# CD4+ T-cell fate optimization for leukemia thresholds

I think that with regard to thymus (T) cell CD4+, and its heterogeneity,

it has already been established, (Carbo, Ghosh, Rothenberg, etc, etc).

This optimization problem, should not be too terribly difficult.

Ie., there are only eight T-help (AKA “types”) that CD4+ can differentiate into

what they call that “Plasticity”.

I think that a useful application is a mixed-integer linear-programming

optimization application can be realized. The problem is a constrained CQM solver problem.

The optimization uses a linear objective and constraints.

The objects are "real-valued" and "integer" values. Eg.,

this relates to the Big-8 proteins(Pts), ie., the subsets of the CD4 T-help

cell in which the "Units" will be;

"continuous", (0 < inf in this app). These are AKA:

"REAL-VALUED" variables."Discrete" units (eg., ACTivation, DIFFerentiation, and Expression,

can be construed to be an ON/OFF state, they are AKA: "INTEGER-VALUED" variables.

The attributes, 6 of them, are embedded into a Pts{{..}} dict, and processed by user input.

( I'll call "Big-8" as (=::) {Tfh, Th9, Th2, iTreg, Tr1, Th22, Th17, Th1})

These may or may not evolve (a threshold exists) from the CD4+ t-cell, (heterogeneity).

Again, there may not be that much of a challenge the hybrid solver for this.

WHAT I’D LIKE HOWEVER, :) is to use this type of optimization on determining the threshold

of CLL, chronic lymphocytic leukemia onset. Any type of leukemia research, CS-wise is

my passion, actually : ) According to Carbo et al, there are 104 proteins, (transcription factors,

receptors, cytokines, interlukens, “component complexes”, up/down regulators etc, in the CD4+ T-cell

compartment). I believe that similar to the above optimization CQM problem, an approach or two

for leukemia thresholds can be accomplished using this CD4+ compartment of 104 proteins.

I’ve already entered all 104 proteins including each of their associated/connected PTs in

an Excel spreadsheet for convenience.

import dimod as dimod

from dwave.system import LeapHybridCQMSampler

import re

# init

strattoamps1 = 0

strACTivation1 = 0

strEXPansion1 = 0

strDIFFerentiation1 = 0

strExpression1 = 0

strPlasticity1 = 0

Units1 = 'continuous'

# This is done for each of the 8 proteins mentioned.

Pts = {'Tfh': {'Attoamps': strattoamps1, 'ACTivation': strACTivation1, 'EXPansion': strEXPansion1,

'DIFFerentiation': strDIFFerentiation1, 'Expression': strExpression1, 'Plasticity': strPlasticity1,

'Units': Units1}, # . . .

# This is also repeated for each of the 8 heterogenous CD4 candidates.

# User input is coded here.

# Constraints are established.

# <Objective and constraint functions are coded>

# <Presentation to the solver is established>

# Lagrange parameters should be explored.

# Analyzing and interpreting results are done.

# Tests and changes to fully optimize to infer E_min is done.

# Comments/notes in the IDE's code.

## Comments

Perez T(Report)Wow, I guess I'm the first to comment on my Comments : ) So -

Merry Christmas and a Happy New Year, all.

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