Complex Biochemical Networks Research Articles

Finding acceptable parameter regions of stochastic Hill functions for multisite phosphorylation mechanism.

Multisite phosphorylation plays an important role in regulating switch-like protein activity and has been used widely in mathematical models. With the development of new experimental techniques and more molecular data, molecular phosphorylation processes emerge in many systems with increasing complexity and sizes. These developments call for simple yet valid stochastic models to describe various multisite phosphorylation processes, especially in large and complex biochemical networks. To reduce model complexity, this work aims at simplifying the multisite phosphorylation mechanism by a stochastic Hill function model. Furthermore, this work optimizes regions of parameter space to match simulation results from the stochastic Hill function with the distributive multisite phosphorylation process. While traditional parameter optimization methods have been focusing on finding the best parameter vector, in most circumstances, modelers would like to find a set of parameter vectors that generate similar system dynamics and results. This paper proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill function based on a quasi-Newton stochastic optimization algorithm. Different objective functions are investigated characterizing different features of the simulation-based empirical data, among which the approximate maximum log-likelihood method is recommended for general applications. Numerical results demonstrate that with an appropriate parameter vector value, the stochastic Hill function model depicts the multisite phosphorylation process well except the initial (transient) period.

Bayesian inference of distributed time delay in transcriptional and translational regulation.

Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. Accompanying code in R is available at https://github.com/cbskust/DDE_BD. Supplementary data are available at Bioinformatics online.

CRA toolbox: software package for conditional robustness analysis of cancer systems biology models in MATLAB

BackgroundIn cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In cancer systems biology, biological networks are typically modeled through Ordinary Differential Equation (ODE) models. Hence, robustness analysis consists in quantifying how much the temporal behavior of a specific node is influenced by the perturbation of model parameters. The Conditional Robustness Algorithm (CRA) is a valuable methodology to perform robustness analysis on a selected output variable, representative of the proliferation activity of cancer disease.ResultsHere we introduce our new freely downloadable software, the CRA Toolbox. The CRA Toolbox is an Object-Oriented MATLAB package which implements the features of CRA for ODE models. It offers the users the ability to import a mathematical model in Systems Biology Markup Language (SBML), to perturb the model parameter space and to choose the reference node for the robustness analysis. The CRA Toolbox allows the users to visualize and save all the generated results through a user-friendly Graphical User Interface (GUI). The CRA Toolbox has a modular and flexible architecture since it is designed according to some engineering design patterns. This tool has been successfully applied in three nonlinear ODE models: the Prostate-specific Pten−/− mouse model, the Pulse Generator Network and the EGFR-IGF1R pathway.ConclusionsThe CRA Toolbox for MATLAB is an open-source tool implementing the CRA to perform conditional robustness analysis. With its unique set of functions, the CRA Toolbox is a remarkable software for the topological study of biological networks. The source and example code and the corresponding documentation are freely available at the web site: http://gitlab.ict4life.com/SysBiOThe/CRA-Matlab.

Stochastic switching in systems with rare and hidden attractors

Complex biochemical networks are commonly characterised by the coexistence of multiple stable attractors. This endows living systems with plasticity in responses under changing external conditions, thereby enhancing their probability for survival. However, the type of such attractors as well as their positioning can hinder the likelihood to randomly visit these areas in phase space, thereby effectively decreasing the level of multistability in the system. Using a model based on the Hodgkin–Huxley formalism with bistability between a silent state, which is a rare attractor, and oscillatory bursting attractor, we demonstrate that the noise-induced switching between these two stable attractors depends on the structure of the phase space and the disposition of the coexisting attractors to each other.

Automated network generation and analysis of biochemical reaction pathways using RING

This paper describes how Rule Input Network Generator (RING), a network generation computational tool, can be adopted to generate a variety of complex biochemical reaction networks. The reaction language incorporated in RING allows representation of chemical compounds in biological systems with various structural complexity. Complex molecules such as oligosaccharides in glycosylation pathways can be described using a simplified representation of their monosaccharide building blocks and glycosidic bonds. The automated generation and topological network analysis features in RING also allow for: (1) constructing biochemical reaction networks in a rule-based manner, (2) generating graphical representations of the networks, (3) querying molecules containing a particular structural pattern, (4) finding the shortest synthetic pathways to a user-specified species, and (5) performing enzyme knockout to study their effect on the reaction network. Case studies involving three biochemical reaction systems: (1) Synthesis of 2-ketoglutarate from xylose in bacterial cells, (2) N-glycosylation in mammalian cells, and (3) O-glycosylation in mammalian cells are presented to demonstrate the capabilities of RING for robust and exhaustive network generation and the advantages of its post-processing features.

An open-source tool for analysis and automatic identification of dendritic spines using machine learning.

Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuronal dendrites. The ability to screen a high number of molecular targets for their effect on dendritic spine structural plasticity will require a high-throughput imaging system capable of stimulating and monitoring hundreds of dendritic spines in various conditions. For this purpose, we present a program capable of automatically identifying dendritic spines in live, fluorescent tissue. Our software relies on a machine learning approach to minimize any need for parameter tuning from the user. Custom thresholding and binarization functions serve to “clean” fluorescent images, and a neural network is trained using features based on the relative shape of the spine perimeter and its corresponding dendritic backbone. Our algorithm is rapid, flexible, has over 90% accuracy in spine detection, and bundled with our user-friendly, open-source, MATLAB-based software package for spine analysis.

Improved Accuracy and Convergence of Intrinsically Disordered Protein Molecular Dynamics Simulations Using the ff14IDPSFF Force Field

Vital components in signaling pathways, regulation, and other complex biochemical networks, intrinsically disordered proteins (IDPs) utilize their conformational flexibility to perform intricate functions, but their flexible conformations remain poorly understood. A mounting body of experimental evidence – ranging from NMR structural studies to X-ray crystallography experiments – has accumulated to probe the conformations of IDPs. However, IDPs remain elusive by standard experimental methods due to their conformational flexibility. Molecular dynamics (MD) simulations can provide detailed insight into these complex structure-function dynamics if they can reproduce available experimental observables. The recently developed ff14IDPSFF force field developed by Song et. al [2017] corrects dihedral backbone terms of all 20 canonical amino acids to achieve this goal. Utilizing this recently-developed force field, 9 unstructured short peptides and the HIVrev IDP were simulated extensively to assess the quality of the new force field and to assess the computational challenge of IDP simulations in general. We extended trajectory lengths for at least one microsecond and to at least ten independent trajectories to comprehensively evaluate the performance, accuracy, and convergence of MD simulations in f14IDPSF. All simulated f14IDPSFF values (Cα chemical shift, J-coupling, RDC) are in close agreement to available NMR-derived experimental values than the generic protein force field ff14SB. Simulated ff14IDPSFF values also converged within nanosecond timescales for the 9 short peptides whereas simulated ff14SB values converged after 1 μs. HIVrev simulated values converged after 1 μs for both ff14IDPSFF and ff14SB, which are expected due to its larger size. Improved accuracy to experimental values and timescale of calculation convergence demonstrate the utility of ff14IDPSFF to explore IDP conformation space and investigate the structure-function dynamics of IDPs.

Detecting the genomic signal of polygenic adaptation and the role of epistasis in evolution.

Over the last decade, the genomic revolution has offered the possibility to generate tremendous amounts of data that contain valuable information on the genetic basis of phenotypic traits, such as those linked to human diseases or those that allow for species to adapt to a changing environment. Most ecologically relevant traits are controlled by a large number of genes with small individual effects on trait variation, but that are connected with one another through complex developmental, metabolic and biochemical networks. As a result, it has recently been suggested that most adaptation events in natural populations are reached via correlated changes at multiple genes at a time, for which the name polygenic adaptation has been coined. The current challenge is to develop methods to extract the relevant information from genomic data to detect the signature of polygenic evolutionary change. The symposium entitled "Detecting the Genomic Signal of Polygenic Adaptation and the Role of Epistasis in Evolution" held in 2017 at the University of Zürich aimed at reviewing our current state of knowledge. In this review, we use the talks of the invited speakers to summarize some of the most recent developments in this field.

ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks

Stochastic simulation and modeling play an important role to elucidate the fundamental mechanisms in complex biochemical networks. The parametric sensitivity analysis of reaction networks becomes a powerful mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, due to overwhelming computational cost, parametric sensitivity analysis is a extremely challenging problem for stochastic models with a high-dimensional parameter space and for which existing approaches are very slow. Here we present an information-theoretic sensitivity analysis in path-space (ISAP) MATLAB package that simulates stochastic processes with various algorithms and most importantly implements a gradient-free approach to quantify the parameter sensitivities of stochastic chemical reaction network dynamics using the pathwise Fisher information matrix (PFIM; Pantazis, Katsoulakis, and Vlachos 2013). The sparse, block-diagonal structure of the PFIM makes its computational complexity scale linearly with the number of model parameters. As a result of the gradientfree and the sparse nature of the PFIM, it is highly suitable for the sensitivity analysis of stochastic reaction networks with a very large number of model parameters, which are typical in the modeling and simulation of complex biochemical phenomena. Finally, the PFIM provides a fast sensitivity screening method (Arampatzis, Katsoulakis, and Pantazis 2015) which allows it to be combined with any existing sensitivity analysis software.

Slow update stochastic simulation algorithms for modeling complex biochemical networks

The stochastic simulation algorithm (SSA) based modeling is a well recognized approach to predict the stochastic behavior of biological networks. The stochastic simulation of large complex biochemical networks is a challenge as it takes a large amount of time for simulation due to high update cost. In order to reduce the propensity update cost, we proposed two algorithms: slow update exact stochastic simulation algorithm (SUESSA) and slow update exact sorting stochastic simulation algorithm (SUESSSA). We applied cache-based linear search (CBLS) in these two algorithms for improving the search operation for finding reactions to be executed. Data structure used for incorporating CBLS is very simple and the cost of maintaining this during propensity update operation is very low. Hence, time taken during propensity updates, for simulating strongly coupled networks, is very fast; which leads to reduction of total simulation time. SUESSA and SUESSSA are not only restricted to elementary reactions, they support higher order reactions too.We used linear chain model and colloidal aggregation model to perform a comparative analysis of the performances of our methods with the existing algorithms. We also compared the performances of our methods with the existing ones, for large biochemical networks including B cell receptor and FcϵRI signaling networks.

ATM-dependent pathways of chromatin remodelling and oxidative DNA damage responses.

Ataxia-telangiectasia mutated (ATM) is a serine/threonine protein kinase with a master regulatory function in the DNA damage response. In this role, ATM commands a complex biochemical network that signals the presence of oxidative DNA damage, including the dangerous DNA double-strand break, and facilitates subsequent repair. Here, we review the current state of knowledge regarding ATM-dependent chromatin remodelling and epigenomic alterations that are required to maintain genomic integrity in the presence of DNA double-strand breaks and/or oxidative stress. We will focus particularly on the roles of ATM in adjusting nucleosome spacing at sites of unresolved DNA double-strand breaks within complex chromatin environments, and the impact of ATM on preserving the health of cells within the mammalian central nervous system.This article is part of the themed issue 'Chromatin modifiers and remodellers in DNA repair and signalling'.

Cross‐Regulation of an Artificial Metalloenzyme

AbstractCross‐regulation of complex biochemical reaction networks is an essential feature of living systems. In a biomimetic spirit, we report on our efforts to program the temporal activation of an artificial metalloenzyme via cross‐regulation by a natural enzyme. In the presence of urea, urease slowly releases ammonia that reversibly inhibits an artificial transfer hydrogenase. Addition of an acid, which acts as fuel, allows to maintain the system out of equilibrium.

Cross-Regulation of an Artificial Metalloenzyme.

Cross‐regulation of complex biochemical reaction networks is an essential feature of living systems. In a biomimetic spirit, we report on our efforts to program the temporal activation of an artificial metalloenzyme via cross‐regulation by a natural enzyme. In the presence of urea, urease slowly releases ammonia that reversibly inhibits an artificial transfer hydrogenase. Addition of an acid, which acts as fuel, allows to maintain the system out of equilibrium.

Bacterial intelligence: imitation games, time-sharing, and long-range quantum coherence.

Bacteria are far more intelligent than we can think of. They adopt different survival strategies to make their life comfortable. Researches on bacterial communication to date suggest that bacteria can communicate with each other using chemical signaling molecules as well as using ion channel mediated electrical signaling. Though in past few decades the scopes of chemical signaling have been investigated extensively, those of electrical signaling have received less attention. In this article, we present a novel perspective on time-sharing behavior, which maintains the biofilm growth under reduced nutrient supply between two distant biofilms through electrical signaling based on the experimental evidence reported by Liu et al., in 2017. In addition, following the recent work by Humphries et al. Cell 168(1):200-209, in 2017, we highlight the consequences of long range electrical signaling within biofilm communities through spatially propagating waves of potassium. Furthermore, we address the possibility of two-way cellular communication between artificial and natural cells through chemical signaling being inspired by recent experimental observation (Lentini et al. 2017) where the efficiency of artificial cells in imitating the natural cells is estimated through cellular Turing test. These three spectacular observations lead us to envisage and devise new classical and quantum views of these complex biochemical networks that have never been realized previously.

Challenges of Integrating Stochastic Dynamics and Cryo-Electron Tomograms in Whole-Cell Simulations

Cryo-electron tomography (cryo-ET) has rapidly emerged as a powerful tool to investigate the internal, three-dimensional spatial organization of the cell. In parallel, the GPU-based technology to perform spatially resolved stochastic simulations of whole cells has arisen, allowing the simulation of complex biochemical networks over cell cycle time scales using data taken from -omics, single molecule experiments, and in vitro kinetics. By using real cell geometry derived from cryo-ET data, we have the opportunity to imbue these highly detailed structural data-frozen in time-with realistic biochemical dynamics and investigate how cell structure affects the behavior of the embedded chemical reaction network. Here we present two examples to illustrate the challenges and techniques involved in integrating structural data into stochastic simulations. First, a tomographic reconstruction of Saccharomyces cerevisiae is used to construct the geometry of an entire cell through which a simple stochastic model of an inducible genetic switch is studied. Second, a tomogram of the nuclear periphery in a HeLa cell is converted directly to the simulation geometry through which we study the effects of cellular substructure on the stochastic dynamics of gene repression. These simple chemical models allow us to illustrate how to build whole-cell simulations using cryo-ET derived geometry and the challenges involved in such a process.

A combined model reduction algorithm for controlled biochemical systems

BackgroundSystems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often, however, not all portions of a model are of equal interest in a given setting. In such situations methods of model reduction offer one possible approach for addressing the issue of complexity by seeking to eliminate those portions of a pathway that can be shown to have the least effect upon the properties of interest.MethodsIn this paper a model reduction algorithm bringing together the complementary aspects of proper lumping and empirical balanced truncation is presented. Additional contributions include the development of a criterion for the selection of state-variable elimination via conservation analysis and use of an ‘averaged’ lumping inverse. This combined algorithm is highly automatable and of particular applicability in the context of ‘controlled’ biochemical networks.ResultsThe algorithm is demonstrated here via application to two examples; an 11 dimensional model of bacterial chemotaxis in Escherichia coli and a 99 dimensional model of extracellular regulatory kinase activation (ERK) mediated via the epidermal growth factor (EGF) and nerve growth factor (NGF) receptor pathways. In the case of the chemotaxis model the algorithm was able to reduce the model to 2 state-variables producing a maximal relative error between the dynamics of the original and reduced models of only 2.8% whilst yielding a 26 fold speed up in simulation time. For the ERK activation model the algorithm was able to reduce the system to 7 state-variables, incurring a maximal relative error of 4.8%, and producing an approximately 10 fold speed up in the rate of simulation. Indices of controllability and observability are additionally developed and demonstrated throughout the paper. These provide insight into the relative importance of individual reactants in mediating a biochemical system’s input-output response even for highly complex networks.ConclusionsThrough application, this paper demonstrates that combined model reduction methods can produce a significant simplification of complex Systems Biology models whilst retaining a high degree of predictive accuracy. In particular, it is shown that by combining the methods of proper lumping and empirical balanced truncation it is often possible to produce more accurate reductions than can be obtained by the use of either method in isolation.

Cysteinet Dysregulation in Muscular Dystrophies: A Pathogenic Network Susceptible to Therapy.

Muscular dystrophies are inherited disorders characterized by progressive skeletal muscle degeneration without curative therapy. The specific defective protein in each type of muscular dystrophy has been associated with different deleterious factors that contribute to the progression of the disease. Among these factors, the impairment of calcium homeostasis, the ubiquitin-proteasome dysfunction, and the oxidative damage of cellular macromolecules seem to be of central importance. Can these different cellular dysfunctions be linked by a common pathogenic mechanism susceptible to therapy? A cellular cysteine network (CYSTEINET) has been proposed previously, as a matrix of interconnected sensitive cysteine-containing proteins (SCCPs) that in addition to reactive species and the cysteine/glutathione cycles can regulate metabolic, redox, and survival cellular pathways by a complex biochemical network of proteins with different functions, but sharing the same regulatory thiol group. Since there are many sensitive cysteine-containing proteins including cysteinedependent enzymes susceptible to redox modifications at cysteine residues that may contribute to muscular degeneration, the aim of this review is to propose that cysteinet dysregulation may explain oxidative damage, calcium disturbances and ubiquitin-proteasome dysfunctions associated with muscular dystrophies. The present review proposes that cysteinet dysregulation in muscular dystrophies may represent a common pathogenic network contributing, in association with the specific protein dysfunction, to muscular degeneration. In this context, N-acetylcysteine may have an important role in the restoration of the proposed cysteinet dysregulation associated with these heterogeneous types of diseases.

Noisy Oscillations in the Actin Cytoskeleton of Chemotactic Amoeba.

Biological systems with their complex biochemical networks are known to be intrinsically noisy. Here we investigate the dynamics of actin polymerization of amoeboid cells, which are close to the onset of oscillations. We show that the large phenotypic variability in the polymerization dynamics can be accurately captured by a generic nonlinear oscillator model in the presence of noise. We determine the relative role of the noise with a single dimensionless, experimentally accessible parameter, thus providing a quantitative description of the variability in a population of cells. Our approach, which rests on a generic description of a system close to a Hopf bifurcation and includes the effect of noise, can characterize the dynamics of a large class of noisy systems close to an oscillatory instability.

Regularization Techniques to Overcome Overparameterization of Complex Biochemical Reaction Networks.

Models of biochemical reaction networks commonly contain a large number of parameters while at the same time there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scientific literature and a significant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data, however, the small amount of available data and the large number of parameters commonly found in these types of models, require the use of regularization techniques to avoid over fitting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross validation rather than fitting results are presented to further emphasize the need for models that generalize well to new data instead of simply fitting the current data.

Interdisciplinary problem- solving: emerging modes in integrative systems biology

Integrative systems biology is an emerging field that attempts to integrate computation, applied mathematics, engineering concepts and methods, and biological experimentation in order to model large-scale complex biochemical networks. The field is thus an important contemporary instance of an interdisciplinary approach to solving complex problems. Interdisciplinary science is a recent topic in the philosophy of science. Determining what is philosophically important and distinct about interdisciplinary practices requires detailed accounts of problem-solving practices that attempt to understand how specific practices address the challenges and constraints of interdisciplinary research in different contexts. In this paper we draw from our 5-year empirical ethnographic study of two systems biology labs and their collaborations with experimental biologists to analyze a significant problem-solving approach in ISB, which we call adaptive problem solving. ISB lacks much of the methodological and theoretical resources usually found in disciplines in the natural sciences, such as methodological frameworks that prescribe reliable model-building processes. Researchers in our labs compensate for the lack of these and for the complexity of their problems by using a range of heuristics and experimenting with multiple methods and concepts from the background fields available to them. Using these resources researchers search out good techniques and practices for transforming intractable problems into potentially solvable ones. The relative freedom lab directors grant their researchers to explore methodological options and find good practices that suit their problems is not only a response to the complex interdisciplinary nature of the specific problem, but also provides the field itself with an opportunity to discover more general methodological approaches and develop theories of biological systems. Such developments in turn can help to establish the field as an identifiably distinct and successful approach to understanding biological systems.