Knowledge-based Simulation of Regulatory Action in ... - CiteSeerX

Knowledge-based Simulation of Regulatory Action in Lambda Phage 3

Tomoaki SHIMADA

3

Masami HAGIYA

Shin-ya NISHIZAKI

y

3

Masanori ARITA

z

Chew Lim TAN

3Dept. of Information Science, Graduate School of Science,

the University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113 Japan

yDept. of Information Technology, Faculty of Engineering,

Okayama University, 3-1-1, Tsushima-Naka, Okayama 700 Japan

zDept. of Information Systems & Computer Science, Faculty of Science, National University of Singapore, Kent Ridge, Singapore 0511

Abstract

In recent years, research on genetic analysis of different biological lives using computers is ourishing. In this research, we have developed a knowledge-based, discrete-event simulation system to simulate proteinsregulated genetic action in lambda phage. Lambda phage is a kind of virus which infects Escherichia coli (E. coli). Speci cally, we simulate the decision between two developmental pathways, that is, lytic growth and lysogenic growth on such conditions as mutation. The novelty of this work is the employment of two dierent levels of abstraction in a genetic model for the purpose of achieving greater precision. Our model is composed of a roughly abstracted level for the noncritical parts which constitute most parts of our model, and a precisely abstracted level for the critical parts. In the former level, our model is a discrete-event simulation in qualitative representation on a knowledgebased system. In the latter level, it is based on reaction formulae in quantitative representation.

1 Introduction In recent years, research on genetic analysis of various biological lives is ourishing under the Human Genome Project, where the nal goal is the analysis of human beings. For the success of the project, it is essential to develop new informatics technologies as well as experimental technologies. We support the genome informatics from the viewpoint of computer scientists rather than from that of molecular biologists. The research in the genome informatics is divided largely into several topics, such as sequence analysis, structure prediction, database, and knowledge processing: Our study is on knowledge processing.

We have developed a knowledge-based simulation system to simulate genetic regulatory action in lambda phage. Lambda phage has genetic regulatory mechanisms which are common in many biological lives and a great number of facts are known about them. Hence, we choose lambda phage as the object of our simulation. In the developmental pathway of the phage, there are two fates, that is, lytic growth and lysogenic growth. We focus our study on determining to which growth each gene is committed on such conditions as mutation. The purpose of our work is to model proteinsregulated genetic action and develop a knowledgebased simulation system for molecular biologists. Based on our experience in the simulation of lambda phage, we shall simulate more general genetic action in the future. Our ultimate object of study is human beings who have very complicated regulatory action, of which a great deal is still unknown. When we know the regulatory action of various biological lives, we can apply the acquired knowledge to human beings and attempt to simulate their genetic action. The novelty of this work is the use of two dierent levels of abstraction in a genetic model for the purpose of achieving higher precision. Our model is composed of a roughly abstracted level for the non-critical parts and a precisely abstracted level for the critical parts. What this novel approach contributes should be considered signi cant, since our model suppresses the details of regulatory action so that it can bring out only the essential parts in full relief and make them conspicuous with accuracy. Most molecular biologists reason about the world of genetics not in quantitative but in qualitative terms like \exist", \non-exist", \much", \little", and so on; nevertheless they insist on precision. Our work basically follows the view of molecular biologists. In our simulation there are two kinds of contribu-

tion to molecular biologists. One is the examination of extended theories and new theories, and the prediction of experimental results. The other is the automation and visualisation of complicated genetic regulatory action. In particular, our simulation can be of great use for showing the causality in complicated proteinsregulated genetic action and for explaining the process of the two fates of lambda phage. It should help molecular biologists ponder on the genetic action, propose new hypotheses, or conduct further experiments. Even if the correctness of our simulation results is not assured to perfection, it can enlighten molecular biologists on their research or saves their precious time, eort, and experimental cost.

Gene Control Circuitry

Very Early N

Nut R

Nut L Early

N

PR

cro

PL

cIII

xis

int

cII

O

P

PL

OL

OL OR

cro

Q

Qut

OR Late

PR

Lytic

R

2 Background

head P R’

tail PR

cro contention

2.1

Lambda phage

activator

Lambda phage [1] is a kind of virus which infects

Lysogenic

Escherichia coli (E. coli). After the infection, the

lambda chromosome circularises and the phage starts to grow by regulation of each protein produced by its own gene. There are two developmental pathways in which the virus can grow, that is, lytic growth and lysogenic growth [2, 3]. Lambda phage provides the best-understood example of the regulation of gene expression by genetic switches. The word switch is employed for regulatory mechanisms, such as repressors, operators, promoters, and anti-terminators, since they act like switches with \on" and \o". In the biological aspects, the developmental pathway of lambpa phage is divided into three stages| very early, early, and late|according to the pattern of gene expression. The rst two stages are identical irrespective of whether the phage is to grow lytically or lysogenically. In lytic growth, the lambda chromosome is extensively replicated, the protein components of new phage particles are produced within the bacterium, and the assembled progeny phage particles are released. On the other hand, in lysogenic growth, the phage chromosome is integrated into the host chromosome. It is passively replicated and quiescently distributed to the progeny bacteria as part of the host chromosome. In a wild type of lambda phage|natural lambda phage|if CII protein is highly active, the infecting phage grows lysogenically; otherwise it grows lytically. In a mutant, the pathway depends upon the kind of mutation. In our simulation, all mutants are assumed to be deleterious and the aected proteins and genetic switches lose their functions completely. Once initiated, the regulatory sequence along each pathway is a cascade, turning on and o groups of genes sequentially. The gene control circuitry by the proteins is illustrated in Figure 1. The location of the proteins and genetic switches is depicted in Figure 2.

OR Figure 1:

cI

P RE

cI

PRM

int

P int

Gene Control Circuitry

Lambda Chromosome

Nut L

P int

PL

P RM OR OL PR cI

N

Nut R P RE

cro

cIII

cII O

xis

P

OR3 OR2 OR1 int

Q P RM att

head

Figure 2:

P R’

PR R cos

Qut

tail

Lambda Chromosome

The description of the mechanism of transcription is necessary to comprehend genetic regulatory action. In the process of transcription, genes within a DNA molecule are transcribed into messenger RNA by an enzyme called RNA polymerase. The process begins with the binding of RNA polymerase near the beginning of a gene to a site called a promoter ; and the RNA polymerase then moves along the linear DNA strand, reading the message on the DNA and simultaneously synthesising RNA. When the RNA polymerase recognises a terminator DNA site, it releases both the DNA and RNA. In its attempt to transcribe a gene, RNA polymerase can be facilitated or hindered by such regulatory proteins as repressors that bind to sites called operators. Conventionally, the repressor is CI protein. Genes can be transcribed beyond the terminator by anti-terminator proteins, such as N proteins that bind to sites called utilisation. The circumstances of the transcription are rendered in Figure 3. Transcription

cI transcript

cro transcript

RNA polymerase Cro protein CI protein (repressor)

Operator OR 3

OR 2

Promoter P RM ON Figure 3:

Lambda DNA OR1

Promoter PR OFF

Transcription

Among some newly produced proteins, Cro proteins and CI proteins are essential factors to determine the bifurcation between two pathways. After the two proteins contention with each other, lambda phage grows lysogenically if the eventual quantity of CI protein surpasses that of Cro protein. If the relation is opposite, it grows lytically. The quantity of each protein is determined by its own production rate. The production rate depends on their existential probabilities at the speci c operator sites. The binding patterns of proteins for the operator sites determine in which direction the transcription of the gene proceeds, as illustrated in Figure 3; the transcription to the left produces CI protein, and the one to the right produces Cro protein. The right operator of lambda phage OR is related to the contention between CI protein and Cro protein, and it is of great importance in the precisely abstracted level of our model. OR comprises three

adjacent sites, OR 1, OR 2, and OR 3, in a row. CI is on, namely, activated on the conditions that:

  Cro

  2.2

nothing is in OR 3, CI

protein is in OR 2.

is on, namely, activated on the conditions that: nothing is in OR 1, nothing is in OR 2. Knowledge-based system

In general, there are two types of problems to which we formulate a solution using computers. One is that of well-structured problems like the problem of solving quadratic equations. There are algorithms for solving these problems, and conventional procedural programming paradigms suce to solve them. The other is that of ill-structured problems like the problem of representing our knowledge gained through experience. Such problems either have no de nite algorithms or are too dicult for us to nd proper algorithms. Knowledge-based systems are suitable for this kind of problems. We consider the simulation of the regulatory action in lambda phage as the latter, because the world of molecular biology has not yet been explored perfectly and precisely. Our simulation has been basically implemented using Smart Elements by Neuron Data [4] which supports the implementation of knowledge-based systems. This software provides object-oriented concepts in frame representation: object structures, message passing and inheritance. Object structures are composed of classes, subclasses, instances, slots and slot values. They may contain relations to other objects and default values. Objects also encapsulate methods, and messages are sent to the objects which then execute appropriate action. Furthermore, slots, slot values and methods allow fully multiple inheritance. 2.3

Qualitative reasoning

Qualitative reasoning yields comprehensible qualitative descriptions of the behaviour of systems. The object of study is usually a physical system, natural or man-made, operating under some physical laws. On that account, it is also called qualitative physics. De Kleer and Brown [5], Forbus [6], and Kuipers [7] laid the foundations of qualitative reasoning. Their models are founded respectively on device structure, physical processes, and constraints between two parameters. They attempted to construct a formalisation of ordinary knowledge about the physical world. In qualitative reasoning, dynamical systems theory is introduced to pursue mathematical aspects of them. For exmaple, Nishida developed the PSX project [8],

in which his group built a highly autonomous system for dynamical systems analysis, called PSX. To determine the behaviour of a system of dierential equations, rst of all the discretely sampled local behaviour is analysed in the vector eld; and then by linking each result of the behaviour analysis together, global behaviour is analysed based on the orbit. The global behaviour is called a phase portrait. We applied the phase portraits in the vector eld to the critical parts in our simulation.

Simulation Model

Roughly abstracted Level Object Knowledge Base Classes . step . protein . switch

Instances Slots & Slot Values

Rule Knowledge Base

3 Simulation

Rules for Synchronisation Rules for Processes

3.1

Structure of simulation

Contention between CI and Cro protein

Our model has two dierent levels of abstraction for the purpose of achieving higher precision: the roughly abstracted level for the non-critical parts and the precisely abstracted level for the critical parts. The contention between Cro protein and CI protein is the critical and crucial part for deciding between two developmental pathways. The roughly abstracted level simulates genetic action regulated by proteins in the common parts of the pathways. It is performed using a knowledge-based system since the precise data are either unknown or unnecessary. In this level, the knowledge is composed of an object knowledge base, for describing various components of our simulation, and a rule knowledge base, for describing the relations among the objects. The precisely abstracted level simulates the contention between the two proteins in qualitative reasoning, based on reaction formulae in quantitative representation. In this level, we make some assumptions and derive the existential probabilities of Cro protein and CI protein from the equilibrium constants of the reaction between proteins and operators; then, we set up the dierential equations based on their probabilities. The bifurcation between lytic growth and lysogenic growth is investigated based on the phase portraits in the vector eld in qualitative reasoning. The result of the precisely abstracted level is embedded in the roughly abstracted level as values in the rule knowledge base. The structure of our simulation model is illustrated in Figure 4. 3.2

Precisely abstracted level

We describe the probability of the binding of a protein to an operator site, which is based on the reaction formula and its equilibrium constant. In the equilibrium state, the binding of a CI protein dimer and a Cro protein dimer to the operator sites is described as CI2 OR n

Cro2 OR n

* ) * )

2CI + OR n 2Cro + OR n

fn = 1; 2; 3g; fn = 1; 2; 3g;

(1) (2)

Precisely abstracted Level Phase Portraits in a Vector Field Figure 4:

Simulation Model

where CI , CI2 , Cro, and Cro2 denote a CI protein monomer, a CI protein dimer, a Cro protein monomer, and a Cro protein dimer, respectively, and OR n fn = 1; 2; 3g denotes a n-th right operator. Each equilibrium constant, namely, dissociation constant is de ned as KCIn KCron

(CI )2 (OR n) (CI2 OR n) (Cro)2 (OR n) = (Cro2 OR n) =

fn = 1; 2; 3g; fn = 1; 2; 3g;

where KCIn and KCron denote the equilibrium constants of the reaction formulae (1) and (2), respectively. The parentheses ( ) in each equation denote molarity, or molar concentration. Considering the condition of the binding patterns of proteins for the operator sites described in Subsection 2.1, we can calculate the probabilities of CI and Cro protein production based on the existential probabilities of the proteins at each operator site. [probability of CI protein production] = PCI =

KCI3 KCro3 + K v2 + K

CI3 Cro3 u2 2 2u ; 2 K K +KCro CI2 Cro2 KCI2 v 2 + KCro2 u2 [probability of Cro protein production] = PCro KCI3 KCro3

=

KCI1 KCro1 + K v2 + K

Cro1 u2

2 K KCro+2 v2 :

CI1 Cro2 We cannot obtain the value of KCro at OR 3 in the literature on lambda phage; therefore, we assume that the value is 1 by the fact that it is a little less than the values of KCro at OR 1 and OR 2 [3]. The data we assumed, written in boldface, and the data [2, 3] we obtained are shown as follows. These values are proportional constants, that is, the actual values are directly proportional to these values. OR 1 OR 2 OR 3 (3) KCI 1 1 25 KCro 8 8 1 KCI1 KCro1

We let the molarity of CI protein and Cro protein be u and v. Given u and v , we can determine which pathway is likely to be chosen and how fast the change is. The former is expressed as the angle of the change and the latter is represented as the magnitude of the change.

Cro 10

0

10

20

30

40

50

CI

Cro

[angle of the change] = Angle

P



Cro ; PCI [magnitude of the change] = M agnitude

= tan01

q

2

CI

2

(4) = PCI + PCro : The value Angle is evaluated at each grid point of the molarity u and v, ranging from 0 to M ax, such that M agnitude > . From these angles at grid points, we can perceive the global behaviour of the quantity of both CI and Cro proteins. The range of u and v depends on , and the range obtained from (4) is approximately 0  u < 50 and 0  v < 10 if and only if  ' 0:01. Within this range, each set of Angle and M agnitude looks like Figure 5. The vector eld is presented in the upper gure, where Angle is indicated by the direction of the arrow and M agnitude is represented by the length of the arrow. This graph is graduated in 2 molarities, and the X -axis and Y -axis represent u and v , respectively. The phase portraits in the vector eld are delineated in the lower gure. It depends upon which is larger between Angle and 1 and how long the arrow is. We can recognise the global ow from the vector eld in Figure p 5. By assuming PCro =PCI = 1, we obtain u = 2 2. Therefore, from the value p of u and the gure, we can conclude that u = 2 2 is the only landmark of the bifurcation between lytic growth and lysogenic growth. Lambda phage chooses lysogenic growth if the eventual quantity of CI protein is more than that of Cro protein. If the relation is opposite, it chooses lytic growth. Hence, from the distribution in the phase portraits, the following information about the global behaviour of u and v is distinguished in terms of u:

Figure 5:







Vector Field and Phase Portraits

The phage is almost surely committed to lytic growth if the p molarity of CI protein is small (0 < u < 2 2). As the phage grows lytically, the production rate of Cro protein is gained and the molarity of Cro protein increases. The phage is almost certainly committed to lysogenic growth p if the molarity of CI protein is not small (u > 2 2). As the phage grows lysogenically, the production rate of CI protein is gained and the molarity of Cro protein does not increase.

The phage is almost certainly committed to both lytic growth and lysogenic growth if the p molarity of CI protein is nearly equal to 2 2 (u ' p 2 2). Since some phages grow lytically and other phages grow lysogenically, a phage can be assumed to grow in both pathways. As the phage grows lytically and lysogenically, the production rate of CI protein is gained and the molarity of Cro protein also increases. We can simulate the critical parts on various conditions by changing the values in (3). In accordance with the assumed value of KCro at OR 3 and the other values, the phase portraits in the vector eld are described and the global behaviour is analysed. The

p

landmark, based on values provided in (3), is 2 2, but it can be changed by modifying the values of (3). 3.3 3.3.1

Roughly abstracted level Object knowledge base

The object knowledge base consists of classes, subclasses, instances, slots, and slots values. There are two main classes: protein and switch. Protein is the class which groups a variety of proteins, and switch is the class which groups the promoters and anti-terminators as genetic switches. In addition to these classes, there is the class step which manages time information, and the class All_Rules which groups all the rules except the rules which control the conditions of time information. The class step has ve subclasses to represent the ve phases: INI (Initial), VE (Very Early), E (Early), L (Late), VL (Very Late). The class step has the slot time and each subclass inherits the slot from the class. The slot value is an integer between 0 and 4. Each slot value of the subclass INI, VE, E, L, and VL is 0, 1, 2, 3, and 4, respectively, throughout the simulation. The class protein has such subclasses as N, Cro, CI, and it has in total 13 subclasses which represent the proteins directly related to the two developmental pathways. The class protein has two kinds of slot: quantity and type. Each subclass inherits the slots from the class. The value of the slot quantity is either \exist" or \none". Only CI protein, however, can take on \little" or \much" after the fate is determined. Likewise, the slot type has the alternative value of \wild" or \mutant". Every subclass has ve instances, each corresponding to a phase. This means that each protein in each phase is implemented as a dierent object. The instance of the subclass of the class protein is also that of the class step. For example, Cro_INI is the instance of both the subclass Cro of the class protein and the subclass INI of the class step. Therefore, the slots and their values have multiple inheritance from the two classes. Likewise, the class switch has such inheritable subclasses as PR, PRM, NutR, and it has in total 9 subclasses which represent the promoters and anti-terminators directly related to the two fates. This class has two kinds of slot: status and type. The slot status has the alternative value of \on" or \o", and the slot type is identical in the class protein. According to the value of the slot status, the value of the slot quantity of the related protein is mainly decided during the execution of simulation. Every subclass has ve instances corresponding to the ve phases and its instance is similar to that of the class protein. For example, PR_INI is the instance of both the subclass PR of the class switch and the subclass INI of the class step.

3.3.2

Rule knowledge base

The main conditions in the rules are about the mutation of proteins, the status of genetic switches, and the quantity of proteins. For example, some mutants decide that the proteins which are activated directly or indirectly by the mutants do not exist. The quantity of some proteins decide the status of the aected genetic switch. The status of the genetic switch also determines the quantity of the in uenced protein. In this manner, the rules are evaluated according to these factors. Each rule, however, has the conditions of time information called time-conditions, in addition to such conditions as the status of genetic switches and the quantity of proteins, since real genetic action depends on time. In our simulation, time is explicitly controlled by rules which control time-conditions, called timerules. It may seem that time-conditions in each rule contradict the essential signi cance of simulation, but they are inevitable for the following two reasons. One reason is that the triggering of some rules is completely dependent upon time, and the other is that the order of rule evaluation and the slot values in each phase should be kept consistent in the knowledge-based system. The rules are composed of two types of rules: the rules for processes and the rules for synchronisation. The former type of rules is the main rules for the simulation of lambda phage. It includes a variety of proteins-regulated genetic action and time-rules are presented. On the other hand, the latter type of rules presents synchronisation between several adequate points. The synchronisation is necessary for our simulation to preserve the order of rule evaluation and to indicate the halfway results by break points. The outline of rule evaluation is as follows. In the rst phase, the system takes an initial set of values as input, while in the following four phases, the set of output values of each previous phase is taken as input. To put it in more detail, in each phase the system evaluates all the rules except time-rules and produces the output values. Time-rules then reset all the rules except time-rules, and the output values are received as input values in the next phase. In the last phase, the system shows the results of simulation. We can know the states of all the rules, and the values of all the variables as well as the slots halfway by putting appropriate break points. 3.4

Example of simulation

Inference during the simulation is achieved through forward chaining, or deduction from asserted facts. In the beginning, the system requires users to input the conditions concerning mutation and CII protein activation as the asserted facts so as to initialise the related slots. Other slots except the slot time with an integral value are assigned \unknown".

After the user has decided which protein is a mutant or which genetic switch is a mutant, the system sends a message concerning the mutation to all the subclasses of the class protein or switch. After these subclasses receive the message, the method attached to them assigns \mutant" to the slot type of either the protein or the genetic switch which has been speci ed as a mutant. It also assigns \wild" to the slot type of the other proteins and genetic switches. Then the system automatically propagates the initial facts to the related rules in the forward chaining fashion. In accordance with the phase, the rules are evaluated in a cascade. The status of each genetic switch is bound to either \on" or \o" by the quantity of proteins and other conditions, such as the status of other genetic switches. Similarly, the quantity of each protein is bound to the alternative value of \exist" or \none" according to the status of genetic switches and other conditions, such as the quantity of other proteins. Hence, our system does not create or delete dynamic objects. Instead, values of such slots as switch and protein are changed dynamically and the simulation proceeds accordingly. An example of the executed simulation is depicted in Figure 6.

Figure 6:

Example of Simulation

In each phase, all the rules except time-rules are evaluated, and only those rules which satisfy timeconditions as well as the other conditions are red. After the evaluation, these rules are reset by timerules at the end of the phase; and then new time information is assigned and evaluated again in the next phase. Thus all the rules are evaluated in each phase, and they are evaluated iteratively ve times in total. In the end, our simulation determines the fate of lambda phage: lytic growth, lysogenic growth, both of them, or neither of them. The system attempts to account for its predictions based on the known facts and on the rules related to those facts. The states of all the proteins and genetic switches in each phase can

be shown after the entire evaluation process nishes. For this purpose, proteins and genetic switches in each phase are implemented as dierent objects. The states of all the rules, and the values of all the variables and slots can also be traced and shown halfway by putting break points. Besides, the overall ow of the processes executed automatically and sequentially can be shown. Thus our simulation can show the causality in the proteins-regulated genetic action and explain explicitly and visually the processes of the two developmental pathways.

4

Related works

Meyers and Friedland are pioneers in knowledgebased simulation in genetics [9]. After their work, several researchers did research on knowledge-based simulation of genetic action. Many of them employed frame representation including object-oriented concepts as methods and message passing. An exception is the work by Mavrovouniotis [10] who employed Lisp language. The objects of their simulation are gene regulation system, metabolic pathways, biochemical reactions, and so on. Their work provide interesting regulatory system for gene expression, such as synthesis, degradation, positive feedback, and negative feedback. Some researchers in this eld applied qualitative physics to biological systems. Koile and Overton [11], Weld [12], and Karp [13] modelled their systems in the framework of qualitative process theory proposed by Forbus [6]; additionally, Koile and Overton's model is based on the system of qualitative dierential equations [5, 7]. Galper and Brutlag et al. [14], Round [15], and Mavrovouniotis simulated genetic action in qualitative representation based on quantitative aspects. Meyers and Friedland also simulated two developmental pathways in lambda phage using a knowledgebased approach. They represented the concepts of time and amounts as real numbers, but these numbers were not strongly supported by experimental data. Therefore, their model is a little lacking in precision. For the purpose of achieving greater precision, our model comprises two dierent levels of abstraction. Like in our model, Weld's Peptide model is also composed of two levels of abstraction. His model consists of discrete process level and continuous cycle level for the purpose of eciency. On the other hand, our model is composed of the roughly abstracted level for the non-critical parts and the precisely abstracted level for the critical parts for the purpose of higher precision.

5

Conclusions and Future Work

Our system simulates two developmental pathways of lambda phage. The overall simulation is knowledge-

based or rule-based in a traditional way, but critical rules are supported with mathematical analysis using chemical equations. In this way, predictable overview of regulatory action is eciently simulated using a knowledge base, and only an unpredictable part is analytically simulated in full relief. Thus, this system can output not only inputted knowledge but also precise prediction by computational analysis, data which helps molecular biologists envision new theories of regulatory action. Our system has not fully automated the interaction between the knowledge-based part and the mathematical analysis part. This interaction, automatic transfer of analyses' result into the knowledge base and vice versa, is our future work. Furthermore, we plan to automatically determine which rule to mathematically investigate. In a really helpful knowledge base for biological systems, an unpredictable rule must be automatically found, mathematically analysed, and eventually re- xed in a knowledge base or simply left to analyse in each evaluation of the rule. We divided the life cycle of lambda phage into ve phases, each grouping time-dependent rules, but this xed number of phases has no biological foundation. Because all the reactions in a real biological body occur simultaneously and indiscriminately, our simulation must do without phases, or at least support dynamic change of the number of phases. This improvement is left to our another future work.

Acknowledgements This work was done while the rst author was visiting National University of Singapore, hosted by the last author. This work bene ted greatly from continuous encouragement by Toshihisa TAKAGI at Human Genome Centre. We also wish to acknowledge Chiew Lan TAI for critically reading the manuscript. This research was supported by a Grant-in-Aid for Scienti c Research on Priority Areas, `Genome Informatics', from the Ministry of Education, Science and Culture of Japan.

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[4] Neuron Data, Inc., Smart Elements Version 2.0 Manual, 1994. [5] J. de Kleer and J. S. Brown, \A qualitative physics based on con uences," Arti cial Intelligence, vol. 24, pp. 7{83, 1984. [6] K. D. Forbus, \Qualitative process theory," Arti cial Intelligence, vol. 24, pp. 85{168, 1984. [7] B. J. Kuipers, \Qualitative simulation," Arti cial Intelligence, vol. 29, pp. 289{388, 1986. [8] T. Nishida, \Towards integration of heterogeneous knowledge for highly autonomous analysis of dynamical systems|preliminary report from the psx project," Journal of Information Processing, vol. 15, no. 3, pp. 350{363, 1992. [9] S. Meyers and P. E. Friedland, \Knowledge-based simulation of genetic regulation in bacteriophage lambda," Nucleic Acids Research, vol. 12, no. 1, pp. 1{9, 1984. [10] M. L. Mavrovouniotis, \Identi cation of qualitatively feasible metabolic pathways," in Arti cial Intelligence and Molecular Biology (L. Hunter, ed.), pp. 325{364, AAAI Press, 1993. [11] K. Koile and G. C. Overton, \A qualitative model for gene expression," in Proceedings of the 1989 Summer Computer Simulation Conference, pp. 415{421, 1989. [12] D. S. Weld, \The use of aggregation in causal simulation," Arti cial Intelligence, vol. 30, pp. 1{ 34, 1986. [13] P. D. Karp, \A qualitative biochemistry and its application to the regulation of the tryptophan operon," in Arti cial Intelligence and Molecular Biology (L. Hunter, ed.), pp. 289{324, AAAI Press, 1993. [14] A. R. Galper, D. L. Brutlag, and D. H. Millis, \Knowledge-based simulation of dna metabolism: Prediction of action and envisionment of pathways," in Arti cial Intelligence and Molecular Biology (L. Hunter, ed.), pp. 365{395, AAAI Press, 1993. [15] A. Barr, P. R. Cohen, and E. A. Feigenbaum, eds., The Handbook of Arti cial Intelligence Volume IV, pp. 323{413, 415{518. Addison-Wesley Publishing, 1989.