New Papers, Old Papers, and Re-convolved Concepts, November edition

I have been busy the past several months fleshing out new ideas and finishing up older ones. The first paper profiled here is "Cellular decision-making bias: the missing ingredient in cell functional diversity", something I published on arXiv [1] last month. This paper is a computational-oriented derivative of the paper "Defining phenotypic respecification diversity using multiple cell lines and reprogramming regimens", published earlier this year in Stem Cells and Development [2].

In [2], it was demonstrated that a series of different cell lines of the same type (e.g. fibroblast) exhibit great variability (many-fold differences) in terms of their direct cellular reprogramming efficiency. The efficiency of this process was measured using phenotypic (e.g. immunocytochemical) assays. This may or may not be due to the underlying genomic processes. Using a limited set of assays analyzed by means of differential gene expression, no smoking gun was found. While we did not investigate candidate epigenetic markers, the phenotypic trend was nevertheless consistent for both human and mouse cells reprogrammed to both generic muscle fiber and generic dopaminergic neurons [3].

The data collected and analyzed here also sets up a series of computational investigations using a method derived from Signal Detection Theory (SDT) and other signal-to-noise characterization methods [4]. SDT is generally used to understand cognitive decision-making in humans and animals. However, decision-making theory has also been used to explain outcomes at the cellular and molecular level, particularly switch-like processes [5]. Using the standard SDT as inspiration, I propose in [1] that cellular and molecular processes can be characterized and analyzed using a technique called cellular SDT.

Major collaborator on the Stem Cells and Development paper [2]: Dr. Steven Suhr, Michigan State University. 

Cellular SDT can uncover something called decision-making bias, which is hypothesized to occur during the conversion of cells from one phenotype to another [3]. In this case, the term bias refers to the magnitude of difference in conversion efficiency for the same cell line given two distinct stimuli. The overarching assumption is that differences observed across different small-scale stimuli (e.g. forced transcription factor activity) can be characterized systematically within and between specific cell types and lines.

My talk to the BEACON Center in May 2013. The first part (YouTube video) focused on modeling diversity in cellular reprogramming (an early version of cellular decision-making bias).

Here is the abstract of the paper. Associated code (on Github) can be found here:

"Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate the scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed."

The second paper profiled here is called "A Semi-automated Peer-review System", a short paper I published on the arXiv earlier this month [6]. The idea of an automated peer review system came to me after preparing a blog post [7] and reading a paper on the most common degree of novelty found among highly influential scientific papers [8]. The paper provides an outline of a human-assisted adaptive algorithm that detects fraud in a set of scientific papers without also filtering out innovative but highly-novel work. As in the case in [1], the approach was based on signal detection theory (SDT). In this case, however, a more conventional application (e.g. standard ROC curves) is used to minimize the number of truly low quality and fraudulent manuscripts while maintaining diversity and novelty in the scientific literature.

Here is the abstract and here is the associated code (mostly pseudo-code) on Github:

"A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse information. Here, the architecture for one potential improvement (a semi-supervised, human-assisted classifier) to the traditional approach will be introduced and evaluated. To evaluate the potential advantages of such a system, hypothetical receiver operating characteristic (ROC) curves for both approaches will be assessed. This will provide more specific indications of how automation would be beneficial in the manuscript evaluation process. In conclusion, the implications for such a system on measurements of scientific impact and improving the quality of open submission repositories will be discussed". 

Finally, I am giving a presentation at the Network Frontiers Workshop at Northwestern University's NICO Institute on the 4th of December. The title of the talk is "From Switches to Convolution to Tangled Webs: evolving sub-optimal, subtle biological mechanisms". The work is an extension of my arXiv paper from 2011 [9] on Biological Rube Goldberg Machines (RGMs), something I also refer to as a convolution architecture. Here is the abstract and here is the associated code on Github:

"One way to understand complexity in biological networks is to isolate simple motifs like switches and bi-fans. However, this does not fully capture the outcomes of evolutionary processes. In this talk, I will introduce a class of process model called convolution architectures. These models demonstrate bricolage and ad-hoc formation of new mechanisms atop existing complexity. Unlike simple motifs (e.g. straightforward mechanisms), these models are intended to demonstrate how evolution can produce complex processes that operate in a sub-optimal fashion. The concept of convolution architectures can be extended to complex network topologies. Simple convolution architectures with evolutionary constraints and subject to natural selection can produce step lengths that deviate from optimal expectation. When convolution architectures are represented as components of bidirectional complex network topologies, these circuitous paths should become “spaghetti-fied”, as they are not explicitly constrained by inputs and outputs. This may also allow for itinerant and cyclic self-regulation resembling chaotic dynamics. The use of complex network topologies also allows us to better understand how higher-level constraints (e.g. hub formation, modularity, preferential attachment) affect the evolution of sub-optimality and subtlety. Such embedded convolution architectures are also useful for modeling physiological, economic, and social complexity". 

And last but not least, a new preprint server has come online called BioRxiv. BioRxiv (administered by Cold Spring Harbor Laboratory) accepts manuscripts from a number of biological disciplines, from Bioinformatics to Molecular Biology to Zoology. I kicked things off in the Zoology category with an older manuscript (originally presented at a conference in 2006) entitled "Filling up the Tree: considering the self-organization of avian roosting behavior" [10]. However, for more theoretical and interdisciplinary work such as the paper in [11], I still plan on using arXiv.

NOTES:

[1] Alicea, B.   Cellular decision-making bias: the missing ingredient in cell functional diversity. arXiv repository, arXiv: 1310:8268 [q-bio.QM] (2013).

[2] Alicea, B., Murthy, S., Keaton, S.A., Cobbett, P., Cibelli, J.B., and Suhr, S.T.   Defining phenotypic
respecification diversity using multiple cell lines and reprogramming regimens. Stem Cells and Development, 22(19), 2641-2654 (2013).

[3]  In this example, conversion refers to direct cellular reprogramming technique (e.g. the creation of iPS cells) that result in the creation of induced neural cells (iNCs) and induced skeletal muscle cells (iSMCs). However, conversion could also refer to carcinogenesis or developmental processes.

Figure 1 from Alicea et.al (2013). Frames A-D, immunocytochemical characterization of iNCs and iSMCs. Frames E-H, diversity in reprogramming efficiency for a range of cell lines.

[4] Schultz, S.R.   Signal-to-noise ratio in neuroscience. Scholarpedia, 2(6), 2046 (2007).

[5] Balazsi, G., van Oudenaarden, A., and Collins, J.J.   Cellular Decision-Making and Biological Noise: From Microbes to Mammals. Cell, 144(6), 910–925 (2011). 

[6] Alicea, B.   A Semi-automated Peer-review System. arXiv: 1311.2504 [cs.DL, cs.HC, cs.SI, physics.soc-ph] (2013).

[7] Alicea, B.   The Novelty-Consensus Dampening.   Synthetic Daisies blog, October 22 (2013). 

[8] Uzzi, B., Mukherjee, S., Stringer, M., and Jones, B.   Atypical Combinations and Scientific Impact. Science, 342, 468-472 (2013).

[9] Alicea,  B.   The ‘Machinery’ of  Biocomplexity:  understanding  non-optimal  architectures  in biological systems. arXiv repository, arXiv: 1104.3559 [nlin.AO, q-bio.QM, q-bio.PE] (2011).

[10] Alicea, B.   Filling up the Tree: considering the self-organization of avian roosting behavior. bioRxiv, doi:10.1101/000349 (2013).

[11] Alicea, B.   The Emergence of Animal Social Complexity: theoretical and biobehavioral evidence. arXiv repository, arxiv:1309.7990 [q-bio.PR, q-bio.NC] (2013).