Portal:Complex Systems Digital Campus/E-Department on Integrative Biology: from molecules to organisms

Portal:Complex_Systems_Digital_Campus/E-Department_on_Integrative Biology: from molecules to organisms From molecules to organisms

Biological investigations are expected to provide knowledge that is transferred at some point to clinical research for handling human physiopathology. This means that we hope to cure better, if possible, or at least to understand better. Yet it is now becoming ever more clear that better understanding will arise from an integrative view of biological systems. We thus need to develop this integrative grasp further and to transfer the knowledge acquired in this framework to clinical research. This interdisciplinary approach should provide novel insights into physiology and pathology. After a brief presentation of the general aims and concepts discussed in this topic, 4 main challenges are listed and detailed. How investigations should be driven in biology is a matter of debate. Should they be data-driven, object-driven or hypothesis-driven? Do we at least agree about the aim of deciphering the causality underlying biological processes? Do we expect models to bring insights and knowledge about the behavior of biological systems, through predictions? Recent advances in functional genomics and in the study of complex diseases (such as cancer, autoimmunity or infectious diseases, mitochondrial diseases, metabolic syndrome) have shown the necessity of an alternative way of thinking in biology, in which pathology and physiology are considered to result from interactions between many processes at various scales. A new scientific field has emerged from this need. Systems biology is the study of gene, protein, and biochemical reaction networks and cell population dynamics, considered as dynamical systems. It explores the biological properties resulting from the interaction of many components, investigating processes at different scales and achieving their integration. Complex systems provide a conceptual framework and effective tools to unravel emergent and immergent features from molecules to organisms and vice versa. The latter, described as immergence, microemergence or downward causation, means that some macro-level constraints are expected to cascade back onto micro-levels. Both emergent and immergent properties should be derived from the multiscale reconstruction of data recorded at the appropriate spatial and temporal scales, to be defined through new types of protocols. We expect to find generic processes (design patterns for computer science) that apply from an upper to a lower organizational level and vice versa and that allow their coupling e.g. synchronisation, reinforcement, amplification, inhibition, achieved through basic processes such as signalling through molecular interactions, diffusion, vesicular transport, ionic transport, electric coupling, biomechanical coupling and regulation of molecules and macromolecules characteristic features (including their concentrations). Complex systems are almost always multiscaled both in time (typically femtoseconds in chemical reactions, seconds in metabolism processes, days to months in cells, years in an living organism) and space (typically nanometers for molecular structures, micrometers for supramolecular assemblies, organelles and cells, centimeters for tissues and organs, meters for organisms). Finding the pertinent space & time scales for experimentation and modeling is a major issue. As a result of evolutionary opportunism (biological tinkering), multiscale space & time correlation is not a priori given. Classical approaches (biochemistry, cellular and molecular biology, behavioral and cognitive studies, etc.) usually have their “preferred” scale by default, mainly due to the fact that protocols and experiments are often designed to work only at a specific scale. This makes back and forth interactions between different scales in observations, experimentations, models and simulations a very exciting transdisciplinary challenge. Variation in biological systems raises the issue of an average, typical or representative behavior. Addressing this point requires characterizing and measuring variability and fluctuations at the molecular, single cell, cell population and physiological levels. The origin, time and space scales, control and functional significance of fluctuations in biological systems are largely unknown. Their functional significance might be approached through their multiscale transmission and possible amplification, reduction/damping or role in mediating bifurcations. Obviously, understanding will not arise from a one-to-one description and modeling of organisms (virtual cell, virtual organism) but rather from the correct identification of which components are relevant for a given problem and the reconstruction of the mechanisms involved. Such a reconstruction should use mathematical and physical tools, some borrowed from out-of-equilibrium thermodynamics and dynamical systems. New tools will also be required to answer specific questions of biology. Ultimately, injecting systemic vision and using complex systems principles and conceptual frameworks for a better understanding of human physio-pathology could lead to novel differential diagnosis and improve medical care.

Main challenges 1. Fluctuations and noise in biological systems 2. Stability in biology 3. Multiscaling 4. Human physiopathology

1. Fluctuations and noise in biological systems Modern biology has developed with the idea of average behaviors or individuals. But this conceptual framework has recently been challenged by empirical observation. Quantitative measurements within living single cells have revealed extensive variability and fluctuation of cellular dynamics between different cells or between different times within the same cell. These observations open a new conceptual framework in biology, in which noise must be fully considered if we are to understand biological systems, while the classical framework tended to consider it as a mere measurement error or as "simple" thermodynamic fluctuations that have to be reduced by cells. This new point of view raises many questions and both practical and theoretical issues that will probably deeply modify our understanding of biological systems. However, to tackle these questions, we need to develop a complete scientific program from precise measurements through to analysis of the origin and functional role of stochasticity in biological systems, at all of their time and space scales. Among the main breakthroughs, we need to: •	Improve the technology for quantitative measurements of noise and fluctuations in single cells, cell populations, tissues, organs and individuals. In particular, it will be necessary to identify the characteristic times at each level of organization and the most appropriate experimental indicators. •	Identify the mechanisms by which noise and fluctuations arise in biological systems. In particular, what are the modalities of multiscale transmission of fluctuations? Are fluctuations amplified or reduced/damped from one scale to the others? Are they important with respect to bifurcations in the organism/cell fate? •	Understand the functional significance of fluctuations in the different biological systems. For instance, it has been proposed that fluctuations can enhance the robustness of living beings. However, other processes can be envisaged (e.g. stochastic resonance, increased signaling rates, cell differentiation, evolution, etc.). Such a functional significance supposes that biological systems are able to control the level of noise. •	Delineate possible mechanisms by which biological systems may control their level of fluctuation (negative/positive feedback loops in biochemical networks, neuronal adaptation in cortical networks, adaptive mutations and mutation hotspots, regulations and networks in the immune system). •	Question the meaning of usual averaging processes in experimental biology. In the case of biochemical networks, can data gathered on cell populations be used to infer the actual network in a given single cell. Similar issues arise in the case of connectivity structures of cortical networks and cell lineage reconstruction. These issues can be addressed in various biological systems including (but not limited to): •	Transcription and regulation networks: it is now clear that the transcriptional activity of the cell is highly stochastic. Some of the molecular causes of this stochasticity have been identified. However, the precise origin and regulation mechanisms of this stochasticity are still to be discovered. This will first require the development of adequate measurement methodologies to enable us to quantify these fluctuations at different time scales in single cells. •	Neurons and neuronal networks: the on-going activity of cortical circuits is a spontaneous activity generated by the recurrent nature of these networks. It has long been considered a mere noise added to the environmental signals. However, more recent studies have proposed a real functional role in which ongoing activity could facilitate signal spreading and be implicated in adaptive processes. Inhibitory effects have been shown to reduce variability at both the single-cell and population level. •	Diversity of the immune system: The immune system is characterized by diversity at different levels. Lymphocyte receptor diversity, populations of effectors and regulators, cell-population dynamics, cell selection and competition, migration through the whole organism are the result of somewhat stochastic or selection mechanisms whose impact in the overall efficiency of the system needs to be further characterized. •	Uncontrolled variability is often accused of being a source of major perturbations in the fate of organisms. Examples can be found in the process of aging, cancer, autoimmunity, infections or degenerative deseases. Yet the precise influence of noise is still open to debate. In particular, one point is to determine to what extent degenerative processes are a consequence of noise accumulation, a consequence of a variation of the noise properties or a consequence of rare stochastic events. •	Variability at the genetic level is the major engine of evolution. But genetic variability may be indirectly regulated according to the spatio-temporal characteristics of the environment (selection for robustness, selection for evolvability). Moreover, clonal individuals may be very different from each other due to intrinsic and extrinsic phenotypic variability. The mechanisms by which heritable and non-heritable variability are regulated still need to be characterized and their influence on the evolutionary process is largely unknown.

Concerning the modeling of fluctuations, several mathematical and physical tools exist, but these need to be improved. Thus: •	Stochastic models are largely used in molecular systems biology. The simulation algorithms (Gillespie algorithm) use the Delbrück-Bartholomay-Rényi representation of biochemical kinetics as jump Markov processes. In order to improve the performance of these methods (which are costly in time) several approximate schemes have been proposed, for instance the approximation of Poisson variables by Gaussians (tau-leap method). Hybrid approximations are more appropriate when the processes are multiscale and these approximations could be developed by combining averaging and the law of large numbers. In certain simple cases, the master equation can be exactly solved. •	It is also interesting to transfer ideas from statistical physics to biology. For instance, fluctuation theorems, which concern the occurrence of out-of-equilibrium fluctuations in heat exchanges with the surrounding environment and work theorems, concerning thermodynamic fluctuations in small systems close to equilibrium, could be applied to characterize fluctuations in gene networks, DNA transcription processes and the unfolding of biomolecules.

2. Stability in biology We encounter various definitions depending on the phenomenon, the model or the community proposing the concept. Homeostasis in relation to metabolic control, the Red Queen concept in evolution describing continuous development to sustain stable fitness in a changing environment, robustness in systems biology referring to insensitivity with respect to perturbations, canalization and attractors in developmental biology and ecology are all forms of stability. Main Challenges

1) Biological systems are only stable on a finite horizon, constantly submitted to perturbations (intrinsic or extrinsic). The notion of steady state, or more generally attractor, has to be revisited. We need new mathematical concepts to describe this type of stability. o Finite-time stability is a concept that can be used to define stability in the case when the system is known to operate or to preserve its structure unchanged over a finite time. We are interested in the conditions under which the system's variables remain within finite bounds. Can we extend such formalism to other properties (oscillations, optimal biomass production, etc.)? o Finite time stability depends on the existence of subsystems with different relaxation times. It is thus important to develop methods allowing to estimate the largest relaxation time of subsystems. For compound systems, how can we relate the relaxation times of the elements to that of the system? o The notion of resilience is also a generalization of stability that is particularly appealing in this context. Indeed, it focuses on the ability to restore or maintain important functions when submitted to perturbations. The formalizations of this concept, founded on dynamical system properties (measure of attraction basin sizes), or even on viability theory (cost to return into a viability kernel) should become more operational to favor a wider diffusion.

2) The functioning of multicellular organisms occurs at the level of the population, not of the individual cell. Furthermore, the stability of a cell population (tissue) is generally different from that of the individual cell. For example, cells extracted from tumours can reverse to normal activity when injected into healthy tissue. In this context, how can we define and study the stability of a population in relation to the stability of individuals? In addition, the same relation should be considered in the context of a developing organism taking into account differentiation and organogenesis. These processes are examples of symmetry-breaking, and we would like to determine whether symmetry arguments can be used in the study of stability properties.

3) Systems biology studies robustness as an important organizing principle of biological systems. As pointed out by H. Kitano, cancer is a robust system with some points of fragility. Thus, finding treatments and cures for diseases may consist in determining the fragility points of a robust system. In order to answer this question, we need good models, new mathematical theories and computer tools to analyse properties of models and new experimental techniques to quantify robustness.

4) Complexity and stability. Models of an organ and models relating several organs to each other imply the collaborative representation of the components. Similarly, gene regulation models gather numerous molecular details. In the modeling process, we should be able to zoom in and out between various levels of complexity. Stable properties of the system could be those that are common to several levels of complexity. More generally, is there a connexion between stability and complexity?

3. Multiscaling Biological processes involve events occurring at many different time and space scales. The hierarchy of these scales enters the scene only because it corresponds to our subjective views of the system, usually based on our various discrete experimental accesses. Multiscale approaches drawn from theoretical physics have been developed essentially in an unidirectional (bottom-up) way, to integrate parameters and mechanisms at a given scale into effective, and hopefully reduced, descriptions at higher scales. However, lower-scale properties are directly coupled with properties of the higher scales (e.g. 3D chromosome distribution in the nucleus partly governs gene expression, which itself participates in nuclear architecture). The very complexity of living systems and biological functions lies partly in the presence of these bidirectional feedbacks between higher and lower scales that have become established over the course of evolution. Self-consistent or iterative “up-and-down” approaches therefore need to be introduced to account for the strong interconnections between the levels and ensuing circular causal schemes. Multiscaling vs. self-scaling

To properly account for the behavior of a biological system, a multiscale approach should jointly tackle all the scales, with no way to skip a priori any microscopic details or macroscopic assemblies. Obviously, such modeling would rapidly reach a high level of complexity, and would ultimately be intractable. This limitation on multiscale descriptions imposes a drastic change in the paradigm underlying the modeling of biological systems. To reduce the complexity level, it has been proposed (Lavelle/Benecke/Lesne) to devise models taking the biological function as a starting point and continuing guideline, driving both integrated modeling and supervised data analysis to parallel the biological functional logic. Decomposition is achieved by dissecting its logic and implementation into basic processes. These elementary processes involve features at different scales and are already integrated in their formulation. More generally, such a decomposition results in “self-scaled” functional modules, independent of the arbitrary description or observation scale. As function-dependent representations are inherently multiscale in nature, and the function cannot be discontinuous, this paradigm-transition consequently requires a scale-continuous model. Scale-continuous descriptions may at first sight look prohibitively complex and non-realistic; however, when such a scale-continuous model is constructed in the context of a function-dependent representation, the dimensionality of the variable-vector to be considered collapses.

Emergence vs. immergence

Modeling of biological systems is required to develop formalisms in order to rebuild the complete system by integration of its hierarchical multiscale levels. It can be achieved by defining "micro to macro" (emergence) and "macro to micro" (immergence, microemergence or downward causation) functions and integrating intra-level (horizontal) and inter-level (vertical) couplings. The definition of pertinent variables at each level of organization and their relations is necessary to obtain emergence (resp. immergence) functions that allow analysis to jump from a microscopic (resp. macroscopic) level to a macroscopic (resp. microscopic) level. Emergence and immergence phenomena are well-known in biology, such as the links between the structure topology of tissues and cell behavior. But these causal relationships are difficult to decypher, mainly because the scales at which they occur are not necessarily those at which observations and experiments are done. •	How should we select relevant space and time scales in our experiments/models/theories (selfscaling rather than exhaustive multiscaling)? Can we correlate multiscale in time and space (at least in some instances) in this sorting? •	How can we perform multiscale reconstruction from data recorded at different scales? On which spatial and temporal scales will the model/simulation obtained be valid?

4. Human physiopathology and animal models Human physio-pathology creates uncertainties with constantly moving frontiers between disciplinary fields, for example, neurology, neurosciences, psychiatry, immunology, cardiovascular, metabolism, endocrinology. Human patho-physiology is characterized by progressive dysfunction and deterioration at multiple space and time scales with non-linear interactions between physiological/biological functions, cognition, emotions, and social consequences. Problems can result initially from local conflict between internal and external signals (e.g. dizziness), but this conflict can expand, diffuse and create additional loops with multiple pathogenic reciprocal interactions. Functional problems could be primary or secondary effects of spontaneous adaptive mechanisms aiming to counter primary injury and dysfunction, and it is important to dissociate them. Two main challenges are: •	to apply complex system principles and theoretical frameworks to the design of experimental studies and the analysis of data at different scales (neurological, physiological, behavioral, neuro-psychological, immunological) from individual or large patient populations; •	to search for cross-correlations and interactions in order to obtain new insights into pathogenic primary or secondary mechanisms. This could lead to new, more sensitive differential diagnostic tools, but also to better medical care or functional re-adaptation. There is a need to go beyond a limited multi-disciplinarity of parallel different approaches and use complex systems tools to cross data from different fields and gain further insight.

This issue concerns the whole internal & general medicine, immunology, neuroscience, psychiatry, geriatrics, pediatrics, functional re-education, public health, and complex systems science. Examples of functional problems, some of which have no measurable organic basis are: vertigo - dizziness and equilibrium problems and fear of falling in the elderly, isolated hearing loss, tinnitus, learning problems – dyslexia, but also neuro-degenerative diseases, types of dementia, Lewy-Body and Alzheimer. What causes the switch from physiological auditory noise to perceived unwanted signal in the case of tinnitus in the absence of neuro-otological findings? Major questions include the significance of instantaneous fluctuations of measurements (physiologic, behavioral, e.g. in the case of dementia) in relation to patho-physiology and progressive degeneration of cortical-subcortical circuits. Other examples could be given in immunology: time and space (lymphoid tissues), analysis of the functionalities of the immune system in physiological (ontogeny to aging, gestation) and pathological conditions (cancer, autoimmunity, infections), and interactions with other biological systems such as the nervous, endocrine, metabolic systems. This is based on dynamics analysis of fluid lymphoid cell populations, quantification and identification of phenotype and functions, repertoires, genomics and proteomics.