Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.     

Quantum-like interference effect in gene expression: glucose-lactose destructive interference

In this note we illustrate on a few examples of cells and proteins behavior that microscopic biological systems can exhibit a complex probabilistic behavior which cannot be described by classical probabilistic dynamics. These examples support authors conjecture that behavior of microscopic biological systems can be described by quantum-like models, i.e., models inspired by quantum-mechanics. At the same time we do not couple quantum-like behavior with quantum physical processes in bio-systems. We present arguments that such a behavior can be induced by information complexity of even smallest bio-systems, their adaptivity to context changes. Although our examples of the quantum-like behavior are rather simple (lactose-glucose interference in E. coli growth, interference effect for differentiation of tooth stem cell induced by the presence of mesenchymal cell, interference in behavior of PrP(C) and PrP(Sc) prions), these examples may stimulate the interest in systems biology to quantum-like models of adaptive dynamics and lead to more complex examples of nonclassical probabilistic behavior in molecular biology.     

Interaction between pancreatic β cell and electromagnetic fields: A systematic study toward finding the natural frequency spectrum of β cell system

Interaction between biological systems and environmental electric or magnetic fields has gained attention during the past few decades. Although there are a lot of studies that have been conducted for investigating such interaction, the reported results are considerably inconsistent. Besides the complexity of biological systems, the important reason for such inconsistent results may arise due to different excitation protocols that have been applied in different experiments. In order to investigate carefully the way that external electric or magnetic fields interact with a biological system, the parameters of excitation, such as intensity or frequency, should be selected purposefully due to the influence of these parameters on the system response. In this study, pancreatic β cell, the main player of blood glucose regulating system, is considered and the study is focused on finding the natural frequency spectrum of the system using modeling approach. Natural frequencies of a system are important characteristics of the system when external excitation is applied. The result of this study can help researchers to select proper frequency parameter for electrical excitation of β cell system. The results show that there are two distinct frequency ranges for natural frequency of β cell system, which consist of extremely low (or near zero) and 100–750 kHz frequency ranges. There are experimental works on β cell exposure to electromagnetic fields that support such finding.     

Quantifying and analyzing the network basis of genetic complexity

Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.     

Evolution of structure,order and complexity in biological systems: Part III of a general theory

In this, Part III of a general theory, the large-scale features of evolution of structure, order, and complexity are considered as characteristic features of the biological state of matter. This starts with a rigorous formal definition of structure, classes of structural order, complexity, measures of complexity, and how these arise through evolution by a cumulative process of storing information in memory systems. Three such memory systems have evolved: the genetic memory, the immune memory, and the memories of the nervous system. The evolution, characteristic parameters and the limitations of these memory systems are explored. From these considerations emerge the large-scale features of the evolutionary pathways of biological structure, function, and complexity.     

Nanotoxicity: the growing need for in vivo study

Nanotoxicology is emerging as an important subdiscipline of nanotechnology. Nanotoxicology refers to the study of the interactions of nanostructures with biological systems with an emphasis on elucidating the relationship between the physical and chemical properties (e.g. size, shape, surface chemistry, composition, and aggregation) of nanostructures with induction of toxic biological responses. In the past five years, a majority of nanotoxicity research has focused on cell culture systems; however, the data from these studies could be misleading and will require verification from animal experiments. In vivo systems are extremely complicated and the interactions of the nanostructures with biological components, such as proteins and cells, could lead to unique biodistribution, clearance, immune response, and metabolism. An understanding of the relationship between the physical and chemical properties of the nanostructure and their in vivo behavior would provide a basis for assessing toxic response and more importantly could lead to predictive models for assessing toxicity. In this review article, we describe the assumptions and challenges in the nanotoxicity field and provide a rationale for in vivo animal studies to assess nanotoxicity.     

In vivo bioluminescence imaging for integrated studies of infection

Understanding biological processes in the context of intact organ systems with fine temporal resolution has required the development of imaging strategies that reveal cellular and molecular changes in the living body. Reporter genes that confer optical signatures on a given biological process have been used widely in cell biology and have been used more recently to interrogate biological processes in living animal models of human biology and disease. The use of internal biological sources of light, luciferases, to tag cells, pathogens, and genes has proved to be a versatile tool to provide in vivo indicators that can be detected externally. The application of this technology to the study of animal models of infectious disease has not only provided insights into disease processes, but has also revealed new mechanisms by which pathogens may avoid host defences during infection.     

Mathematical Models of Cell Motility

Cell motility is an essential biological action in the creation, operation and maintenance of our bodies. Developing mathematical models elucidating cell motility will greatly advance our understanding of this fundamental biological process. With accurate models it is possible to explore many permutations of the same event and concisely investigate their outcome. While great advancements have been made in experimental studies of cell motility, it now has somewhat fallen on mathematical models to taking a leading role in future developments. The obvious reason for this is the complexity of cell motility. Employing the processing power of today’s computers will give researches the ability to run complex biophysical and biochemical scenarios, without the inherent difficulty and time associated with in vitro investigations. Before any great advancement can be made, the basics of cell motility will have to be well-defined. Without this, complicated mathematical models will be hindered by their inherent conjecture. This review will look at current mathematical investigations of cell motility, explore the reasoning behind such work and conclude with how best to advance this interesting and challenging research area.     

Modeling the interaction of gametes and embryos with the maternal genital tract: From in vivo to in silico

Understanding the complex interaction between gametes or embryos and the maternal genital tract requires the use of experimental models. The selection of the right model is an important task to undertake, and despite many new developments in this area, an ideal model system has not yet been developed. In this review article, we focus on how the most appropriate model species and model system can be selected, each with its particular advantages and disadvantages. Selection criteria need to be based on the evaluation of the aim of the experiment, the tools that are available to the scientist, and the ethics that are involved in working with particular animal species and model systems. Society and politics direct scientists to “Refine, Reduce, and Replace” the use of experimental animals, which means that the use of in vivo models is increasingly being discouraged. An in vivo model allows experimentation in the full biological environment of a living organism. In contrast with in vivo models, in vitro models are less complex and are abstracts of in vivo systems, leading often to results that are different from the in vivo situation. If an investigator could understand all the components of a complex biological system and re-create them as individual smaller models in a computer, he or she could create in silico models that would completely represent the complexity of in vivo models. We predict that in the future, in silico modeling will be the natural departure from in vivo, in situ, and in vitro modeling approaches. In addition to numerous advantages that this modeling approach can bring to studying maternal interaction with gametes and embryo, it is perhaps the only true alternative method to animal experimentation.     

Spontaneous switching of frequency-locking by periodic stimulus in oscillators of plasmodium of the true slime mold

Microfabrication technique was used to construct a model system with a living cell of plasmodium of the true slime mold, Physarum polycephalum, a living coupled oscillator system. Its parameters can be systematically controlled as in computer simulations, so that results are directly comparable to those of general mathematical models. As the first step, we investigated responses in oscillatory cells, the oscillators of the plasmodium, to periodic stimuli by temperature changes to elucidate characteristics of the cells as nonlinear systems whose internal dynamics are unknown because of their complexity. We observed that the forced oscillator of the plasmodium show 1:1, 2:1, 3:1 frequency locking inside so-called Arnold tongues regions as well as in other nonlinear systems such as chemical systems and other biological systems. In addition, we found spontaneous switching behavior from certain frequency locking states to other states, even under certain fixed parameters. This technique can be applied to more complex systems with multiple elements, such as coupled oscillator systems, and would be useful to investigate complicated phenomena in biological systems such as information processing.     

Cellular signaling identifiability analysis: A case study

Two primary purposes for mathematical modeling in cell biology are (1) simulation for making predictions of experimental outcomes and (2) parameter estimation for drawing inferences from experimental data about unobserved aspects of biological systems. While the former purpose has become common in the biological sciences, the latter is less common, particularly when studying cellular and subcellular phenomena such as signaling—the focus of the current study. Data are difficult to obtain at this level. Therefore, even models of only modest complexity can contain parameters for which the available data are insufficient for estimation. In the present study, we use a set of published cellular signaling models to address issues related to global parameter identifiability. That is, we address the following question: assuming known time courses for some model variables, which parameters is it theoretically impossible to estimate, even with continuous, noise-free data? Following an introduction to this problem and its relevance, we perform a full identifiability analysis on a set of cellular signaling models using DAISY (Differential Algebra for the Identifiability of SYstems). We use our analysis to bring to light important issues related to parameter identifiability in ordinary differential equation (ODE) models. We contend that this is, as of yet, an under-appreciated issue in biological modeling and, more particularly, cell biology.     

体外模型的发展及其在鲸豚研究中的应用

体外补充替代模型“细胞系”为生命科学研究提供了新的平台,在一定程度上突破了科学研究中伦理、法律、动物福利和动物保护等的限制,从细胞和分子视角更深层次地揭示复杂生命体的生物效应和 调控机制。尤其对于濒危动物,细胞系的建立与超低温冷冻技术相结合,既可以保存濒危动物具有生物表达活性的遗传种质,又可以提供体外保育研究的新平台（如动物毒理学实验）,对动物保护意义 重大。目前细胞培养体系已作为多功能平台被应用于鲸豚类细胞遗传学、病毒学和毒理学的相关研究中,但从物种和组织来源以及细胞类型来看,能长期稳定传代的鲸豚类细胞系仍较单一。优化细胞培 养条件,运用鲸豚类体外细胞揭示更多的生命机制问题,仍是当前鲸豚类体外细胞模型研究所面临的挑战。本文对动物体外模型及其在鲸豚研究中的应用进行了概述,以期推动该技术在鲸豚保育研究中 的创新和发展。     

Synthetic cells and organelles: compartmentalization strategies

The recent development of RNA replicating protocells and capsules that enclose complex biosynthetic cascade reactions are encouraging signs that we are gradually getting better at mastering the complexity of biological systems. The road to truly cellular compartments is still very long, but concrete progress is being made. Compartmentalization is a crucial natural methodology to enable control over biological processes occurring within the living cell. In fact, compartmentalization has been considered by some theories to be instrumental in the creation of life. With the advancement of chemical biology, artificial compartments that can mimic the cell as a whole, or that can be regarded as cell organelles, have recently received much attention. The membrane between the inner and outer environment of the compartment has to meet specific requirements, such as semi‐permeability, to allow communication and molecular transport over the border. The membrane can either be built from natural constituents or from synthetic polymers, introducing robustness to the capsule.