Some inverse problems in biophysics

TL;DR

This paper explores inverse problems in biophysics, including olfactory search algorithms, DNA unzipping dynamics, and neural interaction inference, using theoretical and numerical tools to analyze biological systems.

Contribution

It provides a continuous formulation of the Infotaxis search algorithm, models DNA unzipping dynamics for genetic inference, and develops software for neural interaction analysis.

Findings

Characterized the performance of the continuous Infotaxis algorithm.

Developed a detailed model for DNA unzipping experiments.

Created software for neural interaction inference.

Abstract

During the past few years the development of experimental techniques has allowed the quantitative analysis of biological systems ranging from neurobiology and molecular biology. This work focuses on the quantitative description of these systems by means of theoretical and numerical tools ranging from statistical physics to probability theory. This dissertation is divided in three parts, each of which has a different biological system as its focus. The first such system is Infotaxis, an olfactory search algorithm proposed by Vergassola et al. in 2007: we give a continuous formulation and we characterize its performances. Secondly we will focus on single-molecule experiments, especially unzipping of DNA molecules, whose experimental traces depend strongly on the DNA sequence: we develop a detailed model of the dynamics for this kind of experiments and then we propose several inference algorithm aiming at the characterization of the genetic sequence. The last section is devoted to the description of an algorithm that allows the inference of interactions between neurons given the recording of neural activity from multi-electrode experiments; we propose an integrated software that will allow the analysis of these data.