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.     

组学技术在产油微生物中的应用

微生物油脂是未来燃料和食品用油的重要潜在资源.近年来,随着系统生物学技术的快速发展,从全局角度理解产油微生物生理代谢及脂质积累的特征成为研究热点.组学技术作为系统生物学研究的重要工具被广泛用于揭示产油微生物脂质高效生产的机制研究中,这为产油微生物理性遗传改造和发酵过程控制提供了基础.文中对组学技术在产油微生物中的应用概...     

The influence of cultivation methods on Shewanella oneidensis physiology and proteome expression

High-throughput analyses that are central to microbial systems biology and ecophysiology research benefit from highly homogeneous and physiologically well-defined cell cultures. While attention has focused on the technical variation associated with high-throughput technologies, biological variation introduced as a function of cell cultivation methods has been largely overlooked. This study evaluated the impact of cultivation methods, controlled batch or continuous culture in bioreactors versus shake flasks, on the reproducibility of global proteome measurements in Shewanella oneidensis MR-1. Variability in dissolved oxygen concentration and consumption rate, metabolite profiles, and proteome was greater in shake flask than controlled batch or chemostat cultures. Proteins indicative of suboxic and anaerobic growth (e.g., fumarate reductase and decaheme c-type cytochromes) were more abundant in cells from shake flasks compared to bioreactor cultures, a finding consistent with data demonstrating that “aerobic” flask cultures were O₂ deficient due to poor mass transfer kinetics. The work described herein establishes the necessity of controlled cultivation for ensuring highly reproducible and homogenous microbial cultures. By decreasing cell to cell variability, higher quality samples will allow for the interpretive accuracy necessary for drawing conclusions relevant to microbial systems biology research.     

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.     

Metabolic and evolutionary engineering research in Turkey and beyond

This review discusses metabolic engineering research with an emphasis on evolutionary (whole cell and protein) engineering, which is an inverse metabolic engineering approach. For each section on metabolic, inverse metabolic and evolutionary engineering research, a general review of the major global studies in the literature is made and research examples from Turkey are given and discussed. It is expected that with the rapid development in systems biology and the novel powerful analytical technologies to identify the genetic basis of cellular phenotypes, metabolic and evolutionary engineering research will become widespread and increasingly important in Turkey, following global scientific trends.     

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.     

Metabolic engineering for the utilization of carbohydrate portions of lignocellulosic biomass

The petrochemical industry has grown to meet the need for massive production of energy and commodities along with an explosive population growth; however, serious side effects such as greenhouse gas emissions and global warming have negatively impacted the environment. Lignocellulosic biomass with myriad quantities on Earth is an attractive resource for the production of carbon-neutral fuels and chemicals through environmentally friendly processes of microbial fermentation. This review discusses metabolic engineering efforts to achieve economically feasible industrial production of fuels and chemicals from microbial cell factories using the carbohydrate portion of lignocellulosic biomass as substrates. The combined knowledge of systems biology and metabolic engineering has been applied to construct robust platform microorganisms with maximum conversion of monomeric sugars, such as glucose and xylose, derived from lignocellulosic biomass. By comprehensively revisiting carbon conversion pathways, we provide a rationale for engineering strategies, as well as their features, feasibility, and recent representative studies. In addition, we briefly discuss how tools in systems biology can be applied in the field of metabolic engineering to accelerate the development of microbial cell factories that convert lignocellulosic biomass into carbon-neutral fuels and chemicals with economic feasibility.     

An argument for mechanism-based statistical inference in cancer

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.     

Physiology of microbial cells and metabolic engineering

This review is devoted to the problems of the physiology and cell biology of microorganisms in relation to metabolic engineering. The latter is considered as a branch of fundamental and applied biotechnology aimed at controlling microbial metabolism by methods of genetic engineering and classical genetics and based on intimate knowledge of cell metabolism. Attention is also given to the problems associated with the metabolic limitation of microbial biosyntheses, analysis and control of metabolic fluxes, rigidity of metabolic pathways, the role of pleiotropic (global) regulatory systems in the control of metabolic fluxes, and prospects of physiological and evolutionary approaches in metabolic engineering.     

基于细胞亚群调控提升生物合成效率的研究进展

采用合成生物学与代谢工程技术设计与构建微生物细胞工厂是实现化学品绿色生物制造的重要方法。微生物发酵过程中低产细胞亚群和非生产细胞亚群由于代谢负担轻,更加具有生长优势,会降低产物合成的综合效率。目前,基于响应产物浓度的生物传感器,偶联产物合成与生长的细胞亚群调控系统有助于解决这个问题。综述了细胞亚群调控系统设计和构建的常用方法,重点讨论了目前细胞亚群调控系统存在的问题及其解决策略。     

Physiology of microbial cell and metabolic engineering

This review is devoted to the problems of the physiology and cell biology of microorganisms in relation to metabolic engineering. The latter is considered as a branch of fundamental and applied biotechnology aimed at controlling microbial metabolism by methods of genetic engineering and classical genetics and based on intimate knowledge of cell metabolism. Attention is also given to the problems associated with the metabolic limitation of microbial biosyntheses, analysis and control of metabolic fluxes, rigidity of metabolic pathways, the role of pleiotropic (global) regulatory systems in the control of metabolic fluxes, and prospects of physiological and evolutionary approaches in metabolic engineering.     

Synthetic biology of minimal systems

“Synthetic biology” is a concept that has developed together with, or slightly after, “systems biology”. But while systems biology aims at the full understanding of large systems by integrating more and more details into their models, synthetic biology phrases different questions, namely: what particular biological function could be obtained with a certain known subsystem of reduced complexity; can this function be manipulated or engineered in artificial environments or genetically modified organisms; and if so, how? The most prominent representation of synthetic biology has so far been microbial engineering by recombinant DNA technology, employing modular concepts known from information technology. However, there are an increasing number of biophysical groups who follow similar strategies of dissecting cellular processes and networks, trying to identify functional minimal modules that could then be combined in a bottom-up approach towards biology. These modules are so far not as particularly defined by their impact on DNA processing, but rather influenced by core fields of biophysics, such as cell mechanics and membrane dynamics. This review will give an overview of some classical and some quite new biophysical strategies for constructing minimal systems of certain cellular modules. We will show that with recent advances in understanding of cytoskeletal and membrane elements, the time might have come to experimentally challenge the concept of a minimal cell.     

Using evolutionary genomics,transcriptomics, and systems biology to reveal gene networks underlying fungal development

Fungal model species have contributed to many aspects of modern biology, from biochemistry and cell biology to molecular genetics. Nevertheless, only a few genes associated with morphological development in fungi have been functionally characterized in terms of their genetic or molecular interactions. Evolutionary developmental biology in fungi faces challenges from a lack of fossil records and unresolved species phylogeny, to homoplasy associated with simple morphology. Traditionally, reductive approaches use genetic screens to reveal phenotypes from a large number of mutants; the efficiency of these approaches relies on profound prior knowledge of the genetics and biology of the designated development trait—knowledge which is often not available for even well-studied fungal model species. Reductive approaches become less efficient for the study of developmental traits that are regulated quantitatively by more than one gene via networks. Recent advances in genome-wide analysis performed in representative multicellular fungal models and non-models have greatly improved upon the traditional reductive approaches in fungal evo-devo research by providing clues for focused knockout strategies. In particular, genome-wide gene expression data across developmental processes of interest in multiple species can expedite the advancement of integrative synthetic and systems biology strategies to reveal regulatory networks underlying fungal development.     

Nanotechnology,bionanotechnology and microbial cell factories

Nanotechnology is increasingly using both materials and nano-objects synthesized by living beings, most of them produced by microbial cells. Emerging technologies and highly integrative approaches (such as 'omics and systems biology), that have been largely proven successful for the production of proteins and secondary metabolites are now expected to become fully adapted for the improved biological production of nanostructured materials with tailored properties. The so far underestimated potential of microbial cell factories in nanotechnology and nanomedicine is expected to emerge, in the next years, in the context of novel needs envisaged in the nanoscience universe. This should prompt a careful revisiting of the microbial cell factories as the most versatile biological platforms to supply functional materials for nanotechnological applications.     

Can cellular models revolutionize drug discovery in Parkinson's disease?

The study of mechanisms that underlie Parkinson's disease (PD), as well as translational drug development, has been hindered by the lack of appropriate models. Both cell culture systems and animal models have limitations, and to date none faithfully recapitulate all of the clinical and pathological phenotypes of the disease. In this review we examine the various cell culture model systems of PD, with a focus on different stem cell models that can be used for investigating disease mechanisms as well as drug discovery for PD. We conclude with a discussion of recent discoveries in the field of stem cell biology that have led to the ability to reprogram somatic cells to a pluripotent state via the use of a combination of genetic factors; these reprogrammed cells are termed “induced pluripotent stem cells” (iPSCs). This groundbreaking technique allows for the derivation of patient-specific cell lines from individuals with sporadic forms of PD and also those with known disease-causing mutations. Such cell lines have the potential to serve as a human cellular model of neurodegeneration and PD when differentiated into dopaminergic neurons. The hope is that these iPSC-derived dopaminergic neurons can be used to replicate the key molecular aspects of neural degeneration associated with PD. If so, this approach could lead to transformative new tools for the study of disease mechanisms. In addition, such cell lines can be potentially used for high-throughput drug screening. While not the focus of this review, ultimately it is envisioned that techniques for reprogramming of somatic cells may be optimized to a point sufficient to provide potential new avenues for stem cell-based restorative therapies.