Accelerating a System’s Biology Kernel Using FPGAs

Muhammad Awais11029341Namal college Mianwali

Supervisor: Dr. Waqar Nabi ( University of Glasgow) Dr. Safee Ullah ( LUMS )

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).

Contents Background Gene Regulatory Network of Cortical Area Implementation in Verilog (HDL) Results Conclusion and Future Work Question & Answer

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.

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.

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

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)

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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

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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.

Proposed Design Methodology Steps

Interactions of Genes

Logical Rule

Hardware Description(DHL)

Verification

Xilinx Simulation FPGA

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 interactionsText in italic : Genes ( Fgf8, )Text in up right : Proteins (Fgf8)

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

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) .

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]

Implementation in Verilog (HDL)

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.

Flow Chart Code in Verilog

Dynamics of Boolean Networks and analysis of its output

1.68*107 networks are Simulated. Good and Bad Network

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.

Results Initial Condition of Anterior [ 11 0 0 0 0 0 0 0 0 ] = 768

Best Performing Network

This network is Good. Interaction of Gene were translated into set of Boolean logic Function

Result: Initial State of Anterior [ 11 0 0 0 0 0 0 0 0 ] = 768

Result

Initial State of Posterior = [ 0 0 0 0 0 0 0 0 0 0 ] = 0

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

THANK YOU