Session L40: Focus Session: Systems Biology and Biochemical Networks II

Microbial interaction networks in soil and in silico

Soil harbors a huge number of microbial species interacting through secretion of antibiotics and other chemicals. What patterns of species interactions allow for this astonishing biodiversity to be sustained, and how do these interactions evolve? I used a combined experimental-theoretical approach to tackle these questions. Focusing on bacteria from the genus Steptomyces, known for their diverse secondary metabolism, I isolated 64 natural strains from several individual grains of soil and systematically measured all pairwise interactions among them. Quantitative measurements on such scale were enabled by a novel experimental platform based on robotic handling, a custom scanner array and automatic image analysis. This unique platform allowed the simultaneous capturing of $\sim $15,000 time-lapse movies of growing colonies of each isolate on media conditioned by each of the other isolates. The data revealed a rich network of strong negative (inhibitory) and positive (stimulating) interactions. Analysis of this network and the phylogeny of the isolates, together with mathematical modeling of microbial communities, revealed that: 1) The network of interactions has three special properties: ``balance'', ``bi- modality'' and ``reciprocity''; 2) The interaction network is fast evolving; 3) Mathematical modeling explains how rapid evolution can give rise to the three special properties through an interplay between ecology and evolution. These properties are not a result of stable co-existence, but rather of continuous evolutionary turnover of strains with different production and resistance capabilities.   

Regulatory schemes to achieve optimal flux partitioning in bacterial metabolism

The flux balance analysis (FBA) offers a way to compute the optimal performance of a given metabolic network when the maximum incoming flux of nutrient molecules and other essential ingredients for biosynthesis are specified. Here we report a theoretical and computational analysis of the network structure and regulatory interactions in an E. coli cell. An automated scheme is devised to simplify the network topology and to enumerate the independent flux degrees of freedom. The network organization revealed by the scheme enables a detailed interpretation of the three layers of metabolic regulation known in the literature: i) independent transcriptional regulation of biosynthesis and salvage pathways to render the network tree-like under a given nutrient condition; ii) allosteric end-product inhibition of enzyme activity at entry points of synthesis pathways for metabolic flux partitioning according to consumption; iii) homeostasis of currency and carrier compounds to maintain sufficient supply of global commodities. Using the amino-acid synthesis pathways as an example, we show that the FBA result can be reproduced with suitable implementation of the three classes of regulatory interactions with literature evidence.   

A Realtime Active Feedback Control System For Coupled Nonlinear Chemical Oscillators

We study the manipulation and control of oscillatory networks. As a model system we use an emulsion of Belousov-Zhabotinsky (BZ) oscillators packed on a hexagonal lattice. Each drop is observed and perturbed by a Programmable Illumination Microscope (PIM). The PIM allows us to track individual BZ oscillators, calculate the phase and order parameters of every drop, and selectively perturb specific drops with photo illumination, all in realtime. To date we have determined the native attractor patterns for drops in 1D arrays and 2D hexagonal packing as a function of coupling strength as well as determined methods to move the system from one attractor basin to another. Current work involves implementing these attractor control methods with our experimental system and future work will likely include implementing a model neural network for use with photo controllable BZ emulsions.   

Stochastic gene expression with bursting and positive feedback

Stochasticity (or noise) in the process of gene expression can play a critical role in cellular circuits that control switching between probabilistic cell-fate decisions in diverse organisms. Such circuits often include positive feedback loops as critical elements. In some cases (e.g. HIV-1 viral infections), switching between different cell fates occurs even in the absence of bistability in the underlying deterministic model. To characterize the role of noise in such systems, we analyze a simple gene expression circuit that includes contributions from both transcriptional and translational bursting and positive feedback effects. Using a combination of analytical approaches and stochastic simulations, we explore how the underlying parameters control the corresponding mean and variance in protein distributions.   

mRNA Noise Reveals that Activators Induce a Biphasic Response in the Promoter Kinetics of Highly Regulated Genes

A dominant source of fluctuations in gene expression is thought to be the process of transcription. The statistics of these fluctuations arise from the kinetics of transcription. Multiple studies suggest the bulk of fluctuations can be understood by a simple process where genes are inactive for exponentially distributed times punctuated by geometric bursts of mRNA. Yet it's largely unknown how cis and trans factors affect the two lumped kinetic parameters, burst size and burst frequency, that describe this process. Importantly, how these parameters are regulated in a single gene can qualitatively affect the dynamical behavior of the network it is embedded within. Here, we ask whether transcriptional activators increase gene expression by increasing the burst size or burst frequency. We do so by deducing these parameters from steady-state mRNA distributions measured in individual yeast cells using single molecule mRNA FISH. We find that for both a synthetic and natural promoter, activators appear to first increase burst size, then burst frequency. We suggest this biphasic response may be common to all highly regulated genes and was previously unappreciated because of measurement techniques. Furthermore, its origins appear to relate to cis events at the promoter, and may arise from combinations of basal and activator-dependent bursts. Our measurements shed new light on transcriptional mechanisms and should assist in building synthetic promoters with tunable statistics.   

Determining the stability of genetic switches: Explicitly accounting for mRNA noise

Cells use genetic switches to shift between alternate gene expression states, e.g. to adapt to new environments or to follow a developmental pathway. Here, we study the dynamics of switching in a generic-feedback on/off switch. Unlike protein-only models, we explicitly account for stochastic fluctuations of mRNA, which have a dramatic impact on the genetic switch dynamics. Employing a semi-classical theory to treat the underlying chemical master equations, we obtain accurate results for the quasi-stationary distributions of mRNA and protein copy numbers and for the mean switching time, starting from either state. Our analytical results agree well with extensive Monte Carlo simulations. Importantly, one can use the approach to study the effect of varying biological parameters, and of extrinsic noise, on the switch stability.   

Quantitative Model of microRNA-mRNA interaction

MicroRNAs are short RNA sequences that regulate gene expression and protein translation by binding to mRNA. Experimental data reveals the existence of a threshold linear output of protein based on the expression level of microRNA. To understand this behavior, we propose a mathematical model of the chemical kinetics of the interaction between mRNA and microRNA. Using this model we have been able to quantify the threshold linear behavior. Furthermore, we have studied the effect of internal noise, showing the existence of an intermediary regime where the expression level of mRNA and microRNA has the same order of magnitude. In this crossover regime the mRNA translation becomes sensitive to small changes in the level of microRNA, resulting in large fluctuations in protein levels. Our work shows that chemical kinetics parameters can be quantified by studying protein fluctuations. In the future, studying protein levels and their fluctuations can provide a powerful tool to study the competing endogenous RNA hypothesis (ceRNA), in which mRNA crosstalk occurs due to competition over a limited pool of microRNAs.   

On the control of gene expression in small RNA post-transcriptional regulation pathway: role of conserved weak targets

Small RNA molecules play critical regulatory roles in organisms across all kingdoms of life. Many small RNA families achieve target-specificity via base-pairing of a very short (6-8 nucleotides) ``seed'' region with the targeted mRNA, and consequently many genes carry a matching seed in their sequence. Evidence in bacteria and animals suggest that a single small RNA may regulate the gene expression of many different targets, although most of them very weakly. On the other hand, in all cases we are aware of where the functionality of a small RNA has been carefully studied, only a small number of target genes were identified as being phenotypically relevant. Here, we present a Langevin formalism which describes the dynamics of the different interacting entities (small RNA and targets), including the stochasticity of the underlying biochemical reactions and the effect of transport. Using analytical or numerical computations, we study the influence of (many) weak targets on the mean and noise properties of (few) principal targets. In particular, we argue that the role of these weak targets is to confer robustness to the regulation of the principal targets without significantly affecting their temporal responses to changing environments.   

Approaches to Chemical and Biochemical Information and Signal Processing

We outline models and approaches for error control required to prevent buildup of noise when ``gates'' and other ``network elements'' based on (bio)chemical reaction processes are utilized to realize stable, scalable networks for information and signal processing. We also survey challenges and possible future research. \\[4pt] [1] Control of Noise in Chemical and Biochemical Information Processing, V. Privman, Israel J. Chem. 51, 118-131 (2010).\\[0pt] [2] Biochemical Filter with Sigmoidal Response: Increasing the Complexity of Biomolecular Logic, V. Privman, J. Halamek, M. A. Arugula, D. Melnikov, V. Bocharova and E. Katz, J. Phys. Chem. B 114, 14103-14109 (2010).\\[0pt] [3] Towards Biosensing Strategies Based on Biochemical Logic Systems, E. Katz, V. Privman and J. Wang, in: Proc. Conf. ICQNM 2010 (IEEE Comp. Soc. Conf. Publ. Serv., Los Alamitos, California, 2010), pages 1-9.   

Organizing biochemical reactions: Lessons from cyanobacteria

Cyanobacteria are model organisms for photosynthesis and are of interest for bio-fuel production and carbon dioxide sequestration. I present a mathematical model of the carbon concentrating mechanism (CCM) in cyanobacteria. The CCM is a combination of transporters and enzymes distinctively organized in the cell, which increase the internal concentration of carbon dioxide, and improve the efficiency of converting carbon dioxide to sugar. I find that the internal carbon concentration can be completely described by solutions in two parameter regimes of the model. These solutions correspond to varying transporter and enzymatic activity, which can be directly connected to experimental measurements. I also find a dependence of the carbon concentration on the spatial organization of the reactions within the cell. Understanding the CCM in cyanobacteria gives us insight into design principles for the cellular organization of biological reactions.