Chemiosmotic systems in medicine

The concept of chemiosmotic systems arises from the pioneering work of Peter Mitchell on two fronts. One is concerned with the mechanisms by which molecules are transported across membranes which are generally barriers to such transport. These mechanisms are inevitably molecular, and are now yielding their secrets to a combination of structural protein chemistry and molecular biology. The other front is more physiological, and explores the functional relationships between metabolism and transport. Nevertheless, the two fronts form a continuum of mutally related structure and function. Chemiosmotic systems provide a hierarchy of complexity, starting from say a uniporter reconstituted in a chemically defined bilayer, and proceeding to greater complexity in mitochondria, chloroplasts, eukaryotic and prokaryotic cell membranes, and multicellular systems. Their relationship to medicine is profound, because they provide many opportunities for therapeutic intervention. In this paper I present an overview of chemiosmotic systems at different levels of complexity, both molecular and biological, of their involvements in pathology, and of possible pharmacological treatment or prevention of disease.     

Expert systems in medicine

The emergence of the artificial intelligence (AI) in computer technology and its application in the medical field enables the researchers to carry out such intelligent activities like image processing, medical reasoning systems, clinical decision supporting and natural language understanding, etc. A gastroenterological expert system application is briefly demonstrated in this paper. Similar expert systems can be seen to be useful in the research of gastrointestinal cytoprotection, including the plan of different compounds with cytoprotective effect, experimental and clinical medical research.     

Nonlinear systems in medicine

Many achievements in medicine have come from applying linear theory to problems. Most current methods of data analysis use linear models, which are based on proportionality between two variables and/or relationships described by linear differential equations. However, nonlinear behavior commonly occurs within human systems due to their complex dynamic nature; this cannot be described adequately by linear models. Nonlinear thinking has grown among physiologists and physicians over the past century, and non-linear system theories are beginning to be applied to assist in interpreting, explaining, and predicting biological phenomena. Chaos theory describes elements manifesting behavior that is extremely sensitive to initial conditions, does not repeat itself and yet is deterministic. Complexity theory goes one step beyond chaos and is attempting to explain complex behavior that emerges within dynamic nonlinear systems. Nonlinear modeling still has not been able to explain all of the complexity present in human systems, and further models still need to be refined and developed. However, nonlinear modeling is helping to explain some system behaviors that linear systems cannot and thus will augment our understanding of the nature of complex dynamic systems within the human body in health and in disease states.     

系统生物学对医学的影响

系统生物学是21世纪最前沿的科学之一,它是随着生命科学飞速发展而产生的一门新兴生物学分支[1],它综合数学、信息科学和生物学的各种工具来阐明和理解大量的数据所包含的生物医学意义,从而使人们能够从整体上理解生物医学系统并精确、量化地预测生物医学系统的行为。随着系统生物学的发展及其理论的突破,将在疾病诊治、新药开发、预防医学方面发挥重要的作用,有助于弥补传统医学缺陷并促进其发展。     

Complex systems in pulmonary medicine: a systems biology approach to lung disease

The lung is a highly complex organ that can only be understood by integrating the many aspects of its structure and function into a comprehensive view. Such a view is provided by a systems biology approach, whereby the many layers of complexity, from the molecular genetic, to the cellular, to the tissue, to the whole organ, and finally to the whole body, are synthesized into a working model of understanding. The systems biology approach therefore relies on the expertise of many disciplines, including genomics, proteomics, metabolomics, physiomics, and, ultimately, clinical medicine. The overall structure and functioning of the lung cannot be predicted from studying any one of these systems in isolation, and so this approach highlights the importance of emergence as the fundamental feature of systems biology. In this paper, we will provide an overview of a systems biology approach to lung disease by briefly reviewing the advances made at many of these levels, with special emphasis on recent work done in the realm of pulmonary physiology and the analysis of clinical phenotypes.     

The future of proteomic analysis in biological systems and molecular medicine

Proteomics is the study of proteins and their interactions within complex biological systems. While this field is often associated with mass spectrometry, it is more useful to consider proteomics in the context of an objective: to identify and understand the molecular basis of health and disease at the protein level in vivo. Achieving this objective will require (1) technological developments to resolve current instrument limitations and (2) multidisciplinary integration of biological and protein analysis technologies to answer important questions in both the biological sciences and molecular medicine.     

Boolean modeling techniques for protein co-expression networks in systems medicine

Introduction: Application of systems biology/systems medicine approaches is promising for proteomics/biomedical research, but requires selection of an adequate modeling type.

Areas covered: This article reviews the existing Boolean network modeling approaches, which provide in comparison with alternative modeling techniques several advantages for the processing of proteomics data. Application of methods for inference, reduction and validation of protein co-expression networks that are derived from quantitative high-throughput proteomics measurements is presented. It’s also shown how Boolean models can be used to derive system-theoretic characteristics that describe both the dynamical behavior of such networks as a whole and the properties of different cell states (e.g. healthy or diseased cell states). Furthermore, application of methods derived from control theory is proposed in order to simulate the effects of therapeutic interventions on such networks, which is a promising approach for the computer-assisted discovery of biomarkers and drug targets. Finally, the clinical application of Boolean modeling analyses is discussed.

Expert commentary: Boolean modeling of proteomics data is still in its infancy. Progress in this field strongly depends on provision of a repository with public access to relevant reference models. Also required are community supported standards that facilitate input of both proteomics and patient related data (e.g. age, gender, laboratory results, etc.).     

Bacterial toxin-antitoxin systems and perspectives for their application in medicine

The structure of various toxin-antitoxin (TA) families and the principles of their action are reviewed. TA loci are widely distributed in the genomes of eubacteria and archaea. Most TA systems are two-component and function in a similar way: a stable toxin alters vitally important cell functions and can be inactivated by a labile antitoxin.     

Mathematical models for excitable systems in biology and medicine.

The excitable systems play a very important role in Biology and Medicine. Phenomena such as the transmission of impulses between neurons, the cardiac arrhythmia, the aggregation of amoebas, the appearance of organized structures in the cortex of egg cells, all derive from the activity of excitable media. In the first part of this work a general definition of excitable system is given; we then analyze some cases of excitability, distinguishing between electrical and chemical excitability and comparing experimental observations with simulations carried out by appropriate mathematical models. Such models are almost always formulated by partial differential equations of "reaction-diffusion" type and they have the characteristic to describe propagations of electrical waves (neurons, pacemaker cardiac cells, pancreatic b-cells) or chemical and mechanical waves (propagation of Ca++ waves or mechanical waves in the endoplasmic reticulum). The aim is to put in evidence that the biological systems can show not only excitability of electrical type, but also excitability of chemical nature, which can be observed in the first steps of development of egg cells or, for example, in the formation of pigments in vertebrate skin or in clam shells.     

Elementary network reconstruction: A framework for the analysis of regulatory networks in biological systems

Complexity of regulatory networks arises from the high degree of interaction between network components such as DNA, RNA, proteins, and metabolites. We have developed a modeling tool, elementary network reconstruction (ENR), to characterize these networks. ENR is a knowledge-driven, steady state, deterministic, quantitative modeling approach based on linear perturbation theory. In ENR we demonstrate a novel means of expressing control mechanisms by way of dimensionless steady state gains relating input and output variables, which are purely in terms of species abundances (extensive variables). As a result of systematic enumeration of network species in n×n matrix, the two properties of linear perturbation are manifested in graphical representations: transitive property is evident in a special L-shape structure, and additive property is evident in multiple L-shape structures arriving at the same matrix cell. Upon imposing mechanistic (lowest-level) gains, network self-assembly through transitive and additive properties results in elucidation of inherent topology and explicit cataloging of higher level gains, which in turn can be used to predict perturbation results. Application of ENR to the regulatory network behind carbon catabolite repression in Escherichia coli is presented. Through incorporation of known molecular mechanisms governing transient and permanent repressions, the ENR model correctly predicts several key features of this regulatory network, including a 50% downshift in intracellular cAMP level upon exposure to glucose. Since functional genomics studies are mainly concerned with redistribution of species abundances in perturbed systems, ENR could be exploited in the system-level analysis of biological systems.     

Organelle size control systems: From cell geometry to organelle-directed medicine

Organelles are reaction vessels containing metabolic pathways. As in a chemical factory, the size of the reaction vessels limits the rate of product formation. Organelle size is tuned to metabolic needs, hence reprogramming organelle size could be a novel therapeutic strategy as well as a new tool for metabolic engineering.     

Mode coupling in living systems: implications for biology and medicine

Complex systems, and in particular biological ones, are characterized by large numbers of oscillations of widely differing frequencies. Various prejudices tend to lead to the assumption that such oscillators should generically be very weakly interacting. This paper reviews the basic ideas of linearity and nonlinearity as seen by a physicist, but with a view to biological systems. In particular, it is argued that large couplings between different oscillators of disparate frequencies are common, being present even in rather simple systems which are well-known in physics, although this issue is often glossed over. This suggests new experiments and investigations, as well as new approaches to therapies and human-environment interactions which, without the concepts described here, may otherwise seem unlikely to be interesting. The style of the paper is conversational with a minimum of mathematics, and no attempt at a complete list of references.     

Analysing stratified medicine business models and value systems: innovation-regulation interactions

Stratified medicine offers both opportunities and challenges to the conventional business models that drive pharmaceutical R&D. Given the increasingly unsustainable blockbuster model of drug development, due in part to maturing product pipelines, alongside increasing demands from regulators, healthcare providers and patients for higher standards of safety, efficacy and cost-effectiveness of new therapies, stratified medicine promises a range of benefits to pharmaceutical and diagnostic firms as well as healthcare providers and patients. However, the transition from 'blockbusters' to what might now be termed 'niche-busters' will require the adoption of new, innovative business models, the identification of different and perhaps novel types of value along the R&D pathway, and a smarter approach to regulation to facilitate innovation in this area. In this paper we apply the Innogen Centre's interdisciplinary ALSIS methodology, which we have developed for the analysis of life science innovation systems in contexts where the value creation process is lengthy, expensive and highly uncertain, to this emerging field of stratified medicine. In doing so, we consider the complex collaboration, timing, coordination and regulatory interactions that shape business models, value chains and value systems relevant to stratified medicine. More specifically, we explore in some depth two convergence models for co-development of a therapy and diagnostic before market authorisation, highlighting the regulatory requirements and policy initiatives within the broader value system environment that have a key role in determining the probable success and sustainability of these models.     

Chemical genomics and medicinal systems biology: chemical control of genomic networks in human systems biology for innovative medicine

With advances in determining the entire DNA sequence of the human genome, it is now critical to systematically identify the function of a number of genes in the human genome. These biological challenges, especially those in human diseases, should be addressed in human cells in which conventional (e.g. genetic) approaches have been extremely difficult to implement. To overcome this, several approaches have been initiated. This review will focus on the development of a novel "chemical genetic/genomic approach" that uses small molecules to "probe and identify" the function of genes in specific biological processes or pathways in human cells. Due to the close relationship of small molecules with drugs, these systematic and integrative studies will lead to the "medicinal systems biology approach" which is critical to "formulate and modulate" complex biological (disease) networks by small molecules (drugs) in human bio-systems.