pwc-archive/papers-with-abstracts huggingface dataset:
Parallel implementations of random time algorithm for chemical network stochastic simulations
Authors: Chuanbo Liu, Jin Wang
Date: 2021-02-28 00:00:00
Tasks: CPU

In this study, we have developed a parallel version of the random time simulation algorithm. Firstly, we gave a rigorous basis of the random time description of the stochastic process of chemical reaction network time evolution. And then we reviewed the random time simulation algorithm and gave the implementations for the parallel version of next reaction random time algorithm. The discussion of computational complexity suggested a factor of $M$ (which is the connection number of the network) folds time consuming reduction for random time simulation algorithm as compared to other exact stochastic simulation algorithms, such as the Gillespie algorithm. For large-scale system, such like the protein-protein interaction network, $M$ is on order of $10^8$. We further demonstrate the power of random time simulation with a GPGPU parallel implementation which achieved roughly 100 folds acceleration as compared with CPU implementations. Therefore the stochastic simulation method we developed here can be of great application value for simulating time evolution process of large-scale network.
Is Covid-19 severity associated with ACE2 degradation?
Authors: Ugo Bastolla, Patrick Chambers, David Abia, Maria-Laura García-Bermejo, Manuel Fresno
Date: 2021-02-22 00:00:00

Covid-19 is particularly mild with children, and its severity escalates with age. Several theories have been proposed to explain these facts. In particular, it was proposed that the lower expression of the viral receptor ACE2 in children protects them from severe Covid. However, other works suggested an inverse relationship between ACE2 expression and disease severity. Here we try to reconcile seemingly contradicting observations noting that ACE2 is not monotonically related with age but it reaches a maximum at a young age that depends on the cell type and then decreases. This pattern is consistent with most existing data from humans and rodents and it is expected to be more marked for ACE2 cell protein than for mRNA because of the increase with age of the protease TACE/ADAM17 that sheds ACE2 from the cell membrane to the serum. The negative relation between ACE2 level and Covid-19 severity at old age is not paradoxical but it is consistent with a mathematical model of virus propagation that predicts that higher viral receptor does not necessarily favour virus propagation and it can even slow it down. More importantly, ACE2 is known to protect organs from chronic and acute inflammation, which are worsened by low ACE2 levels. Here we propose that ACE2 contributes essentially to reverse the inflammatory process by downregulating the pro-inflammatory peptides of the angiotensin and bradykinin system, and that failure to revert the inflammation triggered by SARS-COV-2 may underlie both severe CoViD-19 infection and its many post-infection manifestations, including the multi-inflammatory syndrome of children (MIS-C). Within this view, lower severity in children despite lower ACE2 expression may be consistent with their higher expression of the alternative angiotensin II receptor ATR2 and in general of the anti-inflammatory arm of the Renin-Angiotensin System (RAS) at young age.
Multi-species consensus network of DNA strand displacement for concentration-to-strand translation
Authors: Toshiyuki Yamane, Eiji Nakamura, Koji Masuda
Date: 2021-02-21 00:00:00
Tasks: Translation

We propose novel chemical reaction networks to translate levels of concentration into unique DNA strand species, which we call concentration translators. Our design of the concentration translators is based on combination of two chemical reaction networks, consensus network and conversion network with any number of chemical species. We give geometric analysis of the proposed CRNs from the viewpoint of nonlinear dynamical systems and show that the CRNs can actually operate as translator. Our concentration translators exploit DNA strand displacement (DSD) reaction, which is known for a universal reaction that can implement arbitrary chemical reaction networks. We demonstrate two specific types of concentration translators (translator A and B) with different switching behavior and biochemical cost and compared their characteristics computationally. The proposed concentration translators have an advantage of being able to readout the concentration of targeted nucleic acid strand without any fluorescence-based techniques. These characteristics can be tailored according to requirements from applications, including dynamic range, sensitivity and implementation cost.
Steady-state joint distribution for first-order stochastic reaction kinetics
Authors: Youming Li, Da-Quan Jiang, Chen Jia
Date: 2021-02-09 00:00:00

While the analytical solution for the marginal distribution of a stochastic chemical reaction network has been extensively studied, its joint distribution, i.e. the solution of a high-dimensional chemical master equation, has received much less attention. Here we develop a novel method of computing the exact joint distributions of a wide class of first-order stochastic reaction systems in steady-state conditions. The effectiveness of our method is validated by applying it to four gene expression models of biological significance, including models with 2A peptides, nascent mRNA, gene regulation, translational bursting, and alternative splicing.
Graphery: Interactive Tutorials for Biological Network Algorithms
Authors: Heyuan Zeng, Jinbiao Zhang, Gabriel A. Preising, Tobias Rubel, Pramesh Singh, Anna Ritz
Date: 2021-02-06 00:00:00
Tasks: Navigate

Networks provide a meaningful way to represent and analyze complex biological information, but the methodological details of network-based tools are often described for a technical audience. Graphery is a hands-on tutorial webserver designed to help biological researchers understand the fundamental concepts behind commonly-used graph algorithms. Each tutorial describes a graph concept along with executable Python code that visualizes the concept in a code view and a graph view. Graphery tutorials help researchers understand graph statistics (such as degree distribution and network modularity) and classic graph algorithms (such as shortest paths and random walks). Users navigate each tutorial using their choice of real-world biological networks, ranging in scale from molecular interaction graphs to ecological networks. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Discipline-focused tutorials will be essential to help researchers interpret their biological data. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Availability: Graphery is available at https://graphery.reedcompbio.org/.
Solving the chemical master equation for monomolecular reaction systems analytically: a Doi-Peliti path integral view
Authors: John J. Vastola
Date: 2019-11-03 00:00:00
Tasks: Benchmarking

The chemical master equation (CME) is a fundamental description of interacting molecules commonly used to model chemical kinetics and noisy gene regulatory networks. Exact time-dependent solutions of the CME -- which typically consists of infinitely many coupled differential equations -- are rare, and are valuable for numerical benchmarking and getting intuition for the behavior of more complicated systems. Jahnke and Huisinga's landmark calculation of the exact time-dependent solution of the CME for monomolecular reaction systems is one of the most general analytic results known; however, it is hard to generalize, because it relies crucially on properties of monomolecular reactions. In this paper, we rederive Jahnke and Huisinga's result on the time-dependent probability distribution and moments of monomolecular reaction systems using the Doi-Peliti path integral approach, which reduces solving the CME to evaluating many integrals. While the Doi-Peliti approach is less intuitive, it is also more mechanical, and hence easier to generalize. To illustrate how the Doi-Peliti approach can go beyond the method of Jahnke and Huisinga, we also find an explicit and exact time-dependent solution to a problem involving an autocatalytic reaction that Jahnke and Huisinga identified as not solvable using their method. We also find a formal exact time-dependent solution for any CME whose list of reactions involves only zero and first order reactions, which may be the most general result currently known.
Estimate Metabolite Taxonomy and Structure with a Fragment-Centered Database and Fragment Network
Authors: Hansen Zhao, Xu Zhao, Huan Yao, Jiaxin Feng, Sichun Zhang, Xinrong Zhang
Date: 2021-01-11 00:00:00

Metabolite structure identification has become the major bottleneck of the mass spectrometry based metabolomics research. Till now, number of mass spectra databases and search algorithms have been developed to address this issue. However, two critical problems still exist: the low chemical component record coverage in databases and significant MS/MS spectra variations related to experiment equipment and parameter settings. In this work, we considered the molecule fragment as basic building blocks of the metabolic components which had relatively consistent signatures in MS/MS spectra. And from a bottom-up point of view, we built a fragment centered database, MSFragDB, by reorganizing the data from the Human Metabolome Database (HMDB) and developed an intensity-free searching algorithm to search and rank the most relative metabolite according to the users' input. We also proposed the concept of fragment network, a graph structure that encoded the relationship between the molecule fragments to find close motif that indicated a specific chemical structure. Although based on the same dataset as the HMDB, validation results implied that the MSFragDB had a higher hit ratio and furthermore, estimated possible taxonomy that a query spectrum belongs to when the corresponding chemical component was missing in the database. Aid by the Fragment Network, the MSFragDB was also proved to be able to estimate the right structure while the MS/MS spectrum suffers from the precursor-contamination. The strategy proposed is general and can be adopted in existing databases. We believe MSFragDB and Fragment Network can improve the performance of structure identification with existing data. The beta version of the database is freely available at www.xrzhanglab.com/msfragdb/.
Effect of transcription reinitiation in stochastic gene expression
Authors: Rajesh Karmakar, Amit Kumar Das
Date: 2020-09-05 00:00:00

Gene expression (GE) is an inherently random or stochastic or noisy process. The randomness in different steps of GE, e.g., transcription, translation, degradation, etc., leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans. Stochastic gene expression has important consequences for cellular function. The random fluctuations in protein levels produce variability in cellular behavior. It is beneficial in some contexts and harmful to others. These situations include stress response, metabolism, development, cell cycle, circadian rhythms, and aging. Different model studies e.g., constitutive, two-state, etc., reveal that the fluctuations in mRNA and protein levels arise from different steps of gene expression among which the steps in transcription have the maximum effect. The pulsatile mRNA production through RNAP-II based reinitiation of transcription is an important part of gene transcription. Though, the effect of that process on mRNA and protein levels is very little known. The addition of any biochemical step in the constitutive or two-state process generally decreases the mean and increases the Fano factor. In this study, we have shown that the RNAP-II based reinitiation process in gene transcription can have different effects on both mean and Fano factor at mRNA levels in different model systems. It decreases the mean and Fano factor both at the mRNA levels in the constitutive network whereas in other networks it can simultaneously increase or decrease both quantities or it can have mixed-effect at mRNA levels. We propose that a constitutive network with reinitiation behaves like a product independent negative feedback circuit whereas other networks behave as either product independent positive or negative or mixed feedback circuit.
Maximizing Information Gain for the Characterization of Biomolecular Circuits
Authors: Tim Prangemeier, Christian Wildner, Maleen Hanst, Heinz Koeppl
Date: 2021-01-08 00:00:00
Tasks: Experimental Design, Informativeness

Quantitatively predictive models of biomolecular circuits are important tools for the design of synthetic biology and molecular communication circuits. The information content of typical time-lapse single-cell data for the inference of kinetic parameters is not only limited by measurement uncertainty and intrinsic stochasticity, but also by the employed perturbations. Novel microfluidic devices enable the synthesis of temporal chemical concentration profiles. The informativeness of a perturbation can be quantified based on mutual information. We propose an approximate method to perform optimal experimental design of such perturbation profiles. To estimate the mutual information we perform a multivariate log-normal approximation of the joint distribution over parameters and observations and scan the design space using Metropolis-Hastings sampling. The method is demonstrated by finding optimal perturbation sequences for synthetic case studies on a gene expression model with varying reporter characteristics.
Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions
Authors: Juan M. Escorcia-Rodríguez, Andreas Tauch, Julio A. Freyre-González
Date: 2021-01-08 00:00:00
Tasks: Data Integration

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by non-directed experiments, and protein-protein interactions with a direct effect on gene transcription; and sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNAs regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C. glutamicum regulatory network characterization.
On the Fourier transform of a quantitative trait: Implications for compressive sensing
Authors: Stephen Doro, Matthew A. Herman
Date: 2021-01-04 00:00:00
Tasks: Compressive Sensing, Sensitivity

This paper explores the genotype-phenotype relationship. It outlines conditions under which the dependence of a quantitative trait on the genome might be predictable, based on measurement of a limited subset of genotypes. It uses the theory of real-valued Boolean functions in a systematic way to translate trait data into the Fourier domain. Important trait features, such as the roughness of the trait landscape or the modularity of a trait have a simple Fourier interpretation. Ruggedness at a gene location corresponds to high sensitivity to mutation, while a modular organization of gene activity reduces such sensitivity. Traits where rugged loci are rare will naturally compress gene data in the Fourier domain, leading to a sparse representation of trait data, concentrated in identifiable, low-level coefficients. This Fourier representation of a trait organizes epistasis in a form which is isometric to the trait data. As Fourier matrices are known to be maximally incoherent with the standard basis, this permits employing compressive sensing techniques to work from data sets that are relatively small -- sometimes even of polynomial size -- compared to the exponentially large sets of possible genomes. This theory provides a theoretical underpinning for systematic use of Boolean function machinery to dissect the dependency of a trait on the genome and environment.
Stochastic approximations of higher-molecular by bi-molecular reactions
Authors: Tomislav Plesa
Date: 2018-11-07 00:00:00

Biochemical reactions involving three or more reactants, called higher-molecular reactions, play an important role in theoretical systems and synthetic biology. In particular, such reactions underpin a variety of important bio-dynamical phenomena, such as multi-stability/multi-modality, oscillations, bifurcations, and noise-induced effects. However, only reactions with at most two reactants, called bi-molecular reactions, are experimentally feasible. To bridge the gap, in this paper we put forward an algorithm for systematically approximating arbitrary higher-molecular reactions with bi-molecular ones, while preserving the underlying stochastic dynamics. Properties of the algorithm and convergence are established via singular perturbation theory. The algorithm is applied to a variety of higher-molecular biochemical networks, and is shown to play an important role in nucleic-acid-based synthetic biology.
Sensitivity of metabolic networks: perturbing metabolite concentrations
Authors: Nicola Vassena
Date: 2020-12-19 00:00:00
Tasks: Sensitivity

Sensitivity studies the network response to perturbations. We consider local perturbations of the concentrations of metabolites at an equilibrium. We investigate the responses in the network, both of the metabolite concentrations and of the reaction fluxes. Our approach is purely qualitative, rather than quantitative. In fact, our analysis is based, solely, on the stoichiometry of the reaction network and we do not require any quantitative information on the reaction rates. Instead, the description is done only in algebraic terms, and the only data required is the network structure. Biological applications include metabolic control and network reconstruction.
Opportunities at the interface of network science and metabolic modelling
Authors: Varshit Dusad, Denise Thiel, Mauricio Barahona, Hector C. Keun, Diego A. Oyarzún
Date: 2020-06-05 00:00:00

Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimisation principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
Glycolytic pyruvate kinase moonlighting activities in DNA replication initiation and elongation
Authors: Steff Horemans, Matthaios Pitoulias, Alexandria Holland, Panos Soultanas, Laurent Janniere
Date: 2020-12-11 00:00:00

Cells have evolved a metabolic control of DNA replication to respond to a wide range of nutritional conditions. Accumulating data suggest that this poorly understood control depends, at least in part, on Central Carbon Metabolism (CCM). In Bacillus subtilis , the glycolytic pyruvate kinase (PykA) is intricately linked to replication. This 585 amino-acid-long enzyme comprises a catalytic (Cat) domain that binds to phosphoenolpyruvate (PEP) and ADP to produce pyruvate and ATP, and a C-terminal domain of unknown function. Interestingly, the C-terminal domain termed PEPut interacts with Cat and is homologous a domain that, in other metabolic enzymes, are phosphorylated at a conserved TSH motif at the expense of PEP and ATP to drive sugar import and catalytic or regulatory activities. To gain insights into the role of PykA in replication, DNA synthesis was analyzed in various Cat and PEPut mutants grown in a medium where the metabolic activity of PykA is dispensable for growth. Measurements of replication parameters ( ori/ter ratio, C period and fork speed) and of the pyruvate kinase activity showed that PykA mutants exhibit replication defects resulting from side chain modifications in the PykA protein rather than from a reduction of its metabolic activity. Interestingly, Cat and PEPut have distinct commitments in replication: while Cat impacts positively and negatively replication fork speed, PEPut stimulates initiation through a process depending on Cat-PEPut interaction and growth conditions. Residues binding to PEP and ADP in Cat, stabilizing the Cat-PEPut interaction and belonging to the TSH motif of PEPut were found important for the commitment of PykA in replication. In vitro , PykA affects the activities of replication enzymes (the polymerase DnaE, helicase DnaC and primase DnaG) essential for initiation and elongation and genetically linked to pykA . Our results thus connect replication initiation and elongation to CCM metabolites (PEP, ATP and ADP), critical Cat and PEPut residues and to multiple links between PykA and the replication enzymes DnaE, DnaC and DnaG. We propose that PykA is endowed with a moonlighting activity that senses the concentration of signaling metabolites and interacts with replication enzymes to convey information on the cellular metabolic state to the replication machinery and adjust replication initiation and elongation to metabolism. This defines a new type of replication regulator proposed to be part of the metabolic control that gates replication in the cell cycle.
State and parameter estimation from exact partial state observation in stochastic reaction networks
Authors: Muruhan Rathinam, Mingkai Yu
Date: 2020-10-09 00:00:00
Tasks: parameter estimation

We consider chemical reaction networks modeled by a discrete state and continuous in time Markov process for the vector copy number of the species and provide a novel particle filter method for state and parameter estimation based on exact observation of some of the species in continuous time. The conditional probability distribution of the unobserved states is shown to satisfy a system of differential equations with jumps. We provide a method of simulating a process that is a proxy for the vector copy number of the unobserved species along with a weight. The resulting weighted Monte Carlo simulation is then used to compute the conditional probability distribution of the unobserved species. We also show how our algorithm can be adapted for a Bayesian estimation of parameters and for the estimation of a past state value based on observations up to a future time.