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.     

Quantitative phosphoproteomics to characterize signaling networks

Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform for analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify and quantify thousands of phosphorylations, thus providing extensive overviews of the cellular signaling networks. As a result of these developments quantitative phosphoproteomics have been applied to study processes as diverse as immunology, stem cell biology and DNA damage. Here we review the developments in phosphoproteomics technology that have facilitated the application of phosphoproteomics to signaling networks and introduce examples of recent system-wide applications of quantitative phosphoproteomics. Despite the great advances in phosphoproteomics technology there are still several outstanding issues and we provide here our outlook on the current limitations and challenges in the field.     

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.     

The maintenance and regeneration of the planarian excretory system are regulated by EGFR signaling

The maintenance of organs and their regeneration in case of injury are crucial to the survival of all animals. High rates of tissue turnover and nearly unlimited regenerative capabilities make planarian flatworms an ideal system with which to investigate these important processes, yet little is known about the cell biology and anatomy of their organs. Here we focus on the planarian excretory system, which consists of internal protonephridial tubules. We find that these assemble into complex branching patterns with a stereotyped succession of cell types along their length. Organ regeneration is likely to originate from a precursor structure arising in the blastema, which undergoes extensive branching morphogenesis. In an RNAi screen of signaling molecules, we identified an EGF receptor (Smed-EGFR-5) as a crucial regulator of branching morphogenesis and maintenance. Overall, our characterization of the planarian protonephridial system establishes a new paradigm for regenerative organogenesis and provides a platform for exploring its functional and evolutionary homologies with vertebrate excretory systems.     

Bacterial quorum sensing: circuits and applications

Bacterial quorum sensing (QS) systems are cell density—dependent regulatory networks that coordinate bacterial behavioural changes from single cellular organisms at low cell densities to multicellular types when their population density reaches a threshold level. At this stage, bacteria produce and perceive small diffusible signal molecules, termed autoinducers in order to mediate gene expression. This often results in phenotypic shifts, like planktonic to biofilm or non-virulent to virulent. In this way, they regulate varied physiological processes by adjusting gene expression in concert with their population size. In this review we give a synopsis of QS mediated cell–cell communication in bacteria. The first part focuses on QS circuits of some Gram-negative and Gram-positive bacteria. Thereafter, attention is drawn on the recent applications of QS in development of synthetic biology modules, for studying the principles of pattern formation, engineering bi-directional communication system and building artificial communication networks. Further, the role of QS in solving the problem of biofouling is also discussed.     

Plant systems biology: network matters

Systems biology is all about networks. A recent trend has been to associate systems biology exclusively with the study of gene regulatory or protein-interaction networks. However, systems biology approaches can be applied at many other scales, from the subatomic to the ecosystem scales. In this review, we describe studies at the sub-cellular, tissue, whole plant and crop scales and highlight how these studies can be related to systems biology. We discuss the properties of system approaches at each scale as well as their current limits, and pinpoint in each case advances unique to the considered scale but representing potential for the other scales. We conclude by examining plant models bridging different scales and considering the future prospects of plant systems biology.     

Integration of Boolean models exemplified on hepatocyte signal transduction

The number of mathematical models for biological pathways is rapidly growing. In particular, Boolean modelling proved to be suited to describe large cellular signalling networks. Systems biology is at the threshold to holistic understanding of comprehensive networks. In order to reach this goal, connection and integration of existing models of parts of cellular networks into more comprehensive network models is necessary. We discuss model combination approaches for Boolean models. Boolean modelling is qualitative rather than quantitative and does not require detailed kinetic information. We show that these models are useful precursors for large-scale quantitative models and that they are comparatively easy to combine. We propose modelling standards for Boolean models as a prerequisite for smooth model integration. Using these standards, we demonstrate the coupling of two logical models on two different examples concerning cellular interactions in the liver. In the first example, we show the integration of two Boolean models of two cell types in order to describe their interaction. In the second example, we demonstrate the combination of two models describing different parts of the network of a single cell type. Combination of partial models into comprehensive network models will take systems biology to the next level of understanding. The combination of logical models facilitated by modelling standards is a valuable example for the next step towards this goal.     

The German cDNA Network: cDNAs,functional genomics and proteomics

Among the greatest challenges facing biology today is the exploitation of huge amounts of genomic data, and their conversion into functional information about the proteins encoded. For example, the large-scale cDNA sequencing project of the German cDNA Consortium is providing vast numbers of open reading frames (ORFs) encoding novel proteins of completely unknown function. As a first step towards their characterization we have tagged over 500 of these with the green fluorescent protein (GFP), and examined the subcellular localizations of these fusion proteins in living cells. These data have allowed us to classify the proteins into subcellular groups which determines the next step towards a detailed functional characterization. To make further use of these GFP-tagged constructs, a series of functional assays have been designed and implemented to assess the effect of these novel proteins on processes such as cell growth, cell death, and protein transport.Functional assays with such a large set of molecules is only possible by automation. Therefore, we have developed, and adapted, functional assays for use by robotic liquid handling stations and reading stations. A transport assay allows to identify proteins which localize to distinct organelles of the secretory pathway and have the potential to be new regulators in protein transport, a proliferation assay helps identifying proteins that stimulate or repress mitosis. Further assays to monitor the effects of the proteins in apoptosis and signal transduction pathways are in progress. Integrating the functional information that is generated in the assays with data from expression profiling and further functional genomics and proteomics approaches, will ultimately allow us to identify functional networks of proteins in a morphological context, and will greatly contribute to our understanding of cell function.     

Biology of size and gravity]

Gravity is a force that acts on mass. Biological effects of gravity and their magnitude depend on scale of mass and difference in density. One significant contribution of space biology is confirmation of direct action of gravity even at the cellular level. Since cell is the elementary unit of life, existence of primary effects of gravity on cells leads to establish the firm basis of gravitational biology. However, gravity is not limited to produce its biological effects on molecules and their reaction networks that compose living cells. Biological system has hierarchical structure with layers of organism, group, and ecological system, which emerge from the system one layer down. Influence of gravity is higher at larger mass. In addition to this, actions of gravity in each layer are caused by process and mechanism that is subjected and different in each layer of the hierarchy. Because of this feature, summing up gravitational action on cells does not explain gravity for biological system at upper layers. Gravity at ecological system or organismal level can not reduced to cellular mechanism. Size of cells and organisms is one of fundamental characters of them and a determinant in their design of form and function. Size closely relates to other physical quantities, such as mass, volume, and surface area. Gravity produces weight of mass. Organisms are required to equip components to support weight and to resist against force that arise at movement of body or a part of it. Volume and surface area associate with mass and heat transport process at body. Gravity dominates those processes by inducing natural convection around organisms. This review covers various elements and process, with which gravity make influence on living systems, chosen on the basis of biology of size. Cells and biochemical networks are under the control of organism to integrate a consolidated form. How cells adjust metabolic rate to meet to the size of the composed organism, whether is gravity responsible for this feature, are subject we discuss in this article. Three major topics in gravitational and space biology are; how living systems have been adapted to terrestrial gravity and evolved, how living systems respond to exotic gravitational environment, and whether living systems could respond and adapt to microgravity. Biology of size can contribute to find a way to answer these question, and answer why gravity is important in biology, at explaining why gravity has been a dominant factor through the evolutional history on the earth.     

Transient responses and adaptation to steady state in a eukaryotic gene regulation system

Understanding the structure and functionality of eukaryotic gene regulation systems is of fundamental importance in many areas of biology. While most recent studies focus on static or short-term properties, measuring the long-term dynamics of these networks under controlled conditions is necessary for their complete characterization. We demonstrate adaptive dynamics in a well-known system of metabolic regulation, the GAL system in the yeast S. cerevisiae. This is a classic model for a eukaryotic genetic switch, induced by galactose and repressed by glucose. We followed the expression of a reporter gfp under a GAL promoter at single-cell resolution in large population of yeast cells. Experiments were conducted for long time scales, several generations, while controlling the environment in continuous culture. This combination enabled us, for the first time, to distinguish between transient responses and steady state. We find that both galactose induction and glucose repression are only transient responses. Over several generations, the system converges to a single robust steady state, independent of external conditions. Thus, at steady state the GAL network loses its hallmark functionality as a sensitive carbon source rheostat. This result suggests that, while short-term dynamics are determined by specific modular responses, over long time scales inter-modular interactions take over and shape a robust steady state response of the regulatory system.     

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Cell-free systems provide a flexible platform for probing specific networks of biological reactions isolated from the complex resource sharing (e.g., global gene expression, cell division) encountered within living cells. However, such systems, used in conventional macro-scale bulk reactors, often fail to exhibit the dynamic behaviors and efficiencies characteristic of their living micro-scale counterparts. Understanding the impact of internal cell structure and scale on reaction dynamics is crucial to understanding complex gene networks. Here we report a microfabricated device that confines cell-free reactions in cellular scale volumes while allowing flexible characterization of the enclosed molecular system. This multilayered poly(dimethylsiloxane) (PDMS) device contains femtoliter-scale reaction chambers on an elastomeric membrane which can be actuated (open and closed). When actuated, the chambers confine Cell-Free Protein Synthesis (CFPS) reactions expressing a fluorescent protein, allowing for the visualization of the reaction kinetics over time using time-lapse fluorescent microscopy. Here we demonstrate how this device may be used to measure the noise structure of CFPS reactions in a manner that is directly analogous to those used to characterize cellular systems, thereby enabling the use of noise biology techniques used in cellular systems to characterize CFPS gene circuits and their interactions with the cell-free environment.     

Functional analysis of protein kinase networks in living cells: beyond "knock-outs" and "knock-downs"

The identification of over 500 protein kinases encoded by the human genome sequence offers one measure of the importance of protein kinase networks in cell biology. High throughput technologies for inactivating genes are producing an awe-inspiring amount of data on the cellular and organismal effects of reducing the levels of individual protein kinases. Despite these technical advances, our understanding of kinase networks remains imprecise. Major challenges include correctly assigning kinases to particular networks, understanding how they are regulated, and identifying the relevant in vivo substrates. Genetic methods provide a way of addressing these questions, but their application requires understanding the nuances of how different types of mutations can affect protein kinases. The goal of this article is to provide a brief introductory primer into these issues using examples from yeast MAPK cascades and to motivate future systematic genetic analysis focusing on individual residues of protein kinases.     

MicroRNAs from the Planarian Schmidtea mediterranea: a model system for stem cell biology

MicroRNAs (miRNAs) are approximately 22-nt RNA molecules that typically bind to the 3' untranslated regions of target mRNAs and function to either induce mRNA degradation or repress translation. miRNAs have been shown to play important roles in the function of stem cells and cell lineage decisions in a variety of organisms, including humans. Planarians are bilaterally symmetric metazoans that have the unique ability to completely regenerate lost tissues or organs. This regenerative capacity is facilitated by a population of stem cells known as neoblasts. Planarians are therefore an excellent model system for studying many aspects of stem cell biology. Here we report the cloning and initial characterization of 71 miRNAs from the planarian Schmidtea mediterranea. While several of the S. mediterranea miRNAs are members of miRNA families identified in other species, we also identified a number of planarian-specific miRNAs. This work lays the foundation for functional studies aimed at addressing the role of these miRNAs in regeneration, cell lineage decisions, and basic stem cell biology.     

Engineering and control of biological systems: A new way to tackle complex diseases

The ongoing merge between engineering and biology has contributed to the emerging field of synthetic biology. The defining features of this new discipline are abstraction and standardisation of biological parts, decoupling between parts to prevent undesired cross-talking, and the application of quantitative modelling of synthetic genetic circuits in order to guide their design. Most of the efforts in the field of synthetic biology in the last decade have been devoted to the design and development of functional gene circuits in prokaryotes and unicellular eukaryotes. Researchers have used synthetic biology not only to engineer new functions in the cell, but also to build simpler models of endogenous gene regulatory networks to gain knowledge of the "rules" governing their wiring diagram. However, the need for innovative approaches to study and modify complex signalling and regulatory networks in mammalian cells and multicellular organisms has prompted advances of synthetic biology also in these species, thus contributing to develop innovative ways to tackle human diseases. In this work, we will review the latest progress in synthetic biology and the most significant developments achieved so far, both in unicellular and multicellular organisms, with emphasis on human health.     

Evolutionary developmental biology its roots and characteristics

The rise of evolutionary developmental biology was not the progressive isolation and characterization of developmental genes and gene networks. Many obstacles had to be overcome: the idea that all genes were more or less involved in development; the evidence that developmental processes in insects had nothing in common with those of vertebrates.Different lines of research converged toward the creation of evolutionary developmental biology, giving this field of research its present heterogeneity. This does not prevent all those working in the field from sharing the conviction that a precise characterization of evolutionary variations is required to fully understand the evolutionary process.Some evolutionary developmental biologists directly challenge the Modern Synthesis. I propose some ways to reconcile these apparently opposed visions of evolution. The turbulence seen in evolutionary developmental biology reflects the present entry of history into biology.     

Modeling regulatory networks at Virginia Tech

The life of a cell is governed by the physicochemical properties of a complex network of interacting macromolecules (primarily genes and proteins). Hence, a full scientific understanding of and rational engineering approach to cell physiology require accurate mathematical models of the spatial and temporal dynamics of these macromolecular assemblies, especially the networks involved in integrating signals and regulating cellular responses. The Virginia Tech Consortium is involved in three specific goals of DARPA's computational biology program (Bio-COMP): to create effective software tools for modeling gene-protein-metabolite networks, to employ these tools in creating a new generation of realistic models, and to test and refine these models by well-conceived experimental studies. The special emphasis of this group is to understand the mechanisms of cell cycle control in eukaryotes (yeast cells and frog eggs). The software tools developed at Virginia Tech are designed to meet general requirements of modeling regulatory networks and are collected in a problem-solving environment called JigCell.     

The dynamic systems approach to control and regulation of intracellular networks

Systems theory and cell biology have enjoyed a long relationship that has received renewed interest in recent years in the context of systems biology. The term 'systems' in systems biology comes from systems theory or dynamic systems theory: systems biology is defined through the application of systems- and signal-oriented approaches for an understanding of inter- and intra-cellular dynamic processes. The aim of the present text is to review the systems and control perspective of dynamic systems. The biologist's conceptual framework for representing the variables of a biochemical reaction network, and for describing their relationships, are pathway maps. A principal goal of systems biology is to turn these static maps into dynamic models, which can provide insight into the temporal evolution of biochemical reaction networks. Towards this end, we review the case for differential equation models as a 'natural' representation of causal entailment in pathways. Block-diagrams, commonly used in the engineering sciences, are introduced and compared to pathway maps. The stimulus-response representation of a molecular system is a necessary condition for an understanding of dynamic interactions among the components that make up a pathway. Using simple examples, we show how biochemical reactions are modelled in the dynamic systems framework and visualized using block-diagrams.     

蛋白质预算：合成生物学的成本标尺

合成生物学以创建人工生命体系为目的.实践中人们希望人工生命体系具有更强的生产能力、转化能力、环境适应与监测能力,从而获得更优质的生产方式.生命体系的优化涉及到多层次的调控网络,而根本上还是对细胞中蛋白质的含量、定位、活性的控制.在蛋白质表达水平上进行控制是合成生物学元件设计、模块组装以及适配性研究最核心的手段.类似于工厂中的成本计算,合成生物学创建的人工生命体系(人工细胞工厂)以蛋白质预算为依据.优化蛋白质预算的研究策略已经成功应用于合成生物学研究实践中.