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In this paper we propose an extension of a systems/synthetic biology modelling framework based on P systems that explicitly includes modularity. Modularisation in cellular systems can be produced by chemical specificity, spatial localisation and/or temporal modulation within cellular compartments. The first two of these modularisation features, the focus of this paper, can be easily specified and analysed in P systems using sets of rewriting rules to describe chemical specificity and membranes to represent spatial localisation. Our methodology enables the assembly of cell systems biology models by combining modules which represent functional subsystems. A case study consisting of a bacterial colony system is presented to illustrate our approach.

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules.

Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment.

The comprehension of the innate immune system of cell populations is not only of interest to understand systems in vivo but also in vitro, for example, in the control of the release of viral particles for the production of vaccines. In this report I introduce a model, based on dynamical networks, that simulates the cell signaling responsible for this innate immune response and its effect on the infection spread and virus production. The central motivation is to represent a cell population that is constantly mixed in a bio-reactor where there is a cell-to-cell signaling of cytokines (which are proteins responsible for the activation of the antiviral response inside the cell). Such signaling allows the definition of clusters of linked immune cells. Additionally, depending on the density of links, it is possible to identify critical threshold parameters associated to a percolation phase transition. I show that the control of this antiviral response is equivalent to a percolation process.

Proteomics technology is based on the vast analytical power for protein/peptide identification and quantification offered by modern mass spectrometry coupled with hyphenated separation techniques such as two-dimensional gel electrophoresis (2DE) and micro- or nano-scale multidimensional liquid chromatography. The rapid growth of proteomics field provides an array of new tools for the integration of traditional Chinese medicine (TCM) with modern technology and systems biology, and is potentially advancing the progress of modernization and internationalization of TCM. Cho, in this issue of the American Journal of Chinese Medicine, highlights the recent application of 2DE-based and bottom-up proteomics in Chinese medicine research, including the exploration of pharmacological mechanisms of the actions of TCM, the facilitation of herb authentication and identification, and the profiling of protein expression following acupuncture treatment in animal models. Recent development in proteomics has provided further refinement on the analysis of proteins posttranslational modifications as well as quantitative comparison of different proteomes, and enabled the study of proteomes of specific diseases or biological processes under clinically relevant conditions. It is conceivable that the application of technologies developed in proteomics, genomics and metabonomics in the clinical practice and basic research of Chinese medicine will eventually lead to the reconciliation and integration of TCM and contemporary medicine. Chinese medicine is fundamentally a highly personalized medicine; perhaps it is time to embrace the arrival of TCM OMICS era in Chinese medicine research.

Traditional Chinese medicine (TCM), an alternative medicine, focuses on the treatment of human disease via the integrity of the close relationship between body and syndrome analysis. It remains a form of primary care in most Asian countries and its characteristics showcase the great advantages of personalized medicine. Although this approach to disease diagnosis, prognosis and treatment has served the medical establishment well for thousands of years, it has serious shortcomings in the era of modern medicine that stem from its reliance on reductionist principles of experimentation and analysis. In this way, systems biology offers the potential to personalize medicine, facilitating the provision of the right care to the right patient at the right time. We expect that systems biology will have a major impact on future personalized therapeutic approaches which herald the future of medicine. Here we summarize current trends and critically review the potential limitations and future prospects of such treatments. Some characteristic examples are presented to highlight the application of this groundbreaking platform to personalized TCM as well as some of the necessary milestones for moving systems biology of a state-of-the-art nature into mainstream health care.

Rapid progress of experimental biology has provided a huge flow of quantitative data, which can be analyzed and understood only through the application of advanced techniques recently developed in theoretical sciences. On the other hand, synthetic biology enabled us to engineer biological models with reduced complexity. In this review we discuss that a multidisciplinary approach between this sciences can lead to deeper understanding of the underlying mechanisms behind complex processes in biology. Following the mini symposia "Noise and oscillations in biological systems" on Physcon 2011 we have collected different research examples from theoretical modeling, experimental and synthetic biology.

Systems biology aims to describe gene regulatory networks at both experimental and theoretical levels. Mathematical formalisms used at present to describe the behavior of genetic networks range from stochastic to deterministic. The stochastic approach is further subdivided and moves from Langevin to the Master equation. This review presents the Master equation approach.

The present paper describes a novel approach to performing feature extraction and classification in possibly layered circular structures, as seen in two-dimensional cutting planes of three-dimensional tube-shaped objects. The algorithm can therefore be used to analyze histological specimens of blood vessels as well as intravascular ultrasound (IVUS) datasets. The approach uses a radial signal-based extraction of textural features in combination with methods of machine learning to integrate a priori domain knowledge. The algorithm in principle solves a two-dimensional classification problem that is reduced to parallel viable time series analysis. A multiscale approach hereby determines a feature vector for each analysis using either a Wavelet-transform (WT) or a S-transform (ST). The classification is done by methods of machine learning — here support vector machines. A modified marching squares algorithm extracts the polygonal segments for the two-dimensional classification. The accuracy is above 80% even in datasets with a considerable quantity of artifacts, while the mean accuracy is above 90%. The benefit of the approach therefore mainly lies in its robustness, efficient calculation, and the integration of domain knowledge.

This paper proves, via an analytical approach, that 170 (out of 256) Boolean CA rules in a one-dimensional cellular automata (CA) are time-reversible in a generalized sense. The dynamics on each attractor of a time-reversible rule N is exactly mirrored, in both space and time, by its bilateral twin ruleN^†. In particular, all 69 period-1 rules, 17 (out of 25) period-2 rules, and 84 (out of 112) Bernoulli rules are time-reversible.

The remaining 86 CA rules are time-irreversible in the sense that N and N^† mirror their dynamics only in space, but not in time. In this case, each attractor of N defines a unique arrow of time.

A simple "time-reversal test" is given for testing whether an attractor of a CA rule is time-reversible or time-irreversible. For a time-reversible attractor of a CA rule N the past can be uniquely recovered from the future of N^†, and vice versa. This remarkable property provides 170 concrete examples of CA time machines where time travel can be routinely achieved by merely hopping from one attractor to its bilateral twin attractor, and vice versa. Moreover, the time-reversal property of some local rules can be programmed to mimic the matter–antimatter "annihilation" or "pair-production" phenomenon from high-energy physics, as well as to mimic the "contraction" or "expansion" scenarios associated with the Big Bang from cosmology.

Unlike the conventional laws of physics, which are based on a unique universe, most CA rules have multiple universes (i.e. attractors), each blessed with its own laws. Moreover, some CA rules are endowed with both time-reversible attractors and time-irreversible attractors.

Using an analytical approach, the time-τ return map of each Bernoulli στ-shift attractor of all 112 Bernoulli rules are shown to obey an ultra-compact formula in closed form, namely,.

or its inverse map.

These maps completely characterize the time-asymptotic (steady state) behavior of the nonlinear dynamics on the attractors. In-depth analysis of all but 18 global equivalence classes of CA rules have been derived, along with their basins of attraction, which characterize their transient regimes.

Above all, this paper provides a rigorous nonlinear dynamics foundation for a paradigm shift from an empirical-based approach à la Wolfram to an attractor-based analytical theory of cellular automata.

Gene regulatory networks set a second order approximation to genetics understanding, where the first order is the knowledge at the single gene activity level. With the increasing number of sequenced genomes, including humans, the time has come to investigate the interactions among myriads of genes that result in complex behaviors. These characteristics are included in the novel discipline of Systems Biology. The composition and unfolding of interactions among genes determine the activity of cells and, when is considered during development, the organogenesis. Hence the interest of building representative networks of gene expression and their time evolution, i.e. the structure as the network dynamics, for certain development processes. The complexity of this kind of problems makes imperative to analyze the problem in the field of network theory and the evolutionary dynamics of complex systems.

All this has led us to investigate, in a first step, the evolutionary dynamics in generic networks. Thus, the results can be used in experimental researches in the field of Systems Biology. This research aims to decode the transformation rules governing the evolutionary dynamics in a network. To do this, a genetic algorithm has been implemented in which, starting from initial and ending network states, it is possible to determine the transformation dynamics between these states by using simple acting rules. The network description is the following: (a) The network node values in the initial and ending states can be active or inactive; (b) The network links can act as activators or repressors; (c) A set of rules is established in order to transform the initial state into the ending one; (d) Due to the low connectivity, frequently observed, in gene regulatory networks, each node will hold a maximum of three inputs with no restriction on outputs. The "chromosomes" of the genetic algorithm include two parts, one related to the node links and another related to the transformation rules.

The implemented rules are based on certain genetic interactions behavior. The rules and their combinations are compound by logic conditions and set the bases to the network motifs formation, which are the building blocks of the network dynamics.

The implemented algorithm is able to find appropriate dynamics in complex networks evolution among different states for several cases.

The synchronization for two k-valued logical networks of the same dimensions is studied in this paper. First, based on the theory of semi-tensor product of matrices, the master-slave systems (two k-valued logical networks) are converted into discrete-time systems. Second, both open-loop control and feedback control are provided to make the slave network synchronize with the master k-valued logical network. Finally, examples are provided to illustrate the efficiency of the obtained results.

In general biologists are not accustomed to formulating biological problems in the precise mathematical terms that are required to solve the problems analytically or numerically. Although many computational tools for systems biology have been developed recently, our observations indicate that many of these tools are powerful only in the hands of those who know a lot about how to use them. For most biologists, the tools have a protracted learning curve and unfriendly user interface that often diminish their likelihood of being used.

Our long-term goal is to build a knowledge system that allows biologists to synthesize complex biological systems via natural language interactions, and the system is able to generate the corresponding mathematical descriptions so that the often cumbersome communication process between biologists and mathematicians/engineers in formulating complex biological problems in mathematic terms can be performed more easily.

To focus, the first goal in this research is to build a knowledge system prototype that focuses on transport related biological problems that occur from the cellular to tissue level. We address specifically two inter-related problems: (1) Provision of an intelligent system that is capable of automatically synthesizing smaller components into more complex systems; Provision of a user-friendly and natural language interface.

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as “model agnostic” in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction–diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.

Based on our experience in kinetic modeling of coupled multiple metabolic pathways, we propose a generic rate equation for the dynamical modeling of metabolic kinetics. It is symmetric for forward and backward reactions. Its Michaelis-Menten-King-Altman form makes the kinetic parameters (or functions) easy to relate to experimental values in the database and to use in computation. In addition, such a uniform form is ready to arbitrary number of substrates and products with different stiochiometry. We explicitly show how to obtain such rate equations rigorously for three well-known binding mechanisms. Hence, the proposed rate equation is formally exact under the quasi-steady state condition. Various features of this generic rate equation are discussed. In particular, for irreversible reactions, the product inhibition which directly arises from enzymatic reaction is eliminated in a natural way. We also discuss how to include the effects of modifiers and cooperativity.

With the rapid growth of microarray data, it has become a hot topic to reveal complex behaviors and functions of life system by studying the relationships among genes. In the process of reverse network modeling, the relationships with less relevance are generally not considered by determining a threshold when the relationships among genes are mined. However, there are no effective methods to determine the threshold up to now. It is worthwhile to note that the phenotypes of genetic diseases are generally regarded as external representation of the functions of genes. Therefore, two types of phenotype networks are constructed from gene and disease views, respectively, and through comparing these two types of phenotype networks, the threshold of gene network corresponding to a certain disease can be determined when their similarity reaches to maximum. Because the gene network is determined based on the relationships among phenotypes and phenotypes are external representation of the functions of genes, it is considered that relationships in the gene network may show functional relationships among genes in biological system. In this work, the thresholds 0.47 and 0.48 of gene network are determined based on Parkinson disease phenotypes. Furthermore, the validity of these thresholds is verified by the specificity and susceptibility of phenotype networks. Also, through comparing the structural parameters of gene networks for normal and disease stage at different thresholds, significant difference between the two gene networks at threshold 0.47 or 0.48 is found. The significant difference of structural parameters further verifies the efficiency of this method.

This article denotes the strengths and resources of the National Taiwan University.

For the month of August 2021, APBN looks at some of the progress made in cancer research. In Features, we have Yie Hou Lee and Michael Birnbaum from the Singapore-MIT Alliance for Research and Technology Critical Analytics for Manufacturing Personalized-Medicine (SMART-CAMP) to share about the future of CAR T cell manufacturing. Next, a team of researchers from the National Neuroscience Institute, National University of Singapore, and the Duke-NUS Medical School enlightens us on the difficulty of treating glioblastoma brain tumours and how they plan to address its critical issues. Then we have Dr. Chi-Jui Liu and Hsiao Yun Lu to talk about hereditary cancers and how we may improve our odds in this game of roulette. In Columns, we have an analysis by Dr. Ping-Chung Leung on the integrative use of Traditional Chinese Medicine in managing treatment outcomes of COVID-19 patients and a reflection by Dr. Chris Nave on the lessons we can take away from the development of COVID-19 vaccines. Finally, in Spotlights, we share highlights from the Vaccines World Summit 2021 and an interview with Mr. Abel Ang, Group Chief Executive of Advanced MedTech on how their new venture AbAsia Biolabs can help meet Singapore’s need for increased COVID-19 test kits as we enter a new normal.

We provide a systematic analysis of a biological system, the microbial pathogen Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and biocomputational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane and cytosol) of Mtb. We report the compartmentalization of 1,044 identified proteins using proteomics methods. Genome-based biological network analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

Boolean models represent a drastic simplification of complex biomolecular systems, and yet accurately predict system properties, e.g., effective control strategies. Why is this? Parameter robustness has been highlighted as a general feature of biomolecular systems and may play an important role in the accuracy of Boolean models. We argue here that a useful way to view a system’s controllability properties is through its repertoire of self-sustaining positive circuits (stable motifs). We examine attractor control and self-sustaining circuits within the cell cycle restriction switch, a bistable regulatory circuit that allows or prevents entry into the cell cycle. We explore this system using three models: a previously published Boolean model, a Hill kinetics model that we construct from the Boolean model using the HillCube methodology, and a reaction-based model we construct from the literature. We highlight the robustness of stable motifs across these three levels of modeling detail. We also show how consideration of control-robust regulatory circuits can aid in parameter specification.

A growing number of computational models have been proposed over the last few years to help explain the therapeutic effect of deep brain stimulation (DBS) on motor disorders in Parkinson's disease (PD). However, none of these has been able to explain in a convincing manner the physiological mechanisms underlying DBS. Can these models really contribute to improving our understanding? The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD.