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bioRxiv2018

Gene target discovery with network analysis in Toxoplasma gondii

Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasites life cycle. These new findings can contribute to understand of parasite pathogenesis.

systems biology↗
bioRxiv2018

Capturing Multicellular System Designs Using the Synthetic Biology Open Language (SBOL)

Synthetic biology aims to improve the development of biological systems and in-crease their reproducibility through the use of engineering principles, such as stan-dardisation and modularisation. It is important that these systems can be represented and shared in a standard way to ensure they are easily understood, reproduced, and utilised by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterisation. Thus far, this standard has been used to represent designs in homogeneous systems, where the same design is implemented in every cell. In recent years there has been increasing interest in multicellular systems, where biological designs are split across multiple cells to optimise the system behaviour and function. Here we show how the SBOL standard can be used to represent such multicellular systems, and hence how researchers can better share designs with the community.

synthetic biology↗
bioRxiv2019

A quantitative method for proteome reallocation using minimal regulatory interventions

Engineering resource allocation in biological systems for synthetic biology applications is an ongoing challenge. Wild type organisms allocate abundant cellular resources for ensuring survival in changing environments, reducing the productivity of engineered functions. Here we present a novel approach for engineering the resource allocation of Escherichia coli by rationally modifying the transcriptional regulatory network of the bacterium. Our method (ReProMin) identifies the minimal set of genetic interventions that maximise the savings in cell resources that would normally be used to express non-essential genes. To this end we categorize Transcription Factors (TFs) according to the essentiality of the genes they regulate and we use available proteomic data to rank them based on its proteomic balance, defined as the net proteomic charge they release. Using a combinatorial approach, we design the removal of TFs that maximise the release of the proteomic charge and we validate the model predictions experimentally. Expression profiling of the resulting strain shows that our designed regulatory interventions are highly specific. We show that our resulting engineered strain containing only three mutations, theoretically releasing 0.5% of their proteome, has higher proteome budget and show increased production yield of a molecule of interest obtained from a recombinant metabolic pathway. This approach shows that combining whole-cell proteomic and regulatory data is an effective way of optimizing strains in a predictable way using conventional molecular methods.\n\nImportanceBiological regulatory mechanisms are complex and occur in hierarchical layers such as transcription, translation and post-translational mechanisms. We foresee the use of regulatory mechanism as a control layer that will aid in the design of cellular phenotypes. Our ability to engineer biological systems will be dependent on the understanding of how cells sense and respond to their environment at a system level. Few studies have tackled this issue and none of them in a rational way. By developing a workflow of engineering resource allocation based on our current knowledge of E. colis regulatory network, we pursue the objective of minimizing cell proteome using a minimal genetic intervention principle. We developed a method to rationally design a set of genetic interventions that reduce the hedging proteome allocation. Using available datasets of a model bacterium we were able to reallocate parts of the unused proteome in laboratory conditions to the production of an engineered task. We show that we are able to reduce the unused proteome (theoretically 0.5%) with only three regulatory mutations designed in a rational way, which results in strains with increased capabilities for recombinant expression of pathways of interest.\n\nHighlightsO_LIProteome reduction with minimal genetic intervention as design principle\nC_LIO_LIRegulatory and proteomic data integration to identify transcription factor activated proteome\nC_LIO_LIDeletion of the TF combination that reduces the greater proteomic load\nC_LIO_LIRegulatory interventions are highly specific\nC_LIO_LIDesigned strains show less burden, improved protein and violacein production\nC_LI

synthetic biology↗
bioRxiv2017

Subharmonics And Chaos In Simple Periodically-Forced Biomolecular Models

This paper uncovers a remarkable behavior in two biochemical systems that commonly appear as components of signal transduction pathways in systems biology. These systems have globally attracting steady states when unforced, so they might have been considered \"uninteresting\" from a dynamical standpoint. However, when subject to a periodic excitation, strange attractors arise via a period-doubling cascade. Quantitative analyses of the corresponding discrete chaotic trajectories are conducted numerically by computing largest Lyapunov exponents, power spectra, and autocorrelation functions. To gain insight into the geometry of the strange attractors, the phase portraits of the corresponding iterated maps are interpreted as scatter plots for which marginal distributions are additionally evaluated. The lack of entrainment to external oscillations, in even the simplest biochemical networks, represents a level of additional complexity in molecular biology, which has previously been insufficiently recognized but is plausibly biologically important.

systems biology↗
bioRxiv2025

Quantitative system drift

We consider a biological system composed of multiple genetically variable components, the combined result of which is a quantitative trait under stabilizing selection for an optimal value. We show mathematically that, while the mean value of the system is ultimately constrained to remain near its optimum, the values of individual components are free to drift far from their initial values. Each components drift, though qualitatively similar to neutral drift, is slower by a factor that depends on the fraction of the systems genetic variance contributed by the component. Our results provide a population-genetic basis for system drift, the concept that individual components of a biological system can evolve despite selective constraint on their combined product. A special case is a single polygenic trait under stabilizing selection, where our results predict that the mean genetic contributions to the trait of different subregions of the genome, such as the chromosomes, can drift despite constraint on the genome-wide genetic value. We explore the implications of this latter result for selection against interspecific hybrids and selection against turnovers of sex-determining systems. We further apply our general results to a continuous public goods game played between two species, where they predict that individual species contributions to a costly public good can drift freely. Finally, we show that symmetric mutation between alleles that increase and decrease components contributions to the system provides a weak long-term brake on components drift.

evolutionary biology↗
bioRxiv2024

A functional approach to homeostatic regulation

In this work, we present a novel modeling framework for understanding the dynamics of homeostatic regulation. Inspired by engineering control theory, this framework incorporates unique features of biological systems. First, biological variables often play physiological roles, and taking this functional context into consideration is essential to fully understand the goals and constraints of homeostatic regulation. Second, biological signals are not abstract variables, but rather material molecules that may undergo complex turnover processes of synthesis and degradation. We suggest that the particular nature of biological signals may condition the type of information they can convey, and their potential role in shaping the dynamics and the ultimate purpose of homeostatic systems. We show that the dynamic interplay between regulated variables and control signals is a key determinant of biological homeostasis, challenging the necessity and the convenience of strictly extrapolating concepts from engineering control theory in modeling the dynamics of homeostatic systems. This work provides an alternative, unified framework for studying biological regulation and identifies general principles that transcend molecular details of particular homeostatic mechanisms. We show how this approach can be naturally applied to apparently different regulatory systems, contributing to a deeper understanding of homeostasis as a fundamental process in living systems.

physiology↗
bioRxiv2018

A novel structure-based control method for analyzing nonlinear dynamics in biological networks

Exploring complex biological systems requires adequate knowledge of the systems underlying wiring diagram but not its specific functional forms. Thus, exploration actually requires the concepts and approaches delivered by structure-based network control, which investigates the controllability of complex networks through a minimum set of input nodes. Traditional structure-based control methods focus on the structure of complex systems with linear dynamics and may not match the meaning of control well in some biological systems. Here we took into consideration the nonlinear dynamics of some biological networks and formalized the nonlinear control problem of undirected dynamical networks (NCU). Then, we designed and implemented a novel and general graphic-theoretic algorithm (NCUA) from the perspective of the feedback vertex set to discover the possible minimum sets of the input nodes in controlling the network state. We applied our NCUA to both synthetic networks and real-world networks to investigate how the network parameters, such as the scaling exponent and the degree heterogeneity, affect the control characteristics of networks with nonlinear dynamics. The NCUA was applied to analyze the patient-specific molecular networks corresponding to patients across multiple datasets from The Cancer Genome Atlas (TCGA), which demonstrates the advantages of the nonlinear control method to characterize and quantify the patient-state change over the other state-of-the-art linear control methods. Thus, our model opens a new way to control the undesired transition of cancer states and provides a powerful tool for theoretical research on network control, especially in biological fields.\n\nAuthor summaryComplex biological systems usually have nonlinear dynamics, such as the biological gene (protein) interaction network and gene co-expression networks. However, most of the structure-based network control methods focus on the structure of complex systems with linear dynamics. Thus, the ultimate purpose to control biological networks is still too complicated to be directly solved by such network control methods. We currently lack a framework to control the biological networks with nonlinear and undirected dynamics theoretically and computationally. Here, we discuss the concept of the nonlinear control problem of undirected dynamical networks (NCU) and present the novel graphic-theoretic algorithm from the perspective of a feedback vertex set for identifying the possible sets with minimum input nodes in controlling the networks. The NCUA searches the minimum set of input nodes to drive the network from the undesired attractor to the desired attractor, which is different from conventional linear network control, such as that found in the Maximum Matching Sets (MMS) and Minimum Dominating Sets (MDS) algorithms. In this work, we evaluated the NCUA on multiple synthetic scale-free networks and real complex networks with nonlinear dynamics and found the novel control characteristics of the undirected scale-free networks. We used the NCUA to thoroughly investigate the sample-specific networks and their nonlinear controllability corresponding to cancer samples from TCGA which are enriched with known driver genes and known drug target as controls of pathologic phenotype transitions. We found that our NCUA control method has a better predicted performance for indicating and quantifying the patient biological system changes than that of the state-of-the-art linear control methods. Our approach provides a powerful tool for theoretical research on network control, especially in a range of biological fields.

systems biology↗
bioRxiv2024

Chronobiological rhythms control of site- and cell-specific miRNA and mRNA genes and networks across the central nervous system

Biological rhythms control gene expression, but effects on central nervous system (CNS) cells and structures remain undefined. While circadian (24-hour) rhythms are most studied, many genes have periods of greater and less than 24-hours; these fluctuations can be both site- and cell-specific. Identifying patterns of gene rhythmicity across the CNS is necessary for both the study of chronobiology and to make sense of data obtained in the laboratory. We now identify cycling mRNAs, miRNAs, gene networks and novel mRNA-miRNA co-expression pairs in the cortex, hypothalamus, and corpus striatum using high-dimensional datasets. A searchable catalogue (https://www.ghasemloulab.ca/chronoCNS) was created to help refine the analysis of cellular/molecular rhythmicity across the CNS. Immunofluorescence was also used to confirm the rhythmicity of key targets across cells in these structures, with strong cycling signatures in resting oligodendrocytes. Our study sheds light on the contribution of circadian, ultradian, and infradian rhythms and mRNA-miRNA interactions to CNS function.

neuroscience↗
bioRxiv2019

Interplay of cellular states: Role of delay as control mechanism

Delay is everywhere, no matter how small or big it is. Experimental evidences show the existence and importance of time delayed reactions specially in biological systems. The role of delay is found to be multifunctional and is seemed to be system dependent. The analytically solved P(X, t) of gene regulatory process shows universal class of Poisson process at stationary condition. However, time delay creates a possible condition to the system to impart correlation in the stochastic process as sub-Poissonian or noise enhancement process which could be important in regulating and controlling the system. The results of simulation of few biological systems (gene regulation, circadian rhythm, and repressilator) using delay stochastic simulation algorithm show the possibilities of delay induced onset of oscillating states, which could be the active states of the systems, where, the system can establish coherence among the system variables, enhance the signal processing, optimize the system activities, etc. On the other hand, delay can also induce switching off of the oscillating states, which may correspond to inactive state or system failure. Further, delay can also create coherent bistable states of the system variables, at which the system can stay longer to make decisions about the fate of the system.

systems biology↗
bioRxiv2025

GEMsembler: cross-tool structural comparison and ensemble modeling improve metabolic model performance

Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance. Here, we introduce GEMsembler, a Python package designed to compare cross-tool GEMs, track the origin of model features, and build ensemble models containing any subset of the input models. GEMsembler provides comprehensive analysis functionality, including identification and visualisation of biosynthesis pathways, growth assessment and an agreement-based curation workflow. GEMsembler-curated ensemble models built from four Lactiplantibacillus plantarum and Escherichia coli automatically reconstructed models outperform the gold-standard models in auxotrophy and gene essentiality predictions. Optimising Gene-Protein-Reaction (GPR) combinations from ensemble models improves gene essentiality predictions, even in the manually curated gold-standard models. GEMsembler explains model performance by highlighting relevant metabolic pathways and GPRs alternatives, informing experiments to resolve model uncertainty. Thus, GEMsembler facilitates building more accurate and biologically informed metabolic models for systems biology applications.

systems biology↗
bioRxiv2019

Computation of stationary distributions in stochastic models of cellular processes with molecular memory

AbstractModeling stochastic dynamics of intracellular processes has long rested on Markovian (i.e., memoryless) hypothesis. However, many of these processes are non-Markovian (i.e., memorial) due to, e.g., small reaction steps involved in synthesis or degradation of a macroscopic molecule. When interrogating aspects of a cellular network by experimental measurements (e.g., by singlemolecule and single-cell measurement technologies) of network components, a key need is to develop efficient approaches to simulate and compute joint distributions of these components. To cope with this computational challenge, we develop two efficient algorithms: stationary generalized Gillespie algorithm and stationary generalized finite state projection, both being established based on a stationary generalized chemical master equation. We show how these algorithms can be combined in a streamlined procedure for evaluation of non-Markovian effects in a general cellular network. Stationary distributions are evaluated in two models of constitutive and bursty gene expressions as well as a model of genetic toggle switch, each considering molecular memory. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which has the potential to advance both understanding of molecular systems biology and design of synthetic circuits.\n\nAuthor summaryCellular systems are driven by interactions between subsystems via time-stamped discrete events, involving numerous components and reaction steps and spanning several time scales. Such biochemical reactions are subject to inherent noise due to the small numbers of molecules. Also, they could involve several small steps, creating a memory between individual events. Delineating these molecular stochasticity and memory of biomolecular networks are continuing challenges for molecular systems biology. We present a novel approach to compute the probability distribution in stochastic models of cellular processes with molecular memory based on stationary generalized chemical master equation. We map a stochastic system with memory onto a Markovian model with effective reaction propensity functions. This formulation enables us to efficiently develop algorithms under the Markovian framework, and thus systematically analyze how molecular memories regulate stochastic behaviors of biomolecular networks. Here we propose two representative algorithms: stationary generalized Gillespie algorithm and stationary generalized finite state projection algorithm. The former generate realizations with Monte Carlo simulation, but the later compute approximations of the probability distribution by solving a truncated version of stochastic process. Our approach is demonstrated by applying it to three different examples from systems biology: generalized birth-death process, a stochastic toggle switch model, and a 3-stage gene expression model.

systems biology↗
bioRxiv2018

A probabilistic interpretation of PID controllers using active inference

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information theory, statistical physics and machine learning. Furthermore, it has been argued that one such proposal, active inference, combines both information and control theory and has its roots in cybernetics studies of the brain. The connections between information and control theory have been discussed since the 1950s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. How-ever, the implications of the confluence of these two theoretical frame-works for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation once we use only simple linear generative models.

systems biology↗
bioRxiv2016

How domain growth is implemented determines the long term behaviour of a cell population through its effect on spatial correlations.

Domain growth plays an important role in many biological systems, and so the inclusion of domain growth in models of these biological systems is important to understanding how these biological systems function. In this work we present methods to include the effects of domain growth on the evolution of spatial correlations in a continuum approximation of a lattice-based model of cell motility and proliferation. We show that, depending on the way in which domain growth is implemented, different steady-state densities are predicted for an agent population. Furthermore, we demonstrate that the way in which domain growth is implemented can result in the evolution of the agent density depending on the size of the domain. Continuum approximations that ignore spatial correlations cannot capture these behaviours, while those that account for spatial correlations do. These results will be of interest to researchers in developmental biology, as they suggest that the nature of domain growth can determine the characteristics of cell populations.

Systems Biology↗
bioRxiv2021

3D stochastic simulation of chemoattractant-mediated excitability in cells

During the last decade, a consensus has emerged that the stochastic triggering of an excitable system drives pseudopod formation and subsequent migration of amoeboid cells. The presence of chemoattractant stimuli alters the threshold for triggering this activity and can bias the direction of migration. Though noise plays an important role in these behaviors, mathematical models have typically ignored its origin and merely introduced it as an external signal into a series of reaction-diffusion equations. Here we consider a more realistic description based on a reaction-diffusion master equation formalism to implement these networks. In this scheme, noise arises naturally from a stochastic description of the various reaction and diffusion terms. Working on a three-dimensional geometry in which separate compartments are divided into a tetrahedral mesh, we implement a modular description of the system, consisting of G-protein coupled receptor signaling (GPCR), a local excitation-global inhibition mechanism (LEGI), and signal transduction excitable network (STEN). Our models implement detailed biochemical descriptions whenever this information is available, such as in the GPCR and G-protein interactions. In contrast, where the biochemical entities are less certain, such as the LEGI mechanism, we consider various possible schemes and highlight the differences between them. Our stimulations show that even when the LEGI mechanism displays perfect adaptation in terms of the mean level of proteins, the variance shows a dose-dependence. This differs between the various models considered, suggesting a possible means for determining experimentally among the various potential networks. Overall, our simulations recreate temporal and spatial patterns observed experimentally in both wild-type and perturbed cells, providing further evidence for the excitable system paradigm. Moreover, because of the overall importance and ubiquity of the modules we consider, including GPCR signaling and adaptation, our results will be of interest beyond the field of directed migration. Author summaryThough the term noise usually carries negative connotations, it can also contribute positively to the characteristic dynamics of a system. In biological systems, where noise arises from the stochastic interactions between molecules, its study is usually confined to genetic regulatory systems in which copy numbers are small and fluctuations large. However, noise can have important roles when the number of signaling molecules is large. The extension of pseudopods and the subsequent motion of amoeboid cells arises from the noise-induced trigger of an excitable system. Chemoattractant signals bias this triggering thereby directing cell motion. To date, this paradigm has not been tested by mathematical models that account accurately for the noise that arises in the corresponding reactions. In this study, we employ a reaction-diffusion master equation approach to investigate the effects of noise. Using a modular approach and a three-dimensional cell model with specific subdomains attributed to the cell membrane and cortex, we explore the spatiotemporal dynamics of the system. Our simulations recreate many experimentally-observed cell behaviors thereby supporting the biased-excitable hypothesis.

cell biology↗
bioRxiv2024

Chemical Space Localization for Unknown Metabolite Annotation via Semantic Similarity of Mass Spectral Language

Untargeted analysis using liquid chromatography{square}mass spectrometry (LC-MS) allows quantification of known and unknown compounds within biological systems. However, in practical analysis of complex biological system, the majority of compounds often remain unidentified. Here, we developed a novel deep learning-based compound annotation approach via semantic similarity analysis of mass spectral language. This approach enables the prediction of structurally related compounds for unknowns. By considering the chemical space, these structurally related compounds provide valuable information about the potential location of the unknown compounds and assist in ranking candidates obtained from molecular structure databases. Validated with two independent benchmark datasets obtained by chemical standards, our method has consistently demonstrated superior performance compared to existing compound annotation methods. A case study of the tomato ripening process indicates that DeepMASS has significant potential for metabolic biomarker identification in real biological systems. Overall, the presented method shows considerable promise in annotating metabolites, particularly in revealing the "dark matter" in untargeted analysis.

bioinformatics↗
bioRxiv2019

Fully Interpretable Deep Learning Model of Transcriptional Control

The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent work in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Because of the black box nature of DNNs, such assumptions, while useful in practice, are unsatisfactory for scientific analysis. In this paper, we give an example of a DNN in which every layer is interpretable. Moreover, this DNN is biologically validated and predictive. We derive our DNN from a systems biology model that was not previously recognized as having a DNN structure. This DNN is concerned with a key unsolved biological problem, which is to understand the DNA regulatory code which controls how genes in multicellular organisms are turned on and off. Although we apply our DNN to data from the early embryo of the fruit fly Drosophila, this system serves as a testbed for analysis of much larger data sets obtained by systems biology studies on a genomic scale.

systems biology↗
bioRxiv2014

Modelling reactions catalysed by carbohydrate-active enzymes

Carbohydrate polymers are ubiquitous in biological systems and their roles are highly diverse, ranging from energy storage over mechanical stabilisation to mediating cell-cell or cell-protein interactions. The functional diversity is mirrored by a chemical diversity that results from the high flexibility of how different sugar monomers can be arranged into linear, branched or cyclic polymeric structures. Mathematical models describing biochemical processes on polymers are faced with various difficulties. First, polymer-active enzymes are often specific to some local configuration within the polymer but are indifferent to other features. That is they are potentially active on a large variety of different chemical compounds, meaning that polymers of different size and structure simultaneously compete for enzymes. Second, especially large polymers interact with each other and form water-insoluble phases that restrict or exclude the formation of enzyme-substrate complexes. This heterogeneity of the reaction system has to be taken into account by explicitly considering processes at the, often complex, surface of the polymer matrix. We review recent approaches to theoretically describe polymer biochemical systems. All attempts address a particular challenge, which we discuss in more detail. We emphasise a recent attempt which draws novel analogies between polymer biochemistry and statistical thermodynamics and illustrate how this parallel leads to novel insights about non-uniform polymer reactant mixtures. Finally, we discuss the future challenges of the young and growing field of theoretical polymer biochemistry.

Systems Biology↗
bioRxiv2014

Inference of cell-cell interactions from population density characteristics and cell trajectories on static and growing domains

A key feature of cell migration is how cell movement is affected by cell-cell interactions. Furthermore, many cell migratory processes such as neural crest stem cell migration [1, 2] occur on growing domains or in the presence of a chemoattractant. Therefore, it is important to study interactions between migrating cells in the context of domain growth and directed motility. Here we compare discrete and continuum models describing the spatial and temporal evolution of a cell population for different types of cell-cell interactions on static and growing domains. We suggest that cell-cell interactions can be inferred from population density characteristics in the presence of motility bias, and these population density characteristics for different cell-cell interactions are conserved on both static and growing domains. We also study the expected displacement of a tagged cell, and show that different types of cell-cell interactions can give rise to cell trajectories with different characteristics. These characteristics are conserved in the presence of domain growth, however, they are diminished in the presence of motility bias. Our results are relevant for researchers who study the existence and role of cell-cell interactions in biological systems, so far as we suggest that different types of cell-cell interactions could be identified from cell density and trajectory data.

Systems Biology↗