The thesis is organised in nine chapters. The chapters are briefly described below:
• Chapter 1: Introduction. This chapter provides an overview of the thesis.
• Chapter 2: Motivation. In this chapter, we discuss what motivated us to explore computational algorithms that serve the needs of biology and medicine.
• Chapter 3: Literature Survey. A critical review of such algorithms are con- ducted to discern their limitations. We find a major limitation in the computa- tional efficiencies of the algorithms which reconstruct time-varying gene regulatory networks from time-series gene expression datasets.
• Chapter 4: Problem Formulation. The objective of this thesis is set to develop novel algorithms that can offer competitive correctness to that of the existing algorithms, yet, in a significantly more efficient manner.
• Chapter 5: Improving Time-efficiency. We develop our first algorithm. This algorithm outpaces the existing algorithms in runtime; moreover, it outperforms the latter in recall. However, the proposed algorithm is observed to suffer from poor precisions and exponential memory requirements.
• Chapter 6: Balancing Recall and Precision. To overcome poor precisions, we develop our second algorithm. This algorithm offers competitive precisions to that of the existing algorithms while being as time-efficient and recall-powerful as the previously proposed algorithm. Therefore, the only issue that remains is that of exponential memory requirements.
• Chapter 7: Improving Memory-efficiency. We resolve the issue by devel- oping two novel algorithms. These algorithms are equivalent to the previously proposed algorithms, except the former algorithms have linear memory require- ments. At this point, we reach the objective of the thesis, which was to develop algorithms that offer competitive correctness to that of the existing algorithms, in a more efficient manner. However, it is observed that the newly developed algo- rithms are unable to capture a particular type of edges known as transient edges;
such edges remain active for a short period of time but may have a long-lasting effect. We choose to resolve this issue since the broader objective of the thesis is to advance the state-of-the-art of reconstruction algorithms.
• Chapter 8: Capturing Transient Edges. The aforementioned issue is mit- igated by developing four more algorithms. These algorithms are equivalent to the four algorithms proposed previously, except the former algorithms are able to capture significantly higher numbers of edges.
• Chapter 9: Conclusions and Future Directions. Finally, we summarise the contributions and discuss few future directions.
At the end of every chapter, a section called ‘Chapter Summary’ is added. It presents a bird’s eye view of how that chapter relates to the chapters preceding it. Naturally, this rule excludes chapters ‘Introduction’ and ‘Conclusions and Future Directions’.
Chapter 2
Motivation
2.1 Important Questions
Study of biological systems finds its origin at the dawn of human consciousness. Genera- tions of researchers have been dedicating their lives in pursuit of two important answers.
Firstly, the big philosophical question of “What is life?” How does a living system be- have the way a non-living system can not? The answer would not be complete unless we understand how a dynamic living system progresses through different stages of its life. However, living systems typically do not live alone. They interact with other living systems and environmental resources. This brings us to our second question which is of immediate concern for human lives. A human being is a very dynamic and open system.
He or she interacts with a large number of other living systems and natural resources.
Some of the interactions are essential to carry out his or her natural developmental processes. On the other hand, some of them are harmful to the expected developmental progression. Deviations from the expected trajectory can cause diseases. Identifying disease-causing interactions helps us to devise preventive and diagnostic strategies for such diseases. At the same time, finding out the interactions whose effect can potentially nullify that of the harmful interactions is crucial for developing therapeutic strategies.
To summarise, the second question asks “How can we apply our biological knowledge in sustaining health?” The following statistics on burdens of wide-spread diseases in India could help us to fathom the compelling need for finding out the answer.
• Cervical cancer: Every 8 minutes, we lose 1 woman (NICPR, 2016, Statistics)
• TB: (1/4)thof the global incidents every year; 2.1 million citizens in 2013 (Central TB Division, 2015, Chapter 2, p. 22)
• HIV: 86,000 newly infected citizens reported in 2015 (NACO and NIMS, 2015, Figure 3, Estimated New HIV Infections in India, 1998–2015)
and the numbers keep growing . . .
These statistics inevitably raise some questions. Such as -
Q. Complex diseases, like - cancers and diabetes, develop stage-by-stage at the molec- ular level and may take decades before clinical symptoms start appearing. By monitoring an apparently healthy individual at the molecular level, can we pre- dict whether such a disease is under development?
Q. Given data collected from healthy individuals and those suffering from a particular disease, can we find out some novel features that can differentiate between two classes of individuals?
Q. If so many of us are infected, why are not all of us sick? Can we identify a set of features that can distinguish individuals more susceptible to a particular disease than the rest?