I love tackling tough research problems and I am always open to new challenges. This has led me to work on problems in physics, biology, and machine learning. Please check out the following sections for more details of my past projects.
Perception for Autonomous Vehicles
Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom
Sourabh Vora, Alex H. Lang, Bassam Helou, Oscar Beijbom
Epigentic Landscapes and Cellular Reprogramming
Sai Teja Pusuluri*, Alex H. Lang* (co-first authors), Pankaj Mehta, and Horacio E. Castillo
Sai Teja Pusuluri, Alex H. Lang, Pankaj Mehta, and Horacio E. Castillo
I think one of the most surprising recent scientific experiments was Takahashi and Yamanaka's reprogramming of a skin cell to something resembling an embryonic stem cell (ESC), dubbed induced pluripotent stem cells (iPSCs). Following this groundbreaking experiment, other reprogramming protocols have been found so now scientists can switch between a variety of cell types such as ESC, skin, liver, neurons, and cardiomyocytes (heart muscle). This has already revolutionized the understanding of biology and could drastically change the future of medicine.
I am very interested in the answer to two different questions. First, what does it mean to be a cell type if minor perturbations (manipulating only a few genes, specifically 4 transcription factors out of thousands) can result in a completely different cell type? Second, how do we rationally design new reprogramming protocols? If we could make any desired cell type from an individual's own skin, this could eliminate the need for organ transplants.
So why am I, as a physicist, researching this? Well, biologists currently use Waddington's Landscape as an analogy to describe cellular development and cellular reprogramming. As a physicist, the word landscape has a specific mathematical meaning, so my advisor Pankaj Mehta thought it would be a great first project for us to work together to see if we could make the analogy more precise. This project took off and now, several years later, our model describes the biology much better than we had any right to expect.
We developed a model based on spin-glass physics to construct Waddington's landscape. Using real biological data (publically available microarrays), and assuming that the landscape is well described by a Hopfield neural network, we can construct a complete (i.e. all cell types for which we have data) Waddington epigenetic landscape. We believe that, using our model, we can rationally design cellular reprogramming protocols but are still searching for the best experimental test of our model with an awesome group of experimentalists at the BU CReM.
In collaboration with Sai Teja Pusuluri and Horacio E. Castillo, we are working to answer interesting questions on how to control the dynamics of cellular reprogramming. First, we reanalyzed time-series data on cellular reprogramming from differentiated cell types to induced pluripotent stem cells (iPSCs) and showed that gene expression dynamics during reprogramming follow a simple one-dimensional reaction coordinate. Interestingly, this reaction coordinate is independent of both the time it takes to reach the iPSC state as well as the details of experimental protocol used and therefore seems to be a universal characteristic of reprogramming. In a second paper, we examine the sizes of the basins of attractions and show that they are highly depending on the specific correlation structure between cell types. We are currently working on ways to exploit these findings to design new reprogramming methods.
Thermodynamics of Cellular Information Processing
Alex H. Lang, Charles K. Fisher, Thierry Mora, Pankaj Mehta.
Pankaj Mehta, Alex H. Lang, David J Schwab.
An important task of cells is to perform complex computations in response to external signals. Intricate networks are required to sense and process signals, and since cells are inherently non-equilibrium systems, these networks naturally consume energy. Since there is a deep connection between thermodynamics, computation, and information (see Maxwell’s Demon and Landauer’s principle for examples), a natural question is what constraints does thermodynamics place on statistical estimation and learning. In our research, we modeled a single chemical receptor and established a fundamental relationship between the energy consumption and statistical accuracy. Recent advances in non-equilibrium thermodynamics have yet to be fully applied to biological problems, so this remains an exciting field with a multitude of potential problems.
Additionally, I was a part of a review paper which tried to bridge the gap between the thermodynamics of information processing and synthetic biology. Current experiments in synthetic biology are performing complex computations that are beginning to implement unique cellular information processing tasks. However, these biological circuits are still subject to the same general principles laid out by Landauer and others. We summarized recent theoretical work and found that energy consumption in cellular circuits usually serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory.
Bioinformatics and Biological Classification
A.A. Wilson, L. Ying, M. Liesa, C.P. Segeritz, J.A. Mills, S.S. Shen, J.C. Jean, G.C. Lonza, Alex H. Lang, J. Nazaire, A.C. Gower, F.J. Mueller, P. Mehta, A. Ordonez, D. Lomas, L. Vallier, G.J. Murphy, G. Mostoslavsky, A. Spira, O. Shirihai, M. Ramirez, P. Gadue, D.N. Kotton.
K. Dame, S. Cincotta, Alex H. Lang, R. Sanghrajka, R. Zhang, L. Kwok, T. Wilson, S. Monti, P. Mehta, D.N. Kotton, L. Ikonomou.
Biology is now rapidly producing large-scale quantitative data that needs to be analyzed and bioinformatics provides the necessary tools and techniques to handle this big data. In collaboration with researchers at Boston University’s Center for Regenerative Medicine (CREM), I have performed standard statistical analysis of microarrays.
Additionally, we are working on developing new classification methods. Inspired by our work on epigenetic landscapes, we found a new technique based on linear algebra projections that provides an accurate and sensitive tool for discerning differences amongst biological samples. Our analysis method was used by the CREM to provide additional support for their technique to create thyroid progenitors.
Noise of microRNA Regulation and the ceRNA Hypothesis
Javad Noorbakhsh, Alex H. Lang, and Pankaj Mehta.
MicroRNAs are short sequences of RNA that can regulate gene expression by binding to mRNA. The ceRNA hypothesis recently proposed that microRNAs play an important role in gene regulation networks by inducing correlations between mRNAs competing for the same pool of microRNAs.
We mathematically modeled microRNAs using a set of stochastic differential equations. We found that microRNA networks are insensitive to many of the underlying biological details, but are responsive to the binding strength between the microRNA and mRNA. If the ceRNA hypothesis is true, all the microRNA and mRNA interaction rates need to be finely tuned to a similar value. Our work suggests the ceRNA hypothesis is unlikely to play a major role in gene regulation, but specific microRNAs can play an important, albeit limited, role in regulation.
COMETS: Flux Balance Analysis and Spatial Patterning
W.R. Harcombe, W. Riehl, I. Dukovski, B.R. Granger, A. Betts, Alex H. Lang, G. Bonilla, A. Kar, N. Leiby, P. Mehta, C.J. Marx, and D. Segre.
Flux balance analysis (FBA) is a technique to mathematically model metabolic networks. Daniel Segre and his group developed software to implement FBA in extended spatial patterns. This will allow labs to investigate the interactions between various bacteria strains under multiple growing conditions and patterns, mimicking bacterial biofilms. Listen to Daniel explain the project in his video. We helped implement diffusion in this program.
Disentanglement and Decoherence of Qubits
Dong Zhou, Alex H. Lang, and Robert Joynt
Amrit De, Alex H. Lang, Dong Zhou, and Robert Joynt
Quantum computers are a new type of computer built out of quantum based bits, dubbed qubits. This allows large computational speedup for certain types of problems, such as integer factorization. However, noise leads to the quantum qubits becoming classical, which is known as decoherence. In our research, we studied the effect of random telegraph noise and found that certain interactions could delay decoherence.