Behavior Of Biological Systems Research Articles

Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851.

Pseudomonas aeruginosa infections are one of the leading causes of death in immunocompromised patients with cystic fibrosis, diabetes, and lung diseases such as pneumonia and bronchiectasis. Furthermore, P. aeruginosa is one of the main multidrug-resistant bacteria responsible for nosocomial infections worldwide, including the multidrug-resistant CCBH4851 strain isolated in Brazil. One way to analyze their dynamic cellular behavior is through computational modeling of the gene regulatory network, which represents interactions between regulatory genes and their targets. For this purpose, Boolean models are important predictive tools to analyze these interactions. They are one of the most commonly used methods for studying complex dynamic behavior in biological systems. Therefore, this research consists of building a Boolean model of the gene regulatory network of P. aeruginosa CCBH4851 using data from RNA-seq experiments. Next, the basins of attraction are estimated, as these regions and the transitions between them can help identify the attractors, representing long-term behavior in the Boolean model. The essential genes of the basins were associated with the phenotypes of the bacteria for two conditions: biofilm formation and polymyxin B treatment. Overall, the Boolean model and the analysis method proposed in this work can identify promising control actions and indicate potential therapeutic targets, which can help pinpoint new drugs and intervention strategies.

Modeling and simulation of Lac-Operon using reaction-diffusion master equation on heterogeneous parallel platforms

The Lac-Operon is a pivotal molecular mechanism controlling gene expression in response to presence or absence of lactose. Mastering its dynamics aids cellular comprehension and gene circuit design. Employing the multi-particle diffusion reaction-diffusion master equation (MPD-RDME), we model gene expression kinetics. This couples with the Gillespie algorithm-driven Next sub-volume method for particle reactions and diffusion, enabling predictive time-course molecule concentration insights via RDME and Gillespie. Such insights inform experimentation and elucidate cellular operations. Particularly, inducible operon kinetics, like Lac-Operon, hold applications in recombinant protein production and gene therapy. Our work constructs a Lac-Operon gene expression model utilizing MPD-RDME and Next sub-volume on a Heterogeneous Parallel Platform (HPP). Implementation employs the combination of host and GPU, and host and homogeneous GPU pairs. Simulation speed, affected by external inducers, repressor count, and lattice spacing, is compared. Protein and mRNA count under various conditions were simulated on host, GPU, and GPU pairs. Results display 75 %–80 % speed enhancement on the HPP compared to host and GPU combination. This adaptable method extends to explore diverse gene circuits, unraveling molecular-level biological system behaviors.

Agency, Goal-Directed Behavior, and Part-Whole Relationships in Biological Systems

In this essay we aim to present some considerations regarding a minimal but concrete notion of agency and goal-directed behavior that are useful for characterizing biological systems at different scales. These considerations are a particular perspective, bringing together concepts from dynamical systems, combinatorial problem-solving, and connectionist learning with an emphasis on the relationship between parts and wholes. This perspective affords some ways to think about agents that are concrete and quantifiable, and relevant to some important biological issues. Instead of advocating for a strict definition of minimally agential characteristics, we focus on how (even for a modest notion of agency) the agency of a system can be more than the sum of the agency of its parts. We quantify this in terms of the problem-solving competency of a system with respect to resolution of the frustrations between its parts. This requires goal-directed behavior in the sense of delayed gratification, i.e., taking dynamical trajectories that forego short-term gains (or sustain short-term stress or frustration) in favor of long-term gains. In order for this competency to belong to the system (rather than to its parts or given by its construction or design), it can involve distributed systemic knowledge that is acquired through experience, i.e., changes in the organization of the relationships among its parts (without presupposing a system-level reward function for such changes). This conception of agency helps us think about the ways in which cells, organisms, and perhaps other biological scales, can be agential (i.e., more agential than their parts) in a quantifiable sense, without denying that the behavior of the whole depends on the behaviors of the parts in their current organization.

Distilling identifiable and interpretable dynamic models from biological data.

Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occam's razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable.

Spreading behavior of biological SIR system of a COVID-19 disease through a fast Taylor wavelet based numerical algorithm

In this paper, we provide a unique, cost-effective numerical method for solving the SIR model of a COVID-19 disease using the method of Taylor wavelets and collocation technique. The model’s mathematical structure is represented by an ordered collection of ordinary differential equations that are nonlinear. We use the functional matrix of integration of Fibonacci wavelets to translate the given model into a set of computational equations, which we next simplify using the Newton–Raphson method. The given model is solved using the proposed method under various conditions to analyze the outcomes graphically and numerically as well. To demonstrate the efficiency and easy applicability of the method, we have also made a numerical comparison of the solution with some other methods in tables. The behavior of the approximate solutions of susceptible, infectible, and recovery curves under the proposed method is analyzed. MATLAB software is used to perform all the related calculations.

A fractional-order visual neural network for collision sensing in noisy and dynamic scenes

Autonomously sensing collision threats in intelligent mobile machines is a significant challenge. Inspired by the perfect collision sensing system of locusts, various neural networks based on lobular giant motion detectors (LGMD) have been proposed. These networks use integer-order differential operators to simulate the simple and efficient visual neural system of locusts. However, some biological studies have shown that the neurodynamics of neuronal response should be fractional-order, and fractional-order differential operators can accurately describe the biological logic brain neural response process. In view of this, firstly, this paper uses a fractional-order differential operator to simulate the membrane potential response of neurons, and a new fractional-order LGMD collision sensing visual neural network is proposed. This network is designed to accurately detect looming objects and generate reliable response spikes. Then, through experimental analysis, a range [0.36, 0.4] of order in the fractional-order system is given, which is considered to be a reasonable interval for modeling the behavior of biological nervous systems. This maybe contribute to the study of the locust’s fractional-order biological nervous system. Finally, the network was systematically tested in noisy and complex dynamic environments. The experimental results demonstrated that the fractional-order LGMD visual neural network generated responses with high adaptability and reliability, showcasing significantly improved collision detection performance.

Stochastic resonance in a single autapse–coupled neuron

The signal detection ability of nervous system is highly associated with nonlinear and collective behaviors in neuronal medium. Neuronal noise, which occurs as natural endogenous fluctuations in brain activity, is the most salient factor influencing this ability. Experimental and theoretical research suggests that noise is beneficial, not detrimental, for regular functioning of nervous system. In this regard, there is a general agreement that noise at an adequate intensity can engage rhythmic activity in brain and noise-induced oscillations enhances performance of the weak signal processing, especially when frequency of the signal is around that of the noise-induced rhythmic oscillation. This behavior in biological neural systems is explained by the notion of “stochastic resonance”. Another factor that plays a key role in regulating neuronal behaviors, including motor and cognitive tasks by maintaining signaling between cells, is characteristics of synapses different in structure and functioning. Here, we study stochastic resonance in Hodgkin–Huxley neuron that has a peculiar synaptic connection called autapse, known as a biophysical feedback mechanism, under presynaptic noise originating from superposition of inhibitory and excitatory Poisson bombardment. Our results show that, under certain conditions, autapse dynamics are able to improve the weak signal detection performance of Hodgkin–Huxley neuron via stochastic resonance. This study provides novel insights into functional role of autapse in neural information processing by revealing a biophysical aspect of stochastic resonance with numerical computations.

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

We examine the challenging "marriage" between computational efficiency and biological plausibility-A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man's land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.

Bioinspired Vertical Transistors from Artificial Synapse to Neuromorphic System

Neuromorphic devices emulating the brain functionality have achieved a substantial scientific leap in the last decade. Among them, neuromorphic vertical transistors have attracted extensive interests due to their abilities of mitigating physical channel‐length limitations and developing new‐generation 3D integrated electronics. In this review, the recent advances and major trends in the field of emerging neuromorphic devices based on vertical transistor are focused on. It has been started with a brief introduction of biological nervous system and neuromorphic behaviors. Next, the latest advances in the development of vertical transistor and shed light on bio‐related neuromorphic applications are discussed. They are discussed in detail from the aspects of materials and potential applications. Finally, future research challenges in neuromorphic vertical transistor are briefly summarized, and their prospects are discussed.

Multi-omics regulatory network inference in the presence of missing data.

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.

Resonant frequency of coronavirus: The tensegrity approach

COVID-19 is a worldwide health hazard. In discovering a fully efficient vaccination for continually evolving viruses, alternatives such as the sonication treatment methods, which have seen encouraging outcomes in disinfection and medical therapies, are worth exploring. Such treatments incapacitate microbes or diseased cells by selectively invoking large deformation at the resonant frequency that initiates structural failure. Nevertheless, due to biodiversity, the precise range of resonant frequencies for different biological bodies can be widely varying, its determination of which is best achieved with the cost-effective computational simulation approach. The paper proposes, therefore, the numerical determination of the resonant frequency of the coronavirus employing the tensegrity method, due to its efficacy in modeling various biomechanical behaviors of different biological bodies and systems. Initiating from successfully verifying the resonant frequency with existing findings, the model then determines the operative sonication frequencies range for the resonant state of the virus. At these sonication frequencies, the modal analysis exhibits a large structurally destructive deformation of the coronavirus. For practical convenience, the operative range of sonication resonant frequencies of the coronavirus has been mapped alongside healthy human cells. These findings offer an alternative technological avenue in combating the COVID-19 progressive threat.

Insight into response characteristics and inhibition mechanisms of anammox granular sludge to polyethylene terephthalate microplastics exposure

Currently, in-depth understanding of response characteristics and mechanisms of anammox process under microplastics (MPs) stress remains quite limited. This study investigated the influence of 0.1–1.0 g/L polyethylene terephthalate (PET) on anammox granular sludge (AnGS). Compared with the control, 0.1–0.2 g/L PET did not significantly affect the anammox efficiency, while the anammox activity decreased by 16.2% at 1.0 g/L PET. Integrity coefficient and transmission electron microscopy analysis demonstrated that the strength and structural stability of the AnGS became weaken following exposure to 1.0 g/L PET. With the PET increasing, the abundance of anammox genera and genes related to energy metabolism and cofactors and vitamins metabolism decreased. The reactive oxygen species generated in the interaction between microbial cells and PET resulting in cellular oxidative stress was responsible for inhibiting anammox. These findings give novel insights into the anammox behavior in biological nitrogen removal systems treating PET-loaded nitrogenous wastewater.

Mean-shift exploration in shape assembly of robot swarms

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms. Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea is realized by adapting the mean-shift algorithm, which is an optimization technique widely used in machine learning for locating the maxima of a density function. The proposed strategy empowers robot swarms to assemble highly complex shapes with strong adaptability, as verified by experiments with swarms of 50 ground robots. The comparison between the proposed strategy and the state-of-the-art demonstrates its high efficiency especially for large-scale swarms. The proposed strategy can also be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L)step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.

The influence of size and surface chemistry on the bioavailability, tissue distribution and toxicity of gold nanoparticles in zebrafish (Danio rerio)

Gold nanoparticles (AuNPs) are widely used in biomedicine and their specific properties including, size, geometrics, and surface coating, will affect their fate and behaviour in biological systems. These properties are well studied for their intended biological targets, but there is a lack of understanding on the mechanisms by which AuNPs interact in non-target organisms when they enter the environment. We investigated the effects of size and surface chemistry of AuNPs on their bioavailability, tissue distribution and potential toxicity using zebrafish (Danio rerio) as an experimental model. Larval zebrafish were exposed to fluorescently tagged AuNPs of different sizes (10–100 nm) and surface modifications (TNFα, NHS/PAMAM and PEG), and uptake, tissue distribution and depuration rates were measured using selective-plane illumination microscopy (SPIM). The gut and pronephric tubules were found to contain detectable levels of AuNPs, and the concentration-dependent accumulation was related to the particle size. Surface addition of PEG and TNFα appeared to enhance particle accumulation in the pronephric tubules compared to uncoated particles. Depuration studies showed a gradual removal of particles from the gut and pronephric tubules, although fluorescence indicating the presence of the AuNPs remained in the pronephros 96 h after exposure. Toxicity assessment using two transgenic zebrafish reporter lines, however, revealed no AuNP-related renal injury or cellular oxidative stress. Collectively, our data show that AuNPs used in medical applications across the size range 40–80 nm, are bioavailable to larval zebrafish and some may persist in renal tissue, although their presence did not result in measurable toxicity with respect to pronephric organ function or cellular oxidative stress for short term exposures.

Local Assortativity Affects the Synchronizability of Scale-Free Network

Synchronization is critical for system-level behavior in physical, chemical, biological, and social systems. Empirical evidence has shown that the network topology strongly impacts the synchronizability of the system, and the analysis of their relationship remains an open challenge. We know that the eigenvalue distribution determines a network's synchronizability, but analytical expressions that connect network topology and all relevant eigenvalues (e.g., the extreme values) remain elusive. Here, we accurately determine its synchronizability by proposing an analytical method to estimate the extreme eigenvalues using perturbation theory. Our analytical method exposes the role that global and local topology combine to influence synchronizability. We show that the smallest nonzero eigenvalue <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda ^{(2)}$</tex-math></inline-formula> , which determines synchronizability, is estimated by the smallest degree augmented by the inverse degree difference in the least connected nodes. From this, we can conclude that there exists a clear negative relationship between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda ^{(2)}$</tex-math></inline-formula> and the local assortativity of nodes with the smallest degree value. We validate the accuracy of our framework within the setting of a scale-free network and can be driven by commonly used ordinary differential equations (e.g., 3-D Rosler dynamics or Hindmarsh–Rose neuronal circuit). From the results, we demonstrate that the synchronizability of the network can be tuned by rewiring the connections of these particular nodes while maintaining the general degree profile of the network.

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

Solving the chemical master equation is an indispensable tool in understanding the behavior of biological and chemical systems. In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. Solving the chemical master equation directly, however, suffers from the curse of dimensionality. That is, both memory and computational effort scale exponentially in the number of species. In this paper we propose a dynamical low-rank approach that enables the simulation of large biological networks. The approach is guided by partitioning the network into biological relevant subsets and thus avoids the use of single species basis functions that are known to give inaccurate results for biological systems. We use the proposed method to gain insight into the nature of asynchronous vs. synchronous updating in Boolean models and successfully simulate a 41 species apoptosis model on a standard desktop workstation.

Nothing in evolution makes sense except in the light of code biology

This article highlights the potential contribution of biological codes to the course and dynamics of evolution. The concept of organic codes, developed by Marcello Barbieri, has fundamentally changed our view of how living systems function. The notion that molecular interactions built on adaptors that arbitrarily link molecules from different “worlds” in a conventional, i.e., rule-based way, departs significantly from the law-based constraints imposed on livening things by physical and chemical mechanisms. In other words, living entities and non-living things behave like rules and laws, respectively, but this important distinction is rarely considered in current evolutionary theory. The many known codes allow quantification of codes that relate to a cell, or comparisons between different biological systems and may pave the way to a quantitative and empirical research agenda in code biology. A starting point for such an endeavour is the introduction of a simple dichotomous classification of structural and regulatory codes. This classification can be used as a tool to analyse and quantify key organising principles of the living world, such as modularity, hierarchy, and robustness, based on organic codes. The implications for evolutionary research are related to the unique dynamics of codes, or 'Eigendynamics' (self-momentum) and how they determine the behaviour of biological systems from inside, whereas physical constraints are imposed mainly from outside. A speculation on the drivers of macroevolution in light of codes is followed by the conclusion that a meaningful and comprehensive understanding of evolution depends on including codes into the equation of life.

It’s Time to Take Quantum Biology Research Seriously

Understanding the possible quantum-driven behaviors of biological systems could aid in treating injuries or in developing cures for diseases, but research in the field has been pushed to the sidelines. It’s time for that to change.

Effective holistic characterization of small molecule effects using heterogeneous biological networks.

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.