CppCon 2024

Cancer research involves simulating "pseudo tumors" by sampling stochastic processes. Beginning with C++11, there were new features introduced in both the language and Standard Library that proved to be highly beneficial for simulating stochastic processes and analyzing the results. In addition, the linear algebra library Eigen, comprised of template code, makes it simple to perform matrix and vector operations in C++. Using an application in colorectal cancer research, the presentation will feature three topics in C++, namely random number generation, parallel computing, and leveraging the Eigen library.

One of the main attractions of Eigen is that it simplifies the code implementation for people who think mathematically, as the + and * operators are clearly defined in the mathematical sense of matrix addition and row-by-column matrix multiplication.  This talk highlights this aspect by demonstrating how to express the simulation of a stochastic cancer modeling process in C++, a problem that is well formulated in terms of matrix operations.

Simulating these stochastic processes also requires drawing random numbers. Before the new <random> capabilities in C++11, users typically needed to patch together their own uniform random number generators, as well as transformations to their desired distributions such as the Poisson and exponential distributions, which required a large amount of time for implementation and testing. This talk will show that by using methods such as std::discrete_distribution and std::exponential_distribution in <random>, constructing a random process simulator is technically simple in C++.

Cancers with seemingly similar initial conditions may develop into drastically different conditions. Much attention has been paid to events with high variability. To test a theoretical model, a huge number of simulations needs to be done to capture this variability. Therefore, concurrency is in reality a requirement rather than an option. In this application, parallel versions of certain STL algorithms introduced in C++17 are used to obtain descriptive statistics on the simulated data. In addition, an application of task-based concurrency will be used for replacing multithreaded computing formerly based on the pthread library.

In sum, this talk provides an example of using modern C++ in computational biology, with a goal of showing the C++ community that computational biology is a growing domain for applying modern C++ to general science.