algorithm translates a network into an expanded one where each discrete state is represented as a further distinct species. This translation exactly preserves the stochastic dynamics, but the DRE of the expanded network can be interpreted as a correction to the original one. The effectiveness of FSE is demonstrated in models that challenge state-of-the-art techniques due to intrinsic noise, multi-scale populations, and multi-stability.
A system architecture for parallel analysis of flux-balanced metabolic pathways
Authors: Mona Arabzadeh, Mehdi Sedighi, Morteza Saheb Zamani, Sayed-Amir Marashi
Date: 2018-04-09 00:00:00
Tasks: GPU

In this paper, a system architecture is proposed that approximately models the functionality of metabolic networks. The AND/OR graph model is used to represent the metabolic network and each processing element in the system emulates the functionality of a metabolite. The system is implemented on a graphics processing unit (GPU) as the hardware platform using CUDA environment. The proposed architecture takes advantage of the inherent parallelism in the network structure in terms of both pathway and metabolite traversal. The function of each element is defined such that it can find flux-balanced pathways. Pathways in both small and large metabolic networks are applied to the proposed architecture and the results are discussed.
Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks
Authors: Barbara Bravi, Katy J. Rubin, Peter Sollich
Date: 2020-03-19 00:00:00

We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e.g. protein interaction networks involving complex formation and dissociation reactions. We propose the use of model reduction strategies to understand the 'extrinsic' sources of stochasticity arising from the rest of the network. Our approaches are based on subnetwork dynamical equations derived by projection methods and by path integrals. The results provide a principled derivation of the different components of the extrinsic noise that is observed experimentally in cellular biochemical reactions, over and above the intrinsic noise from the stochasticity of biochemical events in the subnetwork. We explore several intermediate approximations to assess systematically the relative importance of different extrinsic noise components, including initial transients, long-time plateaus, temporal correlations, multiplicative noise terms and nonlinear noise propagation. The best approximations achieve excellent accuracy in quantitative tests on a simple protein network and on the epidermal growth factor receptor signalling network.
In Search of Newer Targets for IBD: A Systems and a Network Medicine Approach
Authors: Takashi Kitani, Sushma C. Maddipatla, Ramya Madupuri, James N. Baraniuk, Christopher Greco, Jonathan Hartman, Sona Vasudevan
Date: 2020-06-07 00:00:00

Introduction: Crohn's disease and ulcerative colitis, both under the umbrella of inflammatory bowel diseases (IBD), involve many distinct molecular processes. The difference in their molecular processes is studied by using the different genes involved in each disease, and it is explored further for drug targeting and drug repurposing. Methods: The initial set of genes was obtained by mining published literature and several curated databases. The identified genes were then subject to Systems and Network analysis to reveal their molecular processes and shed some light on their pathogenesis. Such methodologies have identified newer targets and drugs that can be repurposed. Results: We use a Systems and Network Medicine approach to understand the mechanism of actions of genes involved in IBD. From an initial set of genes mined from literature and curated databases, we used the Multi-Steiner Tree algorithm implemented within the CoVex systems medicine platform to expand each disease module by incorporating candidate genes with significant connections to the disease-related seed genes. Such expanded disease modules will identify a larger set of potential targets and drugs. We used the Closeness Centrality algorithm implemented within CoVex to search for newer targets and repurposable drugs. Through a network medicine approach, we provide a mechanistic view of the diseases and point to newer drugs and targets. Conclusion: We demonstrate that the Systems and Network Medicine approach is a powerful way to understand diseases and understand their mechanisms of action.
Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation
Authors: David J. Skelton, Aoesha Alsobhe, Elisa Anastasi, Christian Atallah, Jasmine E. Bird, Bradley Brown, Dwayne Didon, Phoenix Gater, Katherine James, David D. Lennon Jr, James McLaughlin, Pollyanna E. J. Moreland, Matthew Pocock, Caroline J. Whitaker, Anil Wipat
Date: 2020-05-22 00:00:00
Tasks: Knowledge Graphs

Severe acute respiratory syndrome coronavirus two (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents an unprecedented global health challenge. Consequently, a large amount of research into the disease pathogenesis and potential treatments has been carried out in a short time frame. However, developing novel drugs is a costly and lengthy process, and is unlikely to deliver a timely treatment for the pandemic. Drug repurposing, by contrast, provides an attractive alternative, as existing drugs have already undergone many of the regulatory requirements. In this work we used a combination of network algorithms and human curation to search integrated knowledge graphs, identifying drug repurposing opportunities for COVID-19. We demonstrate the value of this approach, reporting on eight potential repurposing opportunities identified, and discuss how this approach could be incorporated into future studies.
Computer-aided whole-cell design: taking a holistic approach by integrating synthetic with systems biology
Authors: Lucia Marucci, Matteo Barberis, Jonathan Karr, Oliver Ray, Paul R. Race, Miguel de Souza Andrade, Claire Grierson, Stefan Andreas Hoffmann, Sophie Landon, Elibio Rech, Joshua Rees-Garbutt, Richard Seabrook, William Shaw, Christopher Woods
Date: 2020-06-04 00:00:00

Computer-aided design for synthetic biology promises to accelerate the rational and robust engineering of biological systems; it requires both detailed and quantitative mathematical and experimental models of the processes to (re)design, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. Computer-aided design strategies require quantitative representations of cells, able to capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems, and we discuss several challenges for the realization of our vision. The possibility to describe and build in silico whole-cells offers an opportunity to develop increasingly automatized, precise and accessible computer-aided design tools and strategies throughout novel interdisciplinary collaborations.
A Dynamics-based Approach for the Target Control of Boolean Networks
Authors: Cui Su, Jun Pang
Date: 2020-06-03 00:00:00

We study the target control problem of asynchronous Boolean networks, to identify a set of nodes, the perturbation of which can drive the dynamics of the network from any initial state to the desired steady state (or attractor). We are particularly interested in temporary perturbations, which are applied for sufficient time and then released to retrieve the original dynamics. Temporary perturbations have the apparent advantage of averting unforeseen consequences, which might be induced by permanent perturbations. Despite the infamous state-space explosion problem, in this work, we develop an efficient method to compute the temporary target control for a given target attractor of a Boolean network. We apply our method to a number of real-life biological networks and compare its performance with the stable motif-based control method to demonstrate its efficacy and efficiency.
In silico identification of potential natural product inhibitors of human proteases key to SARS-CoV-2 infection
Authors: R. P. Vivek-Ananth, Abhijit Rana, Nithin Rajan, Himansu S. Biswal, Areejit Samal
Date: 2020-06-01 00:00:00
Tasks: Molecular Docking, Specificity

Presently, there are no approved drugs or vaccines to treat COVID-19 which has spread to over 200 countries and is responsible for over 3,65,000 deaths worldwide. Recent studies have shown that two human proteases, TMPRSS2 and cathepsin L, play a key role in host cell entry of SARS-CoV-2. Importantly, inhibitors of these proteases were shown to block SARS-CoV-2 infection. Here, we perform virtual screening of 14010 phytochemicals produced by Indian medicinal plants to identify natural product inhibitors of TMPRSS2 and cathepsin L. We built a homology model of TMPRSS2 as an experimentally determined structure is not available. AutoDock Vina was used to perform molecular docking of phytochemicals against TMPRSS2 model structure and cathepsin L crystal structure. Potential phytochemical inhibitors were filtered by comparing their docked binding energies with those of known inhibitors of TMPRSS2 and cathepsin L. Further, the ligand binding site residues and non-covalent protein-ligand interactions were used as an additional filter to identify phytochemical inhibitors that either bind to or form interactions with residues important for the specificity of the target proteases. We have identified 96 inhibitors of TMPRSS2 and 9 inhibitors of cathepsin L among phytochemicals of Indian medicinal plants. The top inhibitors of TMPRSS2 are Edgeworoside C, Adlumidine and Qingdainone, and of cathepsin L is Ararobinol. Interestingly, several herbal sources of identified phytochemical inhibitors have antiviral or anti-inflammatory use in traditional medicine. Further in vitro and in vivo testing is needed before clinical trials of the promising phytochemical inhibitors identified here.
Towards a Scientific Method for Dynamical Systems
Authors: Vincent Wang
Date: 2020-05-08 00:00:00

The premise of this paper is the following value proposition: Models are good when they describe a system of phenomena, and they are better when they can predict the effect of interventions upon the system. We introduce a formalism by which dynamical system models can be obtained by specifying basic constituent processes in the form of petri nets, mediated by the Law of Mass Action. We prove the universality of this procedure with respect to ordinary first order differential equations in rational functions. In addition, we introduce a simple graphical procedure for calculating dynamical systems from petri nets in our formalism, which we use to recover several well-known dynamical systems. For proof-of-concept, we use our method to obtain a differential equation model for the HES1 gene transcription network that predicts post-intervention behaviours of the network.
First-principles prediction of the information processing capacity of a simple genetic circuit
Authors: Manuel Razo-Mejia, Sarah Marzen, Griffin Chure, Rachel Taubman, Muir Morrison, Rob Phillips
Date: 2020-05-07 00:00:00

Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.