“Damn! I was so sure it would be there.” Jamie felt on the verge of tears. “Guess I just wanted there to be a correlation.” Tim put his arm around her shoulders. “Hang in there, Sis. Maybe these are just not the right proteins or whatever.” “I’ve exhausted every combination that had any chance of being related. These were the two that provided at least a glimmer of hope.” She pulled the disk out of the computer drive. “Damn!” She tossed the disk on the desk. “Now what am I going to do?” She felt suddenly tired, like a balloon with the air let out. “I was really counting on this working.” She let out a deep sigh. “Well, thanks for the help anyway.” Later she returned to the lab and sat at her desk for a long time, face propped up in her hands, staring at the pile of scattered graphs. Cool winter sunlight streamed through the windows along the wall. Dust particles gently floated down through the light. Her thoughts wandered back over the last few days, searching for what she was missing, trying to recapture that feeling she had when she looked at the graphs earlier. She picked up the graph they had just rerun with the new regression analysis: M2P Mitacopsis and Citoventasis C116 versus the disease. Think, she chided herself, think! “You represent a group of people who all died from this disease, and who all shared this particular combination of proteins,” she said to the graph. Then she focused on the small cluster of points she had gotten so excited about. But she now knew no correlation lurked there. She opened the folder looking for the second chart she had told Tim about, the one with the same proteins but plotted by age…against the population who had the disease. This was the graph she had run accidentally the other night. Again she saw that grouping, indicating the people were all 66 or 67 years old when they died. “So what? Probably random just like the other one.” She looked at the clump of data for several moments hearing Tim’s words, joking about it, “You get to have the disease and you get to know when you might croak.” Suddenly Jamie felt that familiar tingling sensation. Concentrate! She spun back toward the computer. “Where did I put the rest of the data files?” She looked in the desk drawers. Not there, then she remembered putting them in the cabinet over the computer. She opened the cabinet door and grabbed the stack of disks. “Deaths from natural causes” was marked on the second one in the pile. She slipped it into the computer. “Okay, now we’re going to look at the population of people who died from all natural causes, not just from Pick’s Disease,” she said to the screen. She fed the information into the new regression model Tim had left. It took several minutes to carefully create the table just as he had done earlier. Finally the numbers crawled up the screen. She again ran the regression of the two proteins, M2P Mitacopsis and Citoventasis C116, but this time versus age for the whole population group. The system seemed to take forever to crunch the numbers so she could plot the graph. Jamie realized she wasn’t breathing. She deliberately tried to relax, taking deep breaths. Finally the graph appeared on the screen. She stared in amazement. There was the same grouping…but it extended all across the graph, not just a bunch in one corner. “This can’t be,” she said. “Cannot be!” All these people - who died at the same age - all shared the same two proteins? Regardless of the cause of death? No way! Some might have had cancer, some strokes, and others maybe experienced heart attacks. She knew there were no common pathologies among those diseases. So why would there be any relationship between those deaths and these proteins? Her head pounded so hard she couldn’t think straight. “Okay, there you are,” she said, “right there on the screen, but I gotta tell ya, I’m not believing what I’m seeing.” She studied the screen for several minutes. “All right, one more time…from the beginning.” She went back and reentered the data, once again carefully building the table. Her trembling fingers kept hitting the wrong keys. “Easy, mate. Let’s not get all hyper now.” She reran the regression model. The computer whizzed through the data once more. Her face felt warm, like she was sitting near a blazing fireplace. Out came the numbers and then the graph. She studied it closely. Same result. The graph displayed a consistent correlation. She closed her eyes and tried to rein in her excitement. “Settle down now. This might be important. But get a grip. It’s only for one set of protein combinations.” Would other combinations correlate to other ages? She printed a copy of the graph, sat back in her chair and let out a deep breath. She felt exhausted and bleary-eyed. Was she shivering from the cold or the nervous tension that had suddenly taken over her body? Glancing over at the clock she realized she had been working on this for almost the entire day. And she was supposed to meet Eric later at the café. “I’ve got to take a break. I’ve probably done something really stupid…and just generated a silly graph.” She patted the computer with her hand. “I’ll rerun you guys in the morning. Then we’ll see where I’ve gone wrong.” If not, she wondered, then what? Her mind started darting down brightly colored avenues of what such a discovery might mean. Being able to actually predict a person’s life span? She was out of her chair rapidly pacing around. “That would be so incredible!” she said to the now dark screen. Then she tried to settle herself down. “Hang on, there’s a lot more work to do before anyone starts putting on party hats.” But she couldn’t stop smiling.
|