1. Accelerating a System’s Biology Kernel Using
FPGAs
Muhammad Awais
11029341
Namal college Mianwali
Supervisor:
Dr. Waqar Nabi ( University of Glasgow)
Dr. Safee Ullah ( LUMS )
2. Motivation
A number of computational Approach has been proposed for Modeling and Studying
Biological System.
With the increase in the size of network of Genes, the complexity of Biological Model
increases rapidly.
Field Programmable Gate Array (FPGAs) is one of the best to analyze and study the
behavior of Gene’s Regulatory Network due to its highly Parallel Architecture.
In this project a Complex Model of Gene’s Regulatory network is implemented using
Verilog(HDL).
3. Contents
Background
Gene Regulatory Network of Cortical Area
Implementation in Verilog (HDL)
Results
Conclusion and Future Work
Question & Answer
4. Cerebral Cortex
Divided into many Functionally Distinct Areas Characterized by Different combination of
genes expression.
Genetic mechanisms plays an important role in the development of these area.
My focus will be on the earliest stage of Arealisation: How the patterns of Gene
Expression form early in cortical Area Development.
5. Computational modelling of gene regulatory networks
Why Computational Modelling ?
Complex Behavior is difficult to understand
Simple to implement and to Use
To systematically screen many possible networks.
To predict which regulatory interactions between these genes are important.
Illuminates the design principles of the gene network regulating cortical area development.
6. Boolean Network Model Approach
Boolean Variable:
Representing Genes and Proteins can take only two values i.e. (0 ,1)
Boolean Function
It determines a Boolean-valued output based on certain logical operations.
The basic logical operations include AND, OR and NOT. Operators e.g
D=(A OR B) AND NOT C
Consists of a set of Boolean Variables
{σ1, σ2, σ3, σ4 . . . . . . . , σn}
value of each variable is determined by other variable through a set of Boolean function .
F = {f1, f2, f3, f4, . . . . . . . , fn }
B is the Boolean function corresponding to variable
One function is assigned to a one variable
7. Boolean Network Model Dynamics
Boolean Network is a Graph Consisting of G( V, B)
• Node represents transcription factor
• Edges Represents regulatory input
• Boolean Gate represents Genes expression
X( t+1) = ( A or C, A and C, not(A) or B)
By giving an initial conditions to variable, it reaches to the stable state
where
Xi (t)= Xi ( t+1)
010 001 101 110
8. Boolean Network Model Dynamics
Trajectory
• Series of State Vector Transition
Fixed Point Attractor: A single state that repeats itself
Limit Cycle Attractor: the system visits the same finite set of states
periodically
010 001 101 110
111
010 001 101 110
9. Boolean Network Model of Genes Regulatory
Network for Cortical Area Development
Development Occurs in Two-Dimensional Field
Experiments focus on anterior-posterior patterning
Along the anterior-posterior axis, gradients of Fgf8, Emx2, Pax6, Coup-tfi, and Sp8 play a
particularly strong role in specifying areal identity
Fgf8, Emx2 , Pax6, Coup_tfi , sp8 : Expressed in Gradient across the
Surface of the Cortex.
11. Interactions of Genes
Genes of interests
• Fgf8, Emx2 , Pax6, Coup_tfi , sp8
Combination of interaction Between 5 Genes
• 25 = 32
Some interaction were not considered such as
Emx2 Pax6 or Emx2 ----| Pax6
24 interactions are assumed
+ve : inductive integrations
-ve : repressive interactions
Text in italic : Genes ( Fgf8, )
Text in up right : Proteins (Fgf8)
12. Possible Interactions
According to the table , 24 Possible interactions are summarized
represents the Inductive interaction
---|Represents the Repressive interaction
24 Possible interactions form 224 (1.68*107) networks
13. Logical Rules or Transformed Boolean Function
24 Possible interactions will be converted to set of Boolean Logical
Function using logical operator
---| ( repressive interaction Deals with Not Operator)
Multiple regulator are combined thorough AND Operator
A protein only be active If it corresponding gene is active at previous time
step.
A gene is active when its transcriptional activator is active.
Eg .. (Fgf8 Fgf8, Emx2---| Fgf8, Sp8 Fgf8, and Coup-tfi ---|Fgf8)
Fgf8 = Fgf8 and not(Emx2) and not(Coup-tfi) .
14. Initial States and Desired Steady Sates of Anterior and
Posterior
State of the system is Represented with ten tuple of ‘1’ and ‘0’.
The State of Network will be [ Fgf8 , Fgf8 , Exm2, Emx2, Pax6, Pax6, Coup-tfi, Coup-
tfi. Sp8, Sp8]
Initial state of Anterior Compartment is [ 1, 1, 0, 0, 0, 0, 0, 0, 0 ,0]
Initial state of Posterior Compartment is [ 0, 0, 0, 0, 0, 0, 0, 0, 0 ,0]
Steady state of Anterior [ 1, 1, 0, 0, 1, 1, 0, 0, 1 ,1]
Steady state of Posterior [ 0, 0, 1,1 ,0 , 0, 1, 1, 0 ,0]
16. Finite State Machine
Compute the state of the system at time t+1
Each network is tested either it follow the trajectory from initial state to final States
or not.
18. Dynamics of Boolean Networks and analysis of its
output
1.68*107 networks are Simulated.
Good and Bad Network
19. Implementation of Networks
Dynamic of the Regulatory Network is implemented.
State of Gene depends on the interaction of its regulator at time t.
Network is Converted to the Boolean Logic Function.
24. Conclusion & Future Recommendation
Boolean networks for the Combination of Gene’s interaction are simulated.
Out of 1.68*107 network, 50559 Networks that Follow the Trajectory from initial
states to Steady states.
To find the Combination of interaction of Genes for Good and Bad Networks
