Showing papers in "BioSystems in 2018"

The bioelectric code: An ancient computational medium for dynamic control of growth and form

Abstract: What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering.

Tellurium: An extensible python-based modeling environment for systems and synthetic biology.

Abstract: Here we present Tellurium, a Python-based environment for model building, simulation, and analysis that facilitates reproducibility of models in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source simulation environment composed of multiple libraries, plugins, and specialized modules and methods. Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution. Two interfaces are provided, one based on the Spyder IDE which has an accessible user interface akin to MATLAB and a second based on the Jupyter Notebook, which is a format that contains live code, equations, visualizations, and narrative text. Tellurium uses libRoadRunner as the default SBML simulation engine which supports deterministic simulations, stochastic simulations, and steady-state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from complex algorithms for bifurcation analysis to multidimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and analysis for both novices and experts. Availability: tellurium.analogmachine.org .

Slime mould: The fundamental mechanisms of biological cognition

Abstract: The slime mould Physarum polycephalum has been used in developing unconventional computing devices for in which the slime mould played a role of a sensing, actuating, and computing device. These devices treated the slime mould as an active living substrate, yet it is a self-consistent living creature which evolved over millions of years and occupied most parts of the world, but in any case, that living entity did not own true cognition, just automated biochemical mechanisms. To "rehabilitate" slime mould from the rank of a purely living electronics element to a "creature of thoughts" we are analyzing the cognitive potential of P. polycephalum. We base our theory of minimal cognition of the slime mould on a bottom-up approach, from the biological and biophysical nature of the slime mould and its regulatory systems using frameworks such as Lyon's biogenic cognition, Muller, di Primio-Lengelerś modifiable pathways, Bateson's "patterns that connect" framework, Maturana's autopoietic network, or proto-consciousness and Morgan's Canon.

A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior.

Abstract: Several species of fish live in groups to increase their foraging efficiency and reproduction rates. Such groups are considered self-organized since they can adopt different cooperative actions without the presence of an apparent leader. One of their most interesting collaborative behaviors found in fish is the hunting strategy presented by the Yellow Saddle Goatfish (Parupeneus cyclostomus). In this strategy, the complete group of fish is distributed in subpopulations to cover the whole hunting region. In each sub-population, all fish participate collectively in the hunt considering two different roles: chaser and blocker. In the hunt, a chaser fish actively tries to find the prey in a certain area whereas a blocker fish moves spatially to avoid the escape of the prey. In this paper, we develop the hunting model of Yellow Saddle Goatfish, which at some abstraction level can be characterized as a search strategy for optimization proposes. In the approach, different computational operators are designed in order to emulate this peculiar hunting behavior. With the use of this biological model, the new search strategy improves the optimization results in terms of accuracy and convergence in comparison to other popular optimization techniques. The performance of this method is tested by analyzing its results with other related evolutionary computation techniques. Several standard benchmark functions commonly used in the literature were considered to obtain optimization results. Furthermore, the proposed model is applied to solve certain engineering optimization problems. Analysis of the experimental results exhibits the efficiency, accuracy, and robustness of the proposed algorithm.

Spiking neural P systems with multiple channels and anti-spikes.

Abstract: Spiking neural P systems (SN P systems) with multiple channels are a variant of SN P systems presented recently. By introducing anti-spikes in neurons, SN P systems with multiple channels and anti-spikes are constructed in this work, where both spikes and anti-spikes are used in rules with channel labels. The Turing universality as number generating and accepting devices is proved at first, and then a universal SN P systems with multiple channels and anti-spikes for computing functions is investigated. At last, a small universal system using 65 neurons for computing any Turing computable function is given.

Self-organization in aggregating robot swarms: A DW-KNN topological approach.

Abstract: In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.

A model of optimal protein allocation during phototrophic growth.

Abstract: Photoautotrophic growth depends upon an optimal allocation of finite cellular resources to diverse intracellular processes. Commitment of a certain mass fraction of the proteome to a specific cellular function typically reduces the proteome available for other cellular functions. Here, we develop a semi-quantitative kinetic model of cyanobacterial phototrophic growth to describe such trade-offs of cellular protein allocation. The model is based on coarse-grained descriptions of key cellular processes, in particular carbon uptake, metabolism, photosynthesis, and protein translation. The model is parameterized using literature data and experimentally obtained growth curves. Of particular interest are the resulting cyanobacterial growth laws as fundamental characteristics of cellular growth. We show that the model gives rise to similar growth laws as observed for heterotrophic organisms, with several important differences due to the distinction between light energy and carbon uptake. We discuss recent experimental data supporting the model results and show that coarse-grained growth models have implications for our understanding of the limits of phototrophic growth and bridge a gap between molecular physiology and ecology.

The role of Fenton reaction in ROS-induced toxicity underlying atherosclerosis - modeled and analyzed using a Petri net-based approach

Abstract: The superoxide-driven Fenton reaction plays an important role in the transformation of poorly reactive radicals into highly reactive ones. These highly reactive species (ROS), especially hydroxyl radicals can lead to many disturbances contributing to the endothelial dysfunction being a starting point for atherosclerosis. Although, iron has been identified as a possible culprit influencing formation of ROS, its significance in this process is still debatable. To better understand this phenomenon, the influence of blockade of Fenton reaction in a proposed Petri net-based model of the selected aspects of the iron ROS-induced toxicity in atherosclerosis has been evaluated. As a result of the blockade of iron ions formation in the model, even up to 70% of the paths leading to the progression of atherosclerosis in this model has been blocked. In addition, after adding to the model, the blockade of the lipids peroxidation paths, progression of atherosclerotic plaque has been not observed. This allowed to conclude that the superoxide-driven Fenton reaction plays a significant role in the atherosclerosis.

A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints

Abstract: The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.

Alignment-based and alignment-free methods converge with experimental data on amino acids coded by stop codons at split between nuclear and mitochondrial genetic codes.

Abstract: Genetic codes mainly evolve by reassigning punctuation codons, starts and stops. Previous analyses assuming that undefined amino acids translate stops showed greater divergence between nuclear and mitochondrial genetic codes. Here, three independent methods converge on which amino acids translated stops at split between nuclear and mitochondrial genetic codes: (a) alignment-free genetic code comparisons inserting different amino acids at stops; (b) alignment-based blast analyses of hypothetical peptides translated from non-coding mitochondrial sequences, inserting different amino acids at stops; (c) biases in amino acid insertions at stops in proteomic data. Hence short-term protein evolution models reconstruct long-term genetic code evolution. Mitochondria reassign stops to amino acids otherwise inserted at stops by codon-anticodon mismatches (near-cognate tRNAs). Hence dual function (translation termination and translation by codon-anticodon mismatch) precedes mitochondrial reassignments of stops to amino acids. Stop ambiguity increases coded information, compensates endocellular mitogenome reduction. Mitochondrial codon reassignments might prevent viral infections.

Phi in physiology, psychology and biomechanics: The golden ratio between myth and science.

Abstract: In recent years, there has been a renewed interest in the use of the so-called golden ratio (Phi, ϕ), an irrational number with fractal properties, used in artworks since V century BC. and now for modelling complex biological structures and functions. This number, in fact, recursively pops-up in human history, from Ancient Greeks to Renaissance, and to contemporary scientific studies. Nevertheless, recent scientific results often fall between two extremes: those of a priori sceptic researchers accusing the artificial emergence of ϕ in many studies, and those of researchers that find a mystic meaning in the presence of ϕ in human physiology. This review moves between these two extremes to provide a scientifically based discussion about the possible presence of Phi in human physiology, psychology, and biomechanics of heart and locomotion. We provide scientific evidence, analysing reasons for the presence of Phi, reporting the weakness of some studies overstating the potential meaning of this number, and reporting the reasons for which it could be actually found in some biological structures and functions.

Evolutionary epistemology: Reviewing and reviving with new data the research programme for distributed biological intelligence.

Abstract: Numerous studies in microbiology, eukaryotic cell biology, plant biology, biomimetics, synthetic biology, and philosophy of science appear to support the principles of the epistemological theory inspired by evolution, also known as “Evolutionary Epistemology”, or EE. However, that none of the studies acknowledged EE suggests that its principles have not been formulated with sufficient clarity and depth to resonate with the interests of the empirical research community. In this paper I review evidence in favor of EE, and also reformulate EE principles to better inform future research. The revamped programme may be tentatively called Research Programme for Distributed Biological Intelligence. Intelligence I define as the capacity of organisms to gain information about their environment, process that information internally, and translate it into phenotypic forms. This multistage progression may be expressed through the acronym IGPT (information-gain-process-translate). The key principles of the programme may be summarized as follows. (i) Intelligence, a universal biological phenomenon promoting individual fitness, is required for effective organism-environment interactions. Given that animals represent less than 0.01% of the planetary biomass, neural intelligence is not the evolutionary norm. (ii) The basic unit of intelligence is a single cell prokaryote. All other forms of intelligence are derived. (iii) Intelligence is hierarchical. It ranges from bacteria to the biosphere or Gaia. (iv) The concept of “information” acquires a new meaning because information processing is at the heart of biological intelligence. All biological systems, from bacteria to Gaia, are intelligent, open thermodynamic systems that exchange information, matter and energy with the environment. (v) The organism-environment interaction is cybernetic. As much as the organism changes due to the influence of the environment, the organism’s responses to induced changes affect the environment and subsequent organism-environment interactions. Based on the above principles a new research agenda can be formulated to explore different forms of biological intelligence.

Incorporating gene ontology into fuzzy relational clustering of microarray gene expression data.

Abstract: The product of gene expression works together in the cell for each living organism in order to achieve different biological processes. Many proteins are involved in different roles depending on the environment of the organism for the functioning of the cell. In this paper, we propose gene ontology (GO) annotations based semi-supervised clustering algorithm called GO fuzzy relational clustering (GO-FRC) where one gene is allowed to be assigned to multiple clusters which are the most biologically relevant behavior of genes. In the clustering process, GO-FRC utilizes useful biological knowledge which is available in the form of a gene ontology, as a prior knowledge along with the gene expression data. The prior knowledge helps to improve the coherence of the groups concerning the knowledge field. The proposed GO-FRC has been tested on the two yeast ( Saccharomyces cerevisiae ) expression profiles datasets (Eisen and Dream5 yeast datasets) and compared with other state-of-the-art clustering algorithms. Experimental results imply that GO-FRC is able to produce more biologically relevant clusters with the use of the small amount of GO annotations.

The dynamics of a harvested predator-prey system with Holling type IV functional response.

Abstract: The paper aims to investigate the dynamical behavior of a predator-prey system with Holling type IV functional response in which both the species are subject to capturing. We mainly consider how the harvesting affects equilibria, stability, limit cycles and bifurcations in this system. We adopt the method of qualitative and quantitative analysis, which is based on the dynamical theory, bifurcation theory and numerical simulation. The boundedness of solutions, the existence and stability of equilibrium points of the system are further studied. Based on the Sotomayor's theorem, the existence of transcritical bifurcation and saddle-node bifurcation are derived. We use the normal form theorem to analyze the Hopf bifurcation. Simulation results show that the first Lyapunov coefficient is negative and a stable limit cycle may bifurcate. Numerical simulations are performed to make analytical studies more complete. This work illustrates that using the harvesting effort as control parameter can change the behaviors of the system, which may be useful for the biological management.

The need for a concept of shape homeostasis.

Abstract: A major step forward for developmental biology will be accomplished when someone figures out how to extend the concept of homeostasis to apply to shapes, in the sense of geometric properties of cells, tissues and organs. I propose that the biggest obstacle to this forward step is that biological researchers are not yet familiar with the properties of tensor variables, as compared with scalars. This key difference is that tensor properties can and usually do have different amounts in different directions, whereas scalar properties cannot vary with direction. Examples of tensor variables include stress, strain, curvature, permeability, and stiffness. Examples of scalar properties include chemical concentrations, osmotic pressure, hydrostatic pressure, adhesiveness and electrical voltage. Even D'Arcy Thompson treated mechanical tension (which is the classic example of a tensor variable) as if it were a scalar constant. This greatly reduced the number of geometric shapes that he could explain as being directly produced by forces. For example, in order to generate cylinders, surface contractions need to be twice as strong in one direction as compared with the perpendicular direction. Unless surface contractions vary with direction, only spheres can be generated. Another example of not distinguishing tensors from scalars is the use of suction pipettes to measure stresses of cell surfaces (for example, during cytokinesis). This method of measurement inescapably lumps together directional components of two different tensors (tension and stiffness) as if they were one scalar. Yet another obstacle was that certain scientists argued persuasively, but mistakenly, that attractor basins were evidence of minimization of thermodynamic free energy. Chemical concentrations have no special ability to generate gradients. Neither do any other scalar variables. As will be discussed below, repeated local equilibration of any quantitative variable will generate at least as good a gradient as diffusion can. It is misguided to think of scalars as being in any sense more quantitative than tensors. In fact, tensor variables can convey more information than chemical gradients, often faster and with less vulnerability to disturbance.

The emergent connectome in Caenorhabditis elegans embryogenesis.

Abstract: The relatively new field of connectomics provides us with a unique window into nervous system function. In the model organism Caenorhabditis elegans, this promise is even greater due to the relatively small number of cells (302) in its nervous system. While the adult C. elegans connectome has been characterized, the emergence of these networks in development has yet to be established. In this paper, we approach this problem using secondary data describing the birth times of terminally-differentiated cells as they appear in the embryo and a connectomics model for nervous system cells in the adult hermaphrodite. By combining these two sources of data, we can better understand patterns that emerge in an incipient connectome. This includes identifying at what point in embryogenesis the cells of a connectome first comes into being, potentially observing some of the earliest neuron-neuron interactions, and making comparisons between the formally-defined connectome and developmental cell lineages. An analysis is also conducted to root terminally-differentiated cells in their developmental cell lineage precursors. This reveals subnetworks with different properties at 300 min of embryogenesis. Additional investigations reveal the spatial position of neuronal cells born during pre-hatch development, both within and outside the connectome model, in the context of all developmental cells in the embryo. Overall, these analyses reveal important information about the birth order of specific cells in the connectome, key building blocks of global connectivity, and how these structures correspond to key events in early development.

Integration of intracellular signaling: Biological analogues of wires, processors and memories organized by a centrosome 3D reference system.

Abstract: Background Myriads of signaling pathways in a single cell function to achieve the highest spatio-temporal integration. Data are accumulating on the role of electromechanical soliton-like waves in signal transduction processes. Theoretical studies strongly suggest feasibility of both classical and quantum computing involving microtubules. Aim A theoretical study of the role of the complex composed of the plasma membrane and the microtubule-based cytoskeleton as a system that transmits, stores and processes information. Methods Theoretical analysis presented here refers to (i) the Penrose–Hameroff theory of consciousness (Orchestrated Objective Reduction; Orch OR), (ii) the description of the centrosome as a reference system for construction of the 3D map of the cell proposed by Regolini, (iii) the Heimburg–Jackson model of the nerve pulse propagation along axons’ lipid bilayer as soliton-like electro-mechanical waves. Results and conclusion The ideas presented in this paper provide a qualitative model for the decision-making processes in a living cell undergoing a differentiation process. Outlook This paper paves the way for the real-time live-cell observation of information processing by microtubule-based cytoskeleton and cell fate decision making.

Genome-wide analysis of H3K36me3 and its regulations to cancer-related genes expression in human cell lines.

Abstract: H3K36me3 is a histone modification known to mark active genes. To further understand the effects of H3K36me3 on gene expression levels, we develop predictive models to compute the correlation between the binding signal of H3K36me3 in each bin and the gene expression levels. We find that the bins with stronger H3K36me3 averaged-binding signals present higher correlative strengths with the expression levels of gene. And the higher correlative strengths appear in the downstream regions of the transcription start site. Moreover, we systematically compare the predictive abilities of 11 histone modifications to gene expression levels. The results show that H3K36me3 achieves a higher predictive ability than other modifications, and the higher predictive ability is robust across different mammalian cells and gene groups. Finally, in contrast to the two normal cell lines, our analysis finds that the predictive abilities of H3K36me3 are enhanced in 10 of the 13 bins for oncogenes and are decreased in 10 of 16 bins for tumor-suppressor genes in the cancer cell.

A discriminative test among the different theories proposed to explain the origin of the genetic code: The coevolution theory finds additional support.

Abstract: A discriminative statistical test among the different theories proposed to explain the origin of the genetic code is presented. Gathering the amino acids into polarity and biosynthetic classes that are the first expression of the physicochemical theory of the origin of the genetic code and the second expression of the coevolution theory, these classes are utilized in the Fisher’s exact test to establish their significance within the genetic code table. Linking to the rows and columns of the genetic code of probabilities that express the statistical significance of these classes, I have finally been in the condition to be able to calculate a χ2 value to link to both the physicochemical theory and to the coevolution theory that would express the corroboration level referred to these theories. The comparison between these two χ2 values showed that the coevolution theory is able to explain – in this strictly empirical analysis – the origin of the genetic code better than that of the physicochemical theory.

Prediction of replication sites in Saccharomyces cerevisiae genome using DNA segment properties: Multi-view ensemble learning (MEL) approach.

Abstract: Autonomous replication sequences (ARS) are essential for the replication of Saccharomyces cerevisiae genome. The content and context of ARS sites are distinct from other segments of the genome and these factors influence the conformation and thermodynamic profile of DNA that favor binding of the origin recognition complex proteins. Identification of ARS sites in the genome is a challenging task because of their organizational complexity and degeneracy present across the intergenic regions. We considered a few properties of DNA segments and divided them into multiple subsets (views) for computational prediction of ARS sequences. Our approach utilized these views for learning classification models in an ensemble manner and accordingly predictions were made. This approach maximized the prediction accuracy over the traditional way where all features are selected at once. Our study also revealed that major groove width and major groove depth are the most prominent properties that distinguished ARS from other segments of the genome. Our investigation also provides clue about the most suitable classifier for a given feature set, and this strategy may be useful for finding ARS in other closely related species.

Unique features of nucleotide and codon usage patterns in mycoplasmas revealed by information entropy.

Abstract: Currently, the comparison between GC usage pattern at the 3rd codon position and codon usage index is commonly used to estimate the roles of evolutionary forces in shaping synonymous codon usages, however, this kind of analysis often losses the information about the role of A/T usage bias in shaping synonymous codon usage bias. To overcome this limitation and better understand the interplay between nucleotide and codon usages for the evolution of bacteria at gene levels, in this study, we employed the information entropy method with some modification to estimate roles of nucleotide compositions in the overall codon usage bias for 18 mycoplasma species in combination with Davies-Bouldin index. At gene levels, the overall nucleotide usage bias represents A content as the highest, followed by T, G and C for mycoplasmas, resulting in a low GC content. This feature is universal across these species derived from different hosts, suggesting that the hosts have the limited impact on nucleotide usage bias of mycoplasmas. Information entropy and Davies-Bouldin index can better reveal that the nucleotide usage bias at the 3rd codon position is essential in shaping the overall nucleotide bias for all given mycoplasmas except M. pneumoniae M129. Davies-Bouldin index revealed that the 1st and 2nd codon position play more important role in synonymous codon usage bias than that of the 3rd position at gene levels. To our knowledge, this is the first comprehensive investigation into cooperation between nucleotide and codon usages for mycoplasma and extends our knowledge of the mechanisms that contribute to codon usage and evolution of this microorganism.

Mechanisms of directed evolution of morphological structures and the problems of morphogenesis.

Abstract: Morphogenesis mechanisms are considered from the point of view of complexity. It has been shown that the presence of long-range interactions between biologically important molecules is a necessary condition for the formation and stable operation of morphological structures. A quantum model of morphogenesis based on non-Archimedean analysis and the presence of long-range interactions between biologically important molecules has been constructed. This model shows that the evolution of morphological structures essentially depends on the availability of a priori information on these structures. Critical steps in evolution related to the most important morphological and behavioral findings have been analyzed; the results have shown that the implementation of such steps can only be explained within the framework of a partially directed evolution. Thus, the previously proposed model for a partially directed evolution is established for modeling the evolution of morphological structures.

Dynamic maternal gradients and morphogenetic networks in Drosophila early embryo.

Abstract: Here, we review the latest publications on dynamics and interpretation of morphogen gradients in Drosophila early embryo. The instructive cues provided by these gradients are further interpreted by target genes to enable correct cell fate specification. Moreover, recent studies point on the dynamic and active input from gradients themselves. Latest research has demonstrated that the decay of maternal gradients affects the positional dynamics of target gap gene expression. Study of temporal interpretation of the Bicoid (Bcd) morphogen signal revealed that the Bcd-dependent cell fate specification proceeds sequentially from the posterior towards the anterior. The ‘morphogenetic network’ of maternal and zygotic regulators functions to spatially organize expression of Bcd-dependent target genes. The analysis of molecular mechanisms of Bcd interaction with target enhancers showed that Bcd gradient induces different chromatin states depending on its concentration. All these findings significantly update the model of morphogen gradient interpretation and provide new perspectives to uncover mechanisms of pattern formation in early embryogenesis.

Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES.

Abstract: The purpose of this study was the estimation of ability of the so-called optimal descriptors calculated to be a tool to predict the antimicrobial activity of large pool of peptides. Traditional simplified molecular input-line entry system (SMILES) is an efficient tool to represent the molecular structure of different compounds. Quasi-SMILES represents an extension of traditional SMILES. This approach provides the possibility to involve different eclectic conditions related to analyzed endpoint in the modelling process. In addition, the quasi-SMILES can be used to represent structure of peptides via abbreviations of corresponding amino acids. In this study, quasi-SMILES represents sequences of amino acids in peptides that were tested as the basis to predict antimicrobial activity of 1581 peptides. Predictive potential of binary classification for antimicrobial activity for different splits is quite good when it comes to the training, invisible training, calibration, and validation sets. For the external validation sets, the statistical criteria are ranged: (i) sensitivity 0.82–097; (ii) specificity 0.88–0.99; (iii) accuracy 0.87–0.98; and (iv) Matthews correlation coefficient 0.73–0.97. The suggested optimal descriptors calculated with data on composition of amino acids in peptides can be a tool to predict antimicrobial activity of peptides.

A hyperbolastic type-I diffusion process: Parameter estimation by means of the firefly algorithm.

Abstract: A stochastic diffusion process, whose mean function is a hyperbolastic curve of type I, is presented. The main characteristics of the process are studied and the problem of maximum likelihood estimation for the parameters of the process is considered. To this end, the firefly metaheuristic optimization algorithm is applied after bounding the parametric space by a stagewise procedure. Some examples based on simulated sample paths and real data illustrate this development.

Cell differentiation processes as spatial networks: Identifying four-dimensional structure in embryogenesis.

Abstract: One overarching principle of eukaroytic development is the generative spatial emergence and self-organization of cell populations. As cells divide and differentiate, they and their descendents form a spatiotemporal explicit and increasingly compartmentalized complex system. Yet despite this comparmentalization, there is selective functional overlap between these structural components. While contemporary tools such as lineage trees and molecular signaling networks prvide a window into this complexity, they do not characterize embryogenesis as a global process. Using a four-dimensional spatial representation, major features of the developmental process are revealed. To establish the role of developmental mechanisms that turn a spherical embryo into a highly asymmetrical adult phenotype, we can map the outcomes of the cell division process to a complex network model. This representational model provides information about the top-down mechanisms relevant to the differentiation process. In a complementary manner, looking for phenomena such as superdiffusive positioning and sublineage-based anatomical clustering incorporates dynamic information to our parallel view of embryogenesis. Characterizing the spatial organization and geometry of embryos in this way allows for novel indicators of developmental patterns both within and between organisms.

Towards the development of robot immune system: A combined approach involving innate immune cells and T-lymphocytes.

Abstract: Mobile robots in uncertain and unstructuredenvironments frequently encounter faults. Therefore, an effective fault detection and recovery mechanism is required. One can possibly investigate natural systems to seek inspiration to develop systems that can handle such faults. Authors, in this pursuit, have explored the possibility of designing an artificial immune system, called Robot Immune System (RIS), to maintain a robot’s internal health-equilibrium. This contrasts with existing approaches in which specific robotic tasks are performed instead of developing a self-healing robot. In this respect, a fault detection and recovery methodology based on innate and adaptive immune functions has been successfully designed and developed. The immuno-inspired methodology is applied to a simulated robot using Robot Operating System and Virtual Robot Experimentation Platform. Through extensive simulations in increasingly difficult scenarios, the RIS has proven successful in autonomously detecting the abnormal behaviors, performing the recovery actions, and maintaining the homeostasis in the robot. In addition to being multi-tiered, the developed RIS is also a non-deterministic and population-based system.

Metabolism is the tie: The Bertalanffy-type cancer growth model as common denominator of various modelling approaches.

Abstract: Cancer or tumour growth has been addressed from a variety of mathematical modelling perspectives in the past. Examples are single variable growth models, reaction diffusion models, compartment models, individual cell-based models, clonal competition models, to name only a few. In this paper, we show that the so called Bertalanffy-type growth model is a macroscopic model variant that can be conceived as an optimal condensed modelling approach that to a high degree preserves complexity with respect to the aforementioned more complex modelling variants. The derivation of the Bertalanffy-type model is crucially based on features of metabolism. Therefore, this model contains a shape parameter that can be interpreted as a resource utilisation efficiency. This shape parameter reflects features that are usually captured in much more complex models. To be specific, the shape parameter is related to morphological structures of tumours, which in turn depend on metabolic conditions. We, furthermore, show that a single variable variant of the Bertalanffy-type model can straightforwardly be extended to a multiclonal competition model. Since competition is crucially based on available shared or clone-specific resources, the metabolism-based approach is an obvious candidate to capture clonal competition. Depending on the specific context, metabolic reprogramming or other oncogene driven changes either lead to a suppression of cancer cells or to an improved competition resulting in outgrowth of tumours. The parametrisation of the Bertalanffy-type growth model allows to account for this observed variety of cancer characteristics. The shape parameter, conceived as a classifier for healthy and oncogenic phenotypes, supplies a link to survival and evolutionary stability concepts discussed in demographic studies, such as opportunistic versus equilibrium strategies.

Hyper-restorative non-equilibrium state as a driving force of biological morphogenesis.

Abstract: In 1935 Ervin Bauer formulated a basic principle of organization of living matter defined as the stable non-equilibrium state. The homeostatic stable non-equilibrium is realized internally by selecting the trajectories maintaining stable configurations from those disturbing it and supported by the dynamical structure of metabolism consisting of metabolic cycles, feedback loops and feedforward constraints. In the developing systems, according to Bauer, the principle of stable non-equilibrium is transformed into the principle of increasing external work which is grounded in the hyper-restorative non-equilibrium. The basis of this principle, as formulated by Lev Beloussov, is a hyper-restorative process during conformational relaxation of biomacromolecules which adds extra energy in the system and governs its complexification in which a new optimal coordinate pattern is searched. At the quantum level, this complexification is determined by the parametric refinement process in the field of possibilities. The complexification process takes place at the level of the cytoskeleton which represents a macroscopic enzymatic system and can generate differentiation waves that spread between cells. This may be associated with the function of cytoskeleton of transmitting signals and generating impulses. Different types of waves during morphogenesis correspond to different ranges of wavelengths of emission, from biophoton radiation to the hypersound waves in transmission of neural impulses. It is concluded that biological morphogenesis is based on the hyper-restorative non-equilibrium supported by the functional structure of the cytoskeleton.

A graph-based approach to analyze flux-balanced pathways in metabolic networks.

Abstract: An elementary flux mode (EFM) is a pathway with minimum set of reactions that are functional in steady-state constrained space. Due to the high computational complexity of calculating EFMs, different approaches have been proposed to find these flux-balanced pathways. In this paper, an approach to find a subset of EFMs is proposed based on a graph data model. The given metabolic network is mapped to the graph model and decisions for reaction inclusion can be made based on metabolites and their associated reactions. This notion makes the approach more convenient to categorize the output pathways. Implications of the proposed method on metabolic networks are discussed.