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Rules for Modeling Signal-Transduction Systems
- Science’s STKE
, 2006
"... Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactio ..."
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Cited by 77 (20 self)
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Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system’s behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machine-readable format to enable electronic storage and exchange of models, as well as basic knowledge about protein-protein interactions. Here, we review the motivation for rule-based modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rule-based modeling.
The Calculus of Looping Sequences
"... Abstract. We describe the Calculus of Looping Sequences (CLS) which is suitable for modeling microbiological systems and their evolution. We present two extensions, CLS with links (LCLS) and Stochastic CLS. LCLS simplifies the description of protein interaction at a lower level of abstraction, namel ..."
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Cited by 16 (4 self)
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Abstract. We describe the Calculus of Looping Sequences (CLS) which is suitable for modeling microbiological systems and their evolution. We present two extensions, CLS with links (LCLS) and Stochastic CLS. LCLS simplifies the description of protein interaction at a lower level of abstraction, namely at the domain level. Stochastic CLS allows us to describe quantitative aspects of the modeled systems, such as the frequency of chemical reactions. As examples of application to real biological systems, we show the simulation of the activity of the lactose operon in E.coli and the quorum sensing process in P.aeruginosa, both described with Stochastic CLS. 1
Robustness: confronting lessons from physics and biology
- Biological Reviews of the Cambridge Philosophical Society
, 2008
"... The term robustness is encountered in very different scientific fields, from engineering and control theory to dynamical systems to biology. The main question addressed herein is whether the notion of robustness and its correlates (stability, resilience, self-organisation) developed in physics are r ..."
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Cited by 14 (0 self)
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The term robustness is encountered in very different scientific fields, from engineering and control theory to dynamical systems to biology. The main question addressed herein is whether the notion of robustness and its correlates (stability, resilience, self-organisation) developed in physics are relevant to biology, or whether specific extensions and novel frameworks are required to account for the robustness properties of living systems. To clarify this issue, the different meanings covered by this unique term are discussed; it is argued that they crucially depend on the kind of perturbations that a robust system should by definition withstand. Possible mechanisms underlying robust behaviours are examined, either encountered in all natural systems (symmetries, conservation laws, dynamic stability) or specific to biological systems (feedbacks and regulatory networks). Special attention is devoted to the (sometimes counterintuitive) interrelations between robustness and noise. A distinction between dynamic selection and natural selection in the establishment of a robust behaviour is underlined. It is finally argued that nested notions of robustness, relevant to different time scales and different levels of organisation, allow one to reconcile the seemingly contradictory requirements for robustness and adaptability in living systems.
From Molecules to Organisms: Towards Multiscale Integrated Models of Biological Systems, in "Theoretical Biology Insights
"... Abstract: A consensus has recently emerged that further progress in understanding human physiopathology will demand integrative views of biological systems. In this context, complex systems and related interdisciplinary approaches of biology are expected to help. The aim of this collective paper is ..."
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Cited by 8 (0 self)
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Abstract: A consensus has recently emerged that further progress in understanding human physiopathology will demand integrative views of biological systems. In this context, complex systems and related interdisciplinary approaches of biology are expected to help. The aim of this collective paper is basically to provide a starting point for further discussions and interactions within the community of complex systems biologists. After brie y introducing some general concepts, we present four major challenges that should be tackled in the next years. These represent future directions that we isolated as priority concerns for modern biology. Suggestions of how to reach these destinations are provided, with the hope that they will soon lead to concrete advances towards fully consistent multiscale models of biological systems and a better understanding of physiopathology.
A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems
, 2011
"... One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in case of settings with multiple interacting agents. We apply the Artificial Homeostatic Hormone Systems (AHHS) approach, whic ..."
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Cited by 8 (8 self)
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One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in case of settings with multiple interacting agents. We apply the Artificial Homeostatic Hormone Systems (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHScontrollers is compared to NeuroEvolution of Augmenting Topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multi-modular settings. We analyze the evolved controllers concerning the usage of sensory inputs, the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.
Flexible single molecule simulation of reaction-diffusion processes
- J. Comput. Phys
"... An algorithm is developed for simulation of the motion and reactions of single molecules at a microscopic level. The molecules diffuse in a solvent and react with each other or a polymer and molecules can dissociate. Such simulations are of interest e.g. in molecular biology. The algorithm is simila ..."
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Cited by 6 (3 self)
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An algorithm is developed for simulation of the motion and reactions of single molecules at a microscopic level. The molecules diffuse in a solvent and react with each other or a polymer and molecules can dissociate. Such simulations are of interest e.g. in molecular biology. The algorithm is similar to the Green’s function reaction dynamics (GFRD) algorithm by van Zon and ten Wolde where longer time steps can be taken by computing the probability density functions (PDFs) and then sample from its distribution function. Our computation of the PDFs is much less complicated than GFRD and more flexible. The solution of the partial differential equation for the PDF is split into two steps to simplify the calculations. The sampling is without splitting error in two of the coordinate directions for a pair of molecules and a molecule-polymer interaction and is approximate in the third direction. The PDF is obtained either from an analytical solution or a numerical discretization. The errors due to the operator splitting, the partitioning of the system, and the numerical approximations are analyzed. The method is applied to three different systems involving up to four reactions. Comparisons with other mesoscopic and macroscopic models show excellent agreement.
An optimal number of molecules for signal amplification and discrimination in a chemical cascade
- Biophys. J. 91:2072–2081
, 2006
"... ABSTRACT Understanding the information processing ability of signal transduction pathways is of great importance because of their crucial roles in triggering various cellular responses. Despite continuing theoretical investigation, some important aspects of signal transduction such as a transient re ..."
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ABSTRACT Understanding the information processing ability of signal transduction pathways is of great importance because of their crucial roles in triggering various cellular responses. Despite continuing theoretical investigation, some important aspects of signal transduction such as a transient response and its connection to stochasticity originating from a small number of molecules have not yet been well understood. It is, however, through these aspects that unexpected and nontrivial properties of the information processing emerge. In this article, we analyze the transient behavior of a simple signaling cascade by taking into account the stochasticity originating from the small number of molecules. We identify several properties of the signaling cascade that emerge as a result of the interplay between the stochasticity and transient dynamics of the cascade. We specifically demonstrate that each step of the cascade has an optimal number of signaling molecules at which the average signal amplitude becomes maximal. We further investigate the connection between a finite number of molecules and the ability of the cascade to discriminate between true and error signals, which cannot be inferred from deterministic descriptions. The implications of our results are discussed from both biological and mathematical viewpoints.
A spatial extension to the π calculus
- In Proceedings on the First Workshop “From Biology to concurrency and back
, 2007
"... Spatial dynamics receive increasing attention in Systems Biology and require suitable modeling and simulation approaches. So far, modeling formalisms have focused on population-based approaches or place and move individuals relative to each other in space. SpacePi extends the π calculus by time and ..."
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Spatial dynamics receive increasing attention in Systems Biology and require suitable modeling and simulation approaches. So far, modeling formalisms have focused on population-based approaches or place and move individuals relative to each other in space. SpacePi extends the π calculus by time and space. π processes are embedded into a vector space and move individually. Only processes that are sufficiently close can communicate. The operational semantics of SpacePi defines the interplay between movement, communication, and time-triggered events. A model describing the phototaxis of the Euglena micro-organism is presented as a practical example. The formalism’s use and generality is discussed with respect to the modeling of molecular biological processes like diffusion, active transportation in cell signaling, and spatial structures. Keywords: pi calculus, spatial modeling, systems biology. 1
A Computational Framework for the Topological Analysis and Targeted Disruption of Signal Transduction Networks
"... ABSTRACT In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-sc ..."
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ABSTRACT In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-scale reconstruction of a signaling network composed of nine signaling pathways implicated in prostate cancer. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exist two distinct types of outputs in the signaling network that either can be elicited by many different input combinations or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades. By comparing the obtained disruption targets with lists of drug molecules we find that many of these targets can be acted upon by existing drug compounds, whereas the remaining ones point at so-far unexplored targets. Overall the proposed computational frameworks can help elucidate input/output relationships of signaling networks and help to guide the systematic design of interference strategies.
Visualization of signal transduction processes in the crowded environment of the cell
- in IEEE Pacific Visualization Symposium (PacificVis
"... Figure 1: Different representations of signal transduction in biological cells: microscopic image from an experiment obtained with confocal laser scanning microscopy; microscope-like image generated with one of our visualization techniques; geometric representation emphasizing single proteins and th ..."
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Cited by 4 (3 self)
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Figure 1: Different representations of signal transduction in biological cells: microscopic image from an experiment obtained with confocal laser scanning microscopy; microscope-like image generated with one of our visualization techniques; geometric representation emphasizing single proteins and the structure of the cell; closeup of the highlighted region showing the crowded environment in a simulated cell (from left to right). In this paper, we propose a stochastic simulation to model and analyze cellular signal transduction. The high number of objects in a simulation requires advanced visualization techniques: first to handle the large data sets, second to support the human perception in the crowded environment, and third to provide an interactive exploration tool. To adjust the state of the cell to an external signal, a specific set of signaling molecules transports the information to the nucleus deep inside the cell. There, key molecules regulate gene expression. In contrast to continuous ODE models we model all signaling molecules individually in a more realistic crowded and disordered environment. Beyond spatiotemporal concentration profiles
