Open Discussion of Modeling and Computational Approaches to Cellular Signaling


Open Discussion of Modeling and Computational Approaches to Cellular Signaling

Can computational modeling of neuronal processes help to understand cellular signalling?

Mar 12 2004 11:11AM

Rainer C Spiegel

I am no expert in cellular signalling, but I have expertise in computational modeling of neuronal processes, i.e. when trying to create computational models that simulate interaction among nerve cells. I wonder whether the computational models that are typically applied to simulate neuronal processes (among them artificial neural networks, biologically inspired neural networks, compartmental models, Bayesian methods) were already applied in the cellular signalling community. If not, could these techniques assist to better understand cellular signalling? I would be grateful for any expert opinions in this regard.

Re:Can computational modeling of neuronal processes help to understand cellular signalling?

Mar 22 2004 6:16AM

Upinder S. Bhalla

Dear Dr. Spiegel,

Several of the methods you mention have been used as broad abstractions. For example, Dennis Bray discussed the similarities between ANNs and signaling networks some ten years ago (1). Likewise, many signaling models that incorporate space do so using compartments, and in some cases these overlap directly with electrophysiological effects like calcium diffusion. However, the size-scales of signaling reaction-diffusion systems are often smaller than for compartmental models, and conversely, the time-scales of chemical events are often slower than for electrical ones.

I believe that Bayesian methods have been used to help determine likely interactions between signaling molecules when there are not enough direct data points. I don't recall seeing it applied though in an information encoding sense.

It is my view that detailed electrophysiological models will in due course converge with cellular signaling ones in many cases such as the synapse where there is a lot of cross-talk. The more abstract computational models will in my view remain as informative but not directly applicable.

1. D. Bray, Intracellular signalling as a parallel distributed process. J. Theor. Biol. 143, 215-231 (1990).

Similarities between neural networks and signaling networks

Mar 23 2004 8:29AM

Rainer C Spiegel

Dear Professor Bhalla,

Thank you very much for your advice on the similarities between artificial neural networks and signaling networks. Your point of view and the reference you provided are very much appreciated.

Yours sincerely,
Rainer Spiegel

Mathematical Models in Signaling Systems Conference

Apr 22 2004 9:02AM

Nancy R. Gough

An alert STKE user sent the editors a letter regarding an upcoming conference that may be of interest to those in the modeling community.

Vanderbilt University Summer Conference

Mathematical Models in Signaling Systems

June 16-18, 2004

Vanderbilt University, Nashville, Tennessee

Sponsored by Vanderbilt University, the NationalAcademies Keck Futures Initiative, and AstraZeneca.

This conference will bring together biologists,physicists, mathematicians and computationalscientists, to assess the state of the art inquantitative modeling of signal transductionnetworks. The meeting will identify a set of methodsand ideas and trace future directions for thisemerging field. In addition, it will provide areference forum for students and young scientists whoare training to take interdisciplinary and systemsapproaches to signal transduction, pharmacology andcell biology.

Session topics include:
Detailed Local Models of Signaling Pathways
Cellular Models and Spatial Complexity
Experimental Approaches to Understanding Networks
Analysis of Network Architecture

For more information, visit or call (615) 322-0672.

Funded by a $40 million grant from the W.M. KeckFoundation, the National Academies Keck FuturesInitiative, is a 15-year effort to catalyzeinterdisciplinary inquiry and to enhance communicationamong researchers, funding agencies, universities, andthe general public. The objective of the FuturesInitiative is to stimulate interdisciplinary researchat the frontiers of scientific inquiry. The FuturesInitiative builds on three pillars of vital andsustained research: interdisciplinary encounters thatcounterbalance specialization and isolation; theidentification and exploration of new research topics;and communication that bridges languages, cultures,habits of thought, and institutions. Toward thesegoals, the multifaceted National Academies KeckFutures Initiative incorporates three core activitieseach year: Futures conferences, Futures grants, andNational Academies Communication Awards. For moreinformation, visit

Mathematical Models of Cellular Signaling

Jun 11 2004 4:55AM

Richard G. Lanzara

In the interest of stimulating further discussion of this important topic, I would like to leap into the fray. However, part of the problem is that there is no fray. We need to have a more substantive discussion based upon recognized biophysical principles. Therefore, allow me to attempt an opening.

Cellular signaling is one of the most basic of interactions that requires an understanding of many physical, chemical, and biological concepts. Unfortunately, few people either know or take the time to understand many of these basic principles because they cross manyscientific boundaries. Such older concepts as LeChatlier's principle and the Weber-Fechner law are often ignored when considering the pharmacological modeling of ligand-receptor response. We often don't question some of the underlying concepts assumed by the more recent attempts to fit mathematical models to receptor responses. Some of thequestions that could be raised are fundamental. Such questions as what is really meant by an equilibrium or reaction quotient? Do we really understand the descriptive nature of these expressions and how they relate to what we're trying to describe? I think not (see and

With regard to Weber's law, most modelers don't address why it is that all of our senses appear to obey this basic law (see After all, our senses work as signaling networks too and should be incorporated into a complete model for the receptor response.

Perhaps the key to understand cellular signaling lies with an understanding of rapid receptor desensitization also called tachyphylaxis or fade. This phenomenon occurs for a large number of receptors and normal physiological signaling networks. Some have tried to model desensitization with mathematical models or intracellular events, such as downstream effectors, that seem to become more complex than useful. Many of these intracellular explanations either fail to prove rapid or unequivocal enough to make a believer out of me. In addition, the use of antagonistswith agonists suggest that much of the rapid desensitization can beprevented (see This doesn't fit with most of the recent pharmacologic models or theories.

I don't wish to appear as a "bull in a china shop", but sometimes the emperor has no clothes.

Network Analysis of Cell Signaling

Oct 17 2005 10:29AM

Nancy Gough

Two recent articles describe results on cell signaling that were obtained through the analysis of medium- and large-scale cellular networks. Ma'ayan et al. in Science describe the analysis of a network based on a survey of the literature for signaling in mammalian hippocampal neurons. Stelzl et al. in Cell analyzed a network based on interacting proteins of the human proteome. Both groups used graph theory methods to analyze the properties of the networks and Stelzl et al. identified new participants in the Wnt pathway.

A. Ma'ayan, S. L. Jenkins, S. Neves, A. Hasseldine, E. Grace, B. Dubin-Thaler, N. J. Eungdamrong, G. Weng, P. T. Ram, J. J. Rice, A. Kershenbaum, G. A. Stolovitzky, R. D. Blitzer, R. Iyengar, Formation of regulatory patterns during signal propagation in a mammalian cellular network. Science 309, 1078-1083 (2005). [Abstract] [Full Text] [Editors' Choice summary]

U. Stelzl, U. Worm, M. Lalowski, C. Haenig, F. H. Brembeck, H. Goehler, M. Stroedicke, M. Zenkner, A. Schoenherr, S. Koeppen, J. Timm, S. Mintzlaff, C. Abraham, N. Bock, S. Kietzmann, A. Goedde, E. Toksöz, A. Droege, S. Krobitsch, B. Korn, W. Birchmeier, H. Lehrach, E. E. Wanker, A human protein-protein interaction network: A resource for annotating the proteome. Cell 122, 957-968 (2005). [PubMed] [Editors' Choice summary]

Using Simulation Software: What Software Do You Use?

Aug 9 2006 8:01AM

Nancy R. Gough

In a recent STKE Review, Hlavacek et al. discuss how to use rules based on protein interactions to develop models for studying cell signaling.

Have you developed simulation software or applied any of the existing tools to your system? What kinds of issues have you encountered? How do the existing tools need to evolve? What mark up language are you using for data exchange compatibility? Share your experiences with others people trying to model and simulate cell signaling and biochemical events.

Some rule-based simulation software tools and languages include:

Share your favorite simulation or modeling software!

Highlights from "Managing the Data Explosion in Systems Biology"

Mar 6 2007 7:24AM


At the December 2006 American Society for Cell Biology meeting, there was a Special Interest Subgroup entitled "Managing the Data Explosion in Systems Biology". One focus of this Special Interest Subgroup was issues related to data integration. It appears that, instead of pushing toward a commonly adopted standard, some labs are building tools to allow data in different formats to be mapped and imported for performing analysis and interpretation of high throughput data results.

H. Steven Wiley (Pacific Northwest National Laboratory, WA) introduced the session. Wiley talked about the fact that, although there is a lot of data in cell biology, we are still actually data-poor in terms of the complex data required to really understand cell physiology and the response to signals and changes in the environment. There is a lot of "simple" data--gene sequences, protein interaction information, and structure information--but little "complex" data--information about the dynamics of the system. The research approach thus far has been to figure out the parts and then try to figure out the dynamics. The computational approaches used to analyze the "parts data" to produce dynamic systems biology interpretations and generate hypotheses include both highly specific models and more abstract models. Wiley ranked the various approaches, from most specific to least, as models based on differential equations (these require the most complex and complete data sets), Markov chain models, Boolean models, Bayesian models, and finally statistical models.

There are fundamental issues related to integrating the large, albeit simple, data sets that are generated and processing this information into meaningful knowledge. Some of the specific problems that Wiley mentioned included redundancy in the sequence databases, redundancy in the probe sets used for microarray analysis, uncertainty in the identification of proteins (from mass spectrometry experiments, for example, where the peptides generated could be derived from more than one protein), uncertainty in the protein quantitation data, missing data, and the lack of tools for cross-referencing gene and protein identifiers across databases. Unfortunately, the gene identification standards are not the same as the protein identification standards, so it can be very time-consuming to match a peptide to its protein and then the protein to the gene. Wiley indicated that researchers at the Pacific Northwest National Laboratory can generate 480 Gigabytes of proteomic data per day, but lack sufficient automated mechanisms for identification and interpretation of the results.

Following this introduction, several speakers described efforts related to mapping high throughput data sets to information in existing databases. Some of the tools mentioned relevant to the modeling or computational analysis of cell signaling included

  • Cytoscape network visualization software
  • Onto-Tools statistical gene expression analysis software
  • Gaggle data management application

Feel free to add to this list of tools and comment on any experiences you may have had in working with these or other tools for computational approaches to cell signaling.

Can Mesoscopic Models Test Spatial Mechanisms of Cell Signaling?

Mar 8 2007 6:15AM


Therapeutic intervention in malfunctioning signaling networks requires (at least) three levels of information. The identity of the participating proteins, and knowledge of their active sites; the network of pairwise (and higher)interactions; and the sensitivity of the network to perturbations so as to optimize the intervention. Whereas many possible drug targets are known, and whole networks of interacting proteins are being identified, the spatio- temporal mechanisms underlying signal transmission are largely unknown.

Free diffusion of proteins released from receptors at the plasma membrane to their next signalling partner, while calculationally simple, is extremely unlikely in the crowded environment of the cell (see Takahashi et al. 2005). At the other extreme, a fixed, cross-talk free, spatial arrangement of connections, along the lines of an electrical circuit, is also difficult to imagine. In between are many possible patterns for protein interactions, some associated with the cytoskeleton (Forgacs et al. 2004), whose construction and testing using particle-based mesoscopic models is perhaps now possible.

Do readers of this forum have opinions about the feasibility of constructing spatio-temporal models of a signalling network, on a micron length and millisecond (sec?) timescale, so as to extract the sensitivity of thenetwork to perturbation? Do we have enough information on the protein-protein interactions? What simulation techniques could be used?

K. Takahashi, S. N. Arjunan, M. Tomita, Space in systems biology of signaling pathways--towards intracellular molecular crowding in silico. FEBS Letters 579, 1783-1788 (2005). [PubMed Abstract]

G. Forgacs, S. H. Yook, P. A. Janmey, H. Jeong, C. G. Burd, Role of the cytoskeleton in signaling networks. J. Cell Science 117, 2769-2775 (2004). [Abstract] [Full Text]

Can Mesoscopic Models Test Spatial Mechanisms of Cell Signaling?

Apr 3 2007 5:31AM


Dr. Shillcock has asked how feasible it is to make good particle- level models of membrane-associated signaling, for example, for analyzing sensitivity to perturbation. I am optimistic about this.

While there are still only a few studies of spatio-temporal mechanisms in membrane signaling, I think that this is an area poised for rapid development (e.g., Coggan et al. 2005). Advances in microscopy and reporters have made it possible to track movements of molecules at high time and spatial resolution, sometimes down to the individual molecule level (e.g., Bats et al. 2007).

Currently, the number of molecules we can track in this way is limited. However, the data are more direct and 'in-vivo' than, say, enzyme rate constants. There are also increasingly detailed studies on cytoskeletal organization at and near the membrane that will help set up pretty good models of the signaling environment. Studies such as that of Fogacs et al. (2004) further refine the picture by enumerating interaction sets. Overall, it looks like the data side of the modeling equation is in pretty good shape for beginning detailed spatial modeling.

What simulation techniques could be used? To first order, there are already at least three kinds of simulators for 3-dimensional stochastic reaction-diffusion systems at the single-particle/mesoscopic level: The 'MesoRD' class (Hattne et al. 2005) starts with a spatial grid and follows reactions within grid volumes, and movements of molecules between grid voxels. The 'MCell' class of simulators (Coggan et al. 2005) follows individual molecules and their collisions and can handle complex geometries. The 'Smoldyn' class (Andrews and Bray 2004) uses Smoluchowski dynamics and is also a single-particle simulator. To my knowledge, no one has attempted to compare these methods for the specific problem of reactions in the context of dense cytoskeletal networks near the plasma membrane.

One could probably go a long way with these spatial simulation methods, but I feel that there are two more levels at which the simulator toolkit needs to be enhanced. One is the capability to keep track of multiple states of individual molecular complexes, which grows combinatorially with the number of binding sites. The program Stochsim has had this capability for several years (Le Novere and Shimizu 2001). A more difficult problem is to also keep track of the movement and growth of cytoskeletal elements themselves, including their mechanical properties (reviewed by Karsenti et al. 2006).

To summarize: I think it is indeed becoming feasible to make models to study spatio-temporal signaling in and under the membrane. Many suitable kinds of data are already becoming available. Simulation methods still have some way to go, and the computational load is high, but useful calculations should already be possible.


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Combining Simulation Techniques to Create a Model

May 3 2007 10:35AM


Reading Professor Bhalla's comment, it is clear that there are already several simulation codes that can model aspects of signaling pathways. It seems that the existence of multiple internal states of proteins (phosphorylation states, etc), which result in extremely large numbers of coupled differential equations, is a significant problem with some of these simulation types. Also, the need to follow the behaviour of small numbers of molecules and to capture the fluctuations in populations of proteins is essential. I propose that mesoscopic simulation techniques, such as coarse-grained Molecular Dynamics (MD), Brownian Dynamics (BD), and Dissipative Particle Dynamics (DPD), may be combined to model different aspects of a cell signaling pathway, resulting in a model that can represent the pathway in its normal operation and allow it to be perturbed in clinically-relevant ways.

My background is in physics, so I am not an expert in this field, but I think that in order to construct a useful and predictive model of a signaling pathway it is necessary to represent each particle as an "Object" in the simulation code (Tolle et al. 2006). Objects have identity, position, and internal states. There is no combinatorial explosion of populations in such a model, as only the particles (proteins, ions, monomers,...) present in the pathway need to be represented. In such a simulation, there may be various physical phenomena occurring (ion diffusion, bulky-protein diffusion, tethered proteins floating around a filament, a membrane gently undulating) each of which provides a possible target for physical intervention.

Such a model requires capturing the behaviour of various transport processes: Free particle diffusion, tethered-particle oscillations, cytoskeletal filament assembly and disassembly, protein-protein binding that depends on the proteins' internal states, etc. Each of these processes will have natural length- and time-scales, and combining these different scales in one simulation is quite a challenge. Particularly, the presence of solvent in most MD and DPD simulations of membranes limits them to systems of the order of (100 nm)3. BD can go to much larger systems, but has then to be somehow integrated into the more detailed techniques.

If such a simulation can be constructed, one could explore the dependence of the signaling pathway on generic perturbations and then specialise it to particular pathways. If diffusion of protein A until it binds with protein B is part of the pathway, the effects of such diffusion occurring near a cytoskeletal filament can be tested; the filaments can be spatially distributed in various ways, from a regular 3-D lattice to a fractal network, and the variation of the signal transit time measured (Shafrir et al. 2000). Physical effects, such as depletion layers, occlusion of binding sites when several proteins approach each other, crosstalk between different pathways, will appear naturally in this model. It may be possible to test proposals for how signals are transmitted by proteins bound to the cytoskeleton (Park et al. 2003).

A problem that will then arise is the sheer volume of data that such a detailed simulation will produce. Recent work using DPD simulations to model tension-driven vesicle fusion (Shillcock and Lipowsky 2005), which used vesicles of 30-nm diameter and planar membranes of (100 nm)2, produced about 1 GB of particle-coordinate data per fusion event. If such a simulation were to be scaled up to 1 micron, each event would generate 1 TB of data. Most of this is just the solvent coordinates, but these would be required if a simulation were to be restarted. Discarding the coordinate data requires that analysis must be performed "on the fly", which in turn means that one must anticipate the observables that one wants to measure. But the great benefit of simulations is that they can reveal unexpected effects, which would be missed if only expected observables were monitored. Retaining enough detail in the simulation to represent all relevant physical phenomena, while still being able to handle the data generated, is a nontrivial hurdle.


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