Simpathica: a computational Systems Biology Tool within the Valis Bioinformatics Environment



Download 87.28 Kb.
Date conversion10.10.2017
Size87.28 Kb.

Simpathica: A Computational Systems Biology Tool within the Valis Bioinformatics Environment

(In memory of a friend, and mentor, Dr. Isidore “Izzy” Edelman, 1920 -2004)



Bud Mishra1, Marco Antoniotti, Salvatore Paxia and Nadia Ugel

NYU/ Courant Bioinformatics Group

Courant Institute, New York University,

715 Broadway, New York, NY 10003.






Section 1: Introduction

Biology thrives on complexity, and yet our approaches to decipher complex biological systems have been simple, observational, reductionist and qualitative. The observational nature of biology may even seem self-evident, as for instance expressed below more than three centuries ago by Robert Hooke, whose work Micrographia of 1665 contained his microscopical investigations that included the first identification of biological cells.

“The truth is, the science of Nature has already been too long made only a work of the brain and the fancy. It is now high time that it should return to the plainness and soundness of observations on material and obvious things.”

As we begin to observe, infer and list the fundamental “parts” out of which biology is created, we cannot stop marveling at how these same components, their variants and homologues interconnect, intertwine, and interact using some universal principles that still remain to be fully deciphered. In order to unravel this biological complexity, of which we only have a hint so far, it has become necessary to develop novel tools and approaches that augment, and rigorously formalize those human reasoning processes, which until now could be used for only tiny toy-like subsystems in biology. To this end, the anticipated “Computational Systems Biology Tools” aim to draw upon constructive mathematical approaches developed in the context of dynamical systems, kinetic analysis, computational theory and logic. The resulting toolkit aspires to build powerful simulation, analysis and reasoning facilities that can be used by working biologists for multiple purposes: in making sense of existing data, in devising new experiments and ultimately, in understanding functional properties of genomes, proteomes, cells, organs and organisms. If this ambitious program is to ultimately succeed, there are certain critical components that require special attention of computer scientists, and applied mathematicians. Those we list below:



  1. There is a critical need for powerful computational environments, where novice users can build prototyping tools quickly. An example of such a tool is the multi-scripting Valis environment, which provides rapid prototyping facilities in the same way as Matlab and Mathematica do for other disciplines. See [15].



  1. There is a critical need for research and pedagogic modelling tools that allow a novice user to understand, reason and ponder about large, complex and detailed biochemical systems effectively, efficiently and still effortlessly. Our effort in this direction is exemplified by the modular and hierarchical modelling, simulation and reasoning tool, called Simpathica, which can extract nontrivial temporal properties of diverse classes of biochemical networks, be they regulatory, metabolic or signalling. Simpathica is constructed using the Valis environment. See [3]—[6], [11] and [14].



  1. There is a critical need for further and rapid development of new biotechnological approaches to provide measurements at single-molecule scales with high throughput and enhanced accuracy. We believe that significant improvements will emerge from the confluence of ideas from nanomechanical sensing devices, single-molecule biochemistries, better photochemistry, photonics and microscopy, and clever experiment and algorithmic designs, integrating these complex multi-component devices. See [1],[2], [7], [12] and [13].


  1. Finally, there is a critical need for a catalogue of illustrating examples, where the afore-mentioned methodologies prove their power unambiguously. Given the infancy of this emerging field, these pioneering experiments will face many unpredictable hurdles, but the experience gained will most likely revolutionize our collective scientific viewpoint. Primary among these grand challenges could be the one related to various processes involved in cancer: cell cycle regulation, angiogenesis, DNA repair, apoptosis, cellular senescence, tissue space modeling enzymes, etc. We note that presently there is no clear way to determine if the current body of biological facts—in this instance, the ones related to cancer—is sufficient to explain phenomenology. In these particular cases, rigorous mathematical models with automated tools for reasoning, simulation, and computation can be of enormous help to uncover cognitive flaws, qualitative simplification or overly generalized assumptions.


This paper is organized as follows: We first describe the structure of the computational systems biology toolkit, the Valis environment with related software and database system, in which Simpathica is embedded (section 2). This discussion is followed by a description of Simpathica software architecture and implementation within Valis (section 3) and an illustrative example (Wnt signaling in section 4). We conclude (in section 5) with a list of grand challenges. Section 2 and section 4 should be of interest to systems biologists interested in applying these tools to other examples. Section 3 should interest bioinformaticists engaged in building ever-powerful computational tools for new rapidly arriving biological problems, protocols and technologies. Section 5 should interest systems engineers, mathematicians and computer scientists excited by the new challenges that biology has created for many of our classical fields.

Section 2: Valis and Simpathica Systems

The toolkit combining the Valis software environment and the Simpathica systems-biology reasoning tool is the product of over three years of research and development. While these systems were designed for researchers in the life science community, the basic elements of its design are rather flexible and the tools can be adapted easily for other areas as well (e.g., medical informatics or computational finance).


Currently the NYU computational systems biology toolkit consists of three core components; these are:


  1. Valis, an environment for rapidly integrating bioinformatics research performed by many different groups.

  2. NYU Microarray Database, a database for collecting, sharing, distributing, and analyzing microarray abundance data.

  3. Simpathica, an advanced systems-biology reasoning tool, to simulate and reason about biological processes.

All of the tools are built with an open architecture allowing modular enhancements to be developed easily and integrated rapidly. Because Valis allows rapid prototyping, and Simpathica can model biological domain knowledge these tools allow scientists to quickly develop new hypotheses based on earlier experiments and available literature, and a platform to explore the steps needed to deepen their understanding.


Valis


The bioinformatics environment, Valis, includes tools for visualization of biological information, design and simulation of in silico experiments and storage and communication of biological information. Valis sets itself apart from other environments through two key features:


  1. Language Independent Architecture: The Valis advanced scripting engine can integrate research from multiple groups into a single environment. Researchers using the Valis framework can share both the data and the algorithms for the analysis of that data. Valis’s language independent architecture allows research groups to leverage programs written in different languages. Valis currently supports scripting in R, Perl, Python, JavaScript, SETL and Common Lisp among others. This effectively allows Valis users to seamlessly integrate the major open sourced computational biology platforms Bioconductor, BioPerl, and BioPython. Native libraries can be integrated in the system and used by all the supported languages.
  2. Whole Genome Analysis and Systems Biology Analysis Libraries: Valis is versatile. Custom-built data-structures and algorithms make it possible to perform whole genome analysis as well as simulation and reasoning of large biochemical networks on commodity hardware. As the throughput of sequencing efforts increases, Valis opens up new avenues for comparative genomics studies through computationally efficient large-scale whole-genome analysis tools.

For instance, Valis has been used in conjunction with single molecule physical mapping technology and microarray CGH technology to develop a set of comparative and functional genomic methods that can validate and find errors in genome sequence data, search for copy number variations in cancer cell lines and create models of genome evolution to understand large segmental duplication and functional evolution of genes through duplication or splicing variants. Ability to create new algorithmic approaches rapidly within Valis is hoped to have an immediate and direct impact on the biological community: creating algorithms for understanding and extracting information from genomic and transcriptomic data in a coordinated manner; building, modifying, and correcting existing models to understand biological processes; and creating a common and unified language for biologists to communicate, exchange data, design and disseminate experimental protocols.



NYUMAD


Currently, a significant portion of the experimental biological measurements is focused on gene expression or genomic polymorphisms, and is obtained with microarrays. The wealth of microarray data being generated by biological researchers necessitates a system that can manage, analyze, persist, and distribute this information efficiently to other researchers. Such a system faces numerous challenges including the sheer quantity and complexity of such data, lack of interoperability among systems and the often proprietary methodologies used by the research laboratories, generating the data.

Significant improvement has been accomplished through standardization. For instance, over the last couple of years, MAGE-ML (MicroArray Gene Expression Markup Language) has emerged as the accepted standard for microarray data (http://www.mged.org), allowing for the transmission of XML documents describing this data. A Java object model derived directly from this specification also exists known as the MAGE-OM, thereby allowing MAGE-ML documents to be converted into their corresponding runtime Java objects and vice versa. This standard has grown widely in its adoption, and has made specification in one of its subsets (MIAME) required for most publication in archived journals. As the only currently existent standard for microarray data, MAGE-ML continues to grow in popularity.

We have developed in our toolkit a system to maintain and analyze biological abundance data (for example microarray expression levels or proteomic data) along with associated experimental conditions and protocols. The prototypic system is called the NYU Microarray Database (NYUMAD) and has been expanded to deal with many other related experiments. It uses a relational database management system for the storage of the data and has a flexible database schema designed to store any type of abundance data along with general research data such as experimental conditions and protocols.
NYUMAD is a secure repository for both public and private data. Users can control the visibility of their data. Initially, the data might be private, but after the publication of the results the data can be made visible to the larger research community. Data analysis tools are supplemented with visualization tools. The goal is to provide not only a set of existing techniques but to incorporate ever more sophisticated and mathematically robust methods in the data analysis and to provide links and integration with other NYU tools such as the Valis system.
In addition, we have designed and are implementing the microarray Gene Expression Communication (MAGEC) system, which seeks to fill the need for a robust and interoperable information management system for this large and varied data model. The system has three primary goals:


  • Strict adherence to the MAGE-ML standard for microarray data to provide a foundation for interoperability with other data systems.

  • Modularization of software services to allow easy reuse and deployment of system sub-components based on a specific laboratories research needs.

  • Extensibility to allow developers to quickly create powerful data-editing GUI clients specific to their laboratory needs.

The software system (under development) is a 3-tier system whereby client applications used to edit/manipulate microarray data (GUI applications, analysis tools) exchange data with JAVA Servlets via MAGEC-ML documents. Exchange is specified in MAGEC, a thin wrappers for MAGE-ML documents, which include transaction specific information describing how to use the attached MAGE-ML data.

A different but related database, NYUSIM, is used to store in silico time-course data obtained through various methods of simulation. NYUSIM and NYUMAD share many features in common, and NYUSIM can be used interchangeably, when the microarray data is obtained in vivo or in vitro by a series of experiments, sampling over time. The traces obtained from this database can be analyzed in many different ways: for instance, time-frequency analysis with NYU BioWave, or temporal logic analysis with Simpathica.

Simpathica


The Simpathica system occupies a central role in our systems biology toolkit. It allows biologists to construct and simulate models of metabolic, regulatory and signaling networks and then to analyze their behavior. Biochemical pathways can be drawn on the screen through a visual programming environment or, in a specialized XML format (SBML, see [16]), a language originally designed to promote information exchange between multiple systems and programs. The system allows a biologist to combine simple building blocks representing well-known objects: biochemical reactions and modulations of their effects. The system then simulates the pathways thus entered. Coupled with a natural language system, the Simpathica tool allows a user to ask questions, in plain English, about the temporal evolution of the pathways previously entered.
In general, using modeling tools like Simpathica to simulate biological processes in silico, a biologist can model and study the behavior of complex systems exploring many different scenarios rapidly without relying solely on experimentation.

Theoretical Basis for Simpathica

As noted earlier, Simpathica has a modular and hierarchical design that allows a user to effortlessly construct and rigorously analyze models of biochemical pathways composed out of a set of basic reactions. Each reaction is thought of as a module and belongs to one of many types: reversible and irreversible reactions, synthesis, degradation, and reactions modulated by enzymes and co-enzymes or other reactions satisfying certain stoichiometric constraints. If the stochastic nature of these reactions is ignored (i.e., mass-action models), each of them can be described by a first order algebraic differential equation whose coefficients and degrees are determined by a set of thermodynamic parameters. As an example, a reaction modulated by an enzyme leads to the classical Michaelis-Menten’s formulation of reaction speed as essentially differential equations for the rate of change of the product of an enzymatic reaction. The parameters of such an equation are the constants Km (Michaelis-Menten Constant) and Vmax (maximum velocity of a reaction). In a simple formulation, such as in S-system (see [17] and [18]), this approach provides a convenient way of describing a biochemical pathway as a composition of several primitive reaction modules which can be automatically translated into a set of ODE’s with additional algebraic constraints. Simpathica and XS-system, described in [3]—[6], [11] and [14], (an extension of the basic S-System) retains this modular structure while allowing for a far richer set of modules and constraints.

The Simpathica architecture is composed of two main modules and several ancillary ones. The first main module is a graphical front end that is used to construct and simulate the networks of ODE’s that are part of the model being analyzed. Simpathica uses, among others, the SBML format [16] for exchange. The second module, XSSYS is an analysis module based on a branching time temporal logic, that can be used to formulate questions about the behavior of a system, represented as a set of traces (time course data) obtained from wet-lab experiments or computer simulations. The simplest forms of such queries are about the system steady-states, as there is very little interesting temporal structure to such queries. These queries are of the form “Is it true that staring at a particular initial state, the system can eventually get to a state and remain there without any variation in the states?” Other queries can be about the system robustness (system eventually returns to a state retaining certain properties under various forms of perturbation), reachability analysis (all the states that the system can eventually get to; or all the states from which the system can enter a state with some desirable or undesirable property), frequently visited states, etc. The class of queries in such a branching time temporal logic is rather rich, but yet amenable to efficient computational manipulation.
Thus, starting with a state-trace of a bio-chemical pathway, (i.e. a time-indexed sequence of state vectors representing a numerical simulation of the pathway) as input, Simpathica performs the following operations.


  • Simpathica answers complex questions involving several variables about the behavior of the system. This is rather different from visually examining intertwined sets of simulation traces of a large complex system.
  • Simpathica stores traces in an ancillary database module, NYUSIM, and allows easy search and manipulation of traces in this format. The analysis tools allow these traces to be further examined to extract interesting properties of the bio-chemical pathway.


  • Simpathica classifies several traces (either from a single experiment or from different ones) according to features discernible in their time and frequency domains. Multi-resolution time-frequency techniques can be used to group several traces according to their features: steps, decreases, increases, and even more complex features, such as, memory.

  • Simpathica can automatically generate interesting properties that distinguish one model from a variant in the same family. For instance, by examining cell-cycle models of wild types, mutants and double-mutants, Simpathica can generate a story about how they subtly differ in their temporal behaviors.

With these tools, Simpathica provides an environment to suggest plausible hypotheses and then, refute or validate these hypotheses with experimental analysis of time-course evolution. It also allows investigating conditions or perturbations under which a biochemical pathway may modify its behavior to produce a desired effect (an instance of a control engineering problem).
The XSSYS Simpathica back-end implements a specialized model checking ([8]—[9]) algorithm that, given a “model trace” and a temporal logic formula expressed in an extended CTL form, can state whether the formula is true or false, while providing a counterexample in the latter case: i.e. the system gives an indication at which point in time the formula becomes false.

A full description of the syntax and semantics of the temporal logic language manipulated by Simpathica/XSSYS is beyond the scope of this paper and hence, omitted. For the purpose of the present discussion, it suffices to assume that all the standard CTL operators are available (e.g., modal operators such as “always,” “eventually,” “globally,” “in future,” “until” and the standard Boolean operations such as “and,” “or,” “implies” and “not”). For instance, robustness of a “purine metabolism pathway model,” is succinctly expressed by a statement such as “Always (PRPP > 50 * PRPP1 implies (steady_state() and Eventually (IMP > IMP1) and Eventually (HX < HX1) and Eventually(Always(IMP = IMP1)) and Eventually(Always(HX = HX1)).” This statement captures a very complex notion of biological robustness: “An (instantaneous) increase in the level of PRPP will not make the system stray from the predicted steady state, even if temporary variations of IMP and HX are allowed.”
Thus, the main operators in XSSYS (and CTL) are used to denote possibility and necessity of propositions over time. In our case such propositions involve statements about the value of the variables representing concentrations of molecular species. For instance, to express the query asking whether a certain protein level, p, will eventually grow above a certain threshold value, K,
we write “eventually (p > K).” We also augment the standard CTL language with a set of domain dependent queries. Such queries may be implemented in a more efficient way and express typical questions asked by biologists in their daily data analysis tasks. As an example, we can formulate complex queries like “Always [Globally (X in [L, H]) and eventually (X = L) and eventually (X=H) and globally (X = L implies next (X in (L, H] until X = H)) and globally (X = H implies next (X in [L, H) until X = L)) ]” The query expresses the fact that the value of the X variable “oscillates” between the two values of L and H. Note that our temporal logic deals with time in a topological sense and hence lacks the expressive power to assert that the time period between L and H is constant. On the other hand, this same topological nature of time helps us to express natural ordering among important biological events, independent of whether the events are controlled by processes operating in fast or slow time-scales. Thus, in spite of few obvious shortcomings, CTL is still powerful enough to describe many properties of the system, such as liveness and safety. Furthermore, for those temporal properties expressible in the logic, the analysis tool efficiently constructs counter examples when input query fails to hold true or restricts the conditions under which the query can be satisfied. A more through introduction to XSSYS and its capabilities can be found in the references [5]—[6] and [11].

Section 3: Simpathica within Valis


Next we examine how the possibility of using multiple scripting languages within Valis has proven very useful in rapid construction of tools for bioinformatics and computational biology. To this end, we consider, in this paper, the Simpathica system described earlier and developed as part of the DARPA BioCOMP project.

The Simpathica/XSSYS system is logically divided into a front end and a simulation system, i.e. Simpathica proper and its analysis back-end XSSYS. The two components work together to construct, simulate and analyze the behavior of metabolic and regulatory networks. The biochemical pathways are entered into the system either via the main Simpathica user interface or in an XML format. The system then simulates the pathways entered and produces trace objects. The XSSYS backend, written in Common Lisp, manipulates these traces (or traces produced by other simulation software or experiments) and evaluates queries about the temporal evolution of the pathways in an appropriate temporal logic language. In summary, the following are the key steps:

(i) The Simpathica front end takes as input descriptions of metabolic and regulatory pathways constructed from a set of standard building blocks, which describe a repertoire of biochemical reactions, and can display these pathways in a graphical representation.

(ii) Simpathica then transforms this graph into an internal XML representation that can be also used for data exchange purposes. This internal representation consists of a set of Ordinary Differential Equations (ODEs) along with initial conditions. These ODEs are then translated into Python code, which performs the actual simulation by integrating the set of equations. The result of such a simulation is the trace object to be input into the XSSYS trace analysis system.

(iii) The output of the Simpathica front end consists of an XML model and a trace object produced indirectly by the chosen ODEs integrator (for instance, Python in this specific case).

(iv) Once these are available, the XSSYS system takes the trace object and a temporal logic query and evaluates the truth-value of the query using a model-checking algorithm. If the query turns out to be false over the trace, XSSYS will also return a counterexample (in the form of a time index indicating a point where the trace falsifies the query).

The modules produced for the BIOCOMP project initially used the OAA Object Agent Architecture, to facilitate integration between modules written in different languages and produced by different groups. However the OAA architecture initially selected to speed up prototyping of the BIOCOMP system has many shortcomings:


  1. In this architecture, each agent must register with a “facilitator” (written in Prolog), which centralizes most exchanges.

  2. The facilitator serves to solve queries written in an “Interagent Communication Language” (ICL) that must be built by the clients. The ICL uses most of the power of the unification-based semantics of Prolog. However, this approach requires agent writers to actually know and write in Prolog, which is further compounded by the problem that requests in ICL must be laboriously constructed using an Abstract Syntax Tree library in Java and/or C.

  3. Performance issues arise for in-process calls; limits may be imposed on message sizes.

In Valis we were able to do much better. Once having assembled all the underlying building blocks needed, e.g. the XML parsers, graph viewers, ODE integrators, the XSSYS subsystem, it is possible to prototype in Valis a system like Simpathica/XSSYS in a matter of couple of weeks.



Figure 1: Simpathica Gui Design

A basic graphical user interface can be put together in a Valis form in a few hours, since most of the widgets needed are standard controls of the form manager. The interface can be organized using multiple ‘Tab’ container widgets and using different tabs for I/O, the model editing widgets, the simulation pane, the graphical results of the simulation and the interface with the XSSYS subsystem. The figure seen above shows the tabs and the ‘model editing’ pane.

The code that handles events from the Forms and customizes the interface can readily be written in JavaScript.

The only graphical element needed that is a bit unusual is a viewer for showing a graphical representation of the pathways. For this widget, we use the Adobe SVG viewer. This is a freely available control that can render models written in the SVG language with zooming capabilities.

Since most of the internal data structures with which Simpathica/XSSYS works are based on XML, it is appropriate to use the versatile XML parser from Microsoft to handle them. In Valis this can be made available using just one code line:

xmlparser=CreateObject("Msxml2.DOMDocument.4.0");

A model of a pathway can be easily stored into XML files and retrieved using functionalities provided by the XML parser object. Once loaded and parsed this model is used to update the internal data structures (namely the ‘compounds’ and ‘reactions’ lists) and the corresponding graphical widgets.

We construct a graphical representation of the model from the internal XML representation and feed it to the SVG widget. We use the DOT language (a general graph description language) as an intermediate language for this graphical representation. The DOT code is produced by applying a style sheet to the XML model. For example, a subset of the Wnt Signaling Model, which will be presented in detail later, will yield the following DOT code:

digraph G {

X0 [label="W", style=filled];

X1 [label="Dshi"];

X2 [label="Dsha"];

X1 -> "Yv1" [label="v1", arrowhead=none];

X0 -> "Yv1" [style=dotted];

"Yv1" -> X2;

"Yv1" [shape=point];

X2 -> X1 [label="v2"];

}

In this representation X0 trough X2 and Yv1 are nodes (each one with certain properties, i.e. label, style etc.); a list of the edges follows. The DOT code shows a reversible reaction between Dshi and Dsha that is modulated by Wnt.

The Graphviz system can produce a variety of other graphical representations (among them SVG) once provided with models described in the DOT language. We reworked this system into a standalone control, which is then made available to Valis.

// this function reloads the SVG from the dot string

// dotStr is the DOT description of the model

function updateSVG(dotStr) {

var f, svgStr;


// use the graphviz control to obtain SVG code

svgStr = graphviz.DotToSvg(dotStr);


// save the svg string to file for efficiency purposes

f = fso.CreateTextFile(pathname +"\\diagram.svg",true,false);

f.write(svgStr);

f.close();


// visualize the svg diagram

activeSvgCtl.SRC = pathname + "\\diagram.svg";

}



Figure 2: The SVG viewer embedded in a Valis Form.

This program fragment yields a graph that summarizes the reaction pictorially, as shown above. Furthermore, the system allows the user to navigate through this graph using the SVG viewer. Note that the internal model used to produce the graph representation can be transformed into an intermediate representation suitable for the generation of a set of ODEs. This intermediate representation is obtained with the application of another XML style sheet:


function generateScript4Map() {

var xmlmap = null;

//generate the xml map from the gui

xmlmap = downloadMap();

//transform the map (xmlmap) to the graph internal //representation (xmlgraph) using the style sheet (xslmap2graph)

xmlmap.transformNodeToObject(xslmap2graph, xmlgraph);

writeDebugInfo("Graph", xmlgraph.xml);

//generate the python script for the ODE

return xml2py(xmlgraph);

}

Without much difficulty, we can then dynamically produce some Python code (in the xml2py function above) with the step function for the integrator:



class ___simpathica:

def WntPathway_subset(self, X, t):

xdot=[]

xdot.append(0)



xdot.append(+-1*(+0.182*pow(X[1],1)*pow(X[0],1))++1*(+1.82e-2*pow(X[2],1)))

xdot.append(++1*(+0.182*pow(X[1],1)*pow(X[0],1))+-1*(+1.82e-2*pow(X[2],1)))

return xdot
initial = [ 1,100,0]

compoundsNames = ["W", "Dshi", "Dsha"]

functionName = "___simpathica().WntPathway_subset"

A Python ODE integrator (based on Numeric Python) will integrate the ODEs generated as above.

from Numeric import *

from scipy import *

from scipy.integrate import *

from scipy import gplt


def executeSimulation(script, fT, tT, st):

exec script


global fromTime, toTime, steps, precision, time, Y

fromTime = fT

toTime = tT

steps = st

precision = (toTime - fromTime) / float(steps)

time = arange (fromTime, toTime, precision)

Y = odeint(eval(functionName), initial, time)

gplt.plot(time, Y)


This Python function is called directly from the Simpathica event handlers (written in Javascript) once the simulation is started:
// Call the Python integrator. Pass the equations and the simulation

// parameters

executeSimulation(generateScript4Map(), from, to, steps);

The ‘executeSimulation’ Python function shown above, provides also for a default visualization of the traces of the simulation. It is very easy to customize the current plotting program used by the visualizer, or even to choose another plotting control (e.g. Microsoft Chart control).




Figure 3: Simulation of the Wnt Subset

The XSSYS query event (generated by the button ‘Run XSSys’in the ‘XSSys Query’ pane shown in Figure 4) can be handled by some JavaScript:

function Form1_LoadTraceCommandButton::Click() {

i = Load_Trace(filename);

Select_Trace(filename);

Form1_LoadedTracesListBox.AddItem(filename, i);

}
function Form1_RunXSSysButton::Click() {

Form1_TLResultTextArea.text = "";

Form1_TLResultTextArea.text=Analyze_This(Form1_TLQueryTextArea.text);

}



Figure 4: The XSSys Query Pane

The JavaScript Query-Handler, in turn, calls (the front end to) the XSSYS system in Common Lisp. The XSSYS query pane is shown in Figure 4 above, which indicates how the user may enter the queries and get a response. All of this is integrated in the code in Common Lisp shown below. The Common Lisp code is a simple wrapper around the XSSYS package which implements the core of the Temporal Logic analysis facility (with the identifiers prefixed by xssys:) The Common Lisp integration within Valis and the ActiveX Scripting Engine is as tightly coupled as VisualBasic and much more so than that in Perl or Python. A function defined within Common Lisp appears directly within the ActiveX Scripting Engine namespaces and any function or procedure defined, say, in Perl or Javascript appears as a regular function in a Common Lisp “script.” Of course, Common Lisp is compiled natively, thus enhancing the performance over other “scripting languages”.

The two functions below |Load_Trace| and |Analyze_This| become thus visible in the ActiveX Scripting Engine namespaces and can be referenced by, say, a VisualBasic user interface. No special registration code is necessary.

(defun |Load_Trace| (filename)

(unless (probe-file filename)

(return-from |Load_Trace| –1))

(setf xssys:*the-current-trace*

(xssys:load-trace (pathname filename) :btd))

(or (position (xssys:trace-system-name xssys:*the-current-trace*)

(xssys:list-all-traces)

:test 'string=

:key 'xssys:trace-system-name)

-1))

(defun |Analyze_This| (query)

. . .

(multiple-value-bind (result



satisfying-state-groups

counter-example)

(xssys:analyze-this trace-data form)

(when counter-example


(setf counter-example-index (second counter-example)))

. . .


;;; several variables in this example are introduced elsewhere.

(format *standard-output*


"~&;;; Query ~S prop ~S prop-ag ~S result ~S counter ~S~2%"

query


propositionalp

propositional-always-p

result

counter-example-index)



. . .

)

Section 4: Wnt Signaling Example



There has been a considerable interest in signaling pathways involving Wnt proteins, which form a family of highly conserved secreted signaling molecules. These proteins regulate cell-to-cell interactions during embryogenesis. Furthermore, Wnt genes and Wnt signaling are also implicated in cancer.


Figure 5. Wnt signaling pathway rendered by Simpathia.
While at a qualitative level, scientists now have significant insights into the mechanisms of Wnt action, and data from better experiments through genetics in Drosophila and Caenorhabditis elegans, and gene expression in Xenopus embryos, we still only have a rudimentary understanding of how the complete pathway operates under various situations.

In a widely accepted model of the Wnt pathway, Wnt proteins bind to their receptors on the cell surface, transduce the signal, through several cytoplasmic relay components, to beta-catenin, which then enters the nucleus and forms a complex with TCF to activate transcription of Wnt target genes.

A clear description of this model and an earlier numerical analysis can be found in the paper by Kirschner et al. [10]. The same analysis could be repeated in Simpathica within about a week, as described below and involves few steps.


  • Step 1: First, we took each reactant and each reaction and entered them into Simpathica. All we needed to do was to input the reactants’ names and concentrations and for each reaction list, the reactants, products, and the rate constants. We obtained almost all of the data from the article by Kirschner et al. with one exception. Instead of using a rapid equilibrium approximation as in [10], we made educated guesses for the forward and backward rate constants that would be consistent with fast enzymatic reactions reaching equilibrium quickly. These differences may explain some discrepancies in the scale of the results. Simpathica automatically generates the entire pathway graphically and computes a system of differential equations to simulate the system evolution over time.




Figure 6. List of Reactants in Wnt pathway entered in Simpathica.



Figure 7 . List of Reactions in Wnt pathway entered in Simpathica.

  • Step 2: Next we checked that the system has different steady states under the two different conditions corresponding to the presence or absence of Wnt. These can be tested by queries: “W=0 implies eventually steady_state()” and “W=1 implies eventually steady_state()”. We can now compare the steady-state concentrations generated by our simulation to the experimental data.





Figure 8. Steady-state analysis for Wnt pathway for different values of Wnt.


  • Step 3: Further validation of the model is obtained studying the degradation rate of beta-catenin under different conditions: we can reproduce different experimental settings simply by parameterizing initial concentrations or rate constants through Python scripts.




Figure 9. Kinetics of beta-catenin degradation, see [10].




  • Step 4: Finally we

  • can model the Transient Wnt Stimulation











  • , where Wnt is present at the beginning of the simulation but then decays exponentially.





Figure 10. Beta-cetenin response, see [10].



Figure 11. Axin response, see [10].

Following the analysis, presented in Kirschner et al. paper, we also noticed that beta-catenin’s increase is only temporary, wheras axin remains downregulated. Moreover, the response by axin precedes that of beta-catenin.


Section 5: Conclusion

Many scientists and engineers have articulated that the new biology of the new millennium needs a “regime change” and that the formal tools from systems sciences, with their rigor, and depth, are desperately needed. And yet, in spite of such noble goals, “systems biologists” still wait patiently to be greeted as liberators by the vast majority of biologists. Perhaps, in that lies the grandest of all challenges for the systems biologists.

The most important grand challenge concerns better measurements and experiment design, as well as in making the data available in an electronic public forum. The solution should comprise steps to intervene and measure at the single-molecule and single cell levels, publication of the experimental data using a clear unambiguous lexicon, and ability to conduct experiments inexpensively with facilities that can be shared by the entire community. Community of biologists working within a social framework, where each scientist contributes from his own accumulated knowledge and experience, can create the needed lexicon and ontology. Software to ease the communication among the scientists is not difficult, but does not exist at this point. There should be a public database of biological models at various spatio-temporal resolutions and with as much of the in vitro or in vivo kinetic parameters as possible to compile. Experiments at single-cell and population levels using wild-type cells, mutants, cells perturbed by different conditions or RNA interference should be catalogued with precise time-course measurements. Along these directions, it will be worthwhile to focus on complete map of pathways for one organism, say C. elegans. This digital worm, which can be dubbed C++ elegans, could provide an enhanced environment for in silico experiments. Other pathways of interest could be: cell cycles, proliferation, degradation, and apoptosis. Ultimately, a focus on models of aging and diseases will be of considerable human interest.

Thus, the purely technical grand challenges for this field will be experimental and computational, and will stay with us for a considerably long time. Most of these computational problems deal with accuracy and uncertainty in the model, model complexity and computational complexity. Reactions Models: Instead of just ODE models using DAE’s, one must generalize our tools to PDE’s (incorporating spatial properties), SDE’s (small population size for interacting molecules) and hybrid models (part continuous, part discrete, but also spatial and probabilistic, in one general framework). State Space (Product Space): A number of interacting cells can be modeled by product automata. In addition to the classical “state-explosion problem,” we also need to pay attention to the variable structure due to (a) cell division, (b) apoptosis, and (c) differentiation. Communication: We need to model communication among cells mediated by interactions between extra-cellular factors and external receptors, efficiently and accurately.

We believe that the solution to such computational grand challenges is in reduction of complexity by Hierarchical Modeling and Symbolic Modeling. As we go to more and more complex cellular processes, a clear understanding can be obtained only through modularized hierarchical models. For this process to succeed, we will need to derive simple input-output models of low-level modules by projection (elimination of state variables) or by reduction (state-collapsing), while retaining bisimulation properties. The system dynamics should have a succinct symbolic representation that can be manipulated algebraically (without explicit and exhausting simulation). For instance, in case of a hybrid automaton model, one may be able to represent flow, invariant, jump and reset conditions, with a subset of the kinetic parameters left as unknown variables (e.g., k1, k2, … kn). By algebraically manipulating the equations (also, inequations and inequalities) one can elicit many biological properties of the system in terms of constrains on the unknown and unmeasured variables and parameters. Interestingly enough, because of a similar development of symbolic (and to a less significant degree, hierarchical) model checking procedures in the discrete asynchronous setting, we have been able to tame the computational complexity of computer-aided verification of complex and large engineered systems such as VLSI circuits[8]—[9].


References





  1. Anantharaman, T.S., Mishra, B., and Schwartz, D.C. (1997). “Genomics via Optical Mapping II: Ordered Restriction Maps,” Journal of Computational Biology, 4(2): 91-118.

  2. Anantharaman, T.S., Mysore, V., and Mishra, B. (2005). “Fast and Cheap Genome wide Haplotype Construction via Optical Mapping,” The Pacific Symposium on Biocomputing: PSB 2005, (Eds. R.B. Altman, A.K. Dunker, L. Hunter, T.A. Jung & T.E.Klein), World Scientific.

  3. Antoniotti, M., Park, F.C., Policriti, A., Ugel, N., and Mishra, B. (2003). “Foundations of a Query and Simulation System for the Modeling of Biochemical and Biological Processes.” In Proc. of the Pacific Symposium of Biocomputing (PSB’03), (Eds. R.B. Altman, A.K. Dunker, L. Hunter, T.A. Jung & T.E.Klein), 116-127, World Scientific.

  4. Antoniotti, M., Piazza, C., Policriti, A., Simeoni, M., and Mishra, B. (2003) “Modelling Cellular Behavior with Hybrid Automata: Bisimulation and Collapsing,” International workshop on Computational Methods in Systems Biology, CMSB'03, (Ed. C. Priami), Lecture Notes in Computer Science, LNCS: 2602: 57-74, Springer-Verlag.
  5. Antoniotti, M., Policriti, A., Ugel, N., and Mishra, B. (2002). “XS-systems: extended S-Systems and Algebraic Differential Automata for Modeling Cellular Behaviour.” In Proceedings of HiPC 2002, (Eds. S. Sahni, V.K. Prasanna & U. Shukla), LNCS 2552:431-442, Springer-Verlag.


  6. Antoniotti, M., Policriti, A., Ugel, N., and Mishra, B. (2003). “Model Building and Model Checking for Biological Processes.” Cell Biochemistry and Biophysics, 38:271–286.

  7. Aston, C., Schwartz, D.C., and Mishra, B. (1999). “Optical Mapping and Its Potential for Large-Scale Sequencing Projects,” Trends in Biotechnology, 17:297-302.

  8. Browne, M.C., Clarke, E.M., Dill, D., and Mishra, B.: "Automatic Verification of Sequential Circuits Using Temporal Logic," IEEE Trans. Computers, 35(12): 1035-1044, 1986.

  9. Clarke, E.M., Grumberg, O., and Peled, D., Model Checking, MIT Press, Cambridge, Mass., 1999.

  10. Lee, E., Salic, A., Krüger, Heinrich, R., Kirschner, M.W. (2003) “The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt Pathway.” Public Library of Science, Biology 1: 116-132.

  11. Mishra, B. (2002). “A Symbolic Approach to Modeling Cellular Behavior.” In Proceedings of HiPC 2002, (Eds. S. Sahni, V.K. Prasanna & U. Shukla), LNCS 2552:725-732, Springer-Verlag.

  12. Mishra, B. (2002). “Comparing Genomes,” Special issue on "Biocomputation:" Computing in Science and Engineering. , 42-49.
  13. Mishra, B. (2003). “Optical Mapping,” Encyclopedia of the Human Genome, 4: 448-453, Nature Publishing Group, Macmillan Publishers Limited, London, UK.


  14. Mishra, B., Daruwala, R., Zhou, Y., Ugel, N., Policriti, A., Antoniotti, M., Paxia, S., Rejali, M., Rudra, A., Cherepinsky, V., Silver, N., Casey, W., Piazza, C., Simeoni, M., Barbano, P.E., Spivak, M., Feng, J-W., Gill, O., Venkatesh, M., Cheng, F., Sun, B., Ioniata, I., Anantharaman, T.S., Hubbard, E.J.A., Pnueli, A., Harel, D., Chandru, V., Hariharan, R., Wigler, M., Park, F., Lin, S.-C.., Lazebnik, Y., Winkler, F., Cantor, C., Carbone, A., and Gromov, M. (2003). “A Sense of Life: Computational & Experimental Investigations with Models of Biochemical & Evolutionary Processes,” OMICS - A Journal of Integrative Biology, (Special Issue on BioCOMP, Ed.: S. Kumar), 7(3): 253-268.

  15. Paxia, S., Rudra, A., Zhou, Y., and Mishra, B. (2002). “A Random Walk Down the Genomes: DNA Evolution in VALIS,” (with). Computer, 35(7): 73-79, IEEE Press.

  16. System Biology Markup Language. (2002). http://www.sbml.org.

  17. Voit, E. O. (1991). Canonical Nonlinear Modeling, S-system Approach to Understanding Complexity. Van Nostrand Reinhold, New York.

  18. Voit, E. O. (2000). Computational Analysis of Biochemical Systems A Practical Guide for Biochemists and Molecular Biologists. Cambridge University Press.




1 All correspondence must be addressed to: mishra@nyu.edu. The work reported in this paper was supported by grants from NSF’s ITR programs, Defense Advanced Research Projects Agency (DARPA)’s BioCOMP program, and New York State Office of Science, Technology & Academic Research (NYSTAR).





The database is protected by copyright ©hestories.info 2017
send message

    Main page