Structural systems biology: modelling protein interactions

Much of systems biology aims to predict the behaviour of biological systems on the basis of the set of molecules involved. Understanding the interactions between these molecules is therefore crucial to such efforts. Although many thousands of interactions are known, precise molecular details are available for only a tiny fraction of them. The difficulties that are involved in experimentally determining atomic structures for interacting proteins make predictive methods essential for progress. Structural details can ultimately turn abstract system representations into models that more accurately reflect biological reality.     

Molecules into Cells: Specifying Spatial Architecture

A living cell is not an aggregate of molecules but an organized pattern, structured in space and in time. This article addresses some conceptual issues in the genesis of spatial architecture, including how molecules find their proper location in cell space, the origins of supramolecular order, the role of the genes, cell morphology, the continuity of cells, and the inheritance of order. The discussion is framed around a hierarchy of physiological processes that bridge the gap between nanometer-sized molecules and cells three to six orders of magnitude larger. Stepping stones include molecular self-organization, directional physiology, spatial markers, gradients, fields, and physical forces. The knowledge at hand leads to an unconventional interpretation of biological order. I have come to think of cells as self-organized systems composed of genetically specified elements plus heritable structures. The smallest self that can be fairly said to organize itself is the whole cell. If structure, form, and function are ever to be computed from data at a lower level, the starting point will be not the genome, but a spatially organized system of molecules. This conclusion invites us to reconsider our understanding of what genes do, what organisms are, and how living systems could have arisen on the early Earth.     

Molecules into cells: specifying spatial architecture.

A living cell is not an aggregate of molecules but an organized pattern, structured in space and in time. This article addresses some conceptual issues in the genesis of spatial architecture, including how molecules find their proper location in cell space, the origins of supramolecular order, the role of the genes, cell morphology, the continuity of cells, and the inheritance of order. The discussion is framed around a hierarchy of physiological processes that bridge the gap between nanometer-sized molecules and cells three to six orders of magnitude larger. Stepping stones include molecular self-organization, directional physiology, spatial markers, gradients, fields, and physical forces. The knowledge at hand leads to an unconventional interpretation of biological order. I have come to think of cells as self-organized systems composed of genetically specified elements plus heritable structures. The smallest self that can be fairly said to organize itself is the whole cell. If structure, form, and function are ever to be computed from data at a lower level, the starting point will be not the genome, but a spatially organized system of molecules. This conclusion invites us to reconsider our understanding of what genes do, what organisms are, and how living systems could have arisen on the early Earth.     

Novel recurrent neural network for modelling biological networks: Oscillatory p53 interaction dynamics

Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system – a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more quantitative data become available on individual proteins, the RNN would be able to refine parameter estimation and mapping of temporal dynamics of individual signalling molecules as well as signalling networks as a system. Moreover, RNN can be used to modularise large signalling networks.     

Gravitational effects on biological systems

The possible effects of the earth's gravitational field on biological systems have been studied from a quantitative point of view, focusing the attention to a very simple system, a solution containing proteins, which biochemists might use in experiments. Gravity has been compared with other forces which are known to influence protein activity, including thermic agitation, weak electrostatic interactions, Van der Waals forces and viscous dissipation. Comparisons have been described in terms of the energy of the interaction per mole, referring to some physically simple cases and substances of biological interest. From this study it is evident that the earth's gravitational energy should be taken into account when considering the chemical behaviour of solutions containing substances that have high molecular weight, such as a typical protein, since its value is comparable to other weak interactions. Moreover, since solutions represent the basis of much more complex biological processes taking place inside cells, the influence of gravity should extend also to cellular biochemical behaviour, especially in presence of altered gravity, both in microgravity (such as on satellites orbiting around the earth), and in macrogravity (such as in a centrifugating biological system).     

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.     

Statistical interpretation of the interplay between noise and chaos in the stochastic logistic map

A recurring problem in population biology - as well as other stochastic dynamical systems in biology, the physical and social sciences - is the distinction between the ‘true’ dynamics of a system and observational noise: i.e. can we from present data reliably infer e.g. biological mechanisms, or are signals swamped by noise.Here, we approach this problem using the canonical model for simple systems that exhibit complex behaviour, the logistic map. At each time-point noise is added, which allows us to study the long-term behaviour of a system which exhibits both non-linear dynamics and intrinsic noise.We show that the interplay between deterministic non-linear dynamics and simple Gaussian noise results in a perplexingly simple system when viewed statistically.In particular we show that for the case of Gaussian noise it is possible to derive at very reliable approximations for the time until the system has reached an absorbing state. This generic model allows us, for example, to study the life-time of molecular species involved in noisy feedback loops.     

Detecting disease associated modules and prioritizing active genes based on high throughput data

Background  

The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology.     

The Hofmeister effect and the behaviour of water at interfaces

Starting from known properties of non-specific salt effects on the surface tension at an air-water interface, we propose the first general, detailed qualitative molecular mechanism for the origins of ion-specific (Hofmeister) effects on the surface potential difference at an air-water interface; this mechanism suggests a simple model for the behaviour of water at all interfaces (including water-solute interfaces), regardless of whether the non-aqueous component is neutral or charged, polar or non-polar. Specifically, water near an isolated interface is conceptually divided into three layers, each layer being I water-molecule thick. We propose that the solute determines the behaviour of the adjacent first interfacial water layer (I1); that the bulk solution determines the behaviour of the third interfacial water layer (I3), and that both I1 and I3 compete for hydrogen-bonding interactions with the intervening water layer (I2), which can be thought of as a transition layer. The model requires that a polar kosmotrope (polar water-structure maker) interact with I1 more strongly than would bulk water in its place; that a chaotrope (water-structure breaker) interact with I1 somewhat less strongly than would bulk water in its place; and that a non-polar kosmotrope (non-polar water-structure maker) interact with I1 much less strongly than would bulk water in its place. We introduce two simple new postulates to describe the behaviour of I1 water molecules in aqueous solution. The first, the 'relative competition' postulate, states that an I1 water molecule, in maximizing its free energy (--delta G), will favour those of its highly directional polar (hydrogen-bonding) interactions with its immediate neighbours for which the maximum pairwise enthalpy of interaction (--delta H) is greatest; that is, it will favour the strongest interactions. We describe such behaviour as 'compliant', since an I1 water molecule will continually adjust its position to maximize these strong interactions. Its behaviour towards its remaining immediate neighbours, with whom it interacts relatively weakly (but still favourably), we describe as 'recalcitrant', since it will be unable to adjust its position to maximize simultaneously these interactions.(ABSTRACT TRUNCATED AT 400 WORDS)     

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.     

New insights into the mycobacterial PE and PPE proteins provide a framework for future research

The PE and PPE proteins of Mycobacterium tuberculosis have been studied with great interest since their discovery. Named after the conserved proline (P) and glutamic acid (E) residues in their N-terminal domains, these proteins are postulated to perform wide-ranging roles in virulence and immune modulation. However, technical challenges in studying these proteins and their encoding genes have hampered the elucidation of molecular mechanisms and leave many open questions regarding the biological functions mediated by these proteins. Here, I review the shared and unique characteristics of PE and PPE proteins from a molecular perspective linking this information to their functions in mycobacterial virulence. I discuss how the different subgroups (PE_PGRS, PPE-PPW, PPE-SVP and PPE-MPTR) are defined and why this classification of paramount importance to understand the PE and PPE proteins as individuals and or groups. The goal of this MicroReview is to summarize and structure the existing information on this gene family into a simplified framework of thinking about PE and PPE proteins and genes. Thereby, I hope to provide helpful starting points in studying these genes and proteins for researchers with different backgrounds. This has particular implications for the design and monitoring of novel vaccine candidates and in understanding the evolution of the M. tuberculosis complex.     

Chemical biology approaches in plant stress research

In addition to their importance as essential agrochemicals and life-saving drugs, small molecules serve as powerful research tools to address questions at all levels of biological complexity from protein function to plant biotic interactions. In certain contexts, chemical tools are complementary or even preferred to genetic analysis, since not all experimental systems are amenable for genetic dissection. For example, mutants impaired in oxygen sensing cannot easily be recovered. Pharmacological and chemical genetics approaches have come to the rescue of biologists in unraveling such genetically intractable systems. In this review, I have discussed my own efforts to analyze oxygen deprivation signaling in plants to illustrate the validity of small molecular approaches in elucidating an essential pathway such as oxygen sensing. Chemical biology is also a potent approach to tease out genetically redundant biological processes. The recent breakthrough in identifying the elusive abscisic acid receptors has clearly demonstrated the power of chemical tools in dissecting redundant pathways and led to the blossoming of this area as a distinct discipline of plant biology research. I present a summary of this work and conclude the review with potential challenges in using chemical tools.     

The sociobiology of molecular systems

It is often assumed that molecular systems are designed to maximize the competitive ability of the organism that carries them. In reality, natural selection acts on both cooperative and competitive phenotypes, across multiple scales of biological organization. Here I ask how the potential for social effects in evolution has influenced molecular systems. I discuss a range of phenotypes, from the selfish genetic elements that disrupt genomes, through metabolism, multicellularity and cancer, to behaviour and the organization of animal societies. I argue that the balance between cooperative and competitive evolution has shaped both form and function at the molecular scale.     

The neurobiology of depression—revisiting the serotonin hypothesis. I. Cellular and molecular mechanisms

The serotonin (5-HT) hypothesis of depression dates from the 1960s. It originally postulated that a deficit in brain serotonin, corrected by antidepressant drugs, was the origin of the illness. Nowadays, it is generally accepted that recurring mood disorders are brain diseases resulting from the combination, to various degrees, of genetic and other biological as well as environmental factors, evolving through the lifespan. All areas of neuroscience, from genes to behaviour, molecules to mind, and experimental to clinical, are actively engaged in attempts at elucidating the pathophysiology of depression and the mechanisms underlying the efficacy of antidepressant treatments. This first of two special issues of Philosophical Transactions B seeks to provide an overview of current developments in the field, with an emphasis on cellular and molecular mechanisms, and how their unravelling opens new perspectives for future research.     

Simultaneous origin of homochirality, the genetic code and its directionality

The origin of homochirality in molecules characterizing living systems has remained a mystery since Pasteur's recognition of the problem some 150 years ago.(2-5) Most theories also assume that homochirality emerged in one class of molecules (e.g. ribose) from which it was enriched in other molecules (e.g. amino acids) as well.(2-5)I propose a novel, experimentally testable hypothesis describing a process by which selective chirality in amino acids and ribonucleotides emerged simultaneously and hand-in-hand with the origin and directionality of the genetic code within a system of interactions involving amino acids, peptides, nucleotide bases, their sugars and polynucleotides.